Evil Interstellar Empire Product Management Trial Maxi-Mission
Welcome to the trial maxi-mission #
The Rebel Alliance is more than just a military organization - it’s a coalition of free planets dedicated to overthrowing the Empire and establishing a democratic Republic. And while military might is an important part of our efforts, it’s not the only piece of the puzzle. We also need to demonstrate our strength in economics, science, and engineering innovation in order to truly succeed. That’s why the products we develop for peaceful purposes are just as critical (if not more so) as our military innovations. By showing the galaxy that we are capable of creating innovative and valuable products, we can inspire support for our cause and build a better future for all.
BlueERP is an automated resource planning system that we’ve been selling to construction, mining, and manufacturing companies on free planets as well as planets under imperial control. Recently, we’ve noticed that our win/loss ratio has started to decline. There could be any number of reasons for this - we’ve recently expanded our market to two more planets, we’ve significantly grown our sales department, and the Empire has released an upgrade to their competing system, RedERP.
Your goal is to review the available data and determine the most likely cause for the decrease in our win/loss ratio. This might involve reviewing sales numbers and analyzing feedback from customers and sales managers. With your insights, we can develop a plan to improve BlueERP’s performance and stay ahead of the game.
Q: I do not have enough data to make a definite conclusion.
A: This mission will require you to make a conclusion based on incomplete data. We intentionally keep the data about the BlueERP limited. Focus on the elements that you consider to be important for the win/loss ratio.
Background information #
Your recent emails #
Win/Loss summary information document #
Executive summary #
Win/loss ratio of BlueERP sales has declined during the last three quarters. The root cause is not yet clear.
Data #
Win/loss evolution:
| Quarter | Total BlueERP win/loss |
|---|---|
| Q1 | 0.82 (normal) |
| Q2 | 0.46 (decline start) |
| Q3 | 0.51 |
| Q4 YTD | 0.48 |
Win/loss on free / imperial planets:
| Quarter | Imperial w/l | Free w/l |
|---|---|---|
| Q1 | 0.55 |
1.45 |
| Q2 | 0.31 |
0.81 |
| Q3 | 0.34 |
0.9 |
| Q4 YTD | 0.33 |
0.83 |
Win/loss per industry:
| Quarter | Construction | Manufacturing | Mining |
|---|---|---|---|
| Q1 | 0.7 |
0.851 |
0.849 |
| Q2 | 0.4 |
0.48 |
tbd |
| Q3 | 0.44 |
0.53 |
tbd |
| Q4 YTD | 0.41 |
0.5 |
tbd |
Note: the win/loss data for the last three quarters Mining sector is under re-calculation.
Conclusion #
The decline in our ERP product’s win/loss ratio is a major concern, and we need to find out why as soon as possible. It will be the product team’s responsibility to lead the investigation and come up with hypotheses and solutions.
Conducting win/loss analysis #
Here are a few useful bits of theory on win/loss analysis
Benefits of win/loss analysis
Typical loss reasons
Root cause hypothesis #
Trial mission answer #
The best hypothesis based on the input data is that the new sales team started to approach companies at the wrong time. Smaller companies cannot benefit from the efficiency gains of BlueERP and find the time required to study and operate the product not worth the outcome.
It cannot be the industry switch, since the average figures remain the same for both industries. The missing figures for the Mining industry can be calculated from the averages and the industry lead distribution.
There is no evidence that supports the UX changes in the Q2 release influenced the customers’ reaction to the release.
And finally, the win/loss decline from 0.8 to 0.5 is significant and usually does require action.
What do I do next?
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