Customer Experience Lab

3 routes to enrichment benchmarks

By Joe Novak on February 22, 2018

Improving customer experience increases customer lifetime value. In fact, it dramatically increases it, as research indicates that satisfied customers spend more than twice as much as unsatisfied customers. But how does one begin constructing a business case applying this fact?The Centriam CX Money Map, a guide to the majority of the financial benefits a customer experience program provides, can help.

In a previous post, we outline the purpose of the Money Map and how it can be used to build a business case around customer experience improvement. This post will focus on one of the twelve drivers outlined in the Money Map: customer enrichment. Enrichment —incremental spend generated through cross-sell and upsell— is a common place to start building a customer experience business case.

"Satisfied customers spend more than twice as much as unsatisfied customers. Don't leave money on the table. Check out this post on enrichment benchmarks to jumpstart your business case."  [Click to Tweet!]

Still need to get your copy of the Centriam CX Money Map? Download it now.

Building a CX Business Case

There are many steps to building a business case, but here we are focusing on the benchmarking process. We suggest starting with a proposed customer experience program and thinking carefully about the business outcomes it will impact. For example, will the program:

  • Clarify customer expectations, thus reducing future service costs?
  • Make a touchpoint more enjoyable, increasing the likelihood of repurchase?
  • Increase customer trial, resulting in higher rates of upsell?
  • Raise customer self-service rates, allowing for lower staffing levels?
  • Monitor a key performance metric such as first call resolution or mean time to repair, encouraging improvements?

With the metric(s) finalized, how is a reasonable benchmark chosen? At Centriam we commonly use three different approaches to answer this question. While all three are viable options, we have presented them in our order of preference.

  1. Merge and monetize.

    If your company already employs a customer experience metric, such as NPS or CSAT, connect the results to business outcomes and search for key differences. Start at the most granular level possible. For instance, examine the differences between completely satisfied vs. very satisfied, moderately satisfied vs. somewhat satisfied, etc. If you have secondary metrics, such as likelihood to purchase in the next 30 days, check those too. Next, combine with customer data to assess differences across customer groups. This will ensure benchmarks are sensitive to customer behavior and can help target improvement programs where they’ll have the most impact.
  1. Quantile on data disparities.

    If a CX metric is unavailable, or cannot be tied to customer data, dig into the customer data itself. Use the historic differences between low and high performing groups to set your benchmark. Ask questions of the data such as, if we were able to move one out of five customers in the bottom quartile to the median, what would that be worth? Look at metrics such as units per transaction, unique SKUs per transaction, trip segment composition or service usage, and quantile the data. Distinguishing how customers in the top quantiles differ from those in the bottom across a variety of metrics will direct your benchmarking process. Use the smallest divisions your data allows. We often use ventiles (20 groups), with even further divisions for the top ten percent. The differences between quantiles are rarely uniform; finding the largest of these will guide your focus. We find this methodology particularly helpful when proposed customer experience programs are in short supply.
  1. Poll for past and present.

    If historical data is unavailable, survey data can be used for both historical and future purchase behavior. This has been used effectively, as when American Express redesigned their customer service standards and increased spending by 10%. Their customer feedback indicated that loyalty would increase with conversational customer services, and that proved out when AmEx implemented their conversational service model. However, customer’s stated intentions are often very different from actual future behaviors. Therefore, Centriam prefers to utilize behavioral data to construct more dependable benchmarks, particularly for more subtle business cases.

CX Money Map - Financial Impact of CX

For a representative case study, consider the business case Allianz made for remodeling customer experience.  They utilized the Merge and Monetize route, combining customer purchase history with NPS survey data. They discovered that the largest difference in spend was between high detractors (NPS score 4-6) and passives. Digging further into their NPS, they found the most common complaint of high detractors was lack of feedback. Allianz’s analysis followed industry best practice: it identified a gap, produced multiple metrics and future benchmarks, and ultimately led to the creation of a service recovery initiative. The initiative led to a 16-point gain in NPS, and a corresponding 15% increase in spend for the affected population. This example echoes results from a study of the European insurance market conducted by Cisco’s Business Solutions Group, which estimated that 45% of the financial benefit of improved customer experience stemmed from enrichment gains.

So get started developing your business case for customer experience around enrichment today! For further information about the financial benefits of customer experience, download the Centriam CX Money Map and stay tuned to the blog for discussion of the other main drivers.

Topics: Financial drivers

Author: Joe Novak

Joe is committed to improving the ability of Centriam’s clients to act upon data and insights. Joe brings diverse experience utilizing data and advanced analytics to improve performance across many industries. Prior to Centriam, Joe managed risk for multiple credit card and auto loan portfolios at US Bank. Joe also worked in marketing analytics at Target and survey analytics for the Minnesota State Colleges and Universities system. Joe holds a B.A. in mathematics and computer science from Macalester College and an M.S. in applied mathematics from the University of Colorado.
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