Are Your Customers Coming Back for More?


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“Would you recommend us to a friend?” The familiar Net Promoter Score (NPS). Most people are now very familiar with NPS, especially in the context of automotive sales. If you are not, NPS is where a customer’s willingness to recommend a vendor or brand to their friends is used to measure their customer experience.

In short, I believe that NPS is not as effective a measurement, as it once was. I believe NPS is fundamentally flawed, because it is a delayed indicator of customer loyalty. Is it measuring your customer’s experience for the event in question, or is it more to do with their overall view of how they feel about your brand? It’s tough to say whether NPS measures commitment to a brand or product? Not least, because so few customers respond to NPS questionnaires, usually 5% or less. Marketers also need to consider whether any influence been exerted by their staff. We have all been told, at one time or another, that ‘eight is a pass’, haven’t we?

While NPS ‘supposedly’ represents the customer’s view. The vendor’s view is usually encapsulated in the customer’s lifetime value (CLV). A current view of the value of purchases extrapolated over a longer period. However, what vendors really want to see is a customer’s commitment to a brand, ideally represented in dollars and cents. Why? Because business, especially automotive, is all about selling goods and services that satisfy customers. We all know how highly valued brands usually carry better margins. So, whatever metrics we use to measure customer experience, surely should be tied to a customer’s willingness to make future purchases.

It is no longer good enough to measure a customer’s commitment to a brand or products, we must be able to track it too. E-commerce has made everyone go faster, and if we cannot move at the speed of our customers, we will be left behind. Unfortunately, NPS, which measures customer experience at a given point in time, must wait for another interaction, which may take place months or years later. At the same time, we cannot ask a customer every day or every week to fill out an NPS scorecard, they will soon just stop answering or reading our emails.

Dollars and cents

Let’s come back to that measurement in dollars and cents, measured dynamically, and keyed to outcomes that vendors want to track. If a customer buys a vehicle for $50,000, we want to know about every customer interaction from marketing to warranty claims, to service events, etc. In other words, all the events that would cause that customer to increase or decrease their propensity to purchase another vehicle from us. At what price point, higher or lower, than their original starting point or value.

Utilizing the latest A.I. and ML driven systems, it is now possible to analyze every touchpoint, not just survey results. A customer may have hundreds of touchpoints with a vendor, but never fill in any surveys. The more variety of touchpoints from marketing to sales to support, the better the results. Understanding and deriving customer commitment using sophisticated AI and machine learning models is revolutionizing how businesses should be interacting with their customers.

Not only can these models measure; they can go one step further and recommend the next “best” action or “NBA”, that a marketing team should take with a customer. Each customer, one at a time. Not in super segments, lumping in thousands of customers together based on typical demographics. Instead we can get to know individual customers, treated and valued as individuals. NBAs generated by the latest models can be incredibly helpful and can move at the speed of data, thus giving customers a more targeted offer, deal or action, etc.

Our parents have been telling us for two generations that we are special, that we are unique, now we can finally deal with this in an elegant and profitable manner, even at scale. It makes sense to get more customer interactions into our conversations about customer experience, making it far more comprehensive a measure, and less likely to be manipulated. Where do we get more customer interaction, while at the same time ensure accuracy? For example, when a customer is unhappy with a car service, or their credit card was refused in error. A customer’s experience represents a series of events that reflect many details, such as demographics and changes over time like marriage, salary increases, and changing homes. This also includes sales, marketing, support, social media, and macroeconomic events. Life-changing events, such as a new baby, retirements, are also important pieces of the puzzle.

My team, for instance, has determined that the more we track these events, the more we can understand a pattern of behavior and subsequently, brand commitment. We assign a number to each customer journey that indicates its value. We have found repeatedly that the totality of the customer’s experience, their customer journey with a vendor, is far more indicative of their commitment or customer experience than spot indicators, taken from time to time.

A report by McKinsey states that customer journeys are 30 percent more predictive of customer satisfaction than measuring individual interactions and that using customer journeys can increase customer satisfaction by up to 20 percent. This, in turn, has a positive impact on revenue and churn. I believe artificial intelligence has a unique role to play in allowing companies and organizations to take advantage of the opportunity in the new era of customer experience, bridging the existing gap between the customer as an individual and their journey, to unleash profitable growth.

Jean Belanger
Currently CEO of After graduating (LSE), I joined Wood Gundy. I left to start a VC fund, investing in start-ups. 3 went public. After 15 years in finance, I decided to run companies rather than finance them. The first, programming tools vendor, Metrowerks CodeWarrior, built most of the software used on the Mac in the 1990s. When Metrowerks was acquired by Motorola, I was named VP Biz Dev for their semiconductor business, where I invested over $450M in M&A in 14 months. After Motorola, I started data science supply chain software and IOT pioneer, Reddwerks.


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