The Problem

Our leadership was looking for help making a critical decision, should they expand our metrics catalog to contain forecasting data as a content type, or should they go full steam ahead and create a BI reporting tool? Kelly and I collaborated on this research project and reported back findings that helped steer the company in the right direction.

I created a clickable prototype with several variations on forecasting and alerts and we set forth to record a series of user interviews on the topic. The prototypes were critical in leading the conversation and providing context for users.

Conclusion

• We found that users in our target market use a wide variety of forecasting tools

• Most of them were using Excell formulas for forecasting and shared them in tabular format

• Forcasts normaly have a less frequent granularity than the actuals and viewing them in a time series with different granularity is less valuable than viewing them in tabular format

• Larger companies use more in depth forecasting tools and their data may be much more complicated than the smaller companies we were targeting.

• Forecasts rarely stand alone. Without goals, a forecast is meaningless.

• We concluded that forcast data would be more useful in our product after we build a reporting tool that has tables and goals.