Hi,

This is bad!

The structure looks good, but it misses the entire point!

They specifically ask you to reduce churn. However, your 1st and 2nd buckets don't address this at all!

So, you need to expand upon your buckets #3 and 4. Specifically, talk about reasons churn could occur, how to prevent them, and how to learn lessons from other companies/experiences!

Hi,

Several big things that are missing here:

• Product adoption by the client (e.g. we can have perfect onboarding but the client is not pushing it)
• Customer support
• Pricing, pricing structure and discounts (e.g. for many products pricing will change with usage. In Hubspot the free tier is cheap but limited features, The next Tier is super expensive for SMBs)
• Product itself and customer satisfaction - adressing customer needs. Being better then competitors (Core features, additional features, integrations, etc)
• Churn prevention (e.g. churn forecasting, offers and discounts, etc)

Best

Dear A,

in general a good structure can be evaluated by a certain depth and breadth. The “depth” should be at least 3-4 levels while the “breadth” should cover the entire solution space. You can cross-check this with the MECE principles (For details see respective article on Preplounge), but the CE (collectively exhaustive) part is basically defining your breadth.

Finally, make sure to check for inter-linkages in your structure and point them out.

Simon

Hello!

Careful here, since you open the scope too much.

They give you a very clear, simple and well-scoped task: reduce churn.

Not all the points you mention indeed attack this, and hence they turn into noise that makes the structure longer than needed and foggy.

Points 1 and 2 are totally irrelevant.

Hope it helps!

Cheers,

Clara

To reduce churn, you want to keep users of the app engages. Therefore your first parts of your framework on sign-up and onboarding might not be necessary. In fact, even a MECE framework might not be necessary as only your step 3 really aims at reducing churn.

To do that, I'd recommend to do the analysis on an episode level, rather than on a customer lifetime level as you have done. Then you can optimize them from most churning to least. For that, you determine the potential to frustrate for each user episode. Together with real-time user engagement data, this allows you to prioritize episodes, e.g. ina 2x2 against the potential to frustrate (what % of users churn after going through the episode) vs episode occurance (% of users that go through the episode p.a.). To make it even more detailed, you might break this up by user segment to look at where the most valuable customers churn.

Then you can optimize the episodes along the measures you have defined under your first point (reduce number of forms or clicks required, provide more help, etc.....)