This product line was a major initiative for People.ai and was a response to a challenge straight from the board.  At the time People.ai had good product market fit for its first activity data product.  However, we knew that there were valuable insights to be discovered in that data, and believed that delivering them to users through our web app, could solidify People.ai’s position as a market leader.

Business Goal

Build a product that highlights the insights that our AI discovers to help unlock growth and drive sustained adoption of our web app.

Defining the People Problem

With this business goal in mind, we started to focus on the Frontline Sales Manager persona. These are people who really sit at the core of the sales process, and we knew that they could benefit as much as anyone from the insights around rep activity. Our team held a series of user research calls with Frontline Sales Managers to understand what they struggled with, and two people problems consistently emerged.

Frontline Sales Managers Want To:

• Discover any problems with their team’s deals early enough to get them back on track

• Understand and communicate what activities lead sales reps to the best outcomes

Using Our Design Principles To Shape The Experience

Once we felt like we had well-defined People Problems, we took a look at our design principles and we selected a few that we knew could really help guide the direction of our solutions.

• Simple

  • Based on familiar ideas and concepts

  • Minimize cognitive load by hiding non-task-relevant information

• Personal

  • Adapts to the needs of the user persona

• Scalable

  • Able to handle many data points

With these guides set up, the design team began working closely with a few co-development customers sketching and iterating on ideas until we felt comfortable that we had a solid foundation for a product.

Resulting In-App Workflows

Updates On The Deals That Matter, With Actionable Insights To Keep Them On Track

For this product we created in-app workflows for both of the managers people problems, and the first one we created was to help managers discover any problems with their team’s deals early enough to get them back on track.

This typically happens in the form of a weekly revenue forecast call and involves a sales team getting on a call and reviewing the largest deals for the current quarter, and managers trying to assess if they are on track to close.  And while reps may give updates on each deal, managers have little data to validate the often overly optimistic reports from the sales team.

To arm managers with the data they need we created Deal Room.  When it’s time for a forecast call, a manager can pull up our Deal Room to see a list of the top deals closing this quarter with their largest deals first.

There are several pieces of information on this page that work to give the manager a good understanding of where a deal stands, but there are two specific features that are unique to People.ai and particularly challenging to design.

Engagement Level Score

This is a value from 0 to 100, it is derived from People.ai’s analysis of activity, and is meant to summarize the quality of the activities surrounding a deal.

The design challenge here was how to help managers understand the algorithms that drive this score.

This is a machine learning-driven score that summarizes the activity surrounding a deal. When hovering on a score, the manager can see how the score has trended, as well as an explanation of what factors went into the score.

Actionable Insights

This is where the experience moves beyond just status, and into the tactical information, managers need to catch problems before it is too late. These insights are based on actual activity data and enables managers to validate updates from their reps in a data-driven manner.

For example, in a weekly forecast call a rep might say they had a couple meetings this week and that everything was looking great, but our actionable insights can add vital context.  In this case, while meetings may have happened, the insight points out that there were no executives engaged in any of those meetings.  In the case of enterprise sales, the likelihood of a deal closing without executive involvement is very low.  

Now the manager can see that insight and move the conversation to who has been in these meetings, and instead of being surprised that this deal didn’t close weeks later, they can help their rep chart a path towards executive sponsorship in time to turn the deal around.

Data-Driven 1:1 Rep Coaching Requires Details

The second workflow we created enables managers to understand and communicate what activities lead reps to the best outcomes.

Again there was an existing workflow that we tapped into, 1:1’s. It’s not surprising that managers and companies are concerned about the consistency of 1:1’s especially in the sales org where anecdotes can drive decisions. So we started by envisioning what a data-driven 1:1 conversation could look like.

At the top is a list of metrics, each pertaining to one of the top level questions managers may have.  These are leading indicators, meaning they are meant to give the managers a quick understanding of whether or not the reps current performance will lead to winning deals in the coming weeks.

These metrics represent different parts of a reps sales process, from initial outreach to creating deals to winning them.

If the manager wants to investigate any indicators, they can deep dive into the actual activity. For this example, the rep might be having plenty of meetings, but the manager needs to know if these meetings are with the right accounts. Are they talking to their target accounts or going off plan? Again this is where a manager can step in early in the process to keep the rep on track for the best possible outcome.

Project Results

I am particularly proud of this project because the solutions we designed have made a major impact on the company by both driving revenue and sustained adoption of our web app.

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4 Week Retention