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Loyalty analytics Member engagement models: The foundation of a powerful member retention strategy

PwC | Loyalty analytics member engagement models: The foundation of a powerful member retention strategy 1

The foundation of a powerful member retention strategy

Loyalty programs are nothing new. They date back a century or more to the issuance of coins or stamps redeemable for free goods or discounts on future purchases to reward customers for their patronage. Over the past three decades, loyalty programs have evolved substantially, becoming effective tools in improving a brand’s image and a company’s revenue. As a result, companies compete v igorously to grow their member base, and customers are inundated with offers to join loyalty programs with each in-store or online purchase. According to Colloquy1, the average American household has signed up for nearly 30 loyalty program memberships. Because there are so many programs, simply having one is no longer enough to distinguish a brand. Companies now must develop member-focused marketing strategies that keep the most valued members engaged and prevent them from losing interest in the brand.

1 U.S . Customer Loyalty Program Me mberships Top 3 Billion for the First Time, 2015 Colloquy Census Shows; colloquy.com, February 9, 2015

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The value of member engagement

Seeking a higher return on their loyalty program spend, leading companies are looking toward improving the engagement of existing members rather than prospecting for new members. With vast amounts of data providing significant information about the past behavior of existing members, companies are building member engagement models to aid management in developing strategies to improve member retention and direct targeted marketing to their most valued members. For a relatively modest investment, models can be built that alert companies to highly valued members who are at risk of becoming disengaged. Management can then proactively target marketing strategies to reengage these members with the brand. However, before embarking on the development of a member engagement model, companies must first define what constitutes member engagement, which may vary by member segment.

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The value of member engagement

What is member engagement?

The level of member engagement varies, depending on the type of business and the nature of the member relationship with the brand (e.g. spend, number of stays, cobrand credit card). Loyalty programs, such as those in the hospitality and airline industries, must explicitly define engagement, since members may discontinue or reduce utilization of services at any time without notice. For example, engagement may be defined as a reduction in the utilization of the product or service for a specific period of time, such as one y ear.

Value oriented segmentation strategy

In practice, many companies employ a value oriented segmentation strategy that separates members with distinctly different levels of engagement – high, low, and various levels in between -- into segments, each of which has a different definition of member engagement. For example, an airline program may consider a base member disengaged if the individual has not flown in the last year; whereas, the same program may consider an elite member disengaged after a three month flight-free period. An effective member engagement model recognizes the different behaviors displayed by each segment and focuses on the highest value members. Informed by a member engagement model, companies can implement targeted marketing strategies to prevent disengagement of the highest value members. This approach can reduce revenue variability and increase brand revenue, ty pically by five to ten percent.

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Building a member engagement model

Developing a successful member engagement model involves several distinct steps, beginning with gaining an understanding of the drivers of member engagement based on the underlying data. From there, the company should select and validate the factors to include in the model and determine a segmentation strategy. After building and implementing the member engagement model, companies must monitor and calibrate the model on a regular, ongoing basis.

Assess the data

The first step in creating a predictive analytics solution to model member engagement is to assess the available data in four dimensions:

Number of available data fields

Quality of

data captured

Volume

of data

Ability to attach

external data

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Building a member engagement model

Available data fields. A predictive analytics model can provide more information about member behaviors if a greater number of data fields is available. In addition to transactional information that describes the type, number, and timing of individual transactions, member-specific information (e.g. program tenure, demographics, and loyalty program issued credit card ownership) can provide valuable insights. The model development process can identify non-intuitive relationships between member characteristics and their behavior. For example, members with a particular political affiliation or level of social media activity may utilize a product or service differently from individuals otherwise similar in terms of age, income, and education level.

Data quality. Assessing the quality of the available data, including its reliability and completeness, is important. While 100% completeness is not required, the reliability of any analytics solution is highly dependent on data integrity. Miscoded fields may lead to erroneous conclusions and a company accordingly should identify and correct potential data errors before the modelling process begins.

Data volume. The volume of data required – both in terms of the number of members and length of history captured -- varies by industry and depends on the rate of disengagement. A program with a very low rate of disengagement may require more data to produce suitably predictive results than a program with a higher rate of disengagement. In general, a predictive model will produce more stable results for a larger membership base and longer time period.

Attachment of external data. The ability to attach external data to segment members geographically, demographically, and psychographically can provide significant benefits, particularly for a small data set or data of lesser quality. Several data sources, such as the US Census Bureau, are available at no cost, and others are available for a fee. Psy chographic information -- data about an individual’s values, attitudes and interests — may be obtained from multiple vendors or directly through a member survey. While either method of obtaining psychographic information is an additional expense, the availability of this data leads to a more refined and accurate model.

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Building a member engagement model

Develop a member segmentation strategy

After assessing the data, the next step is to analyze the data to determine an effective member segmentation strategy. A segmentation strategy should identify relatively homogeneous blocks of members that display similar engagement characteristics. As previously noted, the definition of the level of member engagement should vary across the segmentation. For example, the highest value members may utilize the product or service as often as several times each week, compared to only a few times each year for the lowest value members. This step in the analysis may reveal that limited value may result from a predictive model focused on the least engaged members.

Building a predictive model

After assessing the data and implementing an appropriate segmentation strategy, the process of building a model can begin. The first step is to determine the specific factors that affect member engagement, separately for each member segment. After modelling the

selected factors, the modeler should validate the selected factors to prove predictive capabilities. An effective approach to validating a model is to exclude a portion of the data from model development and then apply the final model to this “hold-out” data to see how accurately it identifies members that subsequently disengage.

Tailoring model output to the individual business need, typically using some type of scoring mechanism, is a critical step in the modeling process. Using a scoring mechanism, the model assigns each individual member a score. A higher score represents a higher risk that an individual member will disengage, e.g. member A, with a score of 90, is much more likely to become disengaged in the future than member B, with a score of 20. Models also may provide additional information about members. For example, a member engagement model for an airline frequent flyer program may identify different risks of disengagement for business travelers compared to members that primarily travel for pleasure. Using the scores, as well as additional insights from the model, management can focus marketing actions on a specific individual or segment to keep

members engaged with the program and ultimately the brand.

Following a successful implementation, ongoing, periodic monitoring and calibration of model performance is critical. Changes to loyalty programs, product offerings, and the impact of marketing interventions may significantly affect member behavior. A well-designed model should adapt to these changes, automatically or through manual intervention.

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Building a member engagement model

In conclusion

Companies devote significant resources to identifying and attracting new members, but these same companies often lose sight of the foregone revenue resulting from existing members who become disengaged from their programs. As a result, prescient companies have begun to change their focus, leveraging the vast amount of data they collect from current members to identify valued members at risk of disengagement. By doing so, management can develop effective, targeted marketing strategies to keep members from disengaging in the first place. This analytics approach also provides deep insights into the drivers of customer loyalty, which management can use to design more effective loyalty programs and improve overall member engagement.

Despite the challenges of determining an appropriate approach and dedicating sufficient resources to an analytics solution, companies that stay the course do see measurable results. Member engagement modeling, which helps companies better understand their members and focus their marketing strategy, is a great way for companies to develop predictive analytics.

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To have a deeper conversation about how to leverage data and analytics to enhance your member engagement strategy, please contact:

John Kryczka, FCAS, FCIA, MAAA Managing Director +1 312 298-3746 john.r.kryczka@pwc.com

Mark Jones, ACAS, MAAA Director +1 817 870-0825 mark.j@pwc.com