Customer Experience Science

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In 1620, Francis Bacon broke with the conventions of his time by articulating a new approach to investigating nature. It’s what we now call the Scientific Method, a way of thinking which remains the gold standard for how we comprehend the world today.

Instead of looking to fate or mysticism to explain nature, the Scientific Method asks us to formulate a testable hypothesis and run experiments to measure phenomena. Then, if other scientists conduct the same experiments and get the same results (eliminating flukes and misinterpretation), the hypothesis is confirmed and we’ve expanded our knowledge.

The Scientific Method has enabled us to put a Land Rover on Mars and a computer in our pockets.

With a science-forward approach, you’ll know objectively how the Customer Experience rates and what specifically will make it better.

Because science is the best way to understand the world, by extension, it’s the best way for companies to understand the Customer Experience (CX).

But here’s a glaring fact: Most CX programs are not grounded in science, and as a result, companies are led off course by unreliable data.

How do we know that companies are off course when it comes to the customer experience?

  • The average American Customer Satisfaction Index score is 75.
  • The average Net Promoter Score is 32.

These scores add up to one stark fact: most customers are having mediocre experiences. That’s not what companies want—it’s just what happens when companies lack factual evidence about how their customers truly feel and think.

A science-forward approach to CX solves this problem. With a science-forward approach, you’ll know objectively how the Customer Experience rates and what specifically will make it better.

Verifiable facts? Clear next steps? Isn’t that the point? 

CX: the 5 Science Rules You Need

  1. Remove bias.
  2. Seek representation.
  3. Ensure replicability.
  4. Have multiple tools.
  5. Apply nuanced analysis.
Why spend on CX if you’re failing to find out how your customers and employees feel and think?

Science Rule #1: Remove Bias.

In a lab, you might want to test whether using fertilizer results in larger tomatoes—but if you give your fertilized plants better light and more water, you’re all but ensuring your fertilized plants will do better.

Similarly, with the Customer Experience, you want to eliminate leading questions that steer toward success. You want customers to tell it like it is.

An example of bias we often see is when companies ask vague questions that assume their customers are at least somewhat satisfied, e.g., “How satisfied were you with our engineer?”

Instead, it’s better (more scientific) to ask: “How would you rate our engineer’s expertise?” This wording is specific and asks about the engineer in a neutral way.

But does eliminating bias really matter? Yes!

Why spend on CX if you’re failing to find out how your customers and employees feel and think? Removing bias is the first step toward achieving verifiable facts.

Science Rule 2: Seek Representation.

In a lab, if you were running a health study, you’d work to deliver a sample that represents White, Black, and Hispanic individuals proportionately.

Similarly, with the Customer Experience, your survey data should represent your customer base as a whole, not just those with free time, a gripe, or who gave their email.

For example, for an industrial parts company, Manufacturing Reps are their primary customer. But historically, Reps were not responsive to surveys.

To solve for this, we boosted Rep’s incentives and proactively called a subset of them to ensure adequate representation in the survey data.

When you work to ensure customers are represented proportionality in your survey, your stakeholders are more likely to have confidence in your findings.

Science Rule 3: Confirm Replicability.

In a lab, your peers expect that if they follow your methods, they’ll be able to reproduce your findings.

Similarly, with the Customer Experience, when examining your data, multiple analysts should arrive at the same conclusions.

With Text Analysis and Customer Service Evaluations, it takes special protocols to ensure replicability—this includes when testing for replicability against AI. I’ve written about this earlier this year but in brief, here’s how this works: Start with statistically-valid samples and then build a coding framework. Have multiple Analysts work independently to apply that framework—and use cross coding to prove the degree to which you’ve achieved replicability.

When your findings are replicable, you’re upholding a key pillar of science and proving that your conclusions are verifiable. Replicability builds trust.

Science Rule 4: Have Multiple Tools.

In a lab, you use a telescope to look at the sky and a microscope to examine cellular growth.

Similarly, with the Customer Experience, you need a range of tools to measure different touchpoints, personas, and situations.

When teams only have one tool, it’s usually an NPS Survey. While Net Promoter is useful, there are vast swaths of experience that it fails to illuminate, like how to improve specific departments or gain market share.

For clients who want to monitor their progress year over year, we do tracking studies, and if they need in-depth insights, interviews are our main tool. Having a range of tools expands the scope of objectives that your CX program can address. Moreover, you’re able to take a deep dive into the Customer Experience and uncover detailed next steps.

Science Rule 5: Apply Nuanced Analysis.

You want insights and actions, but you also want to know where you’ll have the most impact and the greatest wins.

In a lab, beyond observing phenomena, you aim to understand root causes or at least correlations among phenomena.

Similarly, with the Customer Experience, more than knowing your outcome scores, you want to know which processes are driving those scores.

Interactive dashboards filtered by score, date, associate, and other variables enable you to analyze your Customer Experience data and find areas to improve on the fly.

With correlation studies, you can dig even deeper to learn what customers care about and how scores are interrelated.

You want insights and actions, but you also want to know where you’ll have the most impact and the greatest wins. Nuanced analysis identifies your priorities.

Evidence-Based Insights

Just like you, my inbox is filled with surveys every day. But most of them are rubbish because they are failing to collect evidence-based insights.

The Scientific Method dates back hundreds of years, yet it remains the best way to make sense of the world. By applying these five science rules, you’ll see the Customer Experience clearly and know how to improve it.

Toward customer experiences enlightened by science-forward thinking!

The post Customer Experience Science appeared first on Interaction Metrics.

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