A new focus on customer information

Much has changed in the relationship between customers and companies since the publication of The Discipline of Market Leaders 15 years ago. The Internet, for example, has empowered customers to know their vendors and control their relationships with them as never before, through easy access to information and the freedom to switch vendors with a mouse-click. Likewise, companies have the ability to know their customers and market to them on a personalized basis as never before, through new cost-effective software solutions for data mining, predictive analytics and decision management – and incorporating new information sources such as email, e-commerce sites, and social media.

These and other forces have converged to both oblige and enable businesses to re-organize around a focus on customer information. The value of using these advanced technologies to analyze all of the available data sources is exactly the difference between knowing and guessing. Knowledge is power – and, ultimately, profit.

Successful businesses today tend to have highly developed capabilities in data analysis. The simple reason is that decisions based on analysis of relevant data have been proven to lead to better results than “gut instinct.” So when a business employs analytics to excel at customer intimacy, it also tends to improve both operational excellence and product leadership.

Deeper and more accurate insight into customers’ needs, wants, opinions, perceptions and values guides companies in developing products that their customers actually want, and to market them based on knowledge of how their customers prefer to be informed. Moreover, the methodology of analytics – of acting on data rather than hunches – begins to permeate the corporate culture and extend throughout all aspects of the business.

Not Just Another Pretty Acronym

Haven’t we been down this road before?” one might think at this point. “In fact, many times?” Indeed, over the years, efforts to achieve Customer Intimacy (CI) have given rise to an alphabet soup of customer-oriented initiatives and technologies. These include VOC (Voice of the Customer), CRM (Customer Relationship Management), CEM (Customer Experience Management) and EFM (Enterprise Feedback Management).

These systems and initiatives have not always delivered as promised. For example, CRM has helped companies manage customer data, but often has failed to transform the customer experience. For many businesses, CRM has become just another system – another data silo that may integrate sales and marketing to some degree, but is isolated from everything else.

That’s the problem. CI implies that your company not only know and understand your customers thoroughly, but also is able to anticipate their needs and adapt to meet and exceed them. The basic definition of CI is this: the ability to segment and tailor offerings to precisely match the needs of customers. Developing this ability requires a holistic approach that takes actionable intelligence derived from the analysis of customer information and then makes this intelligence available to inform decisions and drive actions across the company, from product development to operational functions, as well as vertically within the organization, from the CEO to front-line, customer-facing employees.

CI, in other words, is not a tactical nice-to-have; it is a strategic imperative that can significantly impact your bottom line and your company’s success. Developing CI as a value discipline for your business, however, should not be a disruptive rip-and-replace operation. In fact, your VOC, CRM, CEM and EFM systems don’t go away or get marginalized. Instead, they get new life and provide greater value as information building blocks for your CI initiative.

While CI is strategic, it is not “all or nothing, all at once.” It is an evolution, as you gradually expand your customer information and refine your analysis of it. In the remainder of this paper, we will consider five practical steps that will take you well along the evolutionary path toward CI, with each step leading to the next. We’ll also point out some of the pitfalls to watch out for along the way.

Step One: Plan

Planning is key to a successful CI initiative. Attempting to boil the ocean is self-defeating. But it is important to have a vision.

If your value discipline of focus is Customer Intimacy, you must first describe the ideal state for your company around this value discipline. You need to know what you’re trying to achieve. Next, assess the current state of CI in your business. Identify the gaps between your current state and your ideal state. Define a set of strategic long-term goals to get from point A to point B. This may take several drafts and a lot of vigorous discussion among the functional leaders of your company. Then determine how you will measure progress toward the ideal state of Customer Intimacy.

There are various ways to measure, some of which you may already have in place from a previous CRM project. Measuring CI is another topic for vigorous discussion among your business leaders. Ask yourself what you currently measure and what you do not measure. Relevant measures might include:

• Customer lifetime value
• Percentage of recurring revenue
• Gains in year-over-year customer spending
• Rate of customer attrition

If you have instituted a customer feedback request tracking system, investigate how many employees, in which groups, have access to this system, or to CRM or other customer information. What if more employees not only had access to this information but were measured on how they acted on it? This initial discussion about measuring your progress will be repeated many times over as you move along the evolutionary path toward CI, and continue to refine your methodology and metrics.

You can’t get to the ideal state overnight. As you take the first steps along this strategic path, it’s important to focus on specific, short-term challenges – the most vital opportunities for improving business performance by improving CI. Achieving some high-profile, highimpact wins can motivate the entire company and get everyone, even skeptics, on board. You will continue to advance along the CI continuum as you clean your initial data, bring in new sources and kinds of data, increase your analytical power (both through technology and personnel), and tighten the linkages between knowledge of your customers and your company’s ability to use that knowledge to guide decisions and direct actions.

Get the right people involved in this assessment and planning stage. As a strategic business initiative, CI is a cross-functional effort, involving business sponsors and users across the company and throughout the customer relationship lifecycle. Often, data related to specific points in that lifecycle is siloed – locked in a finance database, or marketing, or customer service, or a call center database (See Figure 1). It is critical to involve all relevant stakeholders in the assessment review and planning stages, including sales, marketing, customer service, R&D, operations at the C-level, and IT. For one thing, it’s critical to understand where the data is siloed, as a preliminary step to bringing it all together in a holistic customer database. For another, it’s key to instill a sense of shared responsibility and to motivate the company-wide collaboration necessary for CI.

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A recent customer engagement survey of over 225 B-to-B marketers conducted for the Business Marketing Association (BMA) by IBM highlighted the cross-functional nature of CI. The survey found that responsibility for managing customer relationships is often spread across several functions. Only 19 percent of respondents reported that responsibility for managing customer engagement and relationships falls solely within marketing. More commonly, it lies within sales (24 percent) or is shared by multiple departments (39 percent). (Reference Figures 1 and 2.)

Whether your business model is primarily B-to-B or B-to-C, crossfunctional buy-in and coordination is critical to any CI initiative. For CI to work at the enterprise level, information collected by one organization, say sales, must be readily available to other departments, such as marketing and customer service, and vice versa.

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At the enterprise level, there needs to be a C-level owner to give CI mission-level prominence in the eyes of employees as well as the strategic momentum needed for business transformation. Again, this transformation will not happen overnight; but it won’t happen at all if not prioritized by your company leadership. If, however, the responsibility is added to an executive’s already-overloaded to-do list, then CI may not get the attention it deserves.

IBM’s B-to-B customer engagement survey highlights the opportunity to bring more focus to customer engagement by adding a role specifically charged with deepening customer relationships and insight. Only a third of the survey respondents currently have such a role. We believe that creating a role specifically to focus on CI – and to ensure collaboration and gain buy-in across business functions – is a best practice.

It’s also especially crucial for IT to have a seat at the table and a role in the process from the beginning. If you’re like most companies, your customer data is siloed in various systems, and it’s not clean – i.e., customers – the same customers – may be represented differently in each system. Names may be represented differently and other identifiers – credit card, marital status, and so on, change over time, splitting one customer into many. A bank customer, for example, with checking and savings accounts, a credit card, a home improvement loan, and access to investment services, may be represented by five different sets of data in five systems, each tagged with customer notes that are at variance with each other. As a strategic-level initiative, CI needs an owner at the 30,000-foot level; but data is where the rubber really hits the road with a CI initiative, and with data, the devil is in the details.

As a team, define your top business goals for CI based on your gap analysis and the most critical needs of your business. Is your priority to acquire new customers? Retain high-value customers? Increase the average shareof-wallet from customers through cross- and up-selling? These priorities will naturally flow out of the problems you’re trying to solve.

Assess the procedures, policies, and technologies you have in place for capturing, analyzing, and acting on customer information across your company. Do you have gaps in data, technology, analytical expertise, business processes or all of the above? As you consider all the transactions and interactions that holistically make up the customer experience, where are the gaps, the holes, the broken links, and the missed opportunities?

It is not about fixing the gaps at this stage, but it is necessary to identify them as you develop an “as-is” picture of your CI capabilities and begin to envision where you want to go. Don’t underestimate the advantage of solving just a few of the most important, root problems. Doing so proves the value of CI to all the business functions which are involved, earns their buy-in, and builds a solid foundation for increasing success.

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Step Two: Capture

It starts with the data. How are you capturing customer data and where is it housed? Is it siloed in different systems? What does your data tell you – and, just as important, what doesn’t it tell you?

Within a particular line of business, you may know your customer well – but this may not be true from an enterprise perspective. You will have found some of these gaps and broken links in your Step One analysis as you addressed critical questions about your customers such as:

• Who are my customers?
• Are there differentiated customer segments and what motivates them?
• Why are some customers leaving and why are some staying?
• What makes them satisfied and loyal?
• Are there product/service enhancements needed?
• What is the value of my customers today, and what is the going forward/projected value?
• Who are my most profitable/least profitable customers?
• What are my customers saying online?

CI requires knowledge and coordination across the enterprise of all the interactions you have with that customer. You also need not just to have, but to be able to analyze and understand all the data available about that customer. You may have some of this available data, but not all.

There are four basic types of customer data: descriptive, behavioral, interaction and attitudinal.

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Behavioral data includes transaction information such as order information, payment history, and shipping addresses (data you typically have in CRM systems). Descriptive data includes the customer’s name and address, along with demographics such as gender, marital status and estimated household income. Interaction data details when, where, and with whom an interaction took place, what was communicated, and what was the outcome.

You’ve probably already taken the first steps, as part of earlier customer initiatives, to clean and consolidate some customer data from different systems. As you move forward, you’ll continue to augment that consolidated data with additional data from operational systems for sales, telemarketing, customer call centers, payment, finance, marketing, etc. You’ll continue to round out the customer with an increasingly holistic view, as you add more of the “who, when, why and how” to the basic “what.“

As you consolidate the data, it is important to look for ways to augment quantitative with qualitative data. For example, an important source of interaction data is web clickstream analysis, including traffic analysis (order of pages visited) and e-commerce analysis (used to determine the effectiveness of a site as a channel-to-market by quantifying the shopper’s behavior). This quantitative data is used by web designers to improve the navigation and conversion rates of your website and can also provide insight into the desirability of your products. Your business, however, may also have a rich trove of qualitative interaction data the value of which you have yet to realize.

Much of your interaction data is likely in the form of unstructured notes from your sales, customer service, telemarketing and other customer-facing personnel. While this information is critical to understanding your customers more intimately, such data often goes un-mined because it is “unstructured” – that is, scattered, free-form and not fitting easily into spreadsheets. In this sense it is similar to the attitudinal data – information on the opinions, preferences, perceptions, moods, etc. – that can be found internally in survey notes, email comments and open-ended inquiries typed into your website; such data can also be found externally from a rich and rapidly growing trove on social media such as Facebook, Twitter, customer review sites and blogs. This is extremely valuable data that can give you important and predictive insights into your customers.

For example, detecting “delight” in a customer’s inbound service email might help to predict the success of the next cross-sell offer, while an indication of “dismay” would instantly trigger a service recovery tactic, such as a special offer, to prevent defection. From Twitter or linkedin. com, you might find prospects seeking information on a particular product or service, such as one company we know of that used such information to recruit testers in their user-centered design process.

New technologies now make it easy and cost-effective for enterprises to take full advantage of all of the data at their disposal. Advanced text mining algorithms and improvements in natural language processing enable you to extract valuable concepts and sentiments from customer survey data and unstructured data to take customer intimacy to the next level. The technologies have also become easier to use and faster at delivering insight. On an individual level, with a few mouse-clicks, business users can build a predictive model within a web browser interface, and run simulations and “what if” scenarios that compare and test the best business outcomes. On a corporate level, using proven methodologies, in a matter of months you can make significant, measurable strides in improving your business by understanding your customers better. One of the customers highlighted on this paper, for example, achieved an ROI of 448 percent in less than three months by focusing on increasing customer conversion rates for direct-mail marketing campaigns.

You may not get all possible data from all possible sources in the early stages of your CI initiative. You determine the right data to start with by mapping back to your original business goals from Step One, and finding the sources of data needed to answer your critical questions. Then, with each additional data source you add, you increase your customer intimacy and the accuracy of your predictive models.

Step Three: Predict

Now that you have all this data, what does it tell you and how do you use it? Your business will gain little benefit from the data unless you use it to better understand your customers and, based on this understanding, to align your business to better meet their needs. Another important finding from IBM’s B-to-B survey referenced earlier is that many marketers feel they are better able to capture customer information than to analyze it for actionable insight. This is an area where the evolution of business capability lags behind the evolution of technology.

Predictive analytics software is a powerful new tool in the corporate arsenal to optimize decisions and outcomes. With predictive analytics software, you can access the various types of data described above and use it to create powerful, predictive models to determine the best course of action for an individual customer. Rather than guessing or inferring a customer’s reaction to a marketing offer or new product, you can accurately predict it

This enables you to know which experience is right for a specific customer before the interaction actually occurs. You can anticipate and thereby enhance customer interactions, according to your business goals and the impact on your most profitable, long-term relationships.

Predictive analytics provides the insight to anticipate what customers will do next – for example, which ones are likely to leave for a competitor or respond favorably to up-sell or cross-sell campaigns. Predictive insights can identify areas of potential risk and fraud or spot new and emerging market opportunities. Organizations that can automate and optimize decisions informed by predictive analytics have a significant advantage over competitors that cannot.

This is no surprise when you consider that, by some estimates, 99 percent of all marketing messages are either ignored by consumers or simply go unnoticed. But when you use predictive analytics to target marketing messages to the customers most likely to respond, your conversion rates increase dramatically while your marketing costs decline equally dramatically. Your marketing spend is now delivering meaningful offers to the few who want them rather than annoying background noise to the many who don’t.

Step Four: Act

Once you have the data to know your customers and can predict their actions, you need to be able to act on this knowledge on a real-time basis. Decision management software enables you to translate knowledge into action – and to use predictive analytics as an integral part of a real-time decision process.

As a closed loop, bi-directional system that continually incorporates valuable feedback into the decision-making process, decision management optimizes and automates your business decisions. It combines the power of predictive analytics and business logic to enable business managers to anticipate and act on customer behavior in real time. They can quickly determine which inbound interactions are the best candidates for an up-sell, cross-sell or retention offer – and then make personalized, real-time recommendations that have the highest likelihood of acceptance by the customer. Decision management gives business managers greater control over the priorities that impact them directly, and ensures the best possible results for both your customers and your company.

Business managers and their teams – working with or without analysts – can create predictive models that score each customer in real time and build corresponding rules based on business processes and regulations. They can execute customized queries, conduct “what if” analyses, and add their unique perspectives into segment definitions or models. They can adjust priorities and processes quickly to optimize decisions. Feedback from the results of actual customer interactions flow back in to the system to create a closed loop of information that enables you to refine future offers and achieve progressively better results.

For example, a customer churn problem may span product development, strategic pricing, channel management, sales and marketing, involving and requiring the viewpoints and the action of all. A decision management system connects these business functions in the same information loop, enabling each functional area to learn from the misfires and successes of the others, and transforming isolated departmental functions into enterprise-wide knowledge and capability

Step Five: Expand

CI is an evolutionary, not a revolutionary, cultural transformation. It doesn’t happen everywhere in your company all at once, and it doesn’t happen overnight.

As we’ve seen, however, it can quickly deliver significant, even gamechanging business improvements: dramatically reducing customer churn and marketing costs while increasing conversion rates and the loyalty of your most profitable customers.

With each such win, more business functions within your company will buy in to the value of CI. With each win, you improve CI while gaining new sources of data that can increase the accuracy of the predictive analytics applied to your customers – and to other aspects of your business such as product planning and inventory management. Each win is, in a sense, another brick added to the CI foundation on which you are building strategic differentiation.

Over time, you’ll refine the processes, expand your accessible data, and involve more business functions. You’ll refine how you measure progress, and get better at it on an individual level as well as a corporate level. You’ll discover previously unidentified pain points as your customers become clearer and gaps in service or experience are revealed. From each step, you’ll apply lessons learned to the next step, as you continue to take your CI initiative toward the ideal state you identified in Step One.

About IBM Business Analytics

IBM Business Analytics software delivers complete, consistent and accurate information that decision-makers trust to improve business performance. A comprehensive portfolio of business intelligence, predictive analytics, financial performance and strategy management, and analytic applications provides clear, immediate and actionable insights into current performance and the ability to predict future outcomes. Combined with rich industry solutions, proven practices and professional services, organizations of every size can drive the highest productivity, confidently automate decisions and deliver better results.

As part of this portfolio, IBM SPSS Predictive Analytics software helps organizations predict future events and proactively act upon that insight to drive better business outcomes. Commercial, government and academic customers worldwide rely on IBM SPSS technology as a competitive advantage in attracting, retaining and growing customers, while reducing fraud and mitigating risk. By incorporating IBM SPSS software into their daily operations, organizations become predictive enterprises – able to direct and automate decisions to meet business goals and achieve measurable competitive advantage. For further information or to reach a representative visit www.ibm.com/spss.