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Strategy for Customer Retention -MECE Framework feedback

case interview preparation Case structure and frameworks customer retention Data Analysis MECE
New answer on Aug 31, 2022
6 Answers
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Anonymous A asked on Sep 14, 2020

Hi everyone, In order to develop a strategy/plan for customer retention for a SaaS prodcut before launch, I wanted to use a MECE framework (hypothesis tree) to strategize and present, is the following one ok? or any better suggestions?

Objective: Keep churn at minimal when we launch (i've tried to use a customer journey like framework with the assumption that more engagement can reduce retention)

1) Account Sign-up

- how many steps to complete signup? How can we keep it as short as possible?

- What info do we need?

2) Onboarding

- How can we onbard the customer? quick tour? live traning?

- How long should the onboarding be ? how many steps & what to cover?

3) Post- Onboarding:

a) User proactive use: Open the app --> Loading time of the app--> type of info on first screen [how can we ensure a smooth process as mentioned], [ what are the regular features that a user would want to enage with? do we have any other non-core features that can encourage enangement]

b) Reactive use: To ensure more enagement can we send proactive updates of data & other info?

4) Analytics

- How can we monitor technical issues to ensure worry free experience?

- Can we create algorithms to flag low enagemeng customer who could churn?

Please give feedback & thoguths if this is ok or bad and your suggestions would be appreciated.

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Anonymous replied on Sep 14, 2020

Hi, as always with cases you need to clearly define your metric, Here the "Churn".

Ask clarification question to understand how does your client define churn (Usually how many customers come back after being activated), how do they define activation? over which period do they consider a user as a churned user?

After understanding what's the current state of the SaaS Business model, you can breakdown the problem into 2 parts, how you can improve the experience before and after the activation.

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StanS on Sep 14, 2020

Ok. Churn metric is 1-2% - 90 days from signup. This is for a NEW product so in this case there is no"issues" but more of hypothesis. So does the "After" activation (post onboarding) framework that I mentioned look good?

Anonymous on Sep 14, 2020

I'm surprised this a low churn for a new product. Anyway, the most important here is to bring value to the right customer and being able to reiterate that. Therefore a possible Framework could be 1) User (Do we attract the right user) 2) Value proposition (Do we manage to provide value as quickly as possible) 3) Engagement (Do we manage to reiterate this value over a long period)

StanS on Sep 14, 2020

Ok thanks for the feedback, the churn rate is more of an intended churn rate rather than an actual one.

Content Creator
replied on Sep 15, 2020
#1 BCG coach | MBB | Tier 2 | Digital, Tech, Platinion | 100% personal success rate (8/8) | 95% candidate success rate


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!

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Anonymous on Sep 15, 2020

Hey Ian, thanks for the feedback, I included 1&2 as I thought that maybe part of the "churn" reasons, eg: No onboarding can cause a user to be lost & not have familiarity leading to drop in return.


Ian on Sep 16, 2020

If they don't get onboarded, they don't sign up. If they don't sign up, they can't leave. Hence, you don't have customer churn!

Ian on Sep 16, 2020

This case is about retention NOT acquisition ;)

Anonymous on Sep 16, 2020

hmm got it. but is it also a possibility that getting incorrect customers to sign up is also contributing to the churn or does this fall under the #3


replied on Sep 15, 2020
McKinsey / Accenture Alum / Got all BIG3 offers / Harvard Business School


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)


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Content Creator
replied on Aug 31, 2022
50+ successful coachings / Ex-Mckinsey JEM & Interviewer / Industry + Engineering background

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.


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Content Creator
replied on Sep 15, 2020
McKinsey | Awarded professor at Master in Management @ IE | MBA at MIT |+180 students coached | Integrated FIT Guide aut


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!



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Anonymous replied on Sep 15, 2020

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.....)

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Anonymous A on Sep 15, 2020

Hey henning, could "no on-boarding" also result in churn as it part of the "user exp"?

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