Cashing in on Customer Insight

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It is becoming increasingly difficult for most companies to differentiate themselves based on their products and prices. The best way to stand out today is by offering a unique customer experience. To do this, companies must demonstrate an in-depth understanding of customers and turn this insight into action to meet their specific needs and preferences. Download this paper to learn how to use predictive analytics and other types of analyses to optimize campaigns and measure customer value.

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Executive Overview

The New York Times columnist Thomas L. Friedman wrote in a January op-ed for the paper that “Average is over.” Friedman was referring to the idea that average skills no longer enable someone to earn an average wage, due to heightened global competition. But he may well have been talking about what it takes to succeed as a business.

Being an “average” company isn’t enough. For companies to succeed in today’s global market, they have to be exceptional in some way. But it’s become increasingly difficult for most companies to differentiate themselves based on their products and prices. The best way to stand out today is by offering a unique customer experience. Doing so starts by demonstrating a thorough understanding of customers and by turning that insight into action to meet their specific needs and preferences.

Customers’ actions and interactions speak volumes. Purchase behaviors, social conversations, contact center interactions, responses to promotions—these actions and discussions provide unique insight into customers’ needs and preferences. Unlocking these insights through customer analytics allows companies to gain the understanding of their customers required to deliver unique customer experiences. It also helps organizations determine optimal ways to improve customer engagement, lifetime value, and satisfaction; and take action to make customer-focused improvements.

With customer analytics—including predictive analytics, social analytics, business intelligence, and decision management—companies are empowered to improve the customer experience and maximize business outcomes by being proactive, rather than reactive. Comments made in social channels about a perceived problem with a checkout tool on a company’s website, for example, can alert company decision-makers about a potential issue before it hits the contact center.

Four ways to leverage customer analytics

First, by analyzing a mix of structured (e.g., transaction data, customer survey data, demographics) and unstructured (e.g., social media chatter, contact center notes) customer behavior and feedback data, decision-makers can gain a much clearer understanding of customer pain points in addition to their needs and preferences. These insights can translate directly into customer experience improvements, process improvements, and business benefits.

For instance, analyzing a mix of structured and unstructured data using advanced and social media analytics can reveal what channels customers prefer to use for interacting with companies and when they prefer to interact, based on such factors as the circumstances surrounding an interaction and the time of day. “Customers tell us a great deal in their different types of interactions with companies,” says Deepak Advani, vice president, business analytics products and solutions, at IBM. “The use of analytics and BI for reporting results can help business leaders to identify what’s important to different customers, pinpoint the next best action, and follow through with the appropriate response that resonates with customers.”

Understanding customers’ needs, pain points, and preferences and acting on those insights is also critical to customer retention, and, thus, to business success. Customer expectations today are higher than ever, and many companies are struggling to retain customers. A recent survey by Accenture of 10,000 consumers across 27 countries found that two thirds of respondents (66 percent) switched brand loyalty in 2011 due to dissatisfaction with customer service. Customer analytics provides insight that curbs churn and boosts retention.

Second, organizations can use customer analytics to help mine disparate data sources to build rich customer profiles that reveal cross- and upsell opportunities to marketing, sales, and customer service operations that may have been overlooked. Retailers can use customer analytics, for example, to conduct market-basket analyses to better understand what products customers have and what they may need. In addition, contact center systems can draw from data to prompt agents with the relevant information and appropriate actions when cross- or upsell opportunities present themselves during support interactions. By gaining this deep, holistic customer insight, companies are better able to increase customer lifetime value. According to a recent Aberdeen Group report, companies that use predictive analytics enjoyed a 73 percent higher sales lift than companies that don’t use these tools. Indeed, advanced analytic and BI tools can provide business leaders with a single version of the truth when it comes to obtaining a full view of each customer.

Third, the strategic use of customer analytics provides companies with myriad opportunities to improve customer satisfaction and loyalty. More relevant and timely interactions and offers, for example, can make customers feel understood and valued. Tracking customer sentiment and responding appropriately, as well as reaching out to customers before dissatisfaction occurs (e.g., providing alerts so customers avoid a banking overdraft or mobile-overage fees), can stimulate customer satisfaction, reduce churn, and help keep customers loyal. Companies can use predictive analytics and decision management tools to gain the kind of insights needed to help retain customers.

Fourth, decision-makers can use customer analytics to measure and report on key customer trends influencing sales, marketing, or customer support strategies. Real-time dashboards and business intelligence tools make it possible for business leaders to act quickly and decisively on critical issues shaping business outcomes. For instance, a decision-maker who sees a sudden downturn in single satisfaction score for business operations in a particular region can drill down to determine the root cause, and then take quick action to resolve the issue.

Companies can then use planning and forecasting analysis to ensure budget optimization while applying predictive analytics to customer data to make the right offer at the right time through the right channel to the right customers. By delivering more relevant and targeted offers, businesses can reduce marketing waste, improve customer satisfaction, and drive higher conversion rates.

“Your competitors are after your best customers, regardless of the industry you operate in,” says Hamit Hamutcu, partner at Peppers & Rogers Group. “Customers have a lot of options, so companies have to work harder at understanding customers’ needs, behaviors, attitudes, and preferences.”

Creating the Full Customer Picture

Customers are complex. Each one has different needs, attitudes, preferences, earning power, and life events, and can’t be defined simply by her most recent purchase or the pages she visited on a company’s website. A compilation of various attributes helps complete the picture of each customer. Without the full range of characteristics, each customer picture is incomplete.

Fortunately, customers divulge a great deal about themselves through their multichannel interactions with companies—both intentionally and inadvertently. For instance, customers’ responses to email and direct mail offers, online behaviors, and contact center interactions (e.g., agent versus interactive voice response [IVR], reasons, and frequency) reveal an abundance of information about themselves and their attitudes and opinions. Additionally, many customers publicize their preferences and attitudes about brands and products on Facebook, Twitter, and other social channels. They also share a great deal about their interests and changes in their life stages (e.g., tweeting about the birth of a first grandchild).

Using customer analytics to dig deeply into information that customers share through their interactions in various channels can reveal granular attributes like why they visited a website or dropped off before completing a transaction. This detailed information can provide business leaders with fodder for analysis that competitors can’t match, because they don’t have access to the same breadth of data. Analysis of this unique blend of customer attributes can provide abundant actionable insights like improvements that can be made to self-service technologies to increase adoption or specific enhancements to product information on a website that can help improve conversion rates.

Additionally, decision-makers can access that insight quickly by using today’s customer analytics tools. “It’s now extremely easy for executives in sales, marketing, and service to have real-time information about what their customers are buying—or not,” Peppers & Rogers Group’s Hamutcu says. As a result, using analytics against these types of customer insights can make it easier for business leaders to answer critical business questions quickly (e.g., What can we do to address the churn risk for this particular set of customers? or Which customers will respond to an offer for X in which channel?). “All of those questions that took a lot more time to answer in the past can now be addressed almost instantaneously,” Hamutcu says.

[Download PDF to see Figure 1]

Too many companies overlook opportunities for using customer information that’s shared with them in social, mobile, email, and other channels. According to the 2011 IBM Global Chief Marketing Officer Study of 1,734 executives, just 26 percent of responding CMOs and their organizations are tracking blogs, 42 percent are tracking third-party reviews, and 48 percent are monitoring consumer reviews to help shape marketing strategies. Companies are preventing themselves from obtaining more holistic views of their customers if they neglect customer attitudes and sentiments shared in social channels. Companies that blend structured and unstructured customer data are able to develop richer and fuller customer profiles that can enable them to build more comprehensive predictive and propensity models that lead to more successful results, IBM’s Advani says.

Assembling the Pieces

Completing the entire customer picture today involves assembling customer insights from multiple sources. This includes traditional sources like customer surveys, in addition to customer sentiment and other information that companies can uncover from comments made in social channels.

nd other information that companies can uncover from comments made in social channels. A recent Harvard Business Review survey of 2,100 companies found that while 66 percent of respondents are either currently using social media channels or have social media plans in the works, just 23 percent are using social media analytics, while just 5 percent are using some form of sentiment analysis. These companies are missing tremendous opportunities for learning about customer needs and preferences that are being shared in social channels.

“To obtain a holistic view of the customer, companies need to do finer-grained customer segmentation down to a segment of one,” IBM’s Advani says. This includes demographic and transactional information about individual customers, but it goes much further than this, according to Advani. To properly understand customer needs, preferences, and motivations, companies also must analyze customer behaviors across channels. Additionally, the analysis within each channel should be multidimensional. For example, what behaviors are customers exhibiting on a company’s website? What did they do during their last visit? How long did they stay? When did they leave and why?

Of course, companies sometimes find themselves missing vital information about customers that’s needed to gain a complete view. This can include critical demographic information like the ages and genders of children in a household or a recent change in earning status for particular customers. By using customer analytics against existing customer information, decision-makers can identify customer information gaps and then conduct very targeted and concise surveys or prompt contact center agents to ask customers specific questions to fill in the blanks, says Erick Brethenoux, director, business analytics and decision management strategy, IBM.

Blending structured and unstructured customer data, including new information uncovered by analyses of information gaps, provides decision-makers with incredibly rich data about their customers. Business leaders who have a clearer picture of each customer are better equipped to develop more accurate propensity models and execute strategies that have a much higher likelihood of success, Peppers & Rogers Group’s Hamutcu says.

A single customer view

Gaining a complete view of customers includes being able to blend, analyze, and act on customer information that is sometimes siloed in separate business divisions and functional areas. Siloed information had been a challenge for Suncorp-Metway Ltd., a diversified financial services company with operations in Australia and New Zealand.


Mergers and acquisitions over the past decade increased its customer base by 200 percent. But this decade-long period of growth also led to 23 different source systems for customer data, making it difficult for it to gain a single view of customers. Suncorp-Metway executives wanted a single, integrated view of its customers to ensure that its marketing campaigns didn’t result in internal conflict between its brands or in a duplication of effort, both of which would have a negative effect on the company’s bottom line.

By creating a master data hub supported by IBM business intelligence, the company reduced 23 million source records into 9 million unique accounts that contain all the data available for each customer in one place. This single view enabled Suncorp-Metway to gain a deeper understanding of each customer’s total value across multiple products. Suncorp-Metway executives now make more informed decisions about which products to promote to what customers.

Suncorp-Metway’s efforts have generated multiple business benefits. Using customer analytics helped the company to reduce its direct mail and related operating costs by eliminating duplicate mailings to the same households and eliminating redundant systems. In addition, the new master data hub helped the company save roughly USD 10 million annually on integration and associated costs. Further, because the company has a richer view of each customer, Suncorp-Metway has been able to significantly increase market share in key product areas without having to increase its marketing spend. “The more information you can collect about your customers, the more accurate your analytic models will be,” Advani says.

For some business leaders, having extensive customer data can be overwhelming. Although many decision-makers express growing interest in the use of “Big Data” (or large sets of both structured and unstructured customer data), some feel intimidated by two factors: the sheer volume of data coming into the organizations and how to determine which data are relevant. By using statistical algorithms, executives can determine what data is and isn’t relevant, thus reducing the pool of potential data sources to the most germane information, says IBM’s Brethenoux.

[Download PDF to see Figure 2]

Pinpointing Profitable Customers

As decision-makers become more experienced using customer analytics, they uncover customer insights and ways of applying analytics that they previously hadn’t considered. These include fresh approaches to devising relevant, compelling offers for customers and to determining the most effective approach for delivering those offers and communicating with customers based on their behaviors and stated or inferred channel preferences.

Business leaders can also use customer analytics to refine their cross- and upsell execution. For example, they can more accurately determine what products customers have purchased and the next logical product to offer a customer based on transaction history, recent web pages visited, and products purchased by customers with similar traits.

Additionally, decision-makers can draw from a blend of structured and unstructured customer data to shape personalized sales, marketing, and customer support strategies that truly resonate with targeted customers. For instance, when a customer calls a contact center, a company can use speech and predictive analytics against the information in real time—as well as a customer’s historical information with the company—to determine the likelihood for that customer to defect, and can then proactively extend a relevant offer or response intended to retain her.

Analyzing customer activity

Providing customers with personalized and relevant communications can help foster customer loyalty while increasing customer value in two ways. First, customers who feel they’re understood and appreciated by a company are more willing to continue doing business with that organization, Peppers & Rogers Group’s Hamutcu says. In addition, engaged customers are also more likely to share the positive experiences they’ve had with a company with friends and associates, thus helping to generate incremental revenue.

First Tennessee Bank had done an effective job of collecting data. And yet, amid this abundance of data, it had a scarcity of cohesive, actionable marketing insights. Bank officials determined that they needed a better way to analyze the large volumes of customer data the bank gathers and to use that information more effectively for decision making. The bank had built a customer data warehouse and gathered information from sources like online banking records, ATMs, and call center interactions. Nonetheless, the bank still had trouble predicting customers’ future behaviors and making decisions because it lacked effective techniques and tools for analyzing the information.

One area where the bank saw room for improvement was in its marketing department’s communications. Each month the bank sent out a mailing designed to get customers to purchase a bank product they weren’t using, such as a checking account or a CD. The campaigns were relatively successful, even though the bank was working with limited data sets and without the use of automated statistical tools. But marketing managers believed the campaigns would have higher success rates if the mailings targeted a broader, more automated analysis of the bank’s customers that included customer preferences, behaviors, and transactional histories.

Consequently, First Tennessee Bank began using IBM predictive analytics solutions with its customer data sources and historical data on the bank’s marketing campaigns. The marketing team experimented with different models to hone their data modeling skills, with the goal of cross-selling more of the bank’s products to customers.

By using IBM customer analytics solutions to analyze data related to customer activity and historical marketing campaigns, the bank has reduced its marketing costs, increased net income, and improved marketing staff productivity. For instance, by analyzing more customer data points, such as ATM habits, transaction volumes, and call center interactions, and matching this information with cross-sell opportunities for checking accounts, savings accounts, CDs, and home equity loans, the bank’s marketing department has increased the conversion rate of its marketing mailings by more than 3 percent. Additionally, because the bank’s marketers are armed with greater insights about its customers, they can send better-targeted, more specific offers. As a result, the bank has slashed its printing, materials, and postage costs by 20 percent. In addition, the marketing team reduced the amount of time spent on marketing campaigns by 8 percent.

Overall, the bank’s use of IBM predictive analytics has delivered a whopping 642 percent ROI, based on an analysis conducted by Nucleus Research. Compelling financial returns like these are a key reason why a growing number of companies like First Tennessee Bank are investing in the use of customer analytics.

Savvy companies use a blend of transactional, demographic, life stage, and other customer data to develop a clearer picture of the real value that a customer is generating for them today and what their potential future value is expected to be, Peppers & Rogers Group’s Hamutcu says. This includes using customer data and analytics to determine which customers are worth keeping and which ones are a cost-drag on the enterprise.

Business leaders can leverage these insights to help them develop relevant offers and to design more customized channel experiences for high-value customers. For instance, understanding how most valuable or most growable customers use a company’s website and why they behave the way in which they do (e.g., pages visited, why they leave) during these interactions can help decision-makers to determine the types of functionality and capabilities that could further improve customer experiences. Such efforts can help companies to engage these customers more effectively and increase their loyalty and lifetime value, IBM’s Advani says. Additionally, insights about customer value and channel preferences and behaviors can also help decision-makers develop strategies for attracting and guiding different customer segments to specific channels based on that insight. Lower-value customers who call the contact center to use the IVR for self-service, for example, may be directed to the website for a more comprehensive self-service experience that also costs the company less.

[Download PDF to see Figure 3]

Companies can also benefit from using customer analytics to provide offers or perform proactive outreach that demonstrates that the company is looking out for customers’ best interests. This could take the form of a reminder, for example, that a product warranty is about to expire or that there may be a less-expensive service plan that’s better suited for them.

Although such actions by a company may temporarily impact short-term revenues, the customer trust that these actions engender will likely lead customers to buy more from the company in the future and to recommend that company to others, thus generating greater long-term customer value, Hamutcu says.

A large retailer IBM works with has used predictive analytics to improve the efficiency and effectiveness of its email marketing campaigns. The retailer has built propensity models to identify the right set of customers for a particular offer and focus its email campaigns on a narrower set of customers that have a higher propensity to buy. These efforts enabled the retailer to improve the performance of its email marketing campaigns, increase the productivity of its email marketing team, and drive higher conversion rates. Meanwhile, the retailer’s customers are receiving more relevant offers that lead them to feel that the company truly understands their needs and preferences, resulting in a more satisfied and loyal customer base.


The amount of customer data now available through an array of channels and sources is staggering. According to Gartner, the world now generates as much information every two days as it did from the dawn of civilization to the year 2003.

Companies need to gather and act on the wide range of customer data available to them. Customer analytics can guide business leaders to make faster, smarter, more effective decisions around specific customer requirements and budding market trends, IBM’s Brethenoux says.

These decision-making capabilities include being able to forecast the amount of investment that a company should make in a particular customer or group of customers who have similar traits, based on the amount of potential value those customers are expected to yield in the future.

Companies’ use of customer analytics often involves tackling a particular business opportunity from a reverse-workflow approach. Business leaders can start by identifying the outcome that a business unit is attempting to achieve and by assessing the organization’s capabilities for achieving that outcome. For example, leaders looking to reduce customer churn by 10 percent can use customer analytics to help identify primary churn triggers and the steps that can be taken to address those issues. As part of these efforts, decision-makers also must determine the key inhibitors (e.g., siloed data) that must be removed before the organization can successfully embark on its customer analytics journey.

Analytics efforts need to be woven into the everyday operations of a business. When customer analytics are managed as a stand-alone function, the value delivered to the business is often much more limited, because it’s likely that the organization’s culture isn’t analytically driven and doesn’t foster an environment of continuous learning and improvement. However, companies that are able to work through obstacles like data silos learn from their use of customer analytics, and then apply best practices going forward have a far greater likelihood of achieving consistent success.

An analytically driven organization takes the time to understand individual customers and uses that information to tailor offers to them. This kind of personalization is critical for companies looking to differentiate themselves with customers. “You have to use the information that’s available to make the customer’s experience special,” IBM’s Advani says, “because if you don’t, your competitors will.”

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