Analyzing in-product behavior to transform the onboarding experience

Linking product usage data to customer data in order to improve product adoption

USABILITY - UX - DATA ANALYSIS - DESIGN THINKING - ONBOARDING - CUSTOMER ACQUISITION - PRODUCT QUALIFIED LEADS - PERSONALIZATION - MARKETING

The subject of this case study is a leading company in the digital signage content management software where I work as a full-time product designer. Here I share my experience with applying a process-driven approach to design based on empathy, data, and the ability to ideate quickly. My goal is to increase the impact of design on product development in every phase to help the company achieve their KPI's.

Problem

A digital signage system has two parts: the content management software and the software that runs on remote displays. These remote displays are driven by a dedicated computer with software installed.

To get this to work, we knew, that having a certain level of technical skills and selecting the appropriate hardware made a big difference. However, the same skills weren't required for the customization and scheduling of content to the remote displays. This resulted in a situation where users with a higher technical skill evaluated and installed the software, while other users were assigned with the task of managing the content, putting the decision to adopt and pay for the product on the hands of two types of users. This created confusion amongst sales, support and marketing teams as their initiatives lacked the input necessary to address the needs of both types of users.

Could I lead the design of a process to help sales, support and marketing teams allocate their efforts better?

Tools and Process for Ideation

Unified Customer Data

At the beginning, the initial requirement was to aggregate customer profile data, customer behavioral data, and production data. I've contacted the teams in charge of marketing, customer support, and customer success and created an inventory of what we were already capturing.

Some of the identified sources of data were:

  • Segment
  • Google Analytis
  • Zendesk
  • Hubspot
  • Bigquery
  • Drift

Dumping all this data together into one data warehouse wasn't enough. We needed the tools to act on this data and to tell us precisely which subset of features delivered more value to each type of user during the different phases of product adoption.

Looking at the right data

  • Have data of paid users behavior and conversion
  • Identify the bottlenecks across all funnels

Product Awareness

The funnels would be defined as follows:

Unaware to Problem aware.

Problem Aware to Solution aware.

Solution aware to Product aware.

So if we were talking to the technical user, we assumed he was already solution aware. But for the non-technical user, we needed to take them through all steps to become product aware.

I knew we needed to be constantly monitoring the monitoring the user’s behavior, and identifying the user profile, so we could determine when a they became product aware in real-time. This would enable every team to prioritize and bring all parts of the plan together for increased product adoption.

Experimentation 

    Implementation and Results

    A lot of what I’ve done has been defining responsibilities around our data tools, educating people on data best practices. I’ve performed some audits of our reports and metrics and uncovered some areas for improvement in our daily operations. Mostly I’ve been doing a lot of learning.