Quantify and qualify – the key to the best customer journey

Rachel Obstler Profile picture for user Rachel Obstler July 19, 2022 Audio mode
Summary:
A complete dataset for all interactions requires context as well as numbers. Rachel Obstler of Heap explains how to get the most from your customer journey data.

Vector business teams statistics with data. Concept of data storytelling with co workers. Business meeting in the open space office © hand idea - Shutterstock
(© hand idea - Shutterstock)

Today, most customer transactions occur digitally versus in person, with McKinsey estimating that 82% of Americans used digital payments last year.

For digital product owners, this affords a great opportunity and new challenges. Unlike a store or sales setting, you are theoretically able to understand immediately the sum total of the entire digital interaction.

This means you are not only able to quickly find and respond to customer-impacting issues and opportunities, but you can then also create a dynamic experience. If users like something, you can show them more of it. You have space for infinite product placement. 

Having all the data means greater insights, but to do this teams need products that can help them manage and leverage all that data. The good news is that the tools used by digital owners are getting better at capturing the data and surfacing insights into the customer journey so businesses can build a better digital experience. 

Understanding the customer’s digital journey comes down to answering a few key questions. What paths do customers follow when interacting with the website or product? Where do they come from — a search engine, an advertisement, an online promotion? Are there points in the digital journey where they are engaged and others where they are consistently dropping off? Where are we losing customers, and why?

Quality meets quantity

The answers to most of these questions require both quantitative data, such as aggregated behavioral metrics, and qualitative data, such as evidence of what an individual user said or did.

Good quantitative data can help map typical journeys and identify common points of friction, as well as expose mechanical issues, like a missing submit button. But without qualitative data, it is difficult to understand context and that all-important “why.” What were they trying to accomplish? Why did they give up? Seeing helps validate what’s going on.

Qualitative data can be gathered by directly capturing the voice of the customer through surveys, talking directly to users, as well as recording and replaying individual user sessions to better understand their journey. Session replay also allows you to analyze some quantitative patterns, such as rage clicks (when a user repeatedly clicks on an element, indicating frustration) and heat maps (where the attention of the customers is being directed on the page).

This sounds good in principle. Yet to be successful, teams must overcome the two big challenges with analytics software today. One is that there is a massive amount of data, but not a lot of insight. Thankfully, today’s tools are getting much better at automatically surfacing points of interest (e.g. users that dropped out of this funnel are getting stuck here).

But a second challenge is that typically tools provide either quantitative or qualitative insights. On one hand, understanding the context, or the “why” of the quantitative insight (e.g. x% of users fall off the journey here) is difficult because it’s hard to “see” what relevant sessions from individual users are doing. On the other hand, digital owners find themselves on qualitative fishing expeditions without knowing if they’ll find something, and if they do, what the impact of the single user’s issue is to their business.

The industry is moving to address this by combining quantitative and qualitative into a single platform that allows companies to zero in on patterns in the quantitative data and map this directly to qualitative insight that pertains to that key moment. With quantitative data alone, you know what the ROI opportunity of fixing the problem is; layering qualitative data means you can more quickly get to a better working hypothesis of how to fix it.

Putting into practice

How does this work? It requires having a complete dataset for all interactions, which makes it possible to match qualitative and quantitative data. If an event surfaces through qualitative analysis, the complete dataset enables a digital owner (e.g. a product manager, growth marketer, UX designer, developer) to determine whether the pattern or event is a one-off issue or repeats itself.

With this complete dataset, it is even possible to quantify what that event is costing the business. If quantitative data surfaces a friction point, qualitative data, like session replay, helps you visually understand what the customer is experiencing so you can come up with better solutions.

Ultimately, integrating quantitative and qualitative data means faster and better insights. The digital journey is more flexible and more quickly changeable than its physical counterparts. The key is to enable your teams with integrated qualitative and quantitative data for a winning combination. Your teams can act with more precision and confidence to not only improve the customer experience but drive business value.

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