Many customer-focused initiatives fail to generate expected returns

Choose your favorite three-letter acronym: VOC (Voice of the Customer), CEM (Customer Experience Management), EFM (Enterprise Feedback Management) or CRM (Customer Relationship Management). Businesses in virtually every industry have implemented some kind of customer-oriented initiative and technology, with CRM being the standard. Some have been massive initiatives, supported by significant investments in technology and designed to shift a company’s orientation from products to customers.

Many of these initiatives, however, are failing to generate expected returns and deliver significant value. These results are partly due to the difficulty of pushing change through established cultures or processes. But another factor is that although operational CRM systems such as sales force automation or call center systems provide the necessary foundation for better customer relationships, they don’t do much to improve an organization’s ability to maximize customer lifetime value. In other words, CRM has become a strictly “customer records management” tool, not a “customer relationship management” strategy.

To deliver a true customer relationship management strategy, organizations need to understand where customers are positioned within their lifecycle. Is this customer in the initial stages of researching a product? Is she a current valuable customer who would respond to a targeted cross-sell offer? Is he exhibiting behaviors that indicate he may defect?

When your organization delivers what customers need at the appropriate time within their lifecycle, customers are more likely to remain open to future marketing efforts, buy more of your products and services and, as a result, become more valuable. This is a win-win relationship for both you and your customer. However, to achieve and maintain this type of relationship requires enhancing your customer-oriented business applications with analytics.

Analytics drives CRM returns

Companies typically start with historical analytics, using a combination of reporting tools, specialized data warehouses, and online analytical processing (OLAP) solutions to drive CRM returns. These solutions help analysts understand and measure the outcome of past decisions and results, and can be useful in narrowing the scope of further investigations. But on their own, they can’t provide your organization with a clear picture of the future.

Industry leaders, however, are evolving their analytical capabilities by adding data mining and other predictive capabilities to their operational CRM systems. Data mining is the process of discovering meaningful and previously unknown correlations, patterns and trends in large amounts of data. To make these discoveries, data mining relies on pattern recognition technologies as well as statistical and mathematical techniques. Because it is forward-looking, data mining helps organizations measure the potential of customer relationships and develop plans to maximize that potential.

The most evolved analytical CRM solutions continuously apply predictive analytics technologies and deploy the results across the enterprise. This way, customers can interact with your organization online, by phone or face-to-face and receive the kind of treatment that meets their present needs and anticipates new ones. This method is designed to encourage customer loyalty as well as additional purchases, thereby increasing a customer’s lifetime value—and your organization’s profits.

For example, one of the world’s largest airlines relies on predictive analytics to optimize revenue for each flight, improve service for its best customers and increase customer loyalty. By using IBM SPSS® Modeler for predictive modeling, its agents and flight staff were able to identify high-value customers and ensure that their needs were met. The resulting increase in customer satisfaction boosted annual revenues to an average of $200 each for its “valuable” customers and $800 each for its “most profitable” customers. Total revenues for the year after implementation increased by $40 million. Over the same period, the airline saved $31 million in operational costs.

The five customer imperatives

Based on extensive experience with a wide range of organizations, the following customer imperatives have been identified as best practices used by leading companies to maximize customer value with business analytics.

• Base your customer strategy on predictive profiles.
• Predict the best way to win the right customers.
• Predict the best way to grow customer relationships.
• Predict the best way to keep the right customers longer.
• Measure and report your results for insight into performance.

1: Base your customer strategy on predictive profiles

Detailed, accurate, predictive profiles are the essential foundation of any customer strategy initiative. To understand your customers better, use analytical tools to create customer segments, and then create predictive profiles of each segment. These profiles, when deployed enterprise-wide, help your entire organization to focus on activities that are most likely to generate the highest returns.

Identify key customer segments

You can define customer segments based on behavioral information drawn from operational systems and attitudinal information obtained through market research and social media. The two approaches complement each other, allowing you to gain a more accurate customer understanding and develop more effective strategies for each customer segment

You can segment customers and prospective customers according to a number of different criteria. For example, you can analyze customers by the amount they spend with you, by their payment pattern, by the length of the relationship, and many other factors. You can split customer segments into smaller sub-segments, even reaching the ultimate one-to-one relationship through micro-segmentation, in which you understand each individual’s needs and preferences.

By understanding which customers are most likely to purchase certain products or services, you can focus marketing programs to obtain the highest possible response on your marketing investments. You can segment customers by value, behavior, demographics and even by attitude.

Create predictive profiles of each segment

Once you’ve identified the segments of customers who use and value your products and services, the next step is to understand what products or services customers in each segment are likely to want next. Adding this predictive element can make your customer relationship significantly more productive and profitable.

One of the world’s leading financial services companies initiated a customer loyalty program using IBM SPSS Modeler. The data mining solution helped executives analyze its data warehouse of 2.5 million customers according to 400 different attributes. By defining a number of different customer segments, the company could focus its marketing campaigns on the one percent of customers who are not only extremely likely to purchase a product or service, but also have the credit rating to do so. It recouped its investment in the project within two years.

2: Predict the best way to win the right customers

Acquiring customers is costly but necessary. Paying too high a price to attract customers, however, or acquiring the wrong types of customers, can have a significant negative impact on profits.

Using inefficient methods to attract customers will result in higher costs and profits that are lower than they should be. Attracting the wrong customers impacts profits, too. For example, if you attract customers who are likely to leave, you may incur the acquisition cost without ever seeing a profit from the customer relationship. Other customers may be loyal, but cost so much to serve that they are only marginally profitable.

IBM SPSS predictive analytics minimizes your organization’s costs by directing programs toward the people most likely to respond. You can further boost profits by focusing on the types of prospects most likely to become profitable customers.

Create a prediction-based customer attraction strategy

Use predictive profiles to determine what types of customers you want to attract. Then create a cost-effective attraction strategy that includes separate plans for each customer segment.

Most companies will want to focus their attraction efforts on winning over prospects that fit the profile of their most profitable customers. But other, less-profitable customer segments may have more room to grow over the long term, or may be more cost-effective to attract. Marketing to these segments may be an attractive option when budgets are tight.

Optimize your customer attraction strategy with response modeling

Precisely adjust your customer attraction plans by using response modeling to predict which marketing programs will generate the highest response. This adjustment can benefit your organization in two ways: you attain the results you want while avoiding the high costs associated with unproductive marketing efforts. In this way, you can see higher profits for the money you invest.

A large Belgium-based insurance company saw its profit margin narrowed and its growth strategy threatened because the cost of adding new customers exceeded revenues from first-year premiums by almost 50 percent. IBM SPSS predictive analytics and decision management technologies allowed the firm to first identify groups most likely to respond to a campaign, and then perform a sophisticated profit-cost analysis. Using this information the company reduced its direct marketing costs by 30 percent and made acquisition campaigns profitable in the first year. In addition, long-term customer profitability increased by 20 percent.

Improve conversion rates with prospect surveys

Market research can be used to improve customer acquisition both before and after marketing campaigns. Beforehand, surveys of groups identified as likely prospects can clarify their reasons for buying your products or services, allowing you to refine your campaign offers. Afterwards, by surveying prospects that did convert and those that did not, you can learn what worked—and what you need to change—to earn prospects’ business in the future. By using this type of predictive intelligence to guide your customer attraction strategy, you can improve the conversion rate for your best prospects.

3: Predict the best way to grow customer relationships

To maximize customer growth and increase customer lifetime value, organizations need to know not only what customers are most likely to want, but also when and how they will want it delivered. With IBM SPSS predictive analytics, you can strive for this level of customer knowledge.

Create a prediction-based customer growth strategy

By using predictive profiles, product-affinity models, segment migration models, response models, survey research and social media analytics, you can generate predictive intelligence about your customers. As a result, your customers will be more satisfied with your service, reinforcing a decision to buy from you again.

Discover product affinities

Customers often purchase products and services together, or in certain sequences. By analyzing “market baskets”—products and services purchased at the same time—organizations can offer customers appropriate additional products at just the right time. Understanding what products your customers buy together can lead to improved product placement, attractive bundling of products in both direct marketing and online offers, and more timely offers. Not only does this increase revenues, it generally improves customer satisfaction and contributes to maximizing customer lifetime value.

IBM SPSS models useful in analytical CRM:

• Response models predict which customers are likely to respond to a new offer
• Product-affinity models predict which sets of products customers are likely to purchase together
• Segment-migration models predict which groups of customers are likely to become more or less valuable
• Attrition models predict which customers are likely to leave

A leading computer retailer in Japan used IBM SPSS Modeler to build a recommendation engine that suggests products to visitors to its website. Recommendations are based on customer profiles and information about prior purchases contained in the company’s database. The first year it was implemented, the recommendation engine resulted in a sales increase of 18 percent, and a profit increase of 200 percent.

Predict segment migration

Applying data mining techniques to historical sales data shows you who buys what. By combining this information with other data, you can also make other kinds of predictions, such as which customer segments will become more valuable and which less valuable, and by what amount. Predictive segmentation modeling shows you which characteristics are linked to migration between customer value segments. Adding this kind of predictive intelligence to your customer growth strategy helps you to realistically plan growth for each segment.

Power CRM systems with prediction

By deploying the results of predictive analytics to every customer touch-point—from your branch offices to your call center to your website—you can achieve greater effectiveness and profitability. Build predictive results into your website, and visitors will be automatically presented with the offer most likely to result in a sale. Or build predictive results into your call center, so that sales representatives know what products or offers are most likely to suit a particular customer’s needs. Every bit of data you have coming in from these systems becomes fuel for driving future customer interactions and realizing higher returns.

Grow relationships by asking customers what they want

Using the data you already have to predict customer needs is a powerful way to improve interactions and lifetime value. But it is also important to systematically ask customers what they want. Surveying your customers and gaining a better understanding of their needs, and why they buy from you, helps your organization to improve your customer growth strategy and maximize customer lifetime value.

4: Predict the best way to keep the right customers longer

Studies have shown that customer acquisition can cost five to 12 times more than retention, and that a five percent improvement in customer retention rate can increase an organization’s profitability from 25 to 100 percent. Obviously, improving customer retention can have a big impact on profits.

Customer attrition is particularly challenging for online retailers and companies in financial services, telecommunications and other industries where customers can change vendors relatively easily.

Create a prediction-based customer retention strategy

Keep your best customers longer by creating attrition models, and then use these models to determine which customers are at risk of defecting. You can enrich these models through survey research and data captured from social media that adds valuable attitudinal information.

Create predictive attrition models

Understand which customers are most likely to leave for competitors and, more importantly, why. By applying data mining techniques to data about your customers, you can develop profiles of customers who are valuable and customers who have previously defected. Then you can develop strategies to keep your valuable customers from leaving.

A European telecommunications company also uses IBM SPSS Modeler to identify customers likely to leave or “churn.” By discovering what types of customers were likely to leave, the company was able to make targeted offers that reduced churn by 20 percent, compared to a similar group that did not receive the offer.

Conduct and analyze satisfaction surveys

Satisfaction surveys are invaluable in determining whether customers are satisfied, why, and in uncovering issues that may affect their future loyalty in time to take corrective action. Even customers who cannot be retained have potential value to your organization. By surveying customers who could not be retained, you can better understand what you need to do to keep customers like them.

A Netherlands hospital that serves nearly 120,000 patients annually needed to comply with the requirements of national healthcare quality legislation. In addition to monitoring mandated quality standards, the hospital also wanted to monitor patients’ opinions and preferences. The hospital chose the IBM SPSS Data Collection survey research suite to collect and manage information and IBM SPSS Statistics software to analyze the data. Survey results pointed to several areas in need of improvement—for instance, a majority of patients indicated they were insufficiently informed about where they could go for emotional support. The hospital used its findings to make improvements and is extending its evaluation process to its nursing and outpatient departments.

Understand customer sentiment with social media analytics

Social media is invaluable in understanding customer preferences, opinions and attitudes; however organizations have struggled to derive true business value from social media, using it strictly as a listening tool. By incorporating social media into predictive analysis, organizations gain deep, granular insight into the sentiment behind what customers are saying about a particular product, brand or service. The IBM Social Media Analytics solution is designed to capture snippets from social media, perform sentiment analysis on key phrases, segment authors by categories such as demographics and influencer score, and uncover evolving topics or trends.

A global financial services provider needed to detect possible risks to its reputation in social media in order to improve customer satisfaction. With IBM Social Media Analytics, it now captures and analyzes opinions on the bank’s services and measures the effectiveness of publicity campaigns concerning the amount of mentions, new products, customer service and corporate news. This information helps bank employees to better understand clients’ and stakeholders’ needs and desires, speeding up responses and supporting proactive campaigns to improve customer satisfaction and loyalty.

5: Use business intelligence to measure, report, forecast and plan

Management gurus tell us that we cannot manage what we do not measure. This is certainly true of customer relationship management. Profitable customer-focused strategies require precise, timely measurement of the factors that affect customer success as well as revenues. This effort requires a combination of historical and predictive technologies: predictive analytics, to identify customer targets for acquisition, up-selling, or cross-selling; and business intelligence, to monitor the results of marketing campaigns and sales programs.

IBM Cognos® Business Intelligence provides critical insight into key areas such as target market trends, micro customer segment behaviors, pricing fluctuations and marketing pipeline behavior. Through real-time alerts, organizations can detect early warning signs related to the health of their business, exposing inefficiencies, below-average return on investment and garnering insights from all information sources to pinpoint root causes of issues. Forecasting and what-if analysis capabilities allow for accurate planning by taking into account the inevitable scenarios, such as budget cuts or market changes, which affect campaigns and other customer-focused initiatives.

Drill down for deeper insight

Insight into key customer-focused initiatives and campaigns is necessary for managers who need a high-level view of the performance of specific campaigns or product portfolios. Managers also need the ability to delve into operational information that might affect pricing, channel and promotional decisions.

Interactive dashboards and analytical reports allows the business user to gain an overall view of the situation at hand, with the ability to go deeper into the details to get a better understanding of the factors driving performance. Fast access to relevant information even when working remotely is key to making better business decisions. Drilling down through increasing levels of detail, business users can view campaign, pricing and customer data by different dimensions, such as campaign responses by discrete geography, channel, event, website offer or by search location.

Conclusion

The ability to create and sustain customer loyalty is a critical capability for organizations. Faced with informed and empowered customers, and massive volumes of readily available customer data, businesses need a way to align their staff and operations to ensure customers are satisfied. IBM solutions for customer loyalty help organizations use customer analytics to gain actionable insights from customer data, and deliver those insights to the people, processes and systems that can improve customer satisfaction and loyalty.