Three Reasons Why You Need Voice of Customer in Your Digital Intelligence Stack

Three Reasons Why You Need Voice of Customer in Your Digital Intelligence Stack

First, let’s agree on one thing: your customers are people, and people are complex — with needs, intentions, motivations, expectations, feelings. (So many feels.)

The risk with today’s digital-first mindset is that it’s easy to get lost in clicks and conversions.

As digital pros, you know it’s important to treat every interaction as an opportunity to ask “why?”

For instance:

Why did a visitor click there?
Why did a visitor leave our site?
Why is a visitor satisfied? Or unsatisfied with their experience?

It easily gets more complex than that, and the only way to really get at the “why” is…

To ask.

Simple right? Well…

A voice of customer (VOC) program — to capture and learn from actual customer voices — should be a critical part of your digital intelligence stack.

When behavioral data is combined with VOC or attitudinal data, you get the fullest picture of your customer. You understand what customers did, why they did it, and how it made them feel.

No doubt you already have multiple VOC efforts underway at your organization. (That’s another topic for another day.)

What I’m suggesting is a more disciplined approach: integrating VOC insights into all your digital decision-making.

What’s the value?

  • More complete customer insights
  • Improved digital decision-making
  • Increased savings and revenue

Here are three ways an integrated approach to VOC can improve the digital experience for customers and business outcomes:

1. Improve website task accomplishment and conversion.

A retail company saw that checkout abandonment increased from one day to the next. In isolation, seeing the increase from 40 percent to 50 percent offered no actionable insight.

But through an “always-on” feedback badge, frustrated customers took the time to share the issue. Through a quick VOC analysis filtered by segment, the product team easily ascertained the page error. An immediate fix to a loading error returned checkout abandonment rates to average levels the next day.

What was the combined data set?

Behavioral data: Checkout abandonment.
Attitudinal data: Customer feedback left on checkout page.

What’s the value?

Reduced potential revenue loss = average order value x percent loss in daily conversions x number of days to discover the issue without VOC.

2. Improve content and usability.

A government agency learned that a large percentage of first-time visitors visited its contact page after just a few minutes on the site. The agency needed to understand why website visitors couldn’t self-serve and were likely calling its contact center.

The agency looked at satisfaction for various segments. VOC data confirmed that first-time visitors were much less satisfied. Comments surfaced through a random digital intercept uncovered an issue with the search field. Visitors were looking for specific forms, but the search results didn’t return expected results. This insight kicked off a months-long project to update its search functionality.

What’s the combined data set?

Behavioral data: Time on site and page visit for first-time visitors.
Attitudinal data: Customer satisfaction and feedback captured through random digital intercept for first-time visitors.

What’s the value?

Reduced call center volume = average cost of calls x number of calls deflected.

3. Improve marketing and advertising effectiveness.

An electrical components supplier created two audience segments to test conversions on return visits: happy and unhappy visitors, defined by CSAT scores.

It found that unhappy visitors were much less likely to convert on a return visit than happy visitors. This insight empowered its digital advertising teams to hold remarketing campaigns to this segment for 30 days, and later lower its bidding levels.

What’s the combined data?

Behavioral data: Conversion for return visitors.
Attitudinal data: Visitor satisfaction.

What’s the value?

Reduction in digital advertising spend = cost per click x clicks by unhappy website visitors.

Data is powerful, but there’s a multiplier effect when you can combine data sets.

When it comes to customers, it’s easy to understand why actual customer voices should be part of all your digital decision-making.

Christine is director of product marketing at ForeSee, a voice of customer solution used by over 2000 global companies.

ForeSee helps you see the whole customer by combining customer behavioral, attitudinal, and interaction insights. Learn more about ForeSee CX Suite, ForeSee Replay, and ForeSee’s bi-directional Adobe integration.

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