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?
Thanks!
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?
(editiert)
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.
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..?
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.
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!