Back to Full Site


The Harte Hanks Blog

IoT and Micro-Moments: Optimizing Big and Small Data to drive Omnichannel Marketing

HarteHanks_MarektingTechnology_ROIIn our last article we discussed how the advent of IoT is bringing marketers an overwhelming amount of data, behemoth data, that can be synthesized into usable knowledge that can drive more effective customer journeys. With companies having access to all of this data, we’d like to talk more about how this data can be optimized and utilized to have the largest impact on your organization.

Beware the overzealous that want to board the big data train too quickly, although they have the very best of intentions. The same “bad data in – bad data out” (incorrect insights or conclusions) rule holds just as true, if not more so, in the world of big data analytics compared to traditional statistical analytics. Big data is compiled from an ever growing number of sources, much of which is unstructured. And simple rules of probability apply here – the larger the pool of data, the higher the likelihood that analysts will miss “dirty” data that can ultimately lead to identifying false positives or false negatives.

Unlike traditional first party data that historically has lived in relational databases, big data often consists of a tremendous amount of unstructured data. Correctly integrating and/or blending this data with more structured first party data is critical so as not to lead to analytic outcomes that are way off in left field. This problem is only exacerbated by the velocity at which data is created, which can largely be attributed to the growing mobile trends discussed earlier where data is transmitted on almost a continuous bases. Also, keep on the lookout for the increasing trend of automobiles being online, yet another massive pool of data generating “devices”. To help ensure that the “signal” can be correctly extracted from the “noise”, it is critical that the appropriate amount of rigor is put behind understanding the quality of the data source, how that data is collected, and how it is integrated and blended with other sources of data.

Despite the value of big data synthesized to be used effectively, there is also extreme value in small data – data that’s about people and emotion (in addition to small datasets gathered from a singular historical event). Small data can be ingested into big data sets, merged with behavioral or trending information derived from machine learning algorithms, and provide clearer insights than we’ve ever had before.

Here’s an example of both: The use of smart labels on medicine bottles is small data which can be used to determine where the medicine is located, its remaining shelf life, if the seal of the bottle has been broken, and the current temperature conditions in an effort to prevent spoilage. Big data can be used to look at this information over time to examine root cause analysis of why drugs are expiring or spoiling. Is it due to a certain shipping company or a certain retailer? Are there reoccurring patterns that can point to problems in the supply chain that can help determine how to minimize these events? 1

The issue here is that we cannot become so obsessed with Big Data we forget about creativity. You have to remember that Big Data is all about analyzing the past, but it has nothing to do with the future. Small Data, can also be defined as seemingly insignificant observations you identify in consumers’ homes. Things like how you place your shoes to how you hang your paintings. These small data observations are likened to emotional DNA that we leave behind. Big Data is about finding correlations, but Small Data is about finding the causation, the reason why. 2

Optimizing Big and Small data into business processes can not only save companies millions of dollars, but creates a buyer and customer journey that are seamless, continuous and maintains context regardless of the touchpoint. This omnichannel marketing approach should be the ultimate goal of marketers – creating a conversation with their buyers and customers based on trust and value exchange – which leads to strong relationships in an increasingly connected on- and off-line world.

Laura Watson is Strategy Director at Harte Hanks, and Korey Thurber is Chief Analytics & Insights Officer at Harte Hanks. Harte Hanks can help your brand create an omnichannel marketing strategy, contact us for a free assessment.


1 Forbes Tech
2 Small Data: The Tiny Clues That Uncover Huge Trends

IoT and Micro-Moments Marketing: Leveraging Big Data to Improve the Customer Journey

4-biggest-challenges_illustrations_2-1_v02-01Being connected via wearables without your mobile device is already a reality with untethered Tech, like Android Wear and the Samsung Gear S2, which both support e-SIMs tapping into your pre-existing cell network at no extra cost. It’s a good bet that every smartwatch brand will have an LTE version by the end of 2016, which means that while there’s a vast number of facts and untold nuggets of information that could surprise even big data’s most ardent followers. Big Data is about to become behemoth data.

Every day, we create 2.5 quintillion bytes of data (that’s 2.5 followed by a staggering 18 zeros!)1 – so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, the Curiosity Rover on Mars, your Facebook video from your latest vacation, purchase transaction records, and cell phone GPS signals to name a few. Google alone processes 3.5 billion requests per day and stores 10 exabytes of data (10 billion gigabytes!)2

Whether it’s tracking driving habits for the purpose of offering insurance discounts, using biometric data to confirm an ATM user’s identity, or using sensors to detect that it’s time for garbage pick-up, the era of the iOT in which “smart” things can seamlessly collect, share and analyze real-time data, is here.

Imagine a world where your watch recognizes that you withdraw cash every Saturday so that you’re ready for the neighborhood lemonade stand and your evening outing, and you haven’t made your usual transaction yet. A helpful alert pops up on your device, and another reminder displays when you’re within a ½ mile of your Bank ATM where a retina scan allows you to withdraw funds. Your Smart Refrigerator identifies that you’re running low on eggs and yogurt, while your wearable identifies an open parking space within 50-feet of your favorite Saturday farm market stop, but cautions you that there’s a marathon starting in 2 hours so you better get a move on. A “ping” in your email indicates that the killer little black dress you’ve wanted just became affordable with a special discount coupon you received as you drive past the store. While you’re away, the sun comes out, so your Smart Home lowers the window shades, turns the A/C up a few degrees and suggests adding popsicles to the grocery list. Like any fabulous assistant, technology not only aids you, but anticipates your needs and helps you make smarter, faster decisions based on “advice” you can trust. This is the best way to use Big Data.

Having the ability to be smarter, faster and always connected without having to carry around a device (or anything at all)…great.

Using Big Data to synthesize all of the fragmented individual data points into an orchestrated, holistic, powerfully intelligent view of the customer to help them during these everyday micro-marketing moments…priceless.

Big Data allows brands to go beyond customer motivation and engagement in driving value exchange to allowing them to foster their brand affinity and cultivate their customer’s evangelism in real-time, responding to their customer’s behaviors even as their activities and likes shift.

Although simple in concept, many brands are struggling to get it right (or get started at all). Leading brands have already gained a powerful competitive advantage by adopting consumer management technology that allows them to understand and engage based on individual consumer preferences and observation of behaviors and buying signals in their Buyer and Customer journey – thus taking a big step toward making Big Data a strategic reality.

Is Big Data, or really behemoth data, really the answer all by itself? There is lot of insight to be garnered from that data, but the key is being able to quickly sift through it all, tuning out the noise to focus on the key patterns and meaningful relationships in that data.

Traditional statistical analytics techniques which focus on finding relationships between variables to predict an outcome simply won’t do when the goal is to optimize decisions using massive pools of data that are growing and evolving on a near-continuous basis. This is where machine learning comes into play and brings the needed “giddy-up” to the analytic component. Machine learning evolved from the study of pattern recognition within the field of artificial intelligence. The easy way to think about it is, it provides computers the ability to learn and improve without a specific program being written to tell the computer to “learn and improve”. Machine learning software identifies patterns in the data in order to group similar data together and to make predictions. Whenever new data is introduced, the software “learns” and creates a better understanding of the optimal decision. Think of it as the automation of the predictive analytic process.

There is certainly a lot of overlap between statistical analytics and machine learning but there is one key difference. The former requires that someone formulate a hypothesis and structure a test to evaluate whether that that hypothesis is true or not. For example, a hypotheses that states a particular marketing lever (i.e. a certain offer or message) will generate or “cause” additional account openings or sales. Machine learning does not worry about hypothesis testing and simply starts with the outcome that you are trying to optimize – sales for example – and uncovers the factors that are the drivers. As more data is introduced, the algorithm learns and improves its predictions in almost real time.
Interestingly, machine learning has been around for decades. But now, due to the massive explosion in data, cheaper cloud based data stores, and huge increases in computing horsepower, the interest in machine learning is really starting to hit its stride.

Laura Watson is Strategy Director at Harte Hanks, and Korey Thurber is Chief Analytics & Insights Officer at Harte Hanks. Harte Hanks can help your brand leverage big data, contact us for a free assessment.
Forbes Tech

IoT and Micro-Moments Marketing: Opportunities and Pitfalls

With the advent of smart technology, we are getting ever closer to the Orsen Wells imagined world of Big Brother oversight in everyday interactions…and many of us are starting to like it because it makes our decision-making easier, our lives more efficient and allows us to do more of the “fun stuff” we’d all rather be doing.

Marketers used to think about the “top of the funnel” with sales and marketing engagement strategies, but most consumers these days are starting their buyer’s journey quietly online through research using video, ratings and reviews and more interactive decision-making short-cuts. And they’re mostly doing it via their mobile devices. Tomorrow is fast-approaching though, as smartwatches mature and the need for “tethering” to a smartphone goes away, devices supporting e-SIMs that are able to tap into your cell network at no extra cost will magnify the Internet of Things (IoT) explosion of use and related data.

The popularity of wearables, especially fitness-related devices, has sky-rocketed over the last couple of years, with 39.5MM US adults using wearables in 2015, including smartwatches and fitness trackers. There’s an expectation that the number will double to 81.7MM users by 2018, or 32% of US adults.1

Wearable devices go way beyond the smartwatch and fitness tracker, with things like FitBark, activity monitoring for Fido, to Athena, a personal security wearable that may help save lives. Verily has a glucose-detecting contact lens and Google is set to use tech to target cardiovascular disease, cancer and mental health problems too. More devices are moving from the nice-to-have category to an integral-to-our-lives status.

With all of this cool, new tech, it’s the nature of marketers to want to use it to sell stuff.

And that’s where we, as marketers, want to caution our compatriots to take the highest marketing road. You can’t get any more personal than something you wear on your body, even sleep with. With great personal engagement comes great responsibility to ensure the consumer experience with your Brand is a beneficial – even trusted – relationship. In digital terms, a break-up takes only seconds. Marketing messages that are annoying in other channels have the potential to take on a new and amplified level of aggravation in personal, wearable devices…running the risk of customers divorcing themselves from your Brand forever.

Yes, new tech means new, small-data points resulting in a big (very big) data explosion measured on the zettabyte scale. (A zettabyte is a 1 followed by 21 zeros.)Finding ways to use that data in a meaningful, mutually beneficial way in micro-moments marketing will ultimately best serve both Brands and their customers.

Laura Watson is Strategy Director at Harte Hanks, and Korey Thurber is Chief Analytics & Insights Officer at Harte Hanks. Harte Hanks can help your brand utilize micro-moments marketing, contact us for a free assessment.


1 eMarketer
2 and the International Data Corporation

B2B vs B2C marketing analytics – the same, but different?

analytics illustrationI’ve spent much of my career working in data-driven marketing roles and delivering insights for B2C brands, but over the last decade the balance has shifted and I now work almost exclusively with B2B businesses. While some of the differences between the two worlds are to be expected, such as the availability of different data types and the more complex buying cycle in B2B, in fact there are a great deal of similarities in the techniques and types of analysis that can be carried out for B2C and B2B. So why aren’t B2B brands making as much use of analytics as their B2C counterparts?

At first I wondered if this was just my isolated view of the world, but a recent study* by B2B Marketing in association with Circleresearch seems to support this. It reveals that 73% of B2B marketers don’t feel their companies make the most of data, with the weakest skills being in the area of Predictive Analytics.

Not a day goes by where we don’t carry out one or more of the following analyses for the B2C brands we work with:

  • Upsell propensity modelling
  • Path to high value analysis
  • Segmentation
  • Share of wallet analysis
  • Cross-sell propensity modelling
  • Churn prediction

And yet, I still don’t see widespread adoption within B2B organisations. Of course there are exceptions, and some readers will be able to recount many examples of insight-driven B2B sales and marketing activities they’ve been part of. But it’s not commonplace.

In simple terms, the marketing objectives facing B2B and B2C businesses are the same. The difference however, is that B2B businesses tend to focus their efforts on acquisition activity, with much less attention given to “in-life” marketing. B2B buyer journeys are much more complex, lead times are longer, and involve multiple decision makers and influencers (Prospect Modelling and Lead Scoring are great examples of analytics used here, particularly with the tools that have been developed in the last 10 years).

While it used to be true that the low volume and variety of data was a limiting factor in B2B, this is no longer true. Data collected through inbound marketing activity and social channels is a rich and current asset, and the tools and platforms available now mean we can quickly convert this into insight.

Here are just a few ways that analyses most often used for B2C can inform marketing programs for B2B organizations:

  • Churn Prediction: Develop a model that predicts which customers are more likely to churn at the end of their contract. For B2B knowing who to contact, and with what message, has historically been tricky. However, analysis of previous contact behaviour and campaign interactions can help optimise future activity and identify the key decision makers.
  • Share of Wallet: Share of Wallet analytics is an area that has great potential in B2B. Most B2B organisations have an account management structure to maintain the relationship with their customers, and this typically means that high value accounts get 1:1 attention while low value accounts just become one of many for a beleaguered account manager. A Share of Wallet analysis can identify those accounts that still have room for growth, and will typically unearth some sleeping giants!
  • Path to High Value: We use this a lot with our B2C clients! Look at today’s ‘best’ customers and identify what was the sequence of events that got them there. Is their first purchase significant, or is it the acquisition channel, or just their firmographics? By recognising tomorrow’s best customers at an early stage, you can implement the right programs that will help nurture that growth

To put all of this into perspective, research by eConsultancy in association with Adobe** shows that only 26% of responding (B2C and B2B) organisations have a solid data-driven marketing strategy in place, so perhaps it’s no surprise that analytics isn’t as widespread in B2B as I’d hope to see. There’s definitely still room for improvement on both sides, so maybe the similarities are greater than I thought!

Harte Hanks has a team of Analysts, Data Scientists and Strategists to help you integrate analytics into your B2B sales and marketing plans. Is your company fully utilizing Analytic driven insights to better inform acquisition, growth and retention activities? Tweet us at @HarteHanks and share your experience.

* “Data Skills Benchmarking Report 2016-17”, April 2016
** “Quarterly Digital Intelligence Briefing: The Pursuit of Data-Driven Maturity”, April 2016

Maximizing Customer Lifetime Value in the Automotive Industry

Screen Shot 2016-04-12 at 6.27.37 PMHow to get over what is keeping you up at night
And, I don’t just mean those crazy neighbors next door. In your competitive market, how can you keep pace with customer needs? How can you be certain that you are investing in the right channels to acquire more customers? What if you were told you had to decrease your budget – where would you first look to cut?

All of these are big questions that we face constantly and can lead to internal strife across departments:

  • Are Sales and Marketing doing what they can?
  • Are our dealers servicing our customers the best way they can?
  • Are our customers getting the education they need from our online properties or call center?

How does your brand ensure that it is investing correctly in every individual customer it has today? Whoa… the answer to that last question, though your boss likely won’t want to hear it, is – you can’t! What you can do is understand whether the value that you expect to receive in the future from each customer is more than what you are currently spending to acquire or retain them.

Whether you are trying to determine who should get the richer offers in your sales events, who to include in the higher level of your service plan, who deserves call resolution escalation rather than being routed to your online community FAQ, etc., it has to be done within a finite budget where not all customers can be treated the same. The experience they receive should match to or exceed the value they are providing, or really their potential value. Through customer lifetime value (CLV) based segmentation, investment can be prioritized to balance delivering a meaningful experience to your customers (i.e. so that they are likely to return), while generating the greatest ROI back to the organization.

Transforming to a Customer-Centric Landscape
In an October 2014 Forbes Insights Study which surveyed 312 senior executives across North America in a broad range of industries, 77% of respondents say CLV is a highly or extremely valuable indicator, but only 58% regularly calculate average CLV*.

There are many ways to calculate lifetime value that can be effective in informing how an organization invests in its customers. The approach is dictated by the data that you have and the understanding of what metrics are foundational to an organization’s success. There must be a vision of continuous improvement that can lead to a more robust solution in later phases. But, there is no reason to wait for the “perfect data” to become available to get started. So let’s get to work!

How to Get Started

  • Data Assessment: What data is readily available today and what is its quality? Are you confident in the results you have seen in the existing reporting and KPIs you are tracking? At this point, you can make assumptions if necessary. For example, if retailers are not sharing specific cost-of-sale data you can estimate a value.
  • Resources: Each data point relates to a customer experience that can aid in predicting for future value. Gathering the data points across your organization, if not already centralized, is a time investment and will likely conflict with the day-to-day priorities your team has already. You may opt to bring on an analytics partner that can receive multiple data files and has expertise in blending that data together.
  • Project Objectives: Since customer lifetime value can and should influence customer experiences across the organization, you will want buy-in from senior management before getting started. What use cases are needed to justify the integration of using this solution? Who will you need to collaborate with early to develop internal partnership that will justify your needs (hint: get to know your Finance person REALLY well)?

Applications of Customer Lifetime Value in Automotive
It is not uncommon to forget that CLV has applications across all areas of your organization.

Build a Loyalty Program that works. In a research study conducted by the International Institute for Analytics and SAS, it was found that companies that had self-rated highly effective programs place a greater value on analytics as a core component of that program. In fact, they are more than twice as likely to view analytics as “very important” (65% vs. 30%)**. Take a moment to be sure your loyalty program is set up in a way that it is generating higher value from your retained customer base. Achieving a higher NPS is good news, but is it leading to a more profitable relationship with your customers? With a CLV foundation, programs and sales or service plan levels can be established that will accelerate your retention rates.

Identify the right channels to invest in. Let’s assume that you are acquiring a significant volume of leads, even qualified leads that have asked for a quote, but your conversion rates are 20% lower than your competitors. Should volume be your sole metric for success? CLV allows you to validate that your acquisition channels are attracting the right customer, with the right conversion rates, which can lead to more efficient acquisition and more loyal customers than your competitors. Cultivating growth in customer value over time starts by acquiring the right customer and then investing in them based on their likelihood to become and remain loyal to your brand.

Remember, CLV goes beyond attributing response or purchase to your collective channel network accurately, but rather it uses that logic to help predict customer value in the future. At the end of the day, don’t worry so much about whom your competitors are converting and you’re not… you have more to be concerned about what you are spending today to acquire the customers you do have.

Harte Hanks has a team of Analysts, Data Scientists and Strategists to help you begin to develop this framework and plan and effectively execute your marketing programs. Is your company fully utilizing Analytic driven insights to better inform customer retention and profitability? Tweet us at @HarteHanks and share your experience.

* “Customers for Life: Technology Strategies for Attracting and Keeping Customers” a Forbes Insights study in association with Sitecore.

** “Successful Loyalty Through Analytics” commissioned by SAS and performed by the International Institute for Analytics.

The Critical Role of Analytics Driven Insight in the Financial Services Sector

There is a critical need for Analytics Driven Insight in the Financial Services (FiServ) sector. The customer journey is no longer solely about the in-branch experience or siloed traditional marketing deployed by marketers. Today, a FiServ institution can influence the customer experience across a multitude of interaction points.

Examining specific sectors within Financial Services, there is a tremendous amount of disruption at the various interaction points across the customer journey:

Retail Banking: The branch network is still highly relevant today but expect routine transactions to continue to migrate from “brick-and-mortar” outlets at the rate of 4% – 5% annually. Financial Services institutions are continuing to turn their attention to the digitization of transactions as well as the digitization of the in-branch experience by integrating digital tools for the branch staff to use to improve service.

Consumer Lending/Credit: Financial Technology – also known “FinTech” companies – are the big drivers of disruption in consumer finance. Companies like borro and LendingClub to name a couple have stormed in and grabbed market share from traditional banks. These same traditional banks are now scrambling to make up lost ground by partnering with or acquiring FinTech firms to create more impactful and relevant interaction points for their customers. In addition, companies like PayPal and bitpay have and will continue to change the way people pay for goods and services, which in turn will continue to influence how we use the old-school “plastic” in our wallets.

Wealth Management: Traditional Wealth Management entities are starting to augment their core, face-to-face wealth management advisor capabilities with online capabilities. Millennials are arguably the most critical segment in the marketplace and as they build wealth, Wealth Management organizations need to be ready to interact and engage with them using the appropriate channels, technology, etc.

State of Analytics Driven Insight in Financial Services
So how do Financial Services institutions best inform marketing and business strategies across the sectors mentioned previously? Analytics driven insight is the key! Marketing analytics have been a mainstream, high-value add in the Financial Services industry for quite some time. In fact, many would agree that marketing analytics essentially “grew-up” in the FiServ sector, driven in large part by the vast amount and quality of data stored by financial service institutions. The FiServ sector is a veritable playground for traditional marketing analysts and statisticians to hone their data mining and insights generating craft.

But the world has changed…..and here is what is behind it:

Exponential Data Growth: More data has been created in the past two years than in the entire history of the human race. By 2020, 1.7 megabytes of new information will be collected every second for each individual on the planet (Forbes). And it’s not only the volume of data….it’s the speed at which it is growing and the variety of sources. Financial Services consumers are generating new data by visiting provider’s websites, transacting online and interacting with various forms of online media. This new pool of data combined with more traditional direct mail, email, telemarketing and first party customer data, is a powerful enabler to better inform spend across a multitude of channel/media choices.

It’s “BIG Insight” that matters: More data is just that….”more data” unless the FiServ entity can wrangle, manipulate and mine that data for better targeting and insight. Financial services organizations have to more closely align themselves around customer’s needs as opposed to traditional product or business lines. Data analytics is driving this trend to enable FiServ institutions to become more customer oriented – not only to know who they are, but where they are (online or branch), and what types of deposit, lending, and wealth management products and services they are interested in.

Increased use of Marketing and Data Management Platforms (DMPs): What used to be available to only the largest financial service institutions is now becoming much more prevalent in mid-tier institutions, enabling them to coordinate and optimize customer interaction points across online and offline channels. By utilizing a DMP the Analysts can more clearly understand WHAT action is being taken and in what channel, WHEN it is being taken, and WHO is taking it. By also incorporating first-party data and having the appropriate tags placed on each page of the digital journey, financial services analysts will have a plethora of data to influence and optimize experiences across the entire customer journey.

Customer-Centric Analytics…NOT Digital Analytics: It wasn’t that long ago that digital marketing was primarily about broad reaching ad buys based less on robust targeting and more on what “felt like the best thing to do”. The “old school” individual/household level data that Financial Services Analysts cut their teeth on has now become a reality in the digital space! Digital marketing is very simply becoming more addressable and more targeted, with a greater portion of ad spend happening at a very targeted individual level. All the analytic disciplines (campaign test design, campaign analytics, predictive models, segmentation, frequency and cadence of touch, etc.) that grew-up in the FiServ sector using individual and household level data, is now being used heavily across addressable digital media – as well as in conjunction with traditional offline data. Everything that was “old” has become “new” again. Please also see my related and recent blog post on fractional attribution.

Harte Hanks has a team of Analysts, Data Scientists and Strategists to help you navigate the new landscape. Is your company fully utilizing Analytics Driven Insights to better inform business and marketing strategy? Tweet us at @HarteHanks and share your experience with us.

Three Marketing Automation Myths That Need to Die

Marketing_AutomationAutomation is a fairly young, up-and-coming concept in the marketing industry, so it is understandable that there would be misconceptions in the beginning about what it is and what it does. As we start 2016 and “marketing automation” becomes less of a buzzword and more of a mainstream strategy, Harte Hanks wants to set the record straight on the facts about marketing automation. Here are three myths that we want to clear up:

1. Marketing Automation is for Scheduling Email Batch-and-Blasts

This is by far the most common myth, and misuse, of marketing automation. Email is just ONE tactic within automation. Most enterprise marketing automation technology platforms can incorporate landing pages, social media, personalized emails, gated content, videos, pay-per-click ads, and third party apps into your campaigns.

“59 percent of companies do not fully use the technology they have available.”Ascend2 “Marketing Technology Strategy” (August 2015)

The beauty of a marketing automation platform is its ability to respond differently depending on the contact. It can be integrated with your CRM and allow you to personalize all emails and touchpoints in a campaign based on this data. For example, a highly personalized email can be sent to a contact who has visited a certain page of your website, while simultaneously a more generic discovery email can be sent to another contact who you know little about or who has never visited your website.

Marketing automation is also much more “aware” than traditional email marketing. Automation tools are sophisticated enough to not only tell whether a customer clicked on a link in your email, but also which product-specific pages they visited after they clicked, whether they filled out a contact form, and even gather geographical and language information from them based on their IP address. Marketing automation tools can then take that user’s activity data and segment him or her into another flow of automated touchpoints (including additional emails, retargeting ads, high value content, etc.) that are specific to their interests.

2. Marketing Automation Means ‘Set It and Forget It’

While it’s true that marketing automation is great for scheduling emails and other campaign activities in advance, simply “setting and forgetting” is a sure-fire way to make sure your investment goes down the drain.

Many marketing automation tools offer robust functionality out of the box, but most are also cloud-based platforms that have new features added on a regular basis. Keeping a pulse on these updates, and participating in product improvement discussions, is important in making the most of your automation software. In fact, Eloqua will be rolling out a new UX experience this spring.

Another reason you should never “set it and forget it” is that with a healthy marketing automation program, your contact database will be continuously growing. Your customer insight will evolve as the system collects more data from your customers and their activities. And as you learn new things about your customers and their preferences, you can use that information to create more meaningful content in your campaigns.

3. Marketing Automation Stops After the Lead Converts to a Customer

Using marketing automation only for lead generation underestimates the power of the tool. As marketers, we know that the best lead source is always your previous customer. Repeat business and customer referrals will always give you the best ROI for your marketing budget. So why not make the most of that source?

“53 percent of marketers say continued communication and nurturing of their existing customers results in moderate to significant revenue impact.” (DemandMetric, Customer Marketing: Improving Customer Satisfaction & Revenue Impact, October 2014)

Luckily, marketing automation is not only a powerful lead generation tool, but it also gives you a platform to keep the conversation going with your new customer(s). When you properly sync your CRM to your automation tool, you can harness the power of segmenting by moving converted customers away from prospects into their own nurturing campaigns. These customer-specific nurturing campaigns open a two-way communication channel allowing your customer to become more engaged with your brand and to fully utilize your product or service.

For example, a customer-specific nurturing campaign can share content on best practices using your product (or service) via weekly newsletters, retargeted ads, and videos. Likewise, you can use those touchpoints to upsell products or services that complement what they’ve already bought. Automated campaigns can also be used to promote customer-only events via email invitations and trigger follow up phone calls from telemarketing or sales representatives.

You will never see the value in your marketing automation strategy if you don’t have a clear understanding of what it can accomplish. Marketing automation is more than the latest corporate buzzword. It’s a powerful marketing strategy and tool that allows companies to nurture prospects with highly personalized, useful content. It helps convert prospects into customers, and customers into brand ambassadors.

Harte Hanks is a full-service marketing agency that can support all aspects of your marketing automation program with minimal ramp up and faster go to market. Contact us for a free audit of your marketing automation programs at 1-844-233-9281.

How to Optimize Spend with Fractional Attribution



When traditional “database marketing” first took off in the early 1990’s, marketing performance measurement and attribution was quite simple. We generated sales and direct mail campaign performance reports using a handful of dimensions. Attribution was easily derived through business reply cards (attached to direct mail pieces), phone numbers or tracking codes. We also used indirect attribution rules by making control group comparisons. We were fairly accurate and the process was easy to execute.

The Current State of Attribution

We all know that the marketing landscape has changed … and it continues to evolve with massive channel proliferation. With so much data and so many options regarding how to best apply a limited marketing budget, how can a CMO receive richer insight to influence tactical decisions that will improve media/channel performance?

Let’s first examine the various states of attribution from the viewpoint of the modern day marketer:

  • Direct Attribution: Still used widely today and still relevant. A specific customer behavior (e.g. a purchase) can be “directly” attributed to a given marketing stimuli via a unique code, landing page/URL, response device, etc. However, other marketing stimuli may have created momentum and been a significant contributor to the consumer’s ultimate decision to purchase.
  • Last Touch Attribution: Attributing the desired customer behavior to the last “known” marketing touch. Similar to “Direct” Attribution, but not always the same, here the marketer attributes the desired customer behavior to the last known touch. This method is very common when there are no specific tracking codes/tags that tie a desired customer behavior directly to a specific marketing stimuli.
  • Multi-Full Attribution: Channel proliferation has led to individual channel/media silos, each with their own unique attribution rules. The separation of traditional offline data and online data is very common. For example, direct mail data is stored in a traditional customer database, email data is stored with the email service provider, and online data is stored by various DMPs, by vendors/partners that are contracted to capture it, each often with their own siloed attribution logic taking FULL credit for the same desired behaviors.
  • Rules Based Attribution: Building on the “Multi-Full Attribution” described above, here marketers use what is often called a “common sense approach” to proportionally assign attribution to very siloed marketing stimuli. For example, a business had recently identified the large overlap between their direct mail and digital channels. For the overlapping purchases identified in both groups, 100% of a given purchase was attributed to direct mail, while simultaneously 100% was also attributed to a combination of digital channels. A rule was then quickly implemented to assign 20% of the attribution to the direct mail channel and proportionally reduce the attribution by 20% across the various forms of digital media. So, it is “fractional” by the simplest definition, but no real math or analytics was being used to assign the “fraction” to each media/channel.

Each of these options contains significant attribution bias towards channels/forms of media, that when taken for face value will result is less than optimal decision-making.


What’s Next and What is Fractional Attribution?

Marketers must now leverage math, science and statistics to analyze and derive insight from large pools of data, much of which can now be integrated across channels to inform decisions across touch points during the customer journey. Fractional Attribution is a necessary tool for understanding campaign performance across a multitude of touch points.

Through advanced (and proven) analytic techniques, a weighting calculation is developed and applied to the various marketing touches during the customer’s buying journey. In short, you are attributing a portion of that customer’s purchase to each of the marketing touches that impacted the customer’s decision to buy.

Harte Hanks has a team of analysts that work with marketing organizations to create a fractional attribution model through a collaborative development process:

  1. Define the overall objectives and identify the behavior metrics you want to positively impact (e.g. response, sales, conversion, product registration, etc.).
  2. Define and implement the roadmap including identification of key performance indicators (KPIs) and setting the overall attribution approach. Companies have used both “quick start” fractional attribution solutions and more robust solutions that require dedicated data stores and data integration tools.
  3. Collect and compile the data.
  4. Execute the fractional attribution solution and create the scenario planning tool.

The “scenario planning tool” is what enables the user to optimize media/channel performance. Using the tool, the analyst or marketer can quickly run “what-if” analyses to estimate the impact of reallocating marketing spend across channel/media or removing a channel/media from the mix altogether. The end result is a much more informed decision that can result in significantly higher returns from your marketing budget. Performance data and insights from the optimization exercise are then used to calibrate and refine the attribution engine going forward.

Fractional Attribution rooted in proven math and statistical techniques is a critical tool to accurately improve and optimize the performance of an incredibly fragmented and complex system of channels and media, both online and offline.


It’s not perfect – no marketing science or advanced marketing analytic solution is. But a robust modeled attribution solution is proven marketing science, and those that leverage it appropriately will generate higher return from their marketing spend and outperform their competitors.

Has your company used fractional attribution to better analyze your marketing spend? Tweet us at @HarteHanks and share your experience with us.

How Pharmaceutical CRMs Can Lead to Healthier Relationships

Boosting physician and patient engagement

pharma CRM postCustomer Relationship Management (CRM) software offers a great deal of potential for the pharmaceutical industry. However, this is a complex sector, riddled with regulations surrounding sensitive data. It is not easy to find a solution that fits business needs while complying with relevant laws. This is especially true at an international level when different rules need to be observed for different countries.

Purchasing a standard CRM solution and trying to adapt it to various business and regulatory requirements is time consuming and difficult. Inevitably it involves compromise and hidden expense.

Instead, many pharmaceutical companies could benefit from international CRM programs that are purpose-built from the ground up by a marketing services provider.

Bespoke CRM for pharmaceuticals

A truly customized approach uses business goals as a starting point and builds a CRM framework around them. This ensures variations across different countries can be accounted for and embraced at an early stage, rather than being bolted on later. The result is a highly specified solution intrinsically optimized to meet business needs. It can have built-in scalability and the flexibility to handle international differences in data laws or standard practice, such as call centre versus nurse-led activity.

Ultimately, custom-built CRM offers better value and efficiency. Adapting existing systems is expensive, license fees can be high and product release cycles can delay the implementation of certain functionalities.

Using an MSP to build, manage and implement the solution brings multiple advantages. Since all aspects – from database management to phone calls, emails and SMS to direct mail – are handled by one organization, the program is more cohesive and affordable. What’s more, sensitive data is all held securely in one place.

Physician and patient communications

The best pharmaceutical CRM programs empower physicians and patients to make better, more informed choices – whether they’re prescribing treatment or following it.

Meeting physicians in person is becoming increasingly difficult for pharmaceutical companies. Physicians are often under pressure to see a certain number of patients per day, leaving limited time for meeting with third parties. Some countries also have complex regulations surrounding personal interaction between pharmaceutical companies and medical professionals. In many cases, direct marketing can play an effective role alongside or in place of face-to-face meetings. It enables physicians to keep abreast of the latest developments in treatments and processes such as pharmaceutical-led patient support.

Patient-focused activity varies depending on the nature of the patient’s condition, where they are in the treatment cycle, the level of data available and nuances of their country of residence. Naturally, when more is known about a patient, activity can be better tailored to their current needs and communications become more meaningful.

A central aim of pharmaceutical CRM should be fostering good relationships between patients and physicians. This means acknowledging the authority of the physician in prescribing drugs, while enabling patients to get more out of their appointments and the overall treatment. Ideally communications should operate progressively, supporting patients as they move from the initial awareness that they may have a certain condition, to actively acknowledging it, then learning to live with it. The latter stage is vital to boost adherence to treatment regimen and enhance overall patient outcomes.

Overcoming challenges

There are many challenges facing the marketing of pharmaceuticals today. However, deeper engagement rooted in custom-built CRM can help navigate many of them.

Direct alignment of patient and physician communications is complex from a data perspective, but with care and attention it can usually be achieved. Bespoke CRM programs can incorporate specific opt-in language to overcome many of the barriers surrounding sensitive data. This ensures that patients who are happy to share their data can access the wider support that is on offer should they need it.

Achieving buy-in from physicians and patients is not easy – nor should it be. Pharmaceutical organizations need to earn trust and loyalty over time. Striving for better, deeper engagement is a critical factor. An effective way to realize this in the short- to medium-term is through the empowerment of patients and physicians, arming them with knowledge and information so they can make informed choices. In the longer term, improved patient outcomes will speak for themselves.


Harte Hanks handles CRM programs for leading global pharmaceutical companies. Patient data is handled sensitively and an integrated approach ensures improved patient support and outcomes. Natalia Gallur has more than ten years’ experience in the sector.


Smarter Demand Gen Awakens

Convergence of Tech and People Will Amplify Demand Generation in 2016

UnknownThe B2B demand-marketing ecosystem continues to evolve at a rapid pace. It’s driven by emerging technologies, tactics and buyer behaviors, alongside other well-established factors that continue to shape the discipline.

Industry influencers and analysts such as SiriusDecisions and Forrester identified a raft of demand generation trends and requirements in 2015. These range from better use of analytics as a foundation for demand planning to buyer journey alignment and operationalizing personas.

The notion of operationalizing personas involves integrating persona intelligence into demand generation efforts. At a fundamental level, it involves dynamic delivery of persona-based content, messaging and offers across email, landing pages and websites. It was first mooted by SiriusDecisions in 2014, but began to take hold last year. During 2016 it will occupy a more central role as we enter the next stage of the journey: smarter demand generation.

Why do we need Smarter Demand Generation?

Many B2B organizations find their demand generation efforts are characterized by small pipelines, missed targets and failure to respond to the needs of today’s buyers. It’s not surprising when you consider the seismic shift in buyer behavior over the past few years.

B2B sales and marketing is becoming increasingly complex and far less linear in its nature. There are multiple influencers, decision makers and stakeholders. There are multiple online and offline marketing channels. And there are multiple interactions and conversations taking place.

In this fractured, multifaceted landscape we need to find a path to more effective, joined-up demand generation. We need an approach that embraces the complex realities of the B2B sector today and handles them with ease. Smarter demand generation is the answer.

What does it mean?

A central feature of smarter demand generation is the convergence of people and technology. This is true throughout the process. Human insight and expertise facilitates the creation and operationalization of personas. It also shapes the development and substance of programs that are augmented and delivered via sophisticated technologies. Finally, individuals at the receiving end of smarter demand generation are served with optimized, highly personalized communications. Content is relevant to their current and future professional needs and it is delivered at an opportune time via the most appropriate platform. The upshot is finely tuned buyer engagement and a more robust pipeline.

This might sound a world away from traditional demand generation. And it’s true that it requires a deeply analytical and intelligent approach expertly integrated with technical capabilities. But every journey begins with a single step. Marketers who set their sights on smarter demand generation can quickly realize benefits at a micro level that can later be replicated at a larger scale.

Exploring smarter demand generation with one segment of your target audience can be a good place to start. Integrating data, technology, people and tactics for the first time isn’t easy – but it is more manageable and achievable at a smaller scale. Ring-fence a project that leverages insight to improve targeting, messaging and optimization. Then closely monitor the results to track the impact on the sales pipeline. Spotlighting the effectiveness of smarter demand generation in this way, and sharing it at a Board level, can create an appetite for more. It might help secure investment in the technologies and skills required for a wider rollout.

The B2B sector has strived for precision marketing for decades. With the awakening of smarter demand generation, it is finally within reach.


Alex Gill explores this theme in a B2B Marketing webinar on 27 January: How to align your marketing for smarter demand generation and stronger ROI. Book your seat here.

The Hottest Three Letter Acronym for 2016: D-M-P


Marketers are overwhelmed with tools and channels, and most of these – OMG! – have a three-letter acronym (TLA) that we use to theoretically make it easier for us to discuss them (and of course, to make us feel like we are in the know!). DSP, SEM, PMD, PLA, SEO, FPD, LOL, CRM, FAN, GDN . . . the list goes on and on. BTW, “LOL” on the previous list refers to “laugh out loud,” ICYM!

IMO, the hot TLA for 2016 will be DMP – data management platform. FYI, a DMP is a data warehouse that “can be used to house and manage any form of information, but for marketers, they’re most often used to manage cookie IDs and to generate audience segments, which are subsequently used to target specific users with online ads.”

For example, let’s say that you have a CRM full of FPD (first-party data) about your customers. You can upload this data to a DMP, enhance the data with third-party behavioral targeting, and then generate audience profiles that you can use to create more targeted and effective ads across your social, search, and display channels. Compared to your competitors without a DMP, your marketing campaigns should resonate better with consumers. Information asymmetry leads to better ROI, so marketers who don’t have a DMP have more to fear than just the FOMO – they may actually be at a significant disadvantage.

All of this assumes, of course, that marketers who invest in a DMP will install it correctly and use it correctly. As anyone who has seen an amazing pitch of marketing technology knows, the product never seems to work quite as well as it does in the canned demo! Setting up a DMP properly is fraught with potential pitfalls, from not properly importing data to incorrect data interpretation. So simply having a DMP is not enough – having the right pilots of data collection and analysis is vital. Given that this is a corporate blog, now would be a good time for me to promote Harte Hanks’ DMP/service solution, which we call Total Customer Discovery.

The future of marketing is always murky, so the centrality of the DMP is still TBD. That said, theoretically DMPs make a lot of sense, and it seems likely that it will be an important component of all online marketing strategies going forward. TTYL!

Harte Hanks Announces Data Refinery to Harness Customer Data and Drive Marketing Results

Data Refinery ProcessMarketers are increasingly looking for innovative ways to get to know their customers better, and to get the most out of the campaigns they create every day. The best way to learn more about your customers is by leveraging data. This isn’t as simple as it sounds. With a plethora of channels at your customers’ disposal, both online and offline, and the growing number of devices that people use, it is difficult to harness all of that data – especially when you’re mining it from multiple sources. Utilizing big data also requires the complexities of hiring a staff to manage data, ensuring best-in-class quality and governance procedures and working with constrained budgets across siloed departments. This is no easy feat.

How do we overcome these challenges together? The answer lies in gathering and storing the most current data on your customers through a data refinery. Data refinery is a scalable platform that allows for on-demand access to compiled customer views that can be accessed by all departments within your organization. The compiled views should be nimble, customizable and rich with proprietary and third-party data sources so they effectively serve the ever-changing marketing demands placed on the various teams that need access, and as a result, empower marketers to know more and communicate better to their customers.

So how does it work?
At the heart of a good data refinery platform is the aggregation of large amounts of various data types from multiple sources and channels, both traditional and digital. A data refinery platform starts with an ideal customer profile that defines data attributes needed to deliver results. This ideal customer profile serves as your “map,” guiding data profiling and sourcing to bring together and enhance owned data with third-party data. The data refinery then cleanses, validates and standardizes the customer profile for output to any downstream marketing or sales application.

Today we are excited to announce that Harte Hanks is launching its very own Data RefinerySM solution. With our solution, access to pre-vetted data sources by vertical and marketing objective are utilized – think of this as an app store for data – reducing the time to value. Selecting data based on reliability and performance metrics optimizes data usage and spending, ensuring campaigns don’t become stagnant. To learn more about Harte Hanks’ Data Refinery click here.

A brand’s success will continue to be dependent on technology, innovation and the ability to connect with the customer in a highly relevant way. A data refinery platform is needed to bring data together and make it foundational to all your marketing and sales efforts.

Next week we’ll review what data sources are available and how best to manage them using the latest open source technologies. In the meantime, start thinking about what you could do if all your data could be harnessed, treated as a single source of the truth and accessed by anyone on demand. The possibilities are almost endless, aren’t they?

Delivering data from all different sources and augmenting it to form purpose-built customer profiles allow you to understand your customers. This insight is powerful and allows you to acquire new customers, reduce churn within your existing customer base, increase repeat purchases and increase customer satisfaction.

A Data Refinery Platform Helps You:

  1. Better understand existing customer base
  2. Create models and segmentation to find better prospects at scale
  3. Understand existing customer behavior, avoid attrition and encourage growth

Marketing Technology: Where’s My ROI?


The modern customer journey is consumer driven and often fractured. Unlike the linear, vendor-led customer journeys of the past, the buyer is now in full control. With endless options – and a bevvy of information about each product or service readily available for consumers – marketers must devise new ways to attract customers and secure brand awareness and loyalty. A slew of new marketing technology, including CRM, marketing automation and inbound marketing platforms, have risen up to solve the new customer journey riddle. But despite the effectiveness of these platforms, too many B2B companies are reporting negative ROI for marketing technology investments. There are a number of reasons why.

Failure to Launch

The B2B sales cycle is a complex process. Unlike B2C products, there is no such thing as an “impulse purchase.” Buyers typically spend weeks, months and sometimes even years researching and deliberating before deciding on a purchase – particularly where big-ticket items are concerned. Marketing technology can help significantly simplify this process, but it isn’t a magic bullet. Marketing platforms aren’t plug and play; they are a set of interconnected tools for marketers to utilize as part of an overall strategy. Too often, B2B companies purchase marketing technology, but fail to allocate the resources necessary to realize their benefits. Marketing systems are a great delivery system, but engaging and strategic content that guides prospects along the customer journey must be created first. You can buy a car, but if you don’t fill it with gas and get behind the wheel, it isn’t going to move.

Scratching the Surface

Most of the marketing technology platforms available today come equipped with an array of features that justify their cost – intelligent analytics, A/B testing, easy integration, etc. Companies who fail to realize ROI on these products are often utilizing only a fraction of the features available to them. These features can significantly enhance the power of the platform and should be utilized whenever possible.

Stove Piping

With so many different types of technology available, B2B companies often have more than one system for sales and marketing. Failure to integrate these systems – particularly marketing automation platforms and CRM software – creates a confusing environment where systems are not communicating with each other and often duplicating efforts. In order to get the most out of marketing software and a favorable ROI, marketing platforms and CRM software should always be integrated.

Putting the Cart Before the Horse

Too many B2B companies dive head first into marketing technology – purchasing platforms without a full understanding of the system or a plan to implement it. B2B marketers often find themselves tasked with becoming technology experts trying to implement and integrate systems they know little, if anything, about. Additionally, systems are often purchased before a strategy has been developed to utilize them.

Boost Your ROI

To fully realize the benefits of marketing technology platforms, B2B marketers must view these platforms as an important tool, but as only part of the process. Creative campaigns, strategic plans and actual customer conversations are all an integral part of the modern customer journey as well. Before purchasing a new marketing technology platform, B2B companies should perform due diligence on the products they wish to purchase and have a plan in place on how they will be utilized.

And if you need help boosting the ROI of your marketing investment, Harte Hanks has extensive experience integrating marketing technology with marketing strategy. We’re here to help!

Back to the Future: Predictive Analytics


What if you knew what your customers wanted, when they wanted it? With predictive marketing analytics, gazing into the future is entirely possible. While predictive analytics is not a new concept – marketers have often tried to use past performance to predict future behavior – the dawn of the information age has amplified its effectiveness and usability. Predictive analytics allow marketers to focus efforts and maximize their budgets by identifying targets who are ready to buy and by eliminating those who aren’t.

Big Data

 To accurately predict consumer behavior, you need more than focus groups and surveys. The era of Big Data has armed marketers with a deluge of information on consumers – including engagement with marketing automation platforms and “intent” data from across the web. The technology to crunch this data and make sense of it is rapidly evolving, providing marketers with a roadmap to reach the right audience at the right time.

Data in Action

The Big Data era has produced an incredible amount of information about habits, desires and tendencies of consumers. Marketers who follow these digital footprints can optimize their marketing efforts to target individual audience segments and personalize messages to speak directly to potential customers. Predictive analytics can help create incredibly specific buyer personas – marketers no longer need to rely on broad demographic data and guestimates of what a particular buyer prefers. Enhanced buyer personas lay the groundwork for highly personalized messaging for nurture campaigns, which multiple studies show leads to significant increases in conversion and revenue. Predictive analytics also provide the benefit of targeted spending. Knowing what audiences to target and which platforms to target them through significantly increases the impact of marketing budgets.

B2B Adoption

B2B marketers have lagged behind their B2C counterparts in the adoption of marketing technology ­­– predictive analytics included. And while it’s true that personalized data from individual consumers offer a more clear view into purchasing habits and tendencies, plenty of data exists for B2B customers that can be utilized to implement more intelligent marketing tactics. Purchase history, for instance, is a great predictor of current and future behavior. If a customer has recently purchased a software system that won’t need an upgrade for three years, targeting that customer with marketing messages is not only inefficient, but could negatively affect that customers’ perception of your brand. Existing software licenses, log-in frequency, help desk calls and firmographics can also help B2B companies predict the need and desire for their products. Normally this kind of data will predict the type of customers that buy your products. Add social data sources to the mix, and you can predict customers that are ready to buy.


Depending on the level of sophistication and budget resources, B2B marketers can deploy analyst-led solutions or automated “black box” solutions to perform predictive analytics. For larger, more comprehensive data operations, an analyst-led approach is preferred. Computers are wonderful, but a human touch – specifically when there are oddities in the data – can more accurately utilize the information output to design programs and messaging that take into account both the customer and the nuances of the company. However, there are various automated solutions that are more than sufficient for less sophisticated marketing automation programs. Both approaches have their own merit, but one thing is clear: predictive analytics allow businesses to focus on what’s important and discard what’s not, leading to amplified revenue growth – and happy customers.


The 4 Biggest Challenges Facing B2B Tech Marketers Today (Part 2)



A couple of weeks ago, I kicked off a blog series about the four biggest challenges faced by B2B tech companies. If you missed the first installment about creating an ecosystem that makes use of all available tools and technologies, you can read about it here.

Today’s challenge is around generating high-quality, real-time data and using it to drive sales and ROI.

CHALLENGE #2: How do I make my data high-quality, real-time and usable to drive sales?

Marketers today are inundated with data. Just when you’ve successfully integrated Instagram into your marketing activities, a new channel is added to the mix, be it a new social network, a mobile app or even virtual reality and interactive holograms. With the army of channels comes a network of devices. From our fitness trackers to our appliances to our cars, almost everything is getting connected to the Internet. With this propagation of channels and devices, we have more data, more sources and more insights than ever before. The challenge now is figuring out whether that data is quality and usable.

How to solve it

At Harte Hanks, we are all about the data. Data analysis and analytics is in our DNA, and we’ve spent the better part of the last decade figuring out how to make data work for us. Here’s what we’ve learned about increasing data quality to effectively run your business:

Obtain Quality Data (Data Remediation)

The first step to driving sales through data insights is to make sure you have quality data. My colleague Seth Romanow recently outlined his proprietary 4-Box model for determining whether data will meet marketing, analytics or campaign requirements. In a nutshell, as a marketer, you must:

Match data requirements with your ideal customer profile and marketing objectives, ensuring that data is “fit for purpose.”

Perform a data audit that implements the 4-Box methodology to segment your data based on completeness against your previously defined ideal profile and engagement.

Identify the gaps and develop a remediation plan that defines clear paths to cleaning, updating, appending and enriching your data.

Execute the remediation by fixing data sources and process issues and incorporating new digital and social data sources to add depth to the record and increase the ability to segment and target more effectively.

Use Quality Data to Drive Sales (Predictive Analytics): Once you have quality data at your disposal, things start to get really interesting. Predictive analytics is a great way to drive bottom line results as it can reduce the need for expensive third-party data or telemarketing support, particularly for acquisition programs.

What It Is – Predictive analytics helps identify when prospects are ready for an up-sell or a cross-sell, but that’s only half of the story. They also enable marketers to focus their efforts and budgets on prospects with a high response rate, and they can tell companies the prospects with which they should not waste their time. For example, targeting an individual who just invested in a product that met their needs and won’t need an upgrade for three years is not a worthy recipient of marketing dollars – not only could it waste time and budget, it could also harm brand equity.

How To Do It – There are a couple of different ways to implement predictive analytics: through an analyst or through a black-box solution. If you suspect your data has oddities or you need precise, robust outcomes, the analyst-led, human approach is best. If budget is a consideration and you are looking for a quick, scalable and repeatable solution, black-box algorithms may be the way to go. With either option, predictive analysts pinpoint firms that have exhibited a desired behavior, extrapolate the common factors about those businesses, and then analyze the behavior and features of the business to help identify others with a similar profile to be prioritized for marketing activity.

With data remediation and predictive analytics, marketers can improve their data quality and use it to more effectively drive targeted sales. So, what’s coming up next? The final two pain points delve deeper into ROI and delivering consistent communications throughout the customer journey.

  • How do I maximize ROI with fewer resources and less investment?
  • How do I unify communication strategies across channels to drive customers through the buyer journey?


Key Takeaways from 3Q Digital Summit (Part 2)

Mobile and Digital Trends

3Q-2Last week, we shared some key insights from our 3Q Digital Growth Summit at Levi’s Stadium, where we hosted 150 colleagues, partners, and clients to discuss the newest and most critical trends in digital.

Today, I’m breaking down the conversations and takeaways from our more intimate fireside chats and client panels and spotlights. Read on for insights from SurvkeyMonkey, PicsArt and more.

Fireside Segment 5: Stay Ahead of the Mobile Curve

Participants: Moderator: Craig Weinberg, VP of Mobile Strategy, 3Q Digital; Wilson Kriegel, CBO, PicsArt

Panel/Topic summary: PicsArt is a full-featured mobile photo editor, collage maker, drawing tool, and social network for artists that draw 99.9% of its audience and engagement from mobile. The company is an example of how to build success and a vast, active audience (it is second to Instagram as highest- rated app) using mobile and emerging mobile audiences as its core.

Key Takeaways

–  Lifecycle management and data is crucial in building mobile strategies.

–  Get analytics in place to measure time of use, frequency of use, and value by users segmented by geo and age

–  Don’t assume the iTunes store works better and gets more valuable users; PicsArt has had equal success on Android – especially given that developing countries tend to skew towards Android.

“Don’t try to compete on desktop if competitors have already nailed the desktop experience. Find new mobile audiences – go young and global.”

Client Panel 6 – Today’s Digital Issues

Participants: Moderator: Neal Ungerleider, Fast Company; Slaton Carter, Director of Digital Marketing, The Real Real; Erica Yoon, Sr. Digital Marketing Manager, Sungevity; Matt O’Day, Digital Marketing Lead, Square

Panel/Topic summary: Our client panel featured experts from Square, Sungevity, and The Real Real – three fast-growing companies experiencing different challenges over their respective arcs.

Key Takeaways

–  Solar provider Sungevity recently added a product for businesses to go with its residential offering; the challenge has been creating messaging that stands out with competitors saying the same thing (save money).

–  For The Real Real, a consignment luxury ecommerce company, personalization is the next hurdle to building on its already rapid growth.

–  Square started as a financial payment solution, but it’s grown and expanded so quickly over the past two years that the next challenge is becoming widely known for everything it provides, not just the flagship product.

– “Gathering the data and bringing it together in an actionable form will be the next big step.”

Client Spotlight 7: SurveyMonkey

Participants: Ada Chen Rekhi, VP of Marketing, SurveyMonkey

Panel/Topic summary: SurveyMonkey, a true Silicon Valley unicorn, has raised over $1B in funding since its founding in 1999. VP of Marketing Ada Chen Rekhi discussed the company’s success factors in international expansion.

Key Takeaways

The market is changing; there are more huge international companies and a greater percentage of international users than ever. To succeed in the international market, a company must:

–  Speak their language.

–  Transact in their currency.

–  Focus on expanding international and content creation processes – including landing 

–  Structure a pre-plan – determine currency and time zones.

–  Keep testing – is this scalable?

–  Partner to go international – leverage partners to scale.

–  Understand translation vs. localization. Translation is straight text for text. Localization is understanding psychographic intent of people and what they prefer in their geos.

“Six years ago, SurveyMonkey had 4 employees. Now we have 650. Much of that is due to international expansion.”

Taking Your Customers from Anonymous to Known: Introducing Total Customer Discovery

A Deeper Dive into the Solution


Today, we are excited to announce our newest solution to enable smarter customer interactions: Total Customer Discovery. You can learn more about the details through our press release, video and digital guide. In this blog post, I’m going to break down some of the technology components that went into creating it.

In a nutshell, Total Customer Discovery provides a holistic, 360-degree profile of customers, merging data from online and offline channels and across devices. This single customer view encompasses data across demographics (contact data, social profiles); psychographics (interests), historical (purchase and promotion history) and influencing power (networks, connections). With this richer customer view, marketers can deliver enhanced and personalized customer experiences, leading to increased acquisition, retention and, ultimately, ROI.

So without further ado, here are the different components of the Total Customer Discovery Solution and what they help address:

Solution Component: Cross Screen Identification

With cross-screen identification, each customer has a persistent, unique ID that carries with them, helping marketers track associated devices with that customer even when customers delete their browsing history (and their cookies). With Total Customer Discovery, we can identify and track customers across various devices (mobile phones, tablets, computers, laptops and so on), learning their behaviors, adding to their customer profiles and offering a seamless brand experiences across touch points that takes into consideration their past purchase history and preferences.

Solution Component: Cross Journey Mapping

To solve the problem of internal silos and overwhelming amounts of data, the cross journey mapping function captures customer’s digital behavior and stores meaningful attributes, such as click, searches, interests, preference, etc. to produce richer, more multi-dimensional customer profiles. These attributes can then be linked with other data sources within an organization such as a Customer Relationship Management (CRM) database. Total Customer Discovery identifies customer interactions across multiple devices and channels, so that we can track a customer throughout their entire journey, from smartphone, to tablet, to computer, to in-store.

Solution Component: Data Onboarding

A single view of customers provides a comprehensive view of the purchase journey. Integrating both online and offline data helps round out the single view of customer for a comprehensive picture of customer behavior for better retargeting and personalization. With data onboarding, online and offline data are merged and customer files are created using email or physical address lists that are matched with a database of advertiser tracking parameters. Particularly for brick-and-mortar stores, integrating online and offline data sources is crucial for delivering relevant content across channels based on the customer identification, from digital interactions on their smartphone to offline purchases at a retail store.

Solution Component: Social Linkage

Personalized, relevant content is the key to driving ROI in today’s world of real-time “micro-moments.” With social linkage, customers’ social interactions and behaviors are tracked across sites to enable deeper customer segmentation. Social linkage takes data from over 150 social sites, including Facebook, LinkedIn, Pinterest, Twitter and Google+, and gives marketers insightful social profile data to inform their social investment decisions and make their digital marketing efforts more effective.

We’d love to tell you more about how Total Customer Discovery takes customers from anonymous to known. For more information, you can visit or email

Key Takeaways from 3Q Digital Growth Summit (Part 1)

Consumer-First Mentality, Landing Page Optimization and Attribution

panelIn digital marketing, the one constant is change. 3Q Digital gathered 150 colleagues, partners, and clients to Levi’s Stadium, the home of the San Francisco 49ers and Super Bowl 50, for a day- long dive into the newest, emergent, and most critical trends in the digital space – including the true growth drivers companies must embrace going forward.

The day of panels, interviews, workshops, great food and drink, and 49ers sightings took place in the BNY Mellon Club West Conference Center, which opens on the ground level to the field’s 50-yard line. Participants included stalwarts from 3Q, Google, Yahoo, Optimizely, SurveyMonkey, PicsArt, Fast Company, The Real Real, Sungevity, and more.

Couldn’t make it to the summit? We’ll break down takeaways and key lessons learned from each of the panel’s here. Tune in next week for a recap of the fireside chats and customer panels.

Panel 1: The Evolution of Agencies and What Clients Should Expect

Participants: Moderator: Scott Rayden, CMO, 3Q Digital; Mason Garrity, VP of Strategy, 3Q Digital;
Ron Fusco, Director of Strategy, 3Q Digital; Marcy Strauss Axelrod, Strategy Lead, 3Q Digital

Panel/Topic summary: Agencies simply doing channel management are doing their clients a disservice. Today’s clients should demand more from an agency: a systematic methodology that identifies and addresses missed opportunities for the client to discover and connect with their customer base.

Key Takeaways

– Incorporate a customer-first mentality into your marketing; it’s not about channels anymore.

–  Focus on the following growth drivers: customer journey, technology, user experience, devices, media channels, and analytics.

– “The future of digital marketing relies on a consumer-first mentality. All strategies must be built around that.”

Panel 2: Fine-Tune Your LPO

Participants: Moderator: Joe Kerschbaum, Account Director, 3Q Digital; Adrienne Abrams, Sr. Director of Creative, 3Q Digital; Sean McEntee, Account Lead, 3Q Digital; Hudson Arnold, Strategy Consultant, Optimizely

Panel/Topic summary: Landing-page optimization requires iterative testing on strong hypotheses to raise conversion rates across mobile and desktop pages. We looked at a case study for CuriosityStream, a new brand that launched in early 2015 and achieved 31% CVR lift in the first three weeks after LPO testing and adjustment.

Key Takeaways

–  Good LPO is not a one-time deal; it requires iterative testing (and each cycle of tests must have hypothesis and a purpose).

–  If you’re new to LPO, invest a little at first, and don’t be afraid of change.

–  If data is inconclusive, examine the testing parameters and adjust.

–  Be smart about the user experience; leverage successful ad copy in landing pages where appropriate.

– “If you haven’t gotten into LPO, just do it. If you’re spending thousands of dollars on ads, lost clicks cost much more than better landing pages.”

Panel 3: Increase Revenue with 1st-Party Data

Participants: Moderator: David Rodnitzky, CEO, 3Q Digital; Brad O’Brien, Director of Social, 3Q Digital; Joe Stephens, Director of Native Advertising, Yahoo; Russell Sprunger, Advanced Data Strategy Lead, Google

Panel/Topic summary: As evidenced by Google’s recent announcement of Customer Match, 1st-party data is one of
the most powerful assets in digital marketing. Whether on social, native, search, display, or retargeting, 1st-party data allows customization, segmentation, and advanced targeting proven to significantly improve ROI. Combined with lookalike targeting, 1st-party data also gives advertisers access to relevant new audiences for their purchase funnels.

Key Takeaways

–  1st-party data quality depends on recency to be at its most relevant.

–  With Custom Audiences on Facebook, a key to using 1st-party data effectively is intelligent segmentation of lists. Take a relevant approach, not a blanket approach. All customers are not created equal.

–  1st-party data can be used for exclusions (e.g. users who have already converted).

–  Be thoughtful and responsible with your data.

“1st-party data lets you know who you’re speaking to and how you’ve spoken to them – and what their last touchpoint with your brand was.”

Panel 4: Device and Channel Diversity – The Attribution Riddle

Participants: Moderator: Ron Fusco, Director of Strategy, 3Q Digital; Ada Pally, Sr. Director of Client Services, 3Q Digital; David Perez, Acquisition Marketing Specialist, Convertro; Ramy Mora, VP of Worldwide eCommerce Marketing, HP

Panel/Topic summary: Attribution is still a bit of a black box in digital marketing; strategies depend on company objectives and preferences. Offline-to-online (and vice versa) interplay is one of the most crucial measurements for companies with both website and brick-and-mortar properties. Attribution isn’t perfect, but it can help measure value of interactions and show companies where the customers expect to be able to interact with them.

Key Takeaways

–  Attribution is not all-or-nothing; start by making sure tracking parameters are in place.

–  Advanced attribution must be holistic; track all channels and device types to gain ability to 
optimize campaigns.

–  Every touchpoint (online/offline, SEM, social, etc.) needs a mobile connection experience.

– “The biggest attribution win is to understand how channels and devices interact with each other. From there, it’s easier to derive the value of each touch point.”

The ABCs of Identifying Your Best-Selling Products

Bestseller red vintage stamp isolated on white backgroundThroughout my years of primary and secondary education, I often heard comments about how a fellow student “screwed up the curve by getting a high grade on the test.”  Ten years after graduation, I’m finding that the concept of weighted distribution is still practical and relevant.

We’ve all seen “New and Best Selling” as a sort option from a favorite retailer. Did you know that weighted distribution enables a company to determine those results?

Now I am going to throw you a math problem, but don’t let it scare you!  I’ll break everything down into simple addition and multiplication. Take a look at your previous 9 months of sales records, and then separate them out by time periods:

  • Time Period 1: Count orders by product in the past 3 months
  • Time Period 2: Count orders by product for previous quarter (3-6 months ago)
  • Time Period 3: Count orders by product for quarter before (6-9 months ago)

Time Period 1 will be most important because it represents your most recent sales.  Time Periods 2 and 3 carry less importance because they are no longer as relevant – perhaps they include an older version of a product that has been discontinued or replaced by a newer style.

Our next step is to apply a weight to each time period.

  • Weighted Time Period 1: Multiply all the totals in Period 1 by 50%
  • Weighted Time Period 2: Multiple all the totals in Period 2 by 35%
  • Weighted Time Period 3: Multiply all the totals in Period 3 by 15%

Add all weighted time periods and sort by the largest weight first.

As a last step, separate the results into 3 categories:

  • Top (AProducts)
  • Middle (BProducts)
  • Low (CProducts)

Based on this weighted model, you now have insight into which products are your “New and Best Selling products,” where you should put your marketing dollars, and how to better manage your inventory.

I’ll give an example of a time that we used this ABC product/inventory report for a client. A technology client of ours markets their sample products to engineering companies and researchers in order to get their products in prototypes. As such, they give out thousands of samples a week through a site that Harte Hanks created and now manages and enhances (I am actually a software engineer on that project).

The client requested that we evaluate how to obtain more organic search traffic by applying search engine optimization to the site.  Based on Internet research, I created a list of tasks to accomplish SEO, and one of the top is to create an XML site map that allows web crawlers to easily identify all products.

The web crawlers/spiders have sophisticated algorithms designed to filter out pages where content appears duplicated.  Since my client’s products are in many cases very similar, there are many similar models being filtered and never showing up when searched on Just yesterday, we had no hits from and one from  Products that are searchable at are often not popular items.

I applied the ABC report to the product list to help populate priority in the XML site map as defined by this spec so that web crawlers would give higher priority to my client’s top shipping products and make them searchable.  By applying ABC, we realized that out of the 33,000 available products on the website, 600 products represented 1/3 of sales, so we prioritized those. The code release is happening now, and we expect to see SEO impact shortly.

The ABC product/inventory report has many uses in business, such as making sure customers can easily navigate to “New and Best Selling” products, marketing hot selling items, supply chain management and inventory management to account for hot selling items, and the management of seasonal sales changes.  Just like in school, a curve (and weighted distribution) can be a business’ best friend.

How Data Refinery Helps Companies Transform Raw Data into Gold

mapr hadoop data refineryCompanies are being bombarded by new sources of data faster than they can consume them. The explosion of emerging customer data sources (social, clickstream, transactions, mobile, sensor, etc.) presents both a huge opportunity and a challenge.

The opportunity is that new data sources can reveal insights for applications that can drive competitive advantage. Businesses want to analyze and integrate more complex types of data to add new insights to what they already know about their customers to improve service and add more value to clients.

The challenge is that managing this growing volume and complexity of data is difficult with traditional database technology. As the volume of data grows, performance goes down. As data complexity increases, more administrators are required to organize data into something meaningful.

Apache Hadoop technology has emerged as a powerful and flexible big data platform for companies to store and process vast quantities of raw data over a long period of time. Companies no longer have to set limits on how much and what kind of data they can ingest into their data repositories. The mantra had become, “Keep it all in case it’s needed”. But to make the hordes of data useful, companies need a mechanism to transform the data into a valuable business asset.  A new mantra is emerging, “Keep it all and let a data refinery sort it out.”

What is a data refinery?

A data refinery is a critical component of a big data strategy, especially for customer-facing enterprises that want to build robust and accurate customer profiles to improve customer interactions.

Think of it as an oil refinery where raw material (oil) comes in and is separated in the different streams for downstream production and products such as gasoline, motor oil, kerosene, and more. Similarly, a data refinery ingests raw material (data) into Hadoop in native format at any scale and can then refine it into other downstream systems or customer-facing applications. Raw data must be refined or explored to understand relationships and whether there is meaning in the data (through tools such as Apache Drill). Next, the data refinery cleans, enriches, and integrates data with other sources of structured data in downstream database or business intelligence solutions to deliver the insights that create more personalized customer relationships.

Harte Hanks builds a data refinery to improve data quality

To serve their clients better, Harte Hanks wanted to ingest and integrate more types of customer data into their clients’ contact databases. They wanted to gain new insights by getting access to the increasing volumes of data generated by people interacting with their clients’ brands over multiple channels. These insights could then feed into their clients’ marketing processes to help drive more effective marketing programs.

Harte Hanks knew their traditional database technology could not manage this huge increase in data volume and complexity, so they selected the MapR Distribution including Hadoop for its big data platform. A key component of the technology platform is the data refinery that cleanses and enriches the growing stream of new data sources that are ingested into the customer databases.

More data yields higher accuracy and new customer insights

The MapR data platform enables Harte Hanks to enhance the performance, scalability and flexibility of its solutions so its clients can more easily and quickly integrate, analyze and store massive quantities of data for deeper insights to better serve customers. This new solution enriches and enhances customer databases by integrating all kinds of digital data, survey data, reference points and more, all while maintaining the performance and ease-of-use they’ve come to expect.

Performance accelerates turnaround time to clients

Harte Hanks is able to increase customer satisfaction through faster time to value and more accurate data sets. Data processing that used to take one to three days can now be accomplished in hours, if not minutes. Their clients can put marketing insights into action immediately for faster results.

Better data = better marketing

The Hadoop-based data refinery can transform a deluge of data into invaluable company assets. Harte Hanks can now offer its clients faster and more accurate customer insights and more complete customer profiles so they can create smarter, more relevant and effective customer interactions.

If you want to learn more about Hadoop or how to get started, MapR provides free on-demand training and examples of big data industry solutions.

About the author

Steve Wooledge, Vice President, Product Marketing, MapR

Steve brings over 12 years of experience in product marketing and business development to MapR. As Vice President of Product Marketing, he is in charge of increasing awareness and driving demand, as well as identifying new market opportunities for MapR. Steve was previously Vice President of Marketing for Teradata Unified Data Architecture. Steve also held various roles at Aster Data, Interwoven and Business Objects, Dow Chemical and Occidental Petroleum.

Steve holds an MBA from the Kellogg School of Management at Northwestern University, and a BS in Chemical Engineering from the University of Akron.

5 Ways to Improve Your Contact Center Through Digital Marketing

Use Your Contact Us Page and Digital Marketing to Improve Customer Satisfaction

five ways

We live in an age where the customer – not the company – dictates your brand. In the old days, you could put out a massive advertising campaign with the moniker “Fly the Friendly Skies” and convince consumers that your airline was the nicest around. Today, an angry customer can create a video called “United breaks guitars,” and millions of consumers will share it in second.

Every interaction with a customer is an opportunity to create a brand advocate or a raving-mad critic. Many companies don’t realize that there are relatively painless ways to use digital marketing to increase the chances that you wow every customer who interacts with you. Here are five ways digital marketing can help:

  1. Help people find your contact info online via search engine optimization. Some companies purposely hide their contact us info deep into their website, in the hopes that customers will end up getting their questions answered without talking to an actual human. While this saves money in the short term by reducing the size of your contact center, the long-term negative hit to your reputation when customers complain to their friends and through social media will cost you dearly. Using search engine optimization (SEO), you can edit your website content and code to increase the visibility of your Contact Us page, making it easier for clients to get their questions answered promptly.
  2. Retarget visitors with a customer satisfaction survey. Retargeting is a form of advertising that serves banner ads to customers who have visited a particular page or completed a particular set of actions on your Web site. While most people use retargeting to convert a customer from a browser to purchaser, contact centers can use this technique to increase their customer satisfaction (CSAT) survey results. Simply retarget everyone who visited the Contact Us page of your website with a banner ad inviting them to give you feedback about their experience with your contact center, and your company overall.
  3. Use analytics to reverse-engineer why customers contact you. When a customer visits your site, your online analytics tool (usually either Google Analytics or Omniture) follows their every move. Which pages did they visit? In what order? How long did they stay on a page? How frequently do they visit the site? All of this information can be mined to figure out how your customer ended up at your Contact Us page. Did the customer look at your “Frequently Asked Questions” (FAQ) page and not find what they wanted? Use this information combined with your call log to create additional FAQs to resolve future customers’ needs. Do numerous customers go to the same product page right before calling customer support? Check the URL of that page; perhaps it is broken.
  4. Test different landing pages to optimize business objectives. The way you design your Contact Us page will influence the way customers interact with it. For example, a page with a giant toll-free number in the middle of it – and not much else – will inevitably lead to lots of calls to your contact center, but a page with links to your FAQs, a live chat option, and a less prominent phone number will increase site interaction at the expense of your contact center. The right balance of contact center versus web-based customer support will vary for every company. The good news is that there are plenty of tools available to help you test different Contact Us page experiences to figure out what look and feel drives the best business success for your business. Tools like Optimizely and Unbounce as well as “landing page optimization” (LPO) experts can help you set up the right tests.
  5. Encourage mobile app installs for customer loyalty. Many companies use their mobile apps to drive sales to their business and also respond to customer questions and concerns. When a customer visits your Contact Us page, why not encourage them to download your app with a prominent link? This is even more relevant if the customer is visiting your mobile website.

Digital marketing is usually known as a way to efficiently drive new customer acquisition and increase existing customer purchases. Using it to enhance your customer satisfaction is just one more great reason to invest in digital marketing.

Customer Data: Why Quality Trumps Quantity + a 4-Step Approach to Remediation

When Less is More

Often times, the discussion about data is around quantity – we think we need more data, more sources, and more insights. But marketers are already inundated with data. Getting more data is not the challenge – getting quality, useful data is.  If we can shift our mindsets to getting more out of the data that we already have, we will open up an easier path of understanding what quality data is telling us.  Quality data results in trustworthy reporting and truer insights, which can help us deliver more effective campaigns thorough better targeting, messaging and content.

But How Do We Find Quality Data?

Over the past two years I have worked with a number of clients to help them improve the quality of their data.  From those engagements and with client input, I developed the 4-Box model to help our clients develop effective data remediation strategies designed to get the most out of their data. At a high level, the 4-Box model provides a methodology to very quickly determine whether data will meet marketing, analytics or campaign requirements. The 4-Box model is a proven approach to evaluating the state of your data and providing recommendations on how to improve and manage the data to ensure that it is fit for purpose.

Step 1: Fit For Purpose

The first step is all about matching your data requirements with your ideal customer profile and marketing objectives. I wrote a blog post a few months ago on how to build the ideal customer profile. The key is to clearly define whom you want to reach based on your marketing or campaign objectives.  It is important to be as specific as possible in defining the ideal profile.  The guideline we use is “what are the minimum attributes” needed to effectively segment customers for a targeted campaign. At the same time, decide which attributes would add significant value if used to append or enrich each of the records at the contact or account level. In addition to standard attributes such as role, title or firmographics, think about using social, digital or behavioral attributes.  These will come into play as part of developing a remediation and enrichment strategy.

Step 2: Data Audit

The data audit is where the 4-Box methodology really starts. By auditing data through the 4-Box lens, you can segment your data based on completeness against a defined ideal profile and engagement.  Engagement may be time based. For example: has contact responded within the past 18 months to a marketing or promotional program? At Harte Hanks we use the Trillium Discovery tool as part of the data audit process to evaluate the integrity and structure of the data.  The data is then matched to a reference file to determine what percentage of records require updating, which is a clue as to the age of the file. We have found that the most effective audit is one that targets a specific segment, such as enterprise customers or customers of certain products or solutions.   4-box data remediation strategy

Step 3: Remediation Plan

After the data audit, you should develop a remediation plan that defines clear paths to cleaning, updating, appending and enriching your data.  This may also include a reactivation program for those customers whose data is complete, but who have not engaged in some time. For many companies, this represents 60 percent of their customer base, so reactivating just 5 percent of these customers will yield significant results.   4-box data remediation strategy

Step 4: Deploy

Once the paths to data refinement are defined, it is time to execute the remediation by fixing data sources and process issues, as well as incorporating new digital and social data sources to add depth to the record and increase the ability to segment and target more effectively. This can be done in a variety of ways, including:

  • Identifying and fixing upstream data capture and issues
  • Selecting third-party data based on the ideal profile framework and marketing strategy
  • Selecting other data sources and types to build out the profile
  • Initiating campaigns based on contact policies for reactivation. Ensure you are compliant with privacy policies and laws.
  • Using contact center and social listening to further profile contacts and accounts and to validate data. This is best used for high value contacts, such as IT or Business Decision Makers or for key accounts.

Quality Data in Action

We worked closely with a global technology client to test the 4-Box methodology.  Our client was looking for ways to improve response rates and, ultimately, MROI.  Using 4-Box, we identified areas for data improvement, evaluated the composition of the roles, titles and number of contacts per account and created a baseline for measuring data quality improvement. The results speak for themselves:

  • 1000:1 ROI for data remediation.  Every dollar spent on data using the 4-Box strategy yielded a $1000 return in incremental revenue.
  • 3.5 percentage point increase in open rates

By improving the overall quality of core data and increasing the data that is “fit for purpose,” the client experienced positive effects on both campaign performance and MROI.

Up Next: Continually Improving Your Data

By aligning marketing requirements with data requirements, we try to ensure that data is “fit for purpose” and therefore unlock more value from existing data assets.  The 4-Box method I outlined above is a very effective and practical way to understand the state of your data and to provide an actionable plan for reactivation and enrichment efforts. By uncovering the value of existing data, you can focus more of your budgets and efforts on creating more powerful customer experiences. Oh, and once you’ve got this part down, we’ll look at a real-time rules-based engine that continually improves your data based on the 4-Box rules that you’ve defined. Stay tuned.

Single Customer View: Going Beyond Cookies to People and Intent

Last week, I responded to web analytics expert Avinash Kaushik’s popular blog Occams Razor where he “de-mystified” misconceptions about digital marketing and analytics. I talked a bit about the proper use of data in effective marketing. This week, I’m going to address why “Cookies! Cookies are all we need!” is a myth, and how other technologies should be used to create a more useful single customer view.

Why cookies aren’t the answer

There is no doubt that cookies give us critical information regarding website or other digital behavior. They help us understand who is visiting a site, how often, what their sessions look like, who is responding to emails and more. Plus, they are a critical part of triggered customer experiences.

But when we rely too heavily on cookies and data, we can depersonalize the user. We often forget that users do things other than visiting our site, and we lose track of them when they log out or leave our site. Most importantly, marketers are starting to question whether cookies should be the primary method for tracking, responding to and gathering insight from site behavior. There are three trends that don’t bode well for cookies:

  1. automatic rejection of 3rd party cookies by Safari and Firefox and higher cookie rejection by users;
  2. use of multiple platforms, particularly mobile and tablets;
  3. the mash-up of work and personal PC use—we may browse on one PC at work and purchase when we get home.

If you really want to get at a user’s full spectrum of behavior and intent to more effectively market to them in a relevant way, you need to go beyond cookies. As Avinash points out, “If we rely on cookies, we are going to make poorer and poorer decisions about our products and our marketing every single day.”

For example, say I’m looking to buy a desk. I may use my PC and my mobile phone to search multiple stores for desks and research pricing. Each store may track my behavior on their specific website with cookies while I browse, but they lose my scent when I move to a competitor’s website or to my mobile. They certainly lose track of me when I take my purchase to a brick and mortar store.

How to cater to each individual’s behavior and intentions

Avinash says he is, “…a bit freaked out that not enough people are worrying about this problem,” of relying solely on cookies. He’s right in pointing out that a cookie-centric strategy just isn’t going to work for businesses moving forward.

There’s a better way to go about tracking your prospects and customers and using the information to provide a more personal, relevant experience.

Sources for Data Enrichment

Having what is now commonly known as a single customer view across sites, across devices and even online to offline will help you to better understand your customers and provide insights to connect with them in a more meaningful and effective way. You achieve this customer view not with cookies, but through technologies that gradually move each individual from an anonymous user to a known identity that you can market to in a personalized, relevant way.

A single customer comes from integrating technologies like:

  • Cross-site data capture that enables personalization through progressive profiling
  • Device-to-individual identification that recognizes a customer across devices (such as PC to mobile to tablet)
  • Social network data and presence to identify unique individuals across social platforms
  • Offline-to-online match that integrates your digital data with your CRM database for better segmentation, targeting and more
  • Email consolidation to identify customers with multiple email addresses and determine the primary address for better campaign response

Learn a bit more about these data integration techniques in this post.

Through a framework that integrates these various technologies, we can more effectively harness siloed information on individual users into a single customer view and begin to offer users personalized content and messaging in real time. By offering each user more relevant content, we encourage users to begin self-identifying online, allowing us to further hone our messaging to better assist prospects and customers in their decision making.

But, a word of caution: user data, given the number of channels and platforms, will never be perfect. This is when aggregating that information can develop meaningful personas and segments to create more relevant and engaging experiences.

Tying it together

Data plays a vital role in business and marketing. And cookies are an important marketing technology. But I wholeheartedly agree with Avinash that neither can stand on its own.

Our focus should be on people first—identifying them and their intent through the use of data and technology. By moving more people from anonymous state to a known entity, we can provide them with the relevant information they want and need, creating smarter, more effective customer interactions that result in better customer experiences.

Which is really what marketing is all about.

Why You’ll Lose with a Data-First Strategy…and What To Do Instead

Avinash KaushikA few weeks ago, Avinash Kaushik, digital marketing evangelist at Google, shared his perspective on how to “de-mystify” misconceptions about digital marketing and analytics on his popular blog, Occams Razor. While he made several excellent points, the myth-busters that resonated most with me were in relation to creating a data-first strategy and…Cookies! Cookies are all we need! I’m going to talk about the proper of use of data in driving effective marketing in this blog, and tune in next week to learn about how to go beyond cookies and use other technologies to create a single customer view.

Looking for data in a gold mine

As a company that specializes in data insights, it may seem odd that we agree that data-first strategies really aren’t the right approach. Let me explain: data on its own is not always useful, and too much data can be equally disadvantageous.

Simply looking for insights in large data sets is like panning for gold. It’s a lot of work for limited treasure. However, if we take a more targeted approach by doing some proper geology, planning and then mining for the gold, it will yield exponentially higher returns.

In other words, to get the most of your data for smarter decision making, data collection should always have a specific purpose, and the data collected should be fit for that purpose. Check out this post for more on making sure your data is fit for purpose: Who Wants to Waste Time or Money on Data? Not Me. 

Data will help you find more useful answers to your problems when you follow these steps:

1. Start with the key business issues that you are trying solve. These should have high impact.

2. Develop a specific question or hypothesis that will guide discovery. Some examples are:

  • Is there a correlation between frequencies of site visits and conversion?
  • Are the most loyal customers also the most profitable?
  • What are the indicators of churn?
  • What are the “best” upsell and cross sell opportunities?

3. Obtain the data that will be most useful to the analysis. Be cognizant of changes in the business that impact the data—including mergers and acquisitions, changes in product lines or go-to-market models. Always remember that behavioral data is just that and can be misleading if you are looking to draw absolutes or not taking context into account.

Data can lead you down the wrong path for the right reasons

A few years ago, when I worked as a product manager at another company, we were looking at click stream data trying to figure out why a certain section of the web site was experiencing a high volume of abandons. We spent a lot of time trying to figure out whether there were issues with the content, page design or navigation. Data was telling us the “what,” not the “why.” So, we decided to ask people via a survey tool why they were leaving the site area. The answer: “I got the information I was looking for.” In other words, they completed the task and we couldn’t see the forest for the trees. We finally got the “why” and realized that the page and content were doing their job.

The lesson learned was that it’s really all about people and intent, and sometimes it’s hard to measure interest and intent with data alone.  You often need to connect with the decision-making behavior on a human level. Which leads to my second point of discussion on why cookies alone aren’t the answer – tune in next week for that post.

Integrated Marketing Through Connected Consumers



Today’s customers are engaging with your brand through an ever-expanding number of devices and channels, giving you unprecedented customer insight.

At least, potentially.

The problem is that data silos in display, email, social, websites, mobile and physical touch points can be tricky to bring together, leaving customers with inconsistent, disconnected experiences.

The good news is that there are plenty of data integration techniques to get rid of silos and create a single view of the customer by connecting all online and offline interactions – ultimately letting you communicate on a one-to-one, relevant basis with your customers and prospects.

A Complete Framework

The greatest benefit comes from an integrated framework that leverages a mix of the following components customized to your key objectives. There are industry leading providers such as BlueConic, BlueCava, FullContact, and LiveRamp that offer these technologies with great success.

1. Cross Site Data Capture: Enable Personalization with Progressive Profiling

Simply put, for every customer visit, their behavior is captured and turned into meaningful attributes. With every click you learn a little more about the needs, interests and behavior of your visitor. It gives you the ability to deliver dynamic, personalized content without changing the site, and it leads to higher conversion rates and a better customer experience.

2. Device-to-Individual Identification: Recognize a Customer Across Devices 

cross screen data integrationMore than 70% of today’s consumers use three or more internet-enabled devices. The challenge with multiple screen usage is that user identification across screens is tough. But you can funnel data across all screens (mobile, desktops and tablets) into a consolidated view of your audience by tracking, analyzing and organizing incoming device data and then connecting screens, consumers and households.

The key here is that this technology enables websites to keep all of the customer history, even when they switch browsers or devices or delete browser history.

3. Social Network Data and Presence: Identify Unique Individuals across Social Platforms

Imagine how much you would know about your individual customers if you could capture data across all of their social accounts. Well, it is possible consolidate data from over 150+ social sites such as Facebook, Twitter, LinkedIn, Google+, Pinterest, etc. to match and create a complete view of a given customer—in real-time. You can enrich bits of data, like email address, Twitter username, Facebook ID or phone number, to full blown individual social profiles.

4. Offline-to-Online Match: Lines Between Traditional & Digital Channels Blur!

offline to online data integrationNow that you have all of this powerful, integrated data, you can combine it with your CRM database to match individuals to both offline and online behavior. The acquired social intelligence in your CRM enables targeting, messaging decisions, design segmentation, experience and scoring strategies around consumer interests, rather than simply relying on purchase history. You could also recognize an offline customer when they visit you online with no login required. Through this you open up new opportunities for retargeting & understand attribution at every touch point.

5. Influencer and Topic of Interest: Identify Brand Advocates and Their Interests

Brand advocates are powerful, but you need to know how to find them and harness their power effectively. By gathering data not only WHO your brand advocates are but also WHAT they are interested in, you can customize a strategy for each defined by preferences, likes and interests. This will help you to nurture your brand advocates for unbiased review and word-of-mouth promotion.

6. Email Consolidation to Individual: Identify Customers with Multiple Email Addresses

Do you have multiple email addresses between personal and professional use? Maybe you even have multiple emails just for personal use? So do lots of your targets. This technology lets you identify customers across all of their email addresses and figure out which is their primary address, improving campaign response.

How to Do Data Integration Right: Bring a Few Techniques Together

Using any one of these techniques will bring your a step closer to integrated customer data, a connected customer experience, and ultimately more revenue. However, the ultimate goal should be to create an integrated framework that utilizes multiple data integration techniques—the whole is greater than the sum of its parts! If you have any questions or need help creating this integrated framework, get in touch.

Connect with US