The “Peak-End Rule,” which is based on research conducted by Daniel Kahneman and Barbara Frederickson, posits that humans assign a greater level of weight or importance to the most intense positive or negative moments, or peaks, during an experience, as well as to the final moments, or end of the experience. One only needs to think about attending a professional sporting event: it is likely you remember the highs (when your team scores or a player makes an incredible play), the lows (when the other team scores or stops your team), and, of course, the result and final score. The remainder of the action, while important to the process of playing the game, simply does not stick out in most humans’ memories.
This peak-end rule can also be applied to customer interactions. Because the human brain does not digest every moment or data point that occurs throughout a customer’s interaction, it is vital to get the positive moments right, limit or eliminate negative moments, and ensure that all interactions end on a positive note.
Organizations can often use artificial intelligence (AI) to find these moments, reducing the amount of time and effort to complete these tasks manually. Analyzing customer interaction transcripts to find instances where customers say thank you, express gratitude, or otherwise indicate that a particular action (taken by a live agent or even an AI-based chatbot), and then make sure that these moments are consistently applied to all customers.
It is also wise to look at situations where there is an opportunity to create a peak moment, by providing additional resources, information, or assistance, to help keep the interaction flowing smoothly. For example, a telecommunications provider could provide a pop-up feature-comparison window for customers that are trying to decide which service plan they would like. A comparison window provides information needed to help them make a choice, and demonstrates to the customer that the telecom provider values them enough to want to help them make an informed choice, rather than putting the onus on the customer to seek out the comparison information themselves.
Still, people tend to remember negative experiences more vividly than positive ones, which can drastically impact the likelihood that an organization can fully atone for the bad experience. That is why it is important to conduct an ongoing analysis of customer interactions to find the moments that trigger negative peaks, and then conduct a root-cause analysis to understand why the experience is poor.
This process should not only include capturing customer feedback via surveys, but should also include capturing in-process data that may yield insights. For example, using the telecommunications provider example above, analyzing metrics such as the time-on page when a choice needs to be made can offer insights into friction points that may be seen as a negative experience.
For physical interactions, it may be useful to utilize video to analyze specific physical actions that may be creating a negative peak. For example, a retail store should study the register queuing patterns to determine if the currently used queues introduce confusion on where the lines begin, or if the design contributes to impeding customer flow within the store. These types of negative peaks may not necessarily be reflected in customer surveys unless they are directly addressed.
Another way to seek out relatively hidden negative peaks is to look at the product usage trends, particularly for devices or services that require some sort of onboarding or electronic activation, such as a smartphone, computer, or streaming service. If there is longer-than-expected lag between the time a product or service has been purchased and when it has been activated, there is likely some sort of disconnect, which may be a negative peak. Perhaps the instructions are not clear, or the activation process is too complicated. In any case, it is important to closely analyze the process so that any negative peaks can be removed.
Eliminating the negative peaks within customer interactions can be used as stepping stones to ensure that the journey concludes on a positive note. As friction levels are reduced, customers are more likely to be receptive to additional cross-sell and upsell offers. If the end of the process ends with a flourish—perhaps a reward or discount – that customer may be more likely to share their positive experience with others, or, in the ideal case, return into the sales funnel in short order.
Author Information
Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
