The Broken Data Promise:How CRM Failed, and Why Businesses Need It More Than Ever

© 2011, Social Media Today, LLC

Brought to you by

Sponsored by

The Broken Data Promise:How CRM Failed, and Why Businesses Need It More Than Ever

Esteban Kolsky

Mark Tamis

Brent Leary

Tyson Hartman

3

During the 1990s and early 2000s, the so-called golden years of CRM deployment, CRM vendors made a promise to their clients: If organizations bought and implemented complete CRM suites; they’d be rewarded with 360º portraits of their customers. These portraits would be generated by the large quantity of transactional and operational data that CRM solutions produce. Vendors claimed that by gathering all data about all interactions between the organization and its customers, companies could then leverage analytical tools within the suite to build deep, meaningful relationships with customers.

This became the 360º view of the customer promise. It was never kept. It’s not because we didn’t try. We kept detailed records of all interactions, all transactions, everything the customer did and said. We gathered more information on customers during the last 10 years than we had since the invention of data collection.

Despite all this data, we still can’t begin to understand what our customers truly want or need. Worse, we can’t use the data we collect to improve our customer relationships—one of the core goals behind the CRM data promise.

How is that possible? How could we fail so completely in the single most important promise made by the most critical piece of front office software to be released in our lifetimes? This eBook will explore the answer to those questions.

We will take a two-pronged approach. First, we’ll explore what is necessary to achieve a holistic view of our customers, what data must be collected, and how that data can be used. We’ll also examine the benefits of this approach, and look at case studies to better understand why organizations must meet the data challenge.

In the second part, we’ll explain how to monitor and store the data needed to create useful customer profiles, and leverage those profiles in different functions. Finally, we’ll present a case study of one organization that successfully used a CRM system to profile its customers.

Foreword

4

It may seem obvious, but still needs to be said: This is all about data. If you want to create a complete profile of any customer segment, you need to collect, store and analyze lots of data. The data we need falls into four main categories:

Demographic – this is what we traditionally think of when we talk about customer data. The core data points are name, address, and phone number, but we also retain gender, age group, education status, income, race, and similar data that will help us classify customers in different segments. One such group might be males aged 18 to 24 years living in New York state. Initially we believed that members of these segments would all behave in the same way, but we found that isn’t always the case. Nevertheless, gathering basic demographic data still helps us identify customers for different purposes.

Behavioral – The promise of CRM systems was that if we retained and analyzed sufficient transactional and operational data about customers, we could determine how they behaved and make predictions about their future behavior. If certain males aged 18 to 24 living in New York state perform a specific action at a specific time, we can infer that the rest of the group will behave in a similar way. Thus, when a 19-year-old male New Yorker interacts with us, we can offer him a particular product with a certain degree of confidence. Later, of course, we discovered that we were missing other core data points needed to make

accurate predictions about behavior within our segments. Nevertheless, all this operational and transactional data is still being stored today, and is still being used as a partial predictor of future behavior.

Attitudinal – This is the missing behavioral link. Organizations tend to collect and store behavioral data from their perspective: What is the customer doing and when? But these questions don’t capture the reasons why customers do what they do, because organizations don’t see the world from the customer’s perspective. The point of gathering attitudinal data is to close this gap by asking customers why they would buy a product. What circumstances determine the attitude that drives the behavior? We collect this type of data via surveys and other feedback events, which normally include satisfaction questions.

Sentimental – Sentimental data refers to the emotions and feelings that a customer has towards the organization, its products, and the relationship as a whole. It has traditionally been materialized in metrics like satisfaction, loyalty and advocacy. Sentimental data can only be captured by direct feedback, and can never be inferred from other metrics. This was one of the biggest pitfalls of the original CRM promise: The original systems tried to guess sentiment by analyzing behaviors, yielding poor or erroneous data.

Customer 360°Esteban Kolsky

Continued on next page

1

5

Customer 360° (cont’d)

In short, a complete customer portrait would tell the organization who the customers are (demographics), what they do (behavioral), what they want (attitudinal), and why they want it (sentimental). Such profiles allow an organization to tailor solutions, products, services, and interactions to what its customers are looking for.

6

2 What Customers WantBrent Leary

About a decade ago, Mel Gibson starred as marketing executive Nick Marshall in a movie called What Women Want. Nick thinks he can use his charm, powers of persuasion, and especially his perception of feminine desires in order to land a major sportswear retailer as a client for his firm. But he never tries to understand what’s important to women until he gets passed over for a major promotion.

Shocked, Nick immerses himself in trying to get inside the minds of his customers—not because he really cares what women want, but to prove that he shouldn’t have been ignored.

After adjusting to his new powers, Nick starts exploiting what he hears for personal gain. He eventually realizes that misusing his new power is an overall negative, so he starts listening in order to really understand women, and not just to validate his preconceived notions. Having changed his own thought processes, he finally learns how to care about women’s needs and concerns, which helps him connect with the audience that he originally took for granted.

While this is only a movie—and I sincerely hope there will never come a time where people can hear what’s going on in my head—we can learn valuable lessons from Nick’s transformation. First, it’s more important than ever to understand what our customers are thinking. Fortunately we don’t need Nick’s extrasensory ability. Customers share

their concerns, likes and dislikes freely via social channels. We can pick up extremely useful insights by knowing where to listen, what to listen to (and for), and, maybe most important, how to listen.

The tools for listening and engaging are already plentiful, and will become easier and easier to use as time goes by. But we still need a strategy for collecting and analyzing what we hear, so that we can translate it into solutions that solve the challenges our customers and prospects face. While listening to our customers and analyzing what they say, we are also creating meaningful interactions with them that lead ultimately to stronger relationships.

The more active our customers are on Facebook, Twitter and other social networks, the more data they create. This presents an opportunity to better understand and engage our customers. It also challenges us to integrate this information with transaction data, activity data and other information that adds up to a layered customer portrait. While the challenges are not trivial, the payoff can be substantial.

Lisa Larson, director of customer care at online pharmacy Drugstore.com, recently shared her experiences integrating social channels in customer service. Here’s what Lisa had to say about the importance of listening to and analyzing the social footprints that customers leave.

Continued on next page

7

What Customers Want (cont’d)

People who aren’t looking at this are missing a key part of their business. You learn the most from just listening to your customers. Years ago, you would have paid amazing amounts of money to get this kind of information. Now it’s free and right there for all of us, we just have to go listen and find it. It’s amazing, the difference. These are really honest conversations that you can listen to and learn from, and possibly join in. You have to decide what the best opportunity is for your company. You are missing out if you are not involved. Besides, it’s fun!

In addition to being fun, social media interaction can help your top and bottom lines. For example, interacting with customers via Twitter and other chat technologies yielded the following results for Drugstore.com:

• The overall phone time that Drugstore.com devoted to customer interaction decreased by 15 percent. E-mail volume shrank by 30 percent. Meanwhile shopping-cart sizes in sales facilitated by chat are now 10 percent to 20 percent larger compared to sales without chat.

• Chat sessions deliver a conversion rate of approximately 25 percent; the site’s overall conversion rate is just 6.4 percent

• Third-quarter 2010 sales grew 17 percent, compared to 2 percent growth in e-commerce overall

• Customer satisfaction scores reached 77 on ForeSee Results’s list of the top 15 online retailers

These are the kind of results that might make it easier to understand the benefits of leveraging social tools to listen, analyze, and engage with customers. But don’t forget Nick—you need to be genuinely interested in understanding your customers, and not just interested in what they can do for you.

8

Brent makes a couple of very interesting points that are worth exploring in more detail. First, he talks about the need to listen. Customers give organizations lots of information, but it typically doesn’t take the form of perfectly structured data. This was the error in the previous methods that tried to generate 360º customer profiles: We relied on structured data provided by transactions and interactions. We assumed that customers who behaved in a certain way once would do the same thing again in a similar situation.

The problem is that we never bothered to ask why our customers behaved in particular ways or what their needs were. We simply assumed that their actions told us everything we needed to know. This was our great error. We can only correct that error by collecting unstructured data, analyzing them, and creating actionable insights from them.

Listening is the first step. Organizations have always known how to create surveys (some good, some awful, most in-between), distribute them, and collect the data that they produce. With the advent of social networks and social channels, we finally found the source for the unstructured data that would complete the thoughts started by traditional structured feedback events such as surveys and focus groups.

For better or worse, both methods generate lots of data. Today, unstructured data volumes collected from customer interactions and transactions are estimated to be between

20 and 100 times larger than structured data volumes. Unstructured data complements structured data; it doesn’t replace it. So organizations must match their new data to existing data about customers and their experiences, and integrate all the data sources to obtain more comprehensive views of their customers.

The problem is that these gigantic data volumes are cumbersome to manage. Organizations have limited capacity and will to parse data and create actionable insights from them. This was the problem that created the second evolution of CRM software: analytical CRM.

COMMENT BY ESTEBAN KOLSKY

9

3Never before in history have we been able to gather and store so much data about customers. We start with contact information, purchases, support interactions, leads and opportunities. The social web allows us to capture data about site navigation and the like. And now we’re adding even more data points: Facebook Likes, Twitter microblogging, and online customer communities.

So how do we turn vast data volumes into actionable insights that are relevant both to our organizations and to the customers we are trying to serve? There’s still a disconnect between data captured by CRM systems and behavioral data that we capture through social media channels as well as traditional channels such as email, surveys and interaction with customer service reps.

The data sets captured in the different channels are hardly ever correlated effectively with data from other sources. We seem satisfied to track Facebook fans and Twitter followers without following through to see whether all these fans and followers were already customers or if they actually bought from us after stating their interest. Nor do we try to capture how fans and followers influence others in their social networks and how this influence affects our brand images and sales.

New analytic tools such as Radian6, Attensity and Lithium try to organize unstructured data by using clever filtering to automatically or manually create CRM system entries.

Although these tools have their merits, they don’t trace connections between the various data points that we have about a given customer. Nor do they help us decide how we should respond to that customer, or even whether we should respond at all.

To state the obvious, business decisions should be guided by customer data and analysis. Although the sheer volume of social data is daunting and the tools far from perfect, we should try to use these data to enhance what we’re already doing with transactional data. Each customer’s cross-channel activity will need to be captured and blended into customer “snapshots,” building on the historical and transactional content of the CRM system.

Step one is finding identifiers that link customers to their identities on the Social Web. Ideally this would happen through an opt-in procedure, for example when the customer visits your community support forum and fills out her profile, sends in her warranty card or signs up for her loyalty card.

The next step is to mine and analyze relevant interactions that we can match with a persona. Micro-segmentation can give us insight into the experiences that our customers expect and suggest appropriate responses.

This matching can be based on personality analytics, sentiment evolution, social and interest graph

The Rise of Analytical CRMMark Tamis

Continued on next page

10

The Rise of Analytical CRM (cont’d)

segmentation, product portfolio, issue anticipation, and so on. The analysis should help us make business decisions regarding the desirability of the customer.

Social data also create opportunities for predictive analysis, due to our real-time access to the customer’s voice. As they say, the best customer service is no service!

You also need to consider where your data are stored. Increasingly this is done via data centers managed by third-party infrastructure providers, otherwise known as “the cloud.” Contrary to popular belief, the real promise of cloud computing is not the ability to outsource your IT management or access applications and data from anywhere. Rather, it’s the ability to quickly connect your datasets to those of your partners, suppliers, and channels, and mine the collective data for customer insights that you would miss if you were only looking at your own data.

The Social Web has given us many new ways to fine-tune customer information. Going forward, the main challenge will be linking customer identities across different interaction channels and blending structured and unstructured data into clean datasets that we can mine for actionable insights.

11

Mark’s vision underscores the fact that we gather social and other data about our customers in order to generate actionable insights. This should be the goal of any company that analyzes the data they collect from their customers and their operations. It’s the main reason to invest in feedback and data management initiatives. But today, most organizations that deploy data analytics seem to believe that actionable insights arise from some magic strike, lucky guess, or black box method.

The five keys to effective data analysis are:

1. Always know what you are seeking. Diving into aBig Data set “just to see what’s there” will only yield frustration.

2. Understand what you have. To achieve useful results,it is critical that you understand what the data are, what they say, how they flow through the systems, how they are used by the organization, and how they relate to other data points.

3. Correlate to KPI. Data insights must be correlatedwith your organization’s key performance indicators. Analytics must articulate with past performance issues and future needs.

4. Define Actions. You can’t have actionable insights without actions. You must know what should happen when data processing yields a value that falls above or below expectations.

5. Iterate. Analytics is not an end game. New technologies and tools allow organizations to take on new challenges and gain a better understanding of what they are after. Make iteration a core part of your strategy.

Unfortunately, these steps don’t guarantee success. Tool interfaces may be getting simpler, but pretty screens also hide the true complexity of analytics. Talented analysts are still the most critical component in analytics—and they are very hard to come by. If you want to succeed at the game of analytics, either hire the expertise you need or train committed individuals to extract vital insights from the sea of data in which all businesses swim.

COMMENT BY ESTEBAN KOLSKY

12

In the global marketplace, businesses and employees are creating and consuming more information than ever before. Gartner predicts that enterprise data in all forms will grow by 650 percent over the next five years, while IDC claims that global data volumes double every 18 months.

According to “The Business Impact of Big Data,” a new global survey of C-level executives and IT decision makers commissioned by Avanade, this data deluge is creating very real challenges for business leaders.

Big Data – Hype or Reality?Across industries, regions and companies, executives report that the exponential growth in data is degrading their ability to access critical information. According to the report, 56 percent of business and IT executives feel overwhelmed by the amount of data their company manages. Many report that important decisions are often delayed because they have too much information.

Despite these challenges, executives see some value in the data deluge. For instance, 61 percent believe that the flood of data entering the enterprise fundamentally changes the way their business operates.

Data AddictionAlthough the onslaught of data can make it more difficult for executives to make decisions, they are still asking for more data, and they want it even faster. This desperation for the right information to make business decisions

puts even more pressure on executives to consume even more information. Which begs a question: Are executives addicted to data? The following data points would suggest that the answer is yes.

• 70 percent of business leaders report that their current IT infrastructure allows employees to get the data they need at the speed they need it.

• 61 percent of executives still want faster access.• One in three say they need even more sources of data

in order to perform their job better.

For many, this data addiction is driven by the inability to find the information they need. In fact, a recent survey found that during the recent recession, more than one-quarter of executives lost business because they couldn’t access the right information. This dearth of accurate information pushes executives to continuously search for better information, creating an addictive behavior pattern.

So what kind of information are executives most concerned about? According to the survey, their top priority is the ability to keep up with customer-service expectations. And when it comes to perceptions of the most data categories, customer information leads the pack. This focus on customers is driving technology investments in CRM systems—67 percent of executives have already invested in CRM or are seriously considering doing so over the next 12 months.

Big Data, Big ProblemsTyson Hartman4

Continued on next page

13

Big Data, Big Problems (cont’d)

Executives are recognizing the opportunity to leverage their customer data in order to create new revenue streams and generate new business. Alarmingly, however, fewer than half of all managers view the available sources of data as strategic differentiators for their organizations. They struggle to understand how Big Data can drive real business value.

Big Data, Big ValueSo how do we get from where we are today to where we want to be? First, companies must develop a “data culture” in which executives, employees, and strategic partners are active participants in managing a meaningful data lifecycle. Companies need to start educating their employees on how to best participate in this process.

This is not just a technology challenge. It’s also a people and process problem. It takes a culture shift among the people who are interacting with the data—whether they are producing or consuming—to be more accountable for data management.

Tomorrow’s successful organizations will be equipped to harness new sources of information and take responsibility for accurate data creation and maintenance. This will enable businesses to turn data into usable information first and then ultimately into true business insights.

14

So what have we learned? Not only did we lack the necessary data to understand our customers holistically, we also lacked the operational capacity to manage and learn from the Big Data sets that we created. How should organizations deal with Big Data? There’s a vast literature of attempts to answer this question, but the following three steps are crucial.

1. Recognize. You must recognize that your current systems, analytic engines, databases, and potentially your architecture are not prepared to handle the deluge of Big Data. Trying to accommodate an aging and inappropriate infrastructure is a recipe for failure.

2. Plan. To accommodate slow growth as opposed to a landslide of data clobbering your systems, figure out what data will be coming from what channels and create a plan to accommodate the various data streams. Once you have the first data stream under control, focus on the second and third, and so on. Plan for a gradual assimilation of the magnitude of data you will receive.

3. Learn By Doing. You are now dealing with a lot of issues that your organization did not have to deal with before. Is it better to store all data and eventually get around to analyzing it? Or would we be better off simply doing real-time analytics and storing the results? How much accuracy is required, and how can we achieve it? You can’t answer these questions until you’ve been confronted with them. Resolve to learn as you go and constantly improve your implementation.

The original CRM vision failed due to the lack of sufficient data and processing ability for the data that existed. We are solving those issues today, but CRM is still not an automatic solution.

Three core areas need to be explored in more detail:

Listening. Listen to the customer’s needs and desires through direct, structured feedback and interactions. This is the first step towards discovery of the necessary data.

Big Data. Unstructured datasets are 20 to 100 times larger in volume than structured datasets. The new social datasets must be understood and then articulated with existing data to create blended datasets that can provide the insights we need.

Analytics and Actionable Insights. Analytics are not, as we used to believe, about understanding the relationship between data and data elements. We need to build an analytical model that produces actionable insight into what our customers need and desire.

Organizations that embark on this journey will be trailblazers, building a repertoire of best practices and lessons learned more than relying on others. Some will reap the reward of nearly perfect knowledge about their customers.

Will your company be one of them?

Conclusions

“It is imperative that companies develop a ‘data culture’ in

which executives, employees, and strategic partners are active

participants in managing a meaningful data lifecycle”

– Tyson Hartman

15

Esteban Kolsky is the principal and founder of ThinkJar, an advisory and research think tank focused on customer strategies.

Brent Leary is co-founder and partner at CRM Essentials LLC, a CRM consulting/advisory firm focused on small and mid-size enterprises.

Mark Tamis is a noted blogger on social CRM with considerable experience in enterprise software.

Tyson Hartman holds the title of Avanade Fellowat Avanade, a Seattle-based technology solutions provider. In this role, Hartman works with the senior technology team to define the vision and road map of Avanade’s solution development practices.

Author Bios

16

SmartData Collective, an online community moderated by Social Media Today, provides enterprise leaders access to the latest trends in Business Intelligence and Data Management. Our innovative model serves as a platform for recognized, global experts to share their insights through peer contributions, custom content publishing and alignment with industry leaders. SmartData Collective is a key resource for executives who need to make informed data management decisions.

About Our SponsorTeradata is the world’s largest company solely focused on creating enterprise agility through database software, enterprise data warehousing, data warehouse appliances, and analytics. They deliver award-winning, integrated, purpose-built platforms based on the most powerful, scalable, and reliable technology platform in the industry, with assets including:• Approximately 7,400 associates in more than 42 countries• Strong diversified client base of over 900 customers

worldwide and companies of all sizes • 2,000+ implementations worldwide

About Us