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Better Attribution Means Better Marketing Results—How to Get Started

optimize marketing spend with fractional attribution

As banks and credit unions face increasing pressure to compete with fintech innovations, peer-to-peer lenders and digital payment channels, it becomes more crucial than ever to optimize marketing spend and understand which channels provide the greatest ROI.

In our last post, we discussed several types of attribution models and determined that a fractional attribution model is a critical tool for optimizing the performance of marketing dollars. Here we will discuss the challenges of implementing a fractional attribution model, and the steps to get started.

Why Aren’t More Companies Using Fractional Attribution?

There is plenty of discussion happening within financial marketing organizations about the importance of marketing attribution. Unfortunately, several challenges are preventing these discussions from gaining any real traction. Common challenges preventing banks from implementing a successful, robust attribution strategy include:

  • The “where do I start?” roadblock. As we saw in the last post, there are many options when it comes to marketing attribution. When faced with an overwhelming number of options, marketers have difficulty selecting the best approach to invest in, let alone deciding where to begin. These folks end up stuck in the same rut.
  • A lack of executive-level sponsorship. The decision to make significant changes in overall organizational structure, talent and technology needs to
be driven from the top, but making this leap is often hindered by competing priorities.
  • Politicized corporate culture. The underlying issue here is often organizational structures that reward the performance of individual channels. When multiple departments compete to take full credit for revenue generation, those department leads are focused on their team’s profits, not the overall health of the company.
  • Inconsistent logic. Offline and online channel marketers still attribute consumer behaviors separately with inconsistent attribution logic. The flawed justification that comes from this practice makes a combined attribution approach seem impossible to achieve (and compounds some of the other challenges listed above).
  • Mobile-added complexity. The ability to track behaviors across various online-enabled devices is a data scientist’s dream, but the complexity of the data can be overwhelming for marketing organizations—and is sometimes perceived as more of a nightmare.
  • Lack of the right skill sets and corresponding technology. Because the demand for data scientists is greater than the supply, it’s time to invest in top data scientists with a broad set of math and statistics skills and a deep understanding of the data landscape.

Fractional Attribution: How to Get Started

Executive-level sponsorship is key to making the transition to a new attribution model. When it comes to gaining executive-level sponsorship, you should stress the point that you can get a clearer picture of what the entire marketing budget is doing by spending just a small fraction of that budget. And by investing a small part of the marketing budget into a fractional attribution model, you can find 15–25 percent of ineffective spend within a few months of implementation. You’re not just getting an accurate map of where your marketing dollars are going, you’re reallocating those dollars in a much more effective manner. When put that way, the transition to a fractional attribution model becomes much more palatable.

You also need to make the shift to a more customer-centric focus and show how this will result in more profitable customer relationships. Here it will be helpful to perform an audit to assess current skill sets and technology infrastructure. Put together a detailed plan that shows how to get to a more robust attribution model with a phased approach—with clear success measures along the way to justify the next step.

Next Step: The Scenario Planning Tool

Implementing a fractional attribution model is only half of the solution; a scenario planning tool is needed to optimize marketing channel performance. With this tool, analysts and marketers can quickly run “what-if ” tests to gauge the impact of reallocating marketing spend. For example, what will happen if you move dollars from digital marketing and instead invest them in direct mail?

The end result is a more informed decision and higher returns from the marketing budget. Furthermore, this process can be used to calibrate and refine the attribution engine going forward.

To create the scenario planning tool, analysts outline a roadmap that identifies key performance indicators (KPIs) and details the overall attribution approach. The most robust attribution solutions require user-level data across multiple online and offline channels that need to be integrated and blended.

Spend Wisely with Better Attribution

The best decisions are data-driven, and in a multichannel world where the customer journey can span across several channels, robust attribution solutions will play a central role in informing marketing-spend decisions. It’s time to leverage analytics to start deriving insight from those large pools of data. A fractional attribution model and a scenario planning tool can optimize media and channel performance, helping you break down the silos at your institution—and showing you exactly where to allocate your marketing dollars.

FiServ Marketers: Your Solution to the Attribution Struggle

Fiserv attribution across the customer journey

An overwhelming amount of financial marketing executives—96 percent, according to The Financial Brand—feel that measuring ROI is a challenge. And 47 percent struggle to accurately quantify their department’s impact. That’s the bad news, the good news is that marketing is increasingly being seen as a revenue center within financial organizations—but with that comes a greater responsibility to deliver, measure and understand the ROI across the different touch points.

This becomes a greater challenge as both marketing and banking services become increasingly digitized and diversified across channels. For a regional bank or credit union, it is crucial to identify the best channels for reaching a desired demographic and then allocate marketing dollars effectively. As we move from viewing marketing as lead-generating to revenue-generating, it is ever more important to be able to attribute revenue to the appropriate efforts and channels.

Improving and assessing customer experience across media platforms is key for financial services, and a CMO now has so many options within the marketing budget, it is impossible to make impactful decisions about improving performance across channels without a robust attribution model. Banks need attribution models to help target customers with the right message, via the right channel at the right time.

Attribution Models—and Their Biases

There are several types of attribution models in use today. Some are more relevant than others, and each comes with an attribution bias toward certain channels and forms of media, which can result in less than optimal allocation of marketing dollars. Let’s take a quick look at these options:

  • Direct attribution ties a specific customer behavior (opening a checking account) to a given marketing stimulus (such as a unique code or landing page). Although it is still relevant, direct attribution ignores the other marketing touch points that contributed to the behavior.
  • Last touch/click attribution credits the customer behavior to the last known marketing touch or click. This is similar to direct attribution, but is typically used when there are no tracking codes linking customer behavior to a specific marketing stimulus. Here the marketer attributes the customer behavior to the last known touch—such as a Facebook ad for a mortgage program. Both direct attribution and last touch/click attribution ignore much of the actual buyer journey.
  • A multi-full attribution model attempts to acknowledge the many touches that lead to a customer action, but goes about it in a less-than-ideal way. With a multi-full attribution model, credit for a customer action is attributed fully to multiple channels. The multi-full attribution approach is becoming more common, and that’s a problem (and one that is compounded by the silos that often spring up at financial institutions around marketers who cover different channels). When equal weight is given to all channels, there is no way to determine where marketing spending is being allocated effectively. Marketing channel silos also lead to data separation, with data being stored in fragmented pockets between various DMPs, vendors and partners contracted to capture it.
  • Rules-based attribution is similar to the multi-full model described above, but in this instance marketers use a “common sense” approach to assign attribution across marketing channels. Where overlapping purchases are being attributed to multiple channels a rule is established that assigns a certain percentage of attribution to one channel and proportionally reduces the attribution by that percentage across other channels. Rules-based attribution has the right intent, but the problem is that guesswork and not analytics are being used to assign the percentages to each channel.

 

attribution in financial services - various models

All of the attribution models listed above leave room for improvement. A good attribution model leverages analytics to derive insight from large pools of data.

Finding the Most Impactful Attribution Model

Which brings us to another model: fractional attribution. It uses math, science and statistics, not guesswork or “common sense,” to integrate data from across channels and turn that data into informed marketing decisions. Through proven analytic techniques, a weighting calculation is developed and applied to all the various touch points during a customer’s buying journey.

 

attribution in financial services - fractional attribution

 

You want to know where you’re wasting marketing dollars? Fractional attribution is a critical tool for optimizing the performance of an incredibly fragmented and complex system of channels and media, both online and offline. No marketing science is perfect, but a robustly modeled attribution solution can help banks leverage marketing data and generate a higher return from their marketing spend—and outperform their competitors.

Stay tuned for our next blog post to find out how to transition to a fractional attribution model and start optimizing media and channel performance. Fractional attribution can help you break down the silos at your institution and tell you exactly where to move your money.

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.

cloudtweaks.com
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 highscalability.com 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.

How to Optimize Spend with Fractional Attribution

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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.

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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.

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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.

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

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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!

Marketing Technology: Where’s My ROI?

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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

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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.

Implementation

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 4)

Unifying Communication Strategies Across Channels Throughout the Customer Journey

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Over the past few weeks, we’ve been exploring the four biggest marketing challenges faced by B2B tech companies.

Whether you’ve been following along or just tuning in now, you can find the first three installments about utilizing all available tools and technologies, leveraging high-quality, real-time data and generating ROI with less budget and fewer resources on our blog.

For the fourth and final challenge, I will discuss the best strategies to unify communications across channels in order to drive the customer journey.

CHALLENGE #4: How do I unify communication strategies across channels to drive customers through the buyer journey?

Your brand is a powerful thing. Not only does it represent the essence and promise of your company, it also embodies the expectations and opinions of your customer as they move through their buying journey. Each touch point with your brand is a chance to enhance – or diminish – a customer’s perception.

That means that each piece of advertising, each call to your contact center and each visit to your landing page should work in tandem to convey a consistent message that represents your brand. Just one negative interaction can damage your customer’s perception. And it’s much more difficult to reverse a negative perception than it is to proactively ensure positive customer interactions from the start of a campaign.

So how can we ensure a single view of customer across their entire journey, with consistent brand touch points and a clear, unified message? Read on:

  1. Start with a clear definition of your brand. First and foremost, you need to clearly define what your brand represents. Your brand platform needs to be articulated and shared with everyone in the company, particularly the external-facing representatives. A marketing program is the creative output built on top of the brand, designed to build awareness and the desire to purchase.
  2. Decide what you are trying to achieve with your marketing efforts. What is your vision of success? What are you trying to do and why are you trying to do it? At this stage, it’s helpful to look at what Harte Hanks Creative Director Alan Kittle calls The Beautiful Intersection. Draw two intersecting circles. In one, write out what you or your client wants to say. In the other, detail what your audience wants to hear. The intersection of this Venn Diagram is your sweet spot – the message that will tell your story while resonating with your audience.
  3. Identify the necessary building blocks and work streams. After you define your end goal and key objective, work backwards to figure out what will get your there. Start with a solid strategist or planner. This individual or team should gather and interpret all available data, and determine how that insight into the customer will enable a connection with the brand. Data intelligence should help form creative briefs and build a campaign message that is highly measurable.
  4. Cut through with a single unifying thought. In a complex, multi-channel, multi-territory campaign, it is essential to have one unifying idea that all marketing efforts tie back to. In fact, the more complex the marketing campaign – the more channels, audiences, periods of time – the simpler the message should be. By looking at the whole picture, you can determine how all the pieces fit together throughout the journey: how an audience reacts to an email, then a phone call a few weeks later and a piece of advertising leading them to a customer landing page a few days after that.
  5. Create an ecosystem of collaboration and information sharing. It is essential that all agencies plug into the brand and work together in a creative, synergistic manner to tell the same story. Branding agencies need to work in tandem with creative teams – the strongest teams collaborate to make a greater sum of their parts.

By following these steps for a new marketing idea, or to increase the effectiveness of an in-progress marketing program, it is possible to unify communications across channels and create that single, unifying thought that weaves through the entire customer journey. Data helps inform and define this thought and to create a cycle of excellence: use data to create something with the best chance of success, then look at what to improve and start the process again.

Global Patient Support Needs to ‘Think Local’

PharmaPatient support programs play a vital role in facilitating better disease management and treatment optimization. Traditionally pharmaceutical companies launched such initiatives on a local level. However, from a regional perspective, this sometimes resulted in patchy and fragmented support. Today, many pharmaceutical companies are driving centralized programs that benefit from a more sophisticated and strategic approach.

This approach brings many advantages around compliance, visibility of success and cost-effectiveness of implementation and maintenance. Yet centralized programs can be inherently complex and unwieldy. This is compounded by the fact that they often need to be coordinated at a global or area level to maximize infrastructure and management efficiencies.

Walking the line between global/regional efficiency and local effectiveness is no mean feat. Patient support is not a ‘one size fits all’ discipline; activity needs to be expertly tailored and carefully orchestrated.

At Harte Hanks, we believe five critical factors underpin patient support that is successful both at a global and a local level.

  1. Gather and leverage local knowledge

Understanding the nuances and intricacies of healthcare provision in different regions is essential. Ideally, you should have people on the ground who have in-depth knowledge of their local system and keep a finger on the pulse of any changes or developments.

Typical patient paths can vary significantly between countries for the same disease. Take the patient touchpoints and interactions for the U.S. healthcare system versus the UK’s NHS or Spain’s Seguridad Social. Prescription behaviours, drug dispensing and the length of time between specialist visits can be entirely different. There can even be differences in the role of healthcare practitioners during treatment, in terms of nurse interaction levels, nurse-led advice, pharmacist involvement and primary or speciality care.

  1. Create space for consultation and collaboration

Regional offices need to have clear channels of communication with the head office, and regular opportunities to report back on the local healthcare environment. They need to know that their observations are taken into account and actively used to shape the delivery of patient support in their territory.

At a strategic level, this collaborative approach enables program goals and objectives to be adapted to the realities of each country and healthcare system. It also needs to work at a tactical level, with regional teams of medical and regulatory professionals reviewing and approving materials before they are issued to healthcare professionals and patients.

Pharmaceutical companies often lack the time and resources required to give adequate attention to each country of a global patient support program. This is especially true when implementation needs to happen in parallel with a product launch or other internal deadlines. Working with a trusted third party can be a mutually beneficial solution for individual countries and the global program as whole. They can offer expert guidance as well as coordinate materials distribution and facilitate knowledge sharing.

  1. Ensure processes and training are water-tight

It’s vital that staff delivering the program, especially those with direct patient contact, understand indicators of pharmacovigilance events. Processes need to be in place to ensure that any spontaneous or solicited reports of adverse effects are handled appropriately and escalated in the right timeframes.

A centralized model can ensure that training compliance efforts are optimized and that all pharmacovigilance processes are managed in a cohesive way. A balance needs to be struck to ensure that training and reporting procedures meet certain standards, while respecting any elements or formats that vary between countries.

  1. Coordinated multi-channel communications

Using a CRM suite to facilitate patient and healthcare provider communications boosts efficiency and enables better control of patient support programs. For example, Harte Hanks can act as a multichannel one-stop-shop which is managed centrally but enables local offices to customize activity, such as:

  • Secure data management and hosting, in-line with local privacy rules
  • SMS, email and direct mail assets (drawing on print-on-demand and personalization capabilities)
  • Creation, development and hosting of personalized online portals for patients and healthcare providers, with self-tracking tools to support all digital communications
  • Advanced reporting and analytics to measure success and monitor progress

CRM and digital services should be flexible enough to accommodate multilingual communications and adaptations for the individual needs of each country. For instance, a global program will encounter various regulatory frameworks and the requirements of medical, legal and regulatory teams differ between countries.

  1. Continual improvement philosophy

If program goals and objectives are tailored to local regions, it follows that KPIs need to be tailored too. For measurement to be meaningful, successes or failures need to be considered in context. And they need to feed into the development of ongoing goals and objectives geared towards a cycle of continual improvement. To facilitate effective management at a macro level, it’s important to ensure global real-time visibility across the entire programme, from high-level KPIs to more detailed local perspectives.

The cornerstone of any successful patient support program is recognition that patients are people. They have their own lives, families, work and hobbies, as well as living with a disease or illness. They deserve to be listened to and helped to live their life to the fullest.

Treating patients as people within a program that operates on a global scale is complicated., but with an intelligent, carefully coordinated approach that draws on local knowledge, it is possible to achieve this. Communicating with patients at the right time with the right message via the most appropriate channel is half of the story. Ensuring information and interventions are precisely tailored to their real needs completes the circle, both supporting the treatment and enhancing the overall patient experience.

Harte Hanks handles patient support programmes 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. To learn more about the services we offer, take a look at our case studies.

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

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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

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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 hartehanks.com/TCD or email TCD@hartehanks.com.

Get to Know the Harte Hanks Team

Belinda Casper, Group Account Director

Casper.headshotBelinda is a leader in the direct marketing industry. She’s been with Harte Hanks for more than 25 years, and has 30+ years of experience in leading operation, strategic and account management teams, primarily supporting financial clients. Belinda’s proven leadership in managing cross-functional teams within our organization insures seamless integration with the data, analytics, strategy and execution teams. She’s the real deal. I think we need to know more about this Harte Hanks rock star, don’t you?

Q: Tell us what a typical day at Harte Hanks looks like from your perspective.

There really isn’t such a thing as a typical day for me, and this is why I’ve loved my job all of these years! Each day and each client brings a new challenge and an opportunity to learn, grow and help our clients continue to evolve and succeed. The only thing that might be typical about my day is the fact I’m on a lot of calls. But each day, these calls are different based on the subject, and who’s on the calls.

Q: What is your role in making customer interactions smarter and how did you get there?

I lead the account team who supports our financial clients. In doing so, we are leading multi-functional teams, which provide strategic, creative, analytics, and database services for our financial clients.

Q: What is your favorite part of working for Harte Hanks?

I love working with many people from different disciplines and backgrounds internally and with many different clients. The evolution of marketing and financial services ensures the opportunity for continuous learning and growth. But, what I love most is the ability to work directly with our clients to solve their marketing and business problems and opportunities.

Q: What about the future of marketing are you most excited about? Trends? Tools? Platforms?

The ability to tie segmentation and personalized messages to create a consistent customer experience in all channels continues to mean different things in each of the industries we support and in each year we evolve to deliver this in different and impactful ways. Knowing that we haven’t yet seen what the impact of wearables will have on marketing is exciting too.

Q: If you could have the skills to do any other job at Harte Hanks not in your current department, what would you like to do?

I would like to be in the creative group. They always have so much fun together and they’re just so . . . creative!

Q: What’s on your bucket list?

I’ve already started checking things off my bucket list the past few years. I jumped out of a plane, went to Italy, and got my 500RYT therapeutic yoga instructor certification all within the last four years. My youngest son went to college four years ago, and I did not miss a beat to make my list of things I wanted to do, and start doing them. Most remaining bucket list items include new and unique places to travel. I hope to travel to Australia, Asia and return to Europe in the next 10 years.

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.

Find Your Target Audience Using Google Analytics

As you may recall, Harte Hanks acquired 3Q Digital earlier this year. This post was originally published on 3Q Digital’s blog. We’ve decided to repost it here because it contains practical tips around how to better understand who your audience is and how they are behaving–to connect with them in a more meaningful way.

At 3Q, we work with a lot of startup companies. In most cases, they have a general idea of who their target audience is before taking their product to market, but this isn’t always the case. Sometimes they are incorrect about their assumptions or they just plain don’t know yet. In these cases, digital marketing could be the quickest way for them to learn who their target audience is. The data that we are able to collect can help them understand who is engaged with their ads and who is interested in their product or service.

Social advertising is definitely the best route to go when it comes to understanding your audience, but what if your client doesn’t want to buy paid social advertising? For the search people out there, I have a solution: Google Analytics.

Recently, I had a client who had done some pretty small surveys to carve out a target market, but their marketing plan was mostly built on assumptions, and we learned very quickly that the assumptions they had made were incorrect. Using Google Analytics’ Audience tab, we were able to very quickly understand who their customers were and where they were located.

find your audience with google analytics

 

Google recently rolled out some pretty big changes to their display network (GDN). Gone are interests and topic targeting (now known as “other audiences”); now our only options are Affinity and In-Market audiences.

For those unfamiliar, here is a quick breakdown of the two types of audiences:

  • Affinity – Google analyzes a person’s interests, lifestyle, and habits and to get a better sense of their overall identity. Sample affinity audiences: Technophile, TV Lover, Film Buff, Pet Lover, Political Junkie
  • In-Market – Google identifies people who are actively searching and comparing your product or service, or, are in the market to purchase. Sample In-Market audiences: Dating services, Home Decor, Residential Properties, Mobile Phones

Using the audience tools in GA, I like to filter for my highest-converting audiences with a certain threshold of sessions (depends on your overall traffic). For this particular client, the threshold I used was 500 sessions, and I wanted to find audiences that convert at a rate of 1% or above.

find audience with google analytics

 

The audiences that rose to the top were not all surprises, but there were a few interesting Affinity audiences I would have never thought to add had I never discovered this functionality in Google Analytics.

I then went through the same process for In-Market and found 5-10 segments that also convert very well for my client.

With these audiences, I built out GDN prospecting campaigns as well as YouTube TrueView campaigns, and the performance has been spectacular!

You can take this one step further by looking at your top-performing gender, age groups and location as well (all information available in AdWords for GDN, but some not for Search).

Good luck!

Uplift Modeling: Not So Scary After All (Case Study)

I’m not terrible with numbers. I’d even go so far to say that I’m data-driven in my marketing. But terms like incremental modeling and uplift modeling still sounded a little intimidating when I was assigned to create some marketing materials on this analytics solution. But I put on my big girl pants and got the run-down from one of our analytics experts.

WOW. I suggest you put aside any anxiety you may have with the scary-to-some concept of modeling for a few minutes because this is some impressive stuff.

What is Uplift Modeling?

Actual results from uplift modeling incremental modelingYou can check out this infographic for a great overview on what uplift modeling (aka incremental modeling) is and how it works, but simply put, uplift modeling does more than identify likely purchasers; it also weeds out those consumers who don’t need a promotion from you to make a purchase. This lets you save big marketing dollars by focusing on the group of consumers who need that extra nudge (aka your marketing) to make a purchase.

See It in Action

One of our clients, a leading specialty retailer, was disappointed with the results of their marketing campaigns. Even during major seasonal events, their direct mail promotions were not generating the expected incremental sales. With high costs associated with direct mail, each poorly targeted customer was making a big dent in the bottom line.

How to Fix It: The Game Plan

To optimize the use of this client’s marketing and direct mail dollars, we took a three-step approach:

  1. Perform an exploratory analysis of hundreds of variables to uncover key purchase drivers that could predict incremental spending.
  2. Build an incremental model to establish the existence of  persuadable customers–those who would only shop if provided a call to action from the retailer.
  3. Improve the effectiveness and efficiency of marketing campaigns by focusing outreach efforts only on the persuadables.

This client’s exploratory data analysis uncovered several data points that could predict incremental spend, including:

  • Number of visits the target made to the store
  • Spending in specific departments
  • Number of months from the target’s first purchase

Big Results

  • The incremental model accurately isolated the top 10% of the retailer’s most persuadable customers.
  • By targeting only this top 10% through direct mail, the retailer could experience an uplift of almost 12% and ROI of 287%.

Learn More

That’s not so scary, right? It’s not too shabby either! If you want to learn more about uplift modeling, check out our infographic or drop us a line.

How to Find Your Most “Persuadable” Targets with Uplift Modeling

Wouldn’t it be great if we could weed out all of those people that won’t buy, regardless of what we send them? AND weed out all of the people that WILL buy, regardless of our marketing? Oh, wait, that’s what incremental modeling (aka uplift modeling) does.

Ever heard of it? Or how it differs from the slightly more common response modeling? Neither had I until a few months ago when I was assigned to create a solution sheet and case study for this specific type of marketing analytics.

My reaction? WOWZA. Why in the world isn’t everyone using this? After running an uplift model and using the results, one of our clients saw an uplift of almost 12% and ROI of 287%–those results are worth learning more.

Check out this infographic for a great run-down on what uplift modeling is, how it works, and what sort of results you could expect to see in your revenue vs. marketing spend.

incremental modeling uplift modeling

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