The conversation intelligence tool is used across a wide variety of use cases from sales rep training, coaching and enablement, to revenue forecasting and customer feedback evaluation.
I joined a small, growing design team and touched every aspect of the product before the company was aquired by ZoomInfo in July 2021.
Our powerful analytics suite is loved by the few data-savvy power users who know how to leverage its many features. But for frontline sales managers who are busy triaging the sales cycle for their team, our solution was overly complex. In turn, our reps kept losing deals to our primary competitor.
Our goal was to remove the feature advantage our competitor held over our intimidating analytics solution.
We targeted frontline sales managers who are end users of this tool as well as sales leaders who heavily influence the buying decision for their organization.
In sales, tracking behaviorial metrics is one way for leaders to quantitatively measure how well their teams are performing. Examples of metrics include: the percentage of time a rep spends talking, the length of their monologues, the number of engaging questions they ask, etc.
Industry experts recommend generalized targets for some of these metrics and sales reps and managers will often aim for these to improve their performance. However sales is a blend of structure and art and leaders that blindly follow an industry target may be missing more unique insights into rep’s individual abilities. We wanted to acknowledge this aspect of behavioral metrics in our solution.
I spoke with our internal sales team and customers to learn about the pitfalls of our existing analytics feature.
I developed principles to steer the design towards values expressed by managers and leaders we talked to:
I designed the prototype to be straightforward and this resonated with existing users. We also learned about a usability issue.
We also learned that to fully trust and act on the metrics we provided, users wanted to “drill down” to the actual moments in conversations that made up the chart data. This would bring clarity to the data and also help diagnose the cause of the results. We incorporated this user request into our roadmap and the feature has since been shipped.
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