Fixing the INNER LOOP BIAS

Sometimes friends ask me what do I do, and then they ask what is customer experience research is for? The simple answer I give is that employees dealing with customers should get feedback on how the customer views the experience. Only this way they can learn and improve.

Simple, isn’t it? This idea is also referred to as the INNER LOOP. It is contrasted with the OUTER LOOP, which tries to initiate learnings from feedback and conclude strategic initiatives for change.

The Inner Loop is set up to make customer-facing employees learn how customers perceive them, give them praise in case of great feedback, but also give an opportunity to follow up with detractors and complaints quickly.

All this is meant to enable the company at the level of the frontline workers to improve.

THE INNER LOOP IS BROKEN

For years I just dealt with the outer loop because the inner loop seemed to be simple and working well. Just recently, I learned that I could not be farther from the truth.

Here is the problem. The idea behind the inner loop is that human reads feedback and learns from it. But this idea is broken for THREE reasons:

REASON #1 — The RAS Filter

Whenever I plan to buy a new car, suddenly everywhere on the streets, I see this car. Suddenly, everyone seems to drive it already.

The reason is a small part of your brain called the Reticular Activation System (RAS). RAS is a bundle of nerves at our brainstem that filters out “unnecessary” information so the important stuff gets through.

The RAS is the reason you learn a new word and then start hearing it everywhere. This is why you can tune out a crowd full of talking people yet immediately snap to attention when someone says your name or something that at least sounds like it.

When reading dozens or even hundreds of feedback, our RAS is bringing those feedback to special intention that somehow caters our personal interests.

A waiter who is frustrated with unpleasant people he needs to serve, will more than others notice complaints as proof of their rudeness rather than looking for ways to satisfy them.

People learn what they want to learn, not what they necessarily need to learn.

REASON #2 — The Frequency-Impact-Illusion:

The most often mentioned reason for the loyalty of speaker users is “great sound”, It is intuitive for us to believe that this is the most important reason, thus the most important thing to further work on.

When using proper cause-effect modeling techniques, you learn that the importance of mentioned topics is hidden and typically NOT correlated with the frequency.

Actually, there are many known mechanisms that explain why this makes sense. First of all, customers “just talk”. They do not have an incentive to be 100% correct and precise. Typically people respond with strongly associated topics that make them talk. As Daniel Kaneman said it “Human brain is like cats. Cats can swim, but avoid it if possible”. Human can think, but if possible, they avoid it because it is exhausting. Even worse, customers are not 100% aware of what drives their own behavior.

This is why when you scroll thru your customer feedback, you will learn the WRONG things because you are primed to believe, frequency means importance.

REASON #3 — Resistance to Critique:

Everyone knows the basic rules of giving feedback. Always start with the good things. It will make the recipient open for critique.

If everyone knows this — why on mother earth are we still taking customer feedback like a dumpster full of thrash and pour it over our frontline coworkers — then expect them to learn productive things from this?

What’s your take? Knowing that people just learn from reading what they want to learn, knowing that what they learn is fooled by its frequency, and knowing that the random sequence of critical feedback sparks more resistance than change.

Knowing all this, does it still make sense to send your coworker the customer feedback verbatims with a kind note “please read“?

The INNER LOOP BIAS FIXER

Here is the promise. With the proper method, you are delivering feedback to the front line so they can:

● Learn what is important and help them to get out of their bubble of own interest.

● Thoroughly enjoy the praise they get and deserve

● Improve frontline workers’ behavior personally by letting them accept critique and focusing on what’s truly important for customers.

The solution requires three things:

First, it is mandatory to institutionalize a modern CX Analytics system. At its core, what it takes is at least:

A) A text analytics system that quantifies the unstructured customer feedback.

B) It needs a proper key driver analytics top of this data that reliably measures the impact and importance of those categories.

Second, by sequencing first the positive, praising comments, you comply with psychological feedback rules.

Third, by batching feedback into important and less important categories, you can help readers to read important feedback first. This automatically frames and primes learning the right way.

The “INNER LOOP BIAS FIXER”-Method works like this: Delivering sequenced feedback in importance-batches:

1. Praise on TOP IMPORTANT topics

2. Critique on IMPACTFUL topics (=Importance X Frequency)

3. Other Praise

4. Other Critique

In A Nutshell

The Inner Loop is meant to enable frontline workers to learn and improve, but this mechanism is broken for three reasons.

People learn from feedback what they want, not what they need. They are fooled by the frequency-importance-illusion, and wrongly sequenced critique makes them less likely to accept critique.

Delivering sequenced feedback in importance-batches is a viable solution. It requires a reliable solution to measure the importance of topics.

The latest systems that combine deep-learning text analytics with causal machine-learning were superior to out-of-the-box solutions.

They deliver 4X higher impact of actions and are thus advised to guide the inner loop process.

CX.AI is such a solution that pioneered this technology. You can even contact CX.AI specialists and get a free consultation.

Your thoughts?

Frank

Founder of CX-AI.com and CEO of Success Drivers // Pioneering Causal AI for Insights since 2001 // Author, Speaker, Daddy