Why read this? : Building a market attractiveness model helps you decide which customers to target. We go through the process of how you build one. Learn from our example how to gather the data and how to calculate market attractiveness scores. Read this to learn how to build a market attractiveness model to make better targeting decisions.
Segmentation, targeting and positioning is a marketing process which helps you :-
- divide the total market into smaller groups.
- decide which group to go after.
- define how you’re going to go after that group.
This article focusses on the decision about which group (or segment) to go after. This is targeting.
It assumes you’ve already done some segmentation. That you’ve divided the total market into groups (segments) who share similar attributes, such as :-
- demographic – e.g. age, gender, income levels.
- occasion – e.g. time of day, usage location, special events.
- attitudinal – e.g. needs, attitudes, motivations.
Targeting is when you decide which segments to focus on. You compare segments with each other, and decide which have the most potential. You can use a tool called the market attractiveness model to make this comparison easier and more objective.
The market attractiveness model
First, you gather data about each segment. The data relates to factors which make a segment attractive. How big it is. How much customers spend. If it’s growing and so on.
You then apply a weighting to each of these factors. The model uses the actual data multiplied by your weighting to calculate an overall market attractiveness score for each segment. The higher the score, the more attractive the segment.
Examples of the types of segment data you can gather and what would make a segment more attractive include :-
- Size – bigger is more attractive.
- Growth rate – growing segments are more attractive.
- Average spend on the category – higher spending segments are more attractive.
- Level of competitor strength – segments with weaker competitors are more attractive.
- Current market share / growth – segments where you’re already strong or growing are more attractive.
- Loyalty / switching rates – If you’re already strong in a segment, high loyalty rates make it more attractive. If you want to move into a new segment, high switching rates make it more attractive.
You need this data to be numeric so the market attractiveness model can run calculations. The result is an objective comparison on the segments based on data. You compare the scores for each segment.
That guides your final decision. However, note that there may be some relevant factors you can’t quantify. It’s not always about the highest scoring segment. You still need to use your judgment to evaluate the scores, and make a final decision on which segment to target.
(See our Six Hats creative thinking article for an example of how other factors can affect market attractiveness decisions).
The 7 steps of the market attractiveness model
As per our segmentation, targeting and positioning guide, there’s 7 steps to creating a market attractiveness model :-
1. Identify variables to evaluate segments.
2. Identify weighting across variables.
3. Identify segments to evaluate.
4. Add up the total size of the variable.
5. Calculate the weighted score.
6. Repeat for each segment.
7. Calculate the segment total score.
Market attractiveness example - Sydney Pizza Company
Let’s work though each of those steps using an example. It’s based on how a typical business might use the market attractiveness model.
Let’s say we’re setting up a new pizza business in Sydney. We have to decide where to base it. We’ve 3 choices of suburb – A,B or C.
In this case, our segments are the suburbs. We’re assuming the customers in each suburb will be relatively similar. That keeps the decision-making relatively simple.
(see also our Ansoff matrix article which uses the same pizza case study to look at sources of growth).
Step 1 – Identify variables to evaluate segments
First, you decide which variables you’ll use to evaluate the segment’s market attractiveness. In this case, we’ve used :-
- population size.
- average price paid.
- level of competition.
We started with population size. A bigger population size means access to more potential customers. That would make a suburb/segment more attractive. Plus, it’s quantifiable and measurable data that’s easy to get hold of.
Average price paid
However, we also want to factor in the potential value of each segment. The more affluent a segment is, the higher the price it’ll be willing to pay. That’s good for market attractiveness.
In other categories we’d have looked at average income or house price for each suburb.
However, in this category there’s a simpler way to measure potential value. That would be average price paid for pizza in each suburb, because you can easily look at what competitors charge.
Level of competition
Finally, we also want a measure of the level of competition in each segment for our market attractiveness model.
In this case, we used the number of pizza shops in each suburb as a way to show that.
Note, we had to alter the calculation here to make a smaller number get a higher score. Clearly a segment is more attractive where there are less competitors.
To do that, we used a fair share calculation.
This assumes all competitors have an equal share. So the more competitors, the lower your fair share will be as there’s more share to spread around.
For example, say there’s 3 competitors in the suburb. If you choose that one and become competitor number 4, your share for that segment is 1 out of 4 or 25 %.
But, if there are 5 competitors and you become number 6, your fair share is only 1 out of 6 or 16.7%.
Calculating the score this way helps make segments with less competitors more attractive in the model.
Strength of competition
The assumption that all competitors are equal and of similar strength is OK for this simple market attractiveness model. But in reality most categories aren’t like that.
Some competitors will be stronger than others. They’ll have different positionings.
For example, you might be taking on a pizza shop run by an Italian family who’ve been running it for 50 years in one suburb. They’ll have lots of loyal customers who’ve used them for years. And in another you might be competing against national pizza chain outlets for example. They have big advertising spends but mainly compete on price. Those would be very different competitive challenges.
One way to do this would be to add in a subjective quality “score” for each competitor. But another more fact-based way would be to use Relative Market Share (RMS).
Relative Market Share
To work it out, you divide your market share by that of your biggest competitor. If you’re market leader, you divide your share by the market #2. Your RMS score will be over 1. If you’re not market leader, you divide your share by the market leader. Your RMS score will be below 1.
In market attractiveness models, having a high RMS in a segment makes it more attractive. You’re already strong in that segment. You have an existing base of customers, and you focus on loyalty rather than winning new business.
In this case however, we couldn’t use RMS as we were new to each segment, so had zero share to start.
Step 2 – Identify weighting across variables
Now we know the variables, we next decide if each variable is “worth” the same in the scoring. In this case, we gave 40% weighting to population size and profitability, and 20% to competition. The weighting has to add up to 100%.
Because your market attractiveness model is in a spreadsheet and uses formulas, you can play around with different weightings and scenarios. These should reflect your priorities and the context of the category.
For example, it would be very different if we decided competitiveness was the most important and gave it 80% and only 20% to the other 2 variables.
The 40-40-20 split was based on our view that population and spend would have a bigger impact on sales.
We knew our competitive strategy was to differentiate. Competition would be less of a factor. Had we chosen cost leadership as a strategy, we’d have weighted the level of competition higher.
In the table those 40-40-20 weightings multiply each of the columns of data for each suburb. So 40% of each segment’s score comes from population, 40% from average price and 20% from competition.
Step 3 – Identify segments to evaluate
We’d already chosen the 3 segment suburbs ahead of building the market attractiveness model. So, the next step here was filling in the relevant data in each column.
Note that we’re using rounded numbers here. It makes the table simpler to read. And with these numbers, rounding won’t affect the final scores.
However, in your market attractiveness model, the differences might not be so great. You need to use less rounded numbers if you think it’ll affect the final scores for each segment.
Step 4 – Add up the total size of the variable
For each variable – population, average price, competitiveness, we next added up each row. That gave us a total for each variable.
So, across the 3 suburbs, there’s a population of 100k people. Add up the average price paid in each suburb for pizza and it’s $50. And in total there’s 10 competitors across the 3 suburbs.
These numbers will be the denominator in the formulas you use to get a weighted score for each variable.
Step 5 – Calculate the weighted score
Next, we multiply each segment’s share of the total score for that variable by the score’s weighting.
That sounds complicated. But take each square one at a time, and it’s actually quite straightforward.
Segment A for example has 35% of the total population. And population is 40% of the total score.
So Segment A gets 35% of 40 or 14 from the population variable towards its overall market attractiveness score.
Segment B has 50% of the population, so it gets 50% of the 40 for a score of 20 on the population variable.
And so on, as you work through each score by variable.
Step 6 – Repeat for each segment
Next, we repeated this step 8 more times to fill in the scores for each segment – variable combination.
If you want to check the formulas are working properly, the “score” in the final column should be the same as the number in the weighting column.
Think about it like this.
Your percentage weighting for each variable is like a set number of points. There’s 40 “points” for population size for example. Each segment gets a share of those 40 points based on its relative share of the total population of the 3 suburbs. 100k people. Add up the points for the 3 suburbs and it should get you back to the total points available for that variable.
Try setting this up in your own spreadsheet if it doesn’t make sense. The easiest way to follow how a market attractiveness model works is by plugging in your own numbers and formulas.
Step 7 – Calculate the segment total score
The final calculation adds up each variable score for each segment. This gives you a total market attractiveness score for each segment.
In this case, Suburb B is the most attractive. That’s mainly driven by its population size.
So, even with a lower average price and more competition, the high population size still makes it the most attractive segment to target.
The thinking behind the market attractiveness model
If you’ve never built a market attractiveness model before, this may all seem a bit complex.
Just pick the one that feels right you’re probably thinking, right?
But using a data-driven approach builds objectivity into your decision making. And that’s a good thing.
It gives you a fair comparison between all the segments based on variables you decide influence market attractiveness.
That’s not to say subjective opinions can’t also come into your decision making.
The rational, fact-based approach of a market attractiveness model is a good way to start your targeting decision. But as we mentioned earlier, some influencing factors can’t be quantified. You start with the results of the market attractiveness model. And then you decide if these other factors are important enough to change which segment to target.
(See our Six Hats creative thinking article for an example of this).
We’ve kept our market attractiveness example here quite simple, so it’s easy to understand.
But, when you use one in real life, there’s other more complex factors you can include.
For example, the factors we used in our model all focus on the segments themselves. What the segment is like, how competitive it is and so on.
But we haven’t included our ability to compete in each segment. There’s no measure which really relates to our business strength.
Is what we do a good fit for that segment, for example? Would we be a strong player there?
This business strength can also influence our choice of which segment to target. To calculate this, you’d gather internal marketing data and score yourself in areas like :-
The brand strength axis features heavily in the well known GE McKinsey matrix market attractiveness model. That model goes a stage further by recommending specific strategies based on the mix of market attractiveness and brand strength :-
- invest in markets where attractiveness is high and you’re relatively strong.
- protect your share in markets where you’re strong (but not attractive) or which are attractive (but you’re not strong).
- harvest or divest segments which aren’t attractive, or where you’re weak.
Conclusion - market attractiveness and targeting
Your choice of target market is important. It shapes where you focus your effort and resources. You prioritise attractive segments, and leave less attractive ones alone.
Your target audience definition defines who your marketing is for.
You use the market attractiveness model as a tool to make better targeting decisions. It’s an objective way to gather relevant data and analyse what makes a segment attractive. The data help you score each segment, so you can see which has the highest potential.
You consider these scores as you make your final decision which segment to go for. You need to know who your marketing is for, before you can decide how you’re going to do it.