Apr 27, 2019
The Customer Is Never Right
But Then They Are Also Never Wrong
The customer is always right.” is actually attributed to both Selfridge and Marshall Fields. Both were famous retailers, both died of pneumonia — but only Selfridge died penny less. You might be better served blaming the Great Depression or his gambling issues. It is at once — neither right nor wrong.
Our theme is hardly unique, many articles have similar themes and titles. But most of them focus on customer service, product development, or employee morale. This article is about behavioral analytics, feedback, and decision-making. A slightly different spin with a slightly different perspective.
Facts vs Feelings
As an analyst, we are taught that facts are paramount. Like everything else, that is only mostly right. Feelings have an important role to play in many types of analysis as well. This is especially true in areas like sentiment analysis, behavioral analysis, and customer analysis in general.
Using that perspective, we can make a bold statement. A statement that is backed by real science, but may sound… well, bold.
Customers rarely get their facts right but, they know exactly how they feel and feelings are never wrong.
Analysts are concerned with truth, not right. So be careful bringing too much philosophy or morality into this equation. Customers often provide false facts. They don’t often provide false feelings and even when they are … mistaken… it is more an issue of the flavor of that emotion than its truth. Whether you have made a customer angry, frustrated, sad, or just unhappy — the key is the negative connotation. Conversely, positive emotions are equally meaningful using a similar rule.
In other words… when a customer provides feedback, the tone and emotion of that feedback should be assumed true and accurate. That sounds pretty right to me. On the other hand, their facts, figures, and details can clearly be held suspect. Science has proven time and again the flaws and bias of human memory. And that sounds sort of wrong.
Analytics can get confusing if you can’t keep these things separate in your mind. If you start believing too much customer testimony without backing it up with raw data OR if you start discounting too much customer sentiment (often because there isn’t enough raw data to prove it) — you run the risk of going astray. Data is essential for detailing facts and activity. It often fails to collect sentiment, although the advent of social media has done much to remedy that. Even then — sentiment can be misjudged. Data isn’t always good at context and is often completely tone deaf.
I have always noted:
In analytics, there is only one absolute.
The corollary might go something like:
There are typically two sides to anything.
In other words, blanket beliefs will almost always prove to have exceptions. It is often the presence of those very exceptions which provide the alternate perspective to provide true insight. Distinguishing these angles, developing the right perspective, and organizing the data to best reflect it — are some key components to most types of customer analytics.
But what about — CVs, NPVs, and LTVs you might ask? That is the topic of another article entirely. Customer valuation models often fail in because of an organization’s inability to properly attribute or allocate revenue and cost. But even if they manage to get that part right, the next landmine falls on customer sentiment and emotion. Most analysts/organizations simply don’t see a way to work that into the numbers. At best, activity and recency rates stand surrogate. That is about all the deeper we can go here.
So remember, customer testimony needs to be validated but customer sentiment is typically best accepted. Take the tone of feedback for what it is worth, but always challenge the details. Humans don’t always get their stories right, but they rarely go so far as to reverse the theme or moral.
Of course, sometimes they do misunderstand why they feel the way they do. They are human after all. But regardless, don’t make the mistake of discounting their emotion just because their causality seems off. Analysts often suck at determining causality, but in the end a trend is a trend and emotion is just as meaningful as stone cold facts.
