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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

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.

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

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.


Staying in Touch With the Zeitgeist


Last week I had lunch with an incredible group of people: an Academy Award-nominated director, a 21-year-old nuclear physicist, and a New York Times columnist. Just another day in the glamorous life of a digital agency executive, right?

Not exactly.

The lunch was just a small part of the amazing parallel universe Google creates once a year called Zeitgeist, which Google describes as “a series of intimate gatherings of top global thinkers and leaders.” And a few lucky agency wonks, apparently!

On one level, Zeitgeist is Google’s version of the TED conference. The topics are eclectic, inspirational, and thought-provoking. This year’s speakers included an astronaut (from space, no less), Kanye West, a North Korean defector, a golf pro, two Nobel Prize winners, a civil rights lawyer, and so on. And, I assume like TED, the hallway discussions were equally if not more interesting than the amazing speeches. I personally chatted with two billionaires, a few Google executives, forward-thinking CEOs, and some great non-profit leaders.

So why does Google put on this lavish event? I’m sure there are many reasons. First, because they can. Google is doing pretty well as a company, so funding a modern-day Bloomsbury Group once a year isn’t going to put much of a dent in their numbers. More importantly, however, I think it reflects the intellectual curiosity of Google’s founders and executives. Remember, this is a company that could have sat back and counted their cash from AdWords but instead has set out to revolutionize everything from cars to diabetes.

And there’s a lesson here for the SEM community. Life is pretty comfortable for the average SEM pro these days. High-paying jobs are easy to come by. (Don’t like your current gig? Don’t worry, someone will no doubt offer you a 30% raise to come across the street to their company.) And whilst SEM continues to change, the industry won’t be going away anytime soon. So if you want to, you can put your head down and do SEM really well and have a great life (for the foreseeable future, at any rate).

Alternatively, you can take the Google path and decide not to rest on your laurels. You can learn Facebook advertising, attribution, mobile acquisition, and audience segmentation. You can experiment with Beacons and Bitcoin. Heck, you can even try to understand branding and out-of-home advertising (call me if you figure this out, because I certainly haven’t).

As an added benefit, expanding your expertise is a good way to maintain that comfy lifestyle you worked so hard to achieve. Some day – maybe even sooner than we think – SEM will decline and possibly disappear entirely. Learning new skills will enable you to effortlessly leave SEM in the dust and move into the next age of digital marketing. Intellectual curiosity aside, there’s strong business strategy behind Google’s forays into video, mobile, shopping, delivery, healthcare, Internet, transportation, and so on: self-preservation.

The tough part about keeping up with the Zeitgeist is that it is fickle and changes quickly and often unpredictably. Whether you’re a multi-billion dollar Internet giant or a really sharp SEM pro, staying on top of the vanguard of online marketing (and really, of the world in general) does more than keep your mind fresh; it is also just a smart business decision.

Now if you’ll excuse me, I believe a Google drone just dropped off my groceries on my doorstep!

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?


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

Tips for Creating Smarter, More Effective Email Marketing

Email MarketingIt’s no secret that marketers have it tougher today than ever before. A saturated market place, overwhelming amounts of brand messages and shorter consumer attention spans are just a few of the challenges we face. Reaching your customers at the right time, on the right channel, with the right message, in less than 30 seconds isn’t exactly a walk in the park.

Email remains one of the most powerful marketing tools at our disposal. When used correctly, it can have a huge impact on your ROI and drive sales. According to a 2014 digital marketing strategy survey by Ascend2 and Research Partners, email is the most effective digital marketing tool and the least difficult to execute. However, given today’s challenges, marketers need to be smarter about executing email campaigns. Here’s what you need to do to elevate your email game:

1. Build your strategy around the right key performance indicators. Many marketers like to boast about strong email open rates. But open rates don’t provide us with important insights into what is resonating or working with customers; they tell us that images have been downloaded but don’t track behavior beyond that. On the other hand, click-through rates should be the industry benchmark we all consider while mining for data insights that will drive email strategy and results. This key metric tells us the consumer read the email and was intrigued enough to take action. Focusing on click-through rates may significantly decrease the volume of data you have to work with, but it also increases the quality of data you can leverage by providing you with actionable results. Click-through data can also help improve unsubscribe rates and create more personalized, relevant content.

2. Don’t underestimate a healthy database. A database with proper and current email addresses and contact information is of utmost importance for an email campaign. Why? Because it helps you segment lists and send hyper-targeted messaging to an audience that wants to be communicated to. The result? Higher-click-through rates, better data quality and insights that will drive your strategy for future communications.

3. Remember that content is king. Gone are the days of blanket emails to your entire database. Nothing will make your customers click “Unsubscribe” faster than generic, irrelevant content that’s been sent to 50,000 plus consumers. Use your database to your advantage and figure out what content is resonating with what segments and then target accordingly. Don’t be afraid to use relevant third-party content. White papers, blog posts and news articles can be leveraged in your email outreach to have a great impact on your program.

4. Make A/B testing mandatory. A/B testing, often referred to as split testing, determines which of two campaign options is the most effective based on open- or click-through-rates. You can simply distribute two variations of one campaign and send them to a small percentage of your total recipients. This provides insights into email elements—like subject lines, color, layout and link placement.

Email marketing truly is a science and, when done properly, it can drive sales, customer satisfaction and brand loyalty. Healthy and effective email campaigns will produce more relevant, personalized interactions with your customers. Taking the steps outlined here can make your brand more effective and maximize your marketing dollars.

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.

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.

Solving the mystery: How does your clean data get so dirty?

By: Traci Varnum

Data Quality Stats on Bad DataBy now, you are probably aware that a lack of accurate, clean data can be a huge problem for us marketers. According to an infographic from Trillium Software, 50% of all companies overestimate the quality of their data. Even more troubling, 50% of companies have absolutely no plan for managing their data problem.

So where the heck does all this bad data come from?

A recent article from Direct Marketing News explains that “dirty data” can rear its ugly head in a number of different ways.  Some common examples include:

  • Consumers failing to update their information
  • Brands failing to update their database as prospect/consumer information changes
  • A lack of communication/sharing between internal departments regarding the data necessary to create consistent or complete profiles
  • Third parties providing data without first performing a proper quality check

Given that the average person will change both jobs and living situations approximately 11 times over the course of their life, clean data can get dirty FAST.

With the large amount of data coming from new, quickly evolving sources like mobile and social media, it is now more important than ever to keep on top of your data. While social networks can provide a plethora of valuable data points, this social information can quickly become outdated. For instance, every single time a user creates a new social profile or updates an existing one, this personal information is stored in a secure database, offering up new information to marketers.

Fortunately, despite all of these extra records to worry about, there are ways to keep your database sparkly clean. Okay, maybe there will be just a tiny bit of dust or an occasional smudge–there’s no such thing as a perfect database. But with the right approach, you can substantially improve your data, and as a result, your overall bottom line.

To learn how small data enhancements can lead to big ROI, download our latest white paper: “Good Data: A Marketer’s Best Weapon.”

7 Reasons to Care About Clean Data

By: Sigrid Seymour

Using clean dataIs your data “clean”? Or does it contain inaccuracies, gaps, duplicate records or old information? Does clean data even matter?

You bet.

From overall corporate strategy to marketing efforts to performance measurement, clean data positions you to make the best possible decisions for all aspects of your business.

Here are seven reasons I have encountered over the past two decades as to why you should be concerned about improving the health of your customer data.

Improve your ability to perform advanced analytics.

A clean customer database that provides a complete view of all your customers’ engagements and transactions paves the way for optimized advanced analytics.  Whether you want to define personas, understand lifetime value, design upsell and cross-sell strategies, or develop sophisticated models, having clean and consolidated data will enable better analytics, improve the performance of your marketing campaigns and maximize your marketing investment.

Produce more effective direct marketing through better customer experience.

Accurate customer data, such as contact information and response data, allows you to better segment your customers based on their individual buying preferences and behaviors, leading to improved campaign targeting and personalized, relevant marketing content and offers—which all lead to a better customer experience and improved marketing results.

Set the stage for integration of online and social data.

A clear, detailed picture of your customer can make a world of difference when it comes to your marketing success. To develop this comprehensive view of your customer, you need to integrate traditional data with data from online sources. This process begins with a customer’s name, postal address or other offline data point and then appends new digital data about the customer, such as a Facebook or Twitter identity or online purchasing behavior. This online data enrichment is more likely to be successful when you begin with clean, accurate offline data. The result is a rich portrait of your customer that empowers high-performance targeting, messaging and experience strategies.

Improve efficiency and save money.

If you’re working with “dirty” data, you’re sending mail to inaccurate addresses, emailing obsolete email addresses and calling disconnected or wrong numbers. Clean data eliminate these wasteful efforts and make sure that your marketing communications actually get delivered, in a timely fashion, saving you valuable marketing dollars.

Properly inform strategic decisions.

“The impact of higher-quality data is most immediately evident in the quality and speed of business decisions. Organizations reporting data accuracy of over 90% were able to put reliable information in the hands of their executives fast enough to meet demand 80% of the time. Companies with lower than 70% data accuracy only succeeded at meeting this demand window half of the time.”

~Aberdeen Group

You need an accurate attribution of customers to stores, branches or regions in order to make solid strategic decisions for your business, such as those surrounding store level merchandizing, real estate assessment or regional segmentation. For example, fully understanding who your customers are and their saturation level at a location or within a geography will help you to decide which product categories to stock, which services best resonate in a particular state, or how seasonal factors will impact your business.

Allow you to accurately measure performance.

Most companies find that transactions alone cannot describe the true health of the business—you must also consider key performance indicators like customer growth, migration and attrition. These KPIs will be more accurate and more useful when based on complete, up-to-date data without duplicate records.

Mitigate risk and deliver on commitment to shareholders.

When there is a data breach, recall or other critical information to relay, clean data help you to mitigate risk by quickly communicating with your customers through accurate contact information.

So, now that we know why clean data is so important, the next step is to learn about cleaning up your dirty data. Look out for our next post on data enrichment FAQs. 

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