Each week it seems there are reports that more and more brands are focusing on "customer experience" as a way to differentiate themselves in a marketplace of increasingly commoditized products and services. Companies like Amazon, Zappos, and USAA are routinely, and rightly, cited as exemplars of customer experience management.
This observation is underscored by a recent study conducted by Forrester Research showing that 90 percent of firms indicate customer experience is a top strategic priority. Incongruously, however, Forrester also notes that nearly the same percentage of companies (86 percent) declare that they do not actually expect to get much value from their customer experience investments.
This seeming contradiction may indicate that companies have yet to see convincing evidence that ties investments in customer experience to gains in company value. It simply isn't good enough to say that improvements in customer experience drive higher customer satisfaction, Net Promoter Scores (NPS), or Forrester's own Customer Experience Index (CXi) scores. The business impacts of these investments need to be tracked beyond these intermediary measures to hardcore financial measures that would interest a CFO.
Customer experience's effect on customer value
To advance this notion, Forrester research shows that high customer experience scores for a brand do correlate with a higher "intent" for customers to purchase again, remain loyal, and recommend the brand. This is a good talking point, but the next step is a careful examination of actual customer data to show these effects.
For example, think of a customer interacting with a home goods supply retailer. The sequence starts with the customer having some contact or engagement with the brand. This could be a product purchase or a non-financial interaction like a phone inquiry or a visit to the brand's website. The process and outcome of that engagement will, to some degree or another, have an effect on the customer's opinion about his experience with the brand. If the experience is a good one, that would likely lead to a subsequent desired behavior, such as another purchase, recommending the brand to a family member, or liking the brand's Facebook page. That behavior will then result in a direct or indirect financial impact to the brand realized as increased sales or improved profit. This financial impact chain of effects, if validated, is what will really get the CFO and CEO excited.
Connect the dots with Customer Experience Value Analysis
So how can a company actually tie customer experience improvements to bottom-line impact? We have created a five-step process called Customer Experience Value Analysis that integrates data analytics with customer value along the entire customer continuum to put the right financial picture into focus.
Step 1: Assemble an analytic data repository
Before you can estimate the impacts of customer experience improvements, you must know as much as possible about your customers. This means not only knowing who the customers are and what they have purchased, but also how they interact with you and how they feel about those interactions. The analysis data set therefore must have data representing all of these categories. We have found that much of this data can be found in existing CRM systems, and so this is a great place from which to pull the core data elements. We then supplement that initial repository with added data elements extracted from channel systems, service centers, and other ancillary customer contact points.
Step 2: Track all customer interactions
Today, customers are encouraged to interact with brands in the ways they most prefer. Our analysis shows that most brands have a mix of single and multichannel users. For instance, many older bank customers prefer conducting their business at a branch, while younger customers generally favor digital channels, and still others in between are hybrid channel users. To gain a firm handle on individual customer experiences, customer interactions across all channels need to be assembled in the analytic data repository. This includes identifying interactions on social networks and mapping them to specific users, when possible. Our recommendation is to compile at least 12 months of history on each customer to account for seasonality and any other usage patterns.
Step 3: Monitor customer experience scores
The trickiest part of the analysis is gathering enough information regarding how customers rate their brand experiences to be able to correlate those scores with subsequent customer behavior. While many companies are now tracking NPS, CXi, or other customer satisfaction measures, typically these scores are gathered on a sample of customers and reflect more of an overall "relational" experience score rather than a "transactional" experience score tied to a specific interaction. That is, they capture what the customer feels about the brand based on an accumulation of experiences, not what he feels based on each experience separately.
In fact, it may be impractical to measure customer experience for each customer, since most customers are not willing to provide information after each interaction. However, customer experience scores can be inferred based on statistical analysis and applied to those that haven't actually answered a customer experience questionnaire. For instance, knowing how long a customer had to wait in queue during a service call and whether the customer's problem was resolved on the first contact can be used to predict with amazing accuracy what his actual customer experience score will be. The key part is to understand the drivers of those customer experience scores.
Fortunately, as more data is collected and analyzed, these predictions should get stronger and stronger. Meanwhile, newer and simpler data collection technologies may also encourage more customers to complete customer experience surveys and provide more feedback.
Additionally, it is important to remember that what drives customer experience scores may differ from segment to segment—some customers may value high touch, concierge-type service, while others more highly value speed and convenience, for example. The analysis must take this into account. Interestingly, we have also seen that the impacts of negative experiences can be somewhat offset by earlier positive experiences (goodwill effects), so knowing as much as you can about each customer's history is important.
Step 4: Determine related customer behavior changes
Linking a customer's experience ratings with his or her subsequent customer behavior is a fairly easy step in the process, once the necessary data elements have been amassed in the analytic data repository. For instance, for a cable operator we determined the impact that different customer service interactions had on the likelihood of a customer disconnecting his service, ranging from single resolved calls, to multiple calls, to escalated calls, to truck rolls. We also used contact center text data to understand the customer sentiment associated with the interactions to further refine those estimates.
Obviously, for customer experience improvements to have value there needs to be an incremental positive difference in customer behavior that can be attributed to them. Statistical modeling approaches are very good at isolating those gains while keeping all other factors about the customer constant.
Step 5: Evaluate customer value impacts
Finally, once all of the incremental behavioral actions have been quantified they need to be translated into a financial impact. In most instances this will be straightforward. However, in some cases, the financial gains of certain purchases may differ depending on who is purchasing them because the product margin may not the same. If, for instance, it is determined that for a particular bank, customer experience improvements have generated 1,000 additional deposit accounts, the value of those can differ significantly by customer because of the balances that are maintained. Similarly, the value of a vehicle purchase may differ between two customers depending on the trim packages and other options that are selected.
This evaluation exercise will ideally cover the aggregate gains in a number of areas from incremental purchases, reduced attrition, lower cost to serve, and greater acquisition (see sidebar), providing a comprehensive estimate of the financial benefits of customer experience improvements.
Take note: the accuracy and reliability of any analysis is highly dependent on a number of crucial factors that may or may not be within the control of the analyst. And that is no different here. In fact, the requirements of performing a Customer Experience Value Analysis may be somewhat more difficult than usual because of the novelty of bringing the components of this particular analysis together. That said, on the positive side, advances in data capture technologies and processing are making this analysis increasingly viable.
Conclusion
Customer Experience Value Analysis isn't a turnkey solution. It requires investment in technology, data experts, and a holistic look at customers and business operations. Yet elements of Customer Experience Value Analysis are beginning to pop up in the business world. There is no denying that drawing customer experience linkages from end-to-end is needed. A successful customer experience of the future will be one whose results can be understood by any CFO or Wall Street analyst. For then, and possibly only then, will the 90 percent of firms that say customer experience is a high priority actually invest in the programs to make customer experiences better.
