How Guru increased customer activation by 71%

From linear journey to decision-tree logic, a little personalization took onboarding a long way for Guru.

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Adam Walker
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[This post is based on an interview with Chelsea Valente, Lifecycle Marketing Manager at Guru. You can watch the full interview here or below]

Guru is a company wiki that lets teams access their organization’s collective information from wherever they work — be it Slack, MS teams, or any website. Chelsea Valente joined the company in February 2021 as Guru’s Lifecycle Marketing Manager with the goal of increasing  activation and driving lifetime value.

Responsible for all user communication post-signup, Chelsea inherited email campaigns that focused on three specific actions for users to take — creating cards, downloading an integration, and taking a training. Guru had determined that teams that took these actions within the first 14 days were more likely to convert, so the email content was spot on. The campaigns were seeing good engagement too, but Chelsea immediately recognized a few areas for improvement:

  1. Personalization — Every user was receiving the same emails urging them to take action, regardless of role or where they were in the customer journey.
  1. Touchpoints — Guru’s onboarding campaign consisted of five emails sent out within the first 14 days, but Chelsea knew the number of communications might not be enough to accomplish everything the company wanted.
  1. Channels — The onboarding campaign was limited to email, which meant Guru was missing out on an opportunity to reach users through different channels, like in-app messaging.

Guru decided to work in phases to capitalize on these opportunities to improve their onboarding communication.

Phase 1: Decision tree logic and in-app messaging

Forty-five percent of consumers will likely take their business elsewhere if a brand fails to offer a personalized experience. Knowing how critical personalization is to the user experience, Guru’s first step was to make sure users were only receiving communication that matched the appropriate stage in their user journey. At the same time, they added in-app messaging to target users through multiple channels.

Guru added decision tree logic to what was formerly a linear journey. The first communication all users receive is an email inviting them to a product tour, but from there, each communication is now dependent on where users are in the customer journey. For example, if a user has created cards, they will receive an in-app message. If they’ve graded their cards, they will then begin receiving emails about integrations. A user who hasn’t taken these steps, on the other hand, will continue receiving emails inspiring them to create and grade cards.

“I don't necessarily need to send an email about integrations to someone who has already used one of our integrations,” says Chelsea. “Now we're sending the right information to people at the right time.”

Phase 2: Personalization based on collected data

Personalization based on a user’s stage in the customer journey was an obvious first step, but any further personalization was a little trickier. Sixty-nine percent of consumers appreciate personalization as long as it’s based on data they’ve shared directly with that company, so we can deduce that anything more can feel intrusive. Guru knew they had a fine line to walk in their personalization efforts.

They decided to test out personalization based on the data they collected at signup, mainly a user’s job role. Guru also asks about use cases as part of their signup flow - but, as an optional field, they weren’t able to collect enough data to personalize communication by use case. Instead, Guru inferred use case by the user’s job role, and then tailored the messaging accordingly.

With the hypothesis that a personalized onboarding experience based on role and use case would increase activation, Guru created an A/B test. Due to bandwidth constraints, they were unable to automate personalization for everyone, so they chose to test their three most-selected roles. This created a fairly small sample size to work with, so instead of using a 50/50 split, Guru sent 80% of each role-based group a personalized communication flow while 20% received generic communication.

Unsurprisingly, those receiving personalized emails and in-app messages had a better response rate. Looking at the cohort as a whole gave Guru statistically significant results that proved their hypothesis. With a 71% lift in activation and a 51% lift in conversion, Guru quickly rolled out the personalized flow to all users in the top three roles.

Phase 3: Plans for personalization at scale

With the results of their A/B test in hand, Guru is eager to roll out personalization to all of its users. “We didn't necessarily have the tech stack to be able to do that [before],” Chelsea explains, “so we are in the process of migrating to a new marketing automation tool that will allow us to drive personalization at scale.”

In the meantime, Guru is working to optimize the general marketing communication series that it delivers to all but the top three roles in order to boost activation.

Guru is also planning to amend its sign-up flow to make use case a mandatory field. In doing so, they will remove a section offering templates and frameworks so that users won’t feel overwhelmed by a long, multi-step sign-up process.

Lessons learned

Safe in the knowledge that personalization can help significantly accelerate growth, Chelsea suggests others start small and continuously iterate as they break into personalized onboarding communication.  

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