Improving Users' Metrics Performance with Actionable Insights
A new feature that alerts users about marketing campaign anomalies
About Dealtale
Dealtale is a data platform for marketers. By creating AI-based reports, users can analyze, optimize, and monitor all their data sources and customer touchpoints in one place.
The Problem
Today Dealtale users are creating new charts for different metrics, from different data sources, and therefore can’t constantly monitor their metric performance. Very often they lack the resources to know when a KPI value goes off the charts in real time.
The Solution - Actionable Insights Feature
We will provide a mechanism that constantly monitors our users’ metrics and helps surface important and relevant business insights.
We will give our users the opportunity to be on top of their most important KPIs performance in real-time, and more importantly - to take actions in order to fix it, by offer them an explanation of the reason for the anomaly detection.
Business Objectives
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Increase ARR for existing customers
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Increase Conversion rate to “closed-won deal”
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Increase Weekly Active Users on the platform
During the process, we will validate our assumption for the feature's high demand, learn how the users are generating value from the product and collect feedback that will help us to grow our business and feature in the right direction - customers driven.
Feature Key Steps and User Flow
We will start working on an MVP version. Once an anomaly is detected we will prompt and alert to the user in a dedicated area on the app (Insights feed). Also, the user will be able to click on the “Learn More” button in order to get further information.
Visualization
Results & Iterations
Once we Released the feature, we started tracking our users’ behavior to check if they noticed the new feature, and if they are using it daily. We also conducted interviews with users to get their insights and thoughts about the feature. After collecting the data we were not happy with the results, and started a few iterations to try fixing the problem.
One main issue we addressed was the bell icon that alerts on new insight. we realized that it wasn’t appealing enough, and users didn’t click on it as we wanted. We decided to replace it with animation that pops up, in order to catch the user’s eye.
Another issue we noticed that needed refinement was the microcopy. The explanation of the anomaly wasn’t always clear to the user and therefore didn’t give them the value we were hoping for. Some of the anomalies looked too similar to each other because of the terminology we used, and some of them just weren’t relevant enough.
