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Loyalty 2.0: Industrialized analytics embedded in your processes

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It is no longer a given that a high initial customer satisfaction rate for a product or service will naturally translate into a loyal customer base. New customers of both the B2B and B2C variety are far more influenced by increased choices, technological advances, and exposure to social media.

At the same time, the channels used to get and keep this new breed of customer often rely on (siloed) brick-and-mortar operations and processes. Remaking enterprise processes that are responsible for delighting customers (and doing it at the right cost) is an analytic and business transformation challenge. The following explores how to “industrialize” and embed analytics into processes to enable intelligent execution at scale.


We live in a world where vast social and e-commerce networks are interwoven globally in ways that are gradually rendering the networks indistinguishable from each other. Buyers are in control as never before. That has made renewing the focus on customer retention and loyalty a top priority regardless of what you are selling. Meeting that priority calls for new business processes, employee practices, and innovation. The need of the hour: Identify and deliver a quality experience across all customer touch points. Effectively meeting this need means creating a “single view” of the customer by integrating multiple data sources, from social media to enterprise data to traditional research.

In this complex environment, one must ask, “How do enterprises increase their ‘intelligence,’ adapt to the new rules of customer loyalty, and drive sustainable growth?”

In this complex environment, one must ask, “How do enterprises increase their ‘intelligence,’ adapt to the new rules of customer loyalty, and drive sustainable growth?” The answer lies in capturing insights from multiple listening posts across touch points and customer relationship life-cycle stages. Through a more comprehensive understanding of buyer opinions and intent, enterprises can use predictive analytics to transform business processes and influence customer behavior.

The growing need for more CEM and less CRM

In recent years, companies have invested a lot of money in Customer Relationship Management (CRM). CRM has empowered the business leader with the muscle of repeatable customer interaction behaviors while enabling the collection of data... some type of data, that is.

Managing customer experience in silos means individual departments worry about their metrics or their processes; the departments do not necessarily look at the overarching goal that connects everything under one frame of reference, the customer’s. Examining only past transactional data, for instance, yields information only on what’s already occurred. Weight must also be given to how up-to-the-moment purchasing preferences evolve based on something customers see and “like” on Facebook or covet enough to “pin” a photo of it to their Pinterest board. In these conditions, the next step is to harness the power of Customer Experience Management (CEM). CEM results in overall customer satisfaction, which, in turn, secures a measure of customer loyalty that would otherwise remain fleeting. It involves the following:

  • Understanding the experience from customers’ perspective
  • Delivering the experience to their expectations, closing the gap between desired and actual experience
  • Integrating customer centricity into an organization’s DNA—through repeatable processes that hit the determinants of “delight”

CEM is not limited to managing end-customer experiences. Rather, it extends to managing and improving the experience of all stakeholders, including influencers, employees, and channel partners. Managing customer experience is not an exercise to be performed by a single team or specific departments. It is about changing how companies do business, how they view their customer, and how flexible they are regarding changing customer needs.

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CEM helps companies look at customer survey data in conjunction with their behavioral data, and thus gives a holistic picture of the customer: Past, current, and probable. It takes a company to the next level of customer centricity by providing access to customer opinion and decision-making processes. Since business leaders are already heavily invested in CRM and still situated on the front-end of its ROI curve, many are understandably skeptical of further investments in CEM. Sometimes even when companies are aware of the importance of CEM they may not know how to manage it. They may collect data but struggle to tie it together into a comprehensive customer view. They may analyze the data but fail to circulate findings to the appropriate people. Some struggle to assign accountability for process improvements. CRM tells you only what the customer does, not what he or she is thinking.

Introducing a holistic CEM framework

We have found that converting raw data into actionable intelligence that can holistically heighten customer satisfaction and loyalty means expanding and sharpening your enterprise’s listening capability in three areas:

  • Traditional research: Feedback is sought in the form of structured ‘Loyalty Surveys’ that study customer perception, attitude, opinion, need, and desire
  • New media research: Steadily monitoring “unsolicited” feedback on websites, forums, blogs, and other social media
  • Enterprise data: Identify and understand key drivers of behavior by studying traits and tendencies during transactional engagements at contact centers and the like

Stage 1: Designing - Experience mapping
Once customer information is gathered from traditional, new media, and enterprise data sources, one can begin to develop a framework for mapping customers’ experiences, measuring their loyalty, and assessing the impact that their opinions can have in relation to how the enterprise responds to those opinions.(Figure 1)

Customer experience must be measured at specific “touch points” throughout his or her journey. This means anywhere he or she comes in contact with the business in any form, be it product, service, customer representative, or word of mouth. A touch point can be divided into three main zones: Pre-purchase, purchase, and post-purchase. Pre-purchase points build customer perception and expectation, which are then evaluated during the purchase stage. Post-purchase points are equally important as they provide the “last impressions” of the product, service, brand, and company that will complete the loop in the Customer Experience Cycle.

There are many ways to chronicle customer experience. Among them are process mapping, customer activity cycles, activity mapping, service blueprint, and touch point analysis. The best results are obtained through a combination of methods. This process helps landscape the entire cycle, from information gathering to purchase decision to actual purchase and post-purchase use to after-sales support. (Figure 2)

This broader landscaping effort involves interviews with all stakeholders in the organization at multiple levels. The stakeholders are spread across functions such as operations, sales and marketing, customer service, design and engineering, and after-sales support. Combining the top management view with front-line operational experience at such initial stages of the CEM program design ensures that the output from the program is operational and is well aligned to the overarching strategic goal. This allows organizations to achieve an “inside-out” view of loyalty in addition to an “outside-in” view.

Stage 2: Execution - Loyalty mapping
Where Stage 1 is about solution design, Stage 2 is about execution. This is the stage when empirical research is conducted and hypotheses formulated and tested. This is also the stage when the methodology for evaluating the CEM program itself is designed and executed.

Design: Based on the input from Stage 1, a study is designed that combines traditional and social media research for a comprehensive customer view. (Figure 3) This design ensures that customer segments across the experience life-cycle stages are covered based on their touch point use. Social media themes provide additional inputs for designing the questionnaire. At this stage, one must identify and finalize the key sources of social media conversations.

Data collection: Data for traditional research can be collected online, on the telephone, face-to-face (F2F), or through a mixed-mode approach depending on the target audience profile. Multiple surveys should be conducted with in-house-data collection centers in multiple geographies and by partnering with third-party agencies for specific regions or target audience profiles. A preliminary analysis of the data and key themes is conducted.

Data analysis: Customer loyalty metrics and their drivers are analyzed to identify the critical drivers. Data collected from traditional research and social media are analyzed independent of each other and in conjunction, for a holistic view.

360 degree view of the customer loyalty

Combining structured feedback from primary research with unsolicited feedback from social media gives a comprehensive view of customer loyalty. Primary research and social media research complement each other and work very well in tandem while closing the inherent gaps in individual methods.

Unsolicited customer chatter: Since this is not structured, it can help identify new themes and new drivers more easily than traditional research. The data are current and free of bias. Additionally, the cost of collecting these data is relatively low, and this type of data can be collected much more frequently than traditional research. Social media data are helpful in providing greater granularity to the individual drivers and issues. This in turn helps drive more action-oriented insights.

Traditional research: The benefits of structured research for checking the customer pulse have been many and undisputed over time. In spite of obvious criticism such as high costs and intense design effort, traditional research helps generate robust insights that can aid business decisions and prioritize actions.

In addition to combining social media and traditional research, a complete view of customer loyalty includes listening to other stakeholders. These stakeholders include not just employees and suppliers but also listening to potential customers who did not convert and competitors’ customers. Then a standard system for listening to customer complaints and offering timely responses must be designed.

Stage 3: Impact assessment
Senior leadership is always looking at the financial impact loyalty has on the business. This stage is thus about combining customer survey data with transactional data to look at “what customers feel” and “what customers do” as a result.

This analysis incorporates merging data from multiple sources, which is sometimes the biggest challenge in this stage. Linkage analysis is a two step process in which Step 1 is about creating a data set for further analysis through merging disparate data sets. Step 2 is about analyzing that data set to identify patterns and trends and build predictive models. This analysis supports customer loyalty efforts and CEM investments in the organization. The numbers help get senior leadership buy-in and predict future investments.

Industrialized analytics can enable a holistic customer view and embed that insight into loyalty-driving processes

Identifying and delivering an overall superior experience to customers is an all-encompassing goal. However, with the demand to create a single view of the customer increasing, there is now a better way to measure, assess, and execute best practices for retaining customers and driving business growth. Pursuing these goals requires a more robust—and more readily scalable—approach to analytics and related data collection. We live in a wired world where the relationship between customer and enterprise is more immediate, fluid, and delicate than it has ever been.

Contact us today to understand how advanced analytics and business processes can enhance your clients’ loyalty rates.

For more information, contact, analytics.marketing@genpact.com and visit, genpact.com/what-we-do/capabilities/analytics/financial-services-analytics

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