Considered by some to be the "pioneer of marketing," John Wanamaker is credited with coining the phrase "Half the money I spend on advertising is wasted; the trouble is I don't know which half." His statement sheds light on the ongoing challenge organizations face when it comes to quantifying the impact of customer interactions. With all the buzz about Customer Experience (CX) and the studies that show quality and consistent interactions create long-term customers and bottom-line performance, CX leaders may face a similar challenge when tasked with prioritizing CX initiatives given their budgets. North Highland’s Experience EconomicsSM research found that 70 percent of customers surveyed believe that their CX efforts will impact business revenue and customer value, but where do they start in measuring precisely how much? In this post, we’ll explore the analytical underpinning of Experience Economics, North Highland’s approach to aligning customer experience investment to financial value.
Organizations commonly start quantifying the value and drivers of customer interaction via recurring customer satisfaction (CSAT) surveys. By asking customers to rate their various interactions across a typical journey, overall customer effort score, and likelihood to recommend, brands can longitudinally track measures like Net Promoter Score (NPS), thereby getting insight into the 'health' trends of their own customers and even how they rank against their competitors. While there are dedicated articles to the shortcoming of measures like NPS, in the context of CX prioritization, this approach has the following limitations:
- NPS is easily understood but doesn’t give clues into what might be driving it outside of what is asked in the survey. The survey analyst typically ignores terabytes of customer interaction.
- The 'silent majority' (customers who haven’t and may never respond to the survey) aren’t considered. Clients likely have sizeable groups of silent detractors; for example, ones they did not know they are going to lose.
Introducing Predictive Profiling
Given that survey respondents can be linked back to a customer database of information and interactions by a unique customer identifier, our team uses a process we call “predictive profiling” to identify actual customer behaviors that correlate to good/neutral/negative sentiment:
- Predictive: After connecting various datasets, we can scan through a sea of customer interactions that precede the survey and "let the data speak" as to what is indicative of future sentiment.
- Profiling: Using significant customer interactions, we surface a profile of likely Promoters, Passives, and Detractors. The profiles can be ranked by the number of customers impacted, the degree to which the client can influence them, and the financial value of improving them.
The Value-Added Elements of Predictive Profiling
Ultimately, predictive profiling takes organizations beyond NPS by providing insight in two key areas:
- The What?: A prioritized list of customer behaviors and interactions that are likely drivers of future sentiment, exposing areas where organizations can invest in CX initiatives to accelerate good experiences or improve bad ones.
- The Who?: A machine learning model that is used to score and rescore all customers to assign individual-level sentiment propensities. This decision support enables highly targeted messaging to key segments.
Predictive Profiling in Action
A healthcare payor managing its member experience across multiple lines of business came to North Highland to help them in the journey of moving from rearview mirror reporting of NPS to 'getting in front of it.'
- 24 percent of group members have recently made claims for general exams, well-child visits, or annual physicals. These members are 29 percent more likely to have reported being a detractor versus a promoter in the wake of these interactions. From check-in to discharge, what are the specific interactions are dissatisfying members?
- 22 percent of individual members have recently had denied claims from certain specialty providers. These members are 51 percent more likely to be detractors. Are these claims being denied due to lack of coverage? Are there systematic or operational issues at the health plan leading to the initial denial of services for specialty care?
We helped the client analyze these and other areas by deploying targeted member surveys asking about these experiences, pulling in additional data sources to further understand the interactions, and creating structured ways to measure the impact of changes made on longer-term metrics. The ultimate goal of our work provided the ability to quantify the financial impact associated with changes to the CX.
Our work with this client is just the beginning of the insights that predictive profiling can generate. Read more about how our approach powers Experience Economics; a system for prioritizing CX activities for maximum impact to the bottom-line.
