In this case, we made an assumption that all pizza shops would be a relatively similar size, so by calculating what our share would be, we were able to flip the numbers round so that less competitors equalled a higher score.
So for example, if there were 3 competitors in the suburb, and we became competitor number 4, if all shops had equal share, our share would be 1 out of 4 or 25 %.
But, if there were 5 competitors in the market and we became number six, our equal share would only be 1 out of 6 or 16.7%.
So, with this logic, a market of 3 competitors is more attractive than a market of 5 competitors.
Step 2 – Identify weighting across variables
Here, we decided to give equal weighting to population size and profitability and less weighting to competition.
We did this because we could see a more direct link between those two variables and our business goal to grow sales.
We did not want to discount the level of competition completely, but given the nature of the category, it seemed like less of a factor than market size and profitability.
Step 3 – Identify segments to evaluate
Here we’d picked three potential suburbs as our segments to evaluate.
Step 4 – Add up the total size of the variable
For each variable – population, average price, competitiveness, we added up each row horizontally to give us a total for each variable (T1, T2, T3 etc)
Step 5 – Calculate the weighted score
So, as an example here. for the ‘score’ for Suburb A based on population size is the population size (a) divided by the total population size of all segments. This is 35 out of 100.
Which you then multiple by the variable weighting of 40%.
So 35 * 40% gets you a score of 14 for the segment variable.
Step 6 – Repeat for each segment
In this case, we basically repeat step 5 a further eight times to fill in the scores for each segment – variable combination.
Step 7 – Calculate the segment total score
Our final calculation them is done by adding up each variable score under each segment.
In this case, it puts Suburb B as the most attractive segment, which is primarily driven by its high population size.
So, even though it has a lower average price and more competition, these are enough to outweigh the attractiveness of it having half the population of all suburbs that we could target.
If you are new to this process, the arithmetic that sits behind the process can be a little off-putting.
But once you grasp the basics, it’s an easily replicated model.
We’ve tried to keep the example above simple to show that it can be applied to any sort of business model.
But in many cases, the model can be expanded out to be even more sophisticated. It can be used to map out potential scenarios and aid decision-making.
While we won’t cover the topics here, we also recommend you check out the Boston Consultancy Group BCG matrix, and the GE McKinsey matrix models.
These are more complex models, but follow the same basic logic process we have covered in this guide.