Approach to Diagnostic Questions without a Numerical KPI

New answer on Jan 29, 2020
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Anonymous A asked on Jan 29, 2020

Hi All,

Is there a general 'thinking frame' that can be applied to diagnostic time case questions where the problem does not relate to a numerical KPI, or a KPI that can't obviously be broken down numerically?

Examples I have seen include:

- Churn rate of employees has increased

- Customer payment times have increased

- In store customer experience has reduced

How would you build a diagnostic issue tree to break down these KPIs, when it is not clear cut like Profit or Revenue?

E.G I am struggling to think of the best way to break down 'churn rate' when there are numerous potential causes like location/team/salary/benefits/envronment etc.?

What is the general advised approach for diagnosing more qualitative issues where the interviewer dooes not define a numerical KPI, even after clarifying?


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updated an answer on Jan 29, 2020
McKinsey Senior EM & BCG Consultant | Interviewer at McK & BCG for 7 years | Coached 350+ candidates secure MBB offers


All your examples can be easily translated into a numerical metric! It is your duty to align with the interviewer, what the EXACT definition of your focus metric is!

For example:

  • Churn rate could be defined as the number of customers who churn (let's say in a year) divided by the total customer base
  • Customer payment time can simply be measured in terms of days until payment. You can then segment into different customer types and identify the problem segment where the number of days is very high
  • Customer experience could, e.g., be measured by number of people who recommend your product/service divided by number of people who would not recommend.

All these are just examples. You can brainstorm a couple of options and discuss with the interviewer, how your objective should be measured. This is your duty as interviewee! And this is how strategy consultants work! Making things measurable is a core element of the job!

Cheers, Sidi

P.S.: There are endless further examples, also in the non-profit space (e.g., "How to improve the education system in country XYZ?" --> you HAVE to define, what improvement means! Otherwise structuring is IMPOSSIBLE. It could, e.g., be number of children entering the school system, or number of graduates generated by the school system, or quality of these graduates, etc. Then you disaggregate this definition into its logical drivers in order to perform the analysis how to influence each dirver).


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Anonymous B on Jan 29, 2020

And where do you go from there then? If you know that churn is (customers that leave/all customers). It might be that more customers leave or that the overall customer base is smaller (which is the same effectively). But how does that help you go from there?


Sidi on Jan 29, 2020

From there you disaggregate the elements of the definition! By logic trees. So you work out the factors that cause customers to leave (e.g., availability of competitor products/substitutes, price sensitivity, is the underlying reason/need for the product/service still there, etc. Once you have done this, you can develop ideas whether/how you can influence each element into the desired direction.

Anonymous A on Jan 29, 2020

but isn't this dis-aggregation just sort of guessing, as it's not a numerical break down. As you say there could be multiple reasons why customers leave or pay late. In the example case I did it was just because the client didn't charge late repayment fees but competitors did. My issue here is how do you 'sensibly' dis-aggregate the criterion holistically, because the answer may just be hidden in another element that you just don't think of, and hence don't ever get to to the root cause etc..?

Sidi on Jan 29, 2020

No, it is quite the opposite of guessing! It is a 2-step-process: Step 1 is a rigorous disaggregation into numerically quantifiable conceptual drivers, Step 2 is a mapping of qualitative influencing factors/reasons to these numerical drivers. So if you feel concerned that you miss out on such qualitative reasons, then this means that you have not drilled deep enough in Step 1 and your numerical categories are still too broad! In my above example, the "number of customers that leave" is of course too broad and needs further disaggregation. For example you could think of "number of people who actively cancel" + "number of people who just don't renew" (if we are talking about subscriptiom models). You can further drill down if needed, in order to reach the level of concreteness where you don't need to "guess" anymore. I hope this is understandable, but tbh I believe it is very hard to grasp it via exchanging explanatory messages. This is a methodology that needs to be taught.

Anonymous A on Jan 29, 2020

This makes sense and I understand your point RE numerical issue tree, then qualitative mapping. My main concern which links back to the original question, is for metrics that are not easy to break down numerically at first sight, how do you just come up with the dis-aggregation on the spot without just asking the interviewer to define the metric for you!! This is my issue here, not the qualitative mapping, but how to create the rigorous logic tree on the spot for numerical problems that are not clear cut like revenues or profit!

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replied on Jan 29, 2020
McKinsey / Accenture Alum / Got all BIG3 offers / Harvard Business School


MECE is not a requirement and it can not always be applied.

You should build a structure using your previous experience, based on:

  • Objective (Should have a metric and time-frame)
  • Context
  • Type of the case

E.g market entry cases can have completely different objectives:

  • Should we enter the market?
  • Which top 3 markets out of 10 we should enter?
  • Can we get xx% market share on the new market?
  • Can we get xx ROI if we enter this market?
  • etc

Depending on the Objective and the Context you should come up with a proper structure to address the problem. Of course, you may use the patterns that you've learned while solving the other cases.

There is a number of ways how you can approach the structure:

  1. Mathematical issue tree (e.g. Total time spent on cleaning operation = # of people x Frequency x Hours per cleaning per person) or formula (e.g. output rate = total number of people being served / time to serve one person) or common industry drivers (e.g. revenues = # of customers x av. check) (e.g Passengers on the plane = capacity x Load Factor) or the industry revenue streams (Fuel revenues / non-fuel revenues for the gas station)
  2. Drivers (e.g. drivers influencing the birth rate in a country)
  3. Buckets structures (e.g. for the problems in sales : Sales strategy / sales people and allocation / motivation / sales process). Very often it's a real framework used by the consultants (e.g. the famous Bain Cap framework for PE due dills: Market / Competitors / Company / Feasibility of exit)
  4. Frameworks (e.g. People / Process / Technology) (e.g. The famous McKinsey framework - People don't want to do smth / they can't do smth / smth prevents them from doing that). Even academical frameworks in the rare cases (e.g. Product / Distribution / Price / Marketing (Also known as 4P))
  5. Value chains / Customer Journey / Process steps
  6. Consecutive Steps of the project (Analyzing cost structure / Benchmarking to calculate the cost savings potential / Analyzing the processes to reach the potential / Calculating costs and benefits)
  7. Etc

There is no magic pill to learn building the MECE issue trees. It comes with a lot of Practice and reflection and building proper industry and functional knowledge.


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McKinsey Senior EM & BCG Consultant | Interviewer at McK & BCG for 7 years | Coached 350+ candidates secure MBB offers
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