A Strategic Segmentation of the Global Population for Covid 19
Well, it’s not every day one starts a blog post with such a title; but these are not ordinary times. So let’s dive in…
Key Point 1: What we are facing can be seen as a global, citizen management challenge; by which we mean a big part of moving through and beyond the Covid 19 pandemic will come from understanding the affected base , drilling into the key drivers of differentiation and from there deriving a customer/ citizen management plan to achieve the best possible outcomes. I say ‘customer’ solely to remind that many of the disciplines required to build and implement the plan are those that have bubbled up through the customer management/ CRM disciplines. But in this situation, Citizen Management is the better framing.
Key Point 2: Given the above, the start point is to define and agree on a strategic segmentation of the relevant population (i.e. everyone on the planet). Strategic segmentation can be described as identifying the defining the key groups in a population with distinct characteristics that line up with the ability to drive knowledge and take actions. One would typically expect these segments to be mutually exclusive, and one would hope for no more than 8-10 in order that they become embedded and drive strategic plans.
Key Point 3: One would expect many tactical segmentation approaches to then emerge beneath the strategic one; the tactical ones are more aimed at drilling into specific facets of a strategic segment to build understanding and enable actions.
This approach to strategic segmentation for Covid 19 is being evolved in the MyData Community, where there is a huge array of work underway to bring human-centric data approaches to the Covid 19 problem. Here’s how we see that strategic segmentation at this point.
This visual below is our first attempt at defining the strategic variables and allocating the population into segments. The actual numbers in each segment are best guess derived from published sources, and that will always be the case in this situation.
We see the key strategic variables at this stage being 1) Covid 19 Medical Status (5 options), and 2) Key Worker Status or not. The former reflects where each individual is on their potential Covid 19 journey from no symptoms/ assumed not infected, through symptomatic but not tested or tested and found not infected, to confirmed current case, confirmed recovery and finally confirmed fatality. The latter reflects the extent to which the individual has a different risk profile because they are a key worker of some form and thus will necessarily have differing behaviours to those who are not key workers.
More important than the number within each segment are the movements between segments over time; in this case in almost real time. This is where one would be looking to apply ‘actionable knowledge’, i.e. a chart that not only tells me something I did not know before, but also allows for action to be taken by people who have the necessary access permissions to drill into and use the underlying data. The ideal data-set for analysing and using data in this way will blend top down published or scraped data with bottom up, permissioned based data from individuals. That approach would allow for maximum accuracy in the data-set and extensive usability, not least from the individual contributors to the data-set themselves who could personalise that actionable knowledge to their own specific requirements.
We’re working on a prototype of the above now, so hopefully have something to show in the next few days.