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Using the market attractiveness model for targeting

Market segment attractiveness - pizza shop example

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Why read this? : A market attractiveness model helps you decide which segments to target. We share how it works with an example of a common targeting decision. Learn the types of data you can use, and the formulas which drive the model. Read this to learn how to find the most attractive market segments.

Segmentation, targeting and positioning is the process you use to :-

  • break the total market down into smaller groups.
  • choose which group to go after.
  • decide how you’re going to go after that group. 

In this article, we focus on targeting. That’s the decision on which group (or segment) to go after.

It follows the segmentation step, where you take the total potential market and break it down into groups (segments) who share similar attributes. 

These attributes are usually a mix of :-

  • demographic – e.g. age, gender, income levels.
  • occasion – e.g. time of day, usage location, special events.
  • attitudinal – e.g. needs, attitudes, motivations.

You can’t go after every segment, so targeting is where you decide which ones to prioritise. You work out which segment has the most potential. To do that, you use a tool called the market attractiveness model.

The market attractiveness model

The market attractiveness model is a decision-making tool which gathers and organises relevant data about each segment.

You use this data to weigh up the relative attractiveness of each segment and that gives you an attractiveness “score”. The higher the score, the more attractive the market. 

Data is key to how the tool works.

You need to be able to gather relevant data about each segment for it to work. Typical types of data you can use includes :-

Market segment attractiveness blank template
  • Size – bigger is normally 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 are 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. It you want to move into a new segment, high switching rates make it more attractive. 

You usually build the model in a spreadsheet. It works on calculated scores, so all the data needs to be quantifiable i.e. numbers based.

This does lead to its only downside however. 

There may be some factors influencing your decision which can’t be quantified. Usually, you use the numbers from the market attractiveness model to give you an initial view on which segment has the most potential. But then you’d review and include any other factors which the model can’t cover before making your final decision. 

So essentially market attractiveness is usually a 2 step process. A data and fact based initial view on the segments. And then an expert, more subjective review of the recommendation.

(You can see an example of how this second stage works in our article on Six Hats creative thinking article).

The 7 steps of the market attractiveness model

As per our segmentation, targeting and positioning guide, there’s 7 steps in 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.

We’ll show how this works by plugging data into the model with an actual business example.

Market attractiveness example - Sydney Pizza Company

Let’s say we’re planning to set up a new pizza shop in Sydney. We have to decide in which of 3 suburbs to base it – A,B or C.

Or in other words, we have to decide which of 3 segments (A,B or C) we want to target. 

(We use this pizza shop example in elsewhere on the site by the way. See for example our articles on the Ansoff matrix and Six Hats creative thinking).

We’ve hired a really good marketing coach and they’re helping us work through the process.

Our segments are purely based on location. That keeps things simple for this marketing decision

Sydney Pineapple Pizza Company mock up company image - says Bondi Beach, has two pineapple icons, a large pizza slice in the background and superimposed on image of a turquoise sea.

(as we said earlier, in reality, segment definitions are usually more complex than just one variable). 

Step 1 – Identify variables to evaluate segments

First, we need to decide which variables will make a market more or less attractive. In this case, we’ve gone with these 3 :-

  • population size.
  • average price paid.
  • level of competition. 

Population size

We started with population size. A couple of reasons for that. First, it’s quantifiable and measurable data that’s easy to get hold of. It’s also a relatively safe assumption that the higher the population size, the more potential customers we can get. So, it’s a good indicator of market attractiveness.

Average price paid

However, we also want to consider the economic value of each segment too.

The more affluent or wiling to pay a higher price a segment is, the more attractive it is. In this case, we could have tried to dig out average income or average house price for each suburb for example. 

But we decided to go with a much simpler and more directly relevant factor. Because we could see what other pizza shops charged, we could work out an average price paid for pizza in each suburb. 

Glass jar knocked over on floor with coins spilled out onto the floor

(As an aside, both of these factors would also be helpful in the financial plan. Population size could help forecast the number of orders for example. And you’d multiply that by the average price paid to work out your sales value line in the profit and loss). 

Level of competition

Finally, we’d also want to include a measure of the level of competition in each segment in our market attractiveness model. 

In this case, we used the number of pizza shops in each suburb as a way to show that.

That did mean we had to slight play around with the formula here as here smaller is better. A segment is more attractive when there are less competitors. 

To do that, we assumed each competitor has similar market share.

Close up shot of a set of white chess pieces, with a single black pawn replacing one of the white pawns to show differentiation and distinctiveness

And that if we went into that segment, we could get an equal share to them.

But the more competitors, the lower that equal share would be. The share represents the attractiveness score for that market segment. 

For example, say there’s 3 competitors in the suburb. We became competitor number 4, so if all shops have equal share, our share is 1 out of 4 or 25 %.

But, with 5 competitors and us becoming number 6, our equal share would be 1 out of 6 or 16.7%.

Doing it this way makes markets with fewer competitors more attractive because your potential share score is higher.

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 won’t be like that. 

Competitors will differ in strength and in their positioning. You might be taking on a to taking on a pizza shop run by an Italian family who’ve been running it for 50 years in one suburb. And in another you’re competing against a bunch of national pizza chain outlets for example. 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

RMS is a measure of competitiveness taken from the BCG matrix (also known as the Boston Box). It’s often used in market attractiveness models by more well-established business who have good access to marketing data

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 example though, RMS wouldn’t have worked. We’re completely new to each segment. Our RMS would be zero for all 3. So it wouldn’t have help pull apart the segments. 

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 these numbers. Different weightings will generate different overall scores.

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. 

Market segment attractiveness - pizza shop example

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 eacb segments 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.

We should point out we’re using rounded numbers here. It makes the table simpler to read. And there’s big enough differences between these numbers for rounding to overly influence the overall scores. 

However, with more complex market attractiveness models, that may not be the case. If there are less differences and closer scores, you may want to use less rounded numbers. 

Step 4 – Add up the total size of the variable

For each variable – population, average price, competitiveness, we next added up each row to give 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.

Close up of old style Texas Instruments calculator

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

In this case, we basically repeat step 5 a further eight 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. You get a total market attractiveness score for each segment based on its summed score on all the variables. 

In this case, Suburb B is the most attractive segment. That’s mainly driven by its high relative population size.

So, even with a lower average price and more competition, the high population size still makes it the most attractive segment to go for. 

The thinking behind the market attractiveness model

If you’ve never built a market attractiveness model before, this may all seem overly complex.

Just pick the one that feels right you’re probably thinking, right? 

But this approach forces you to start your decision making with data and objectivity. And once you work though each variable and segment, the logic behind it will make more sense. 

That’s not to say there’s not place for subjective opinion in your decision making.

man in a blue T-shirt looking at the ceiling

The rational, fact-based approach of a market attractiveness model is a good start point for targeting decisions. But as we mentioned earlier, there can be other influencing factors which the market attractiveness model can’t handle. 

(See our Six Hats creative thinking article for examples of this). 

Business strength

We’ve kept our market attractiveness here quite simple, so it’s easy to understand.

But, when you use one, there’s another factor you can use that improves the model, but also makes it more complex.

The factor’s we’ve used in our model here all focus on the segments themselves. What the segment is like, and how competitive it is for example. 

What we haven’t factored in though is our ability to compete in each segment.

Person's hand resting on a wooden sign saying you got this

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. 

It’s implied a little in the Relative Market Share (RMS) calculation we mentioned earlier. You’re stronger in segments where you have high share. But in terms of choosing which segment to go after, you could also build in scores to cover your internal strengths. 

To do this, you’d use marketing data and score yourself in areas like :-

  • brand choice e.g. awareness, consideration, trial, loyalty.
  • brand equity e.g. imagery statements like quality, value and innovation. 
  • customer metrics e.g. website visits, CRM members, customer service interactions.

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 are 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 are not 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. It saves you money because you also choose which segments to NOT go after. 

Your target audience definition appears in almost everything else you do in marketing. 

It’s the first part of your positioning statement for example. It shapes your brand identity. And it drives your market research, your marketing planning and every brief you write. 

Archery target with arrows in bullseye to symbolise marketing targeting

The market attractiveness model helps you make a better targeting decisions. It’s a logical way to gather relevant data and think about what makes a segment attractive. Plug the objective facts in, and you get a recommendation on which segment has the highest potential.

You use that recommendation to make your final choice of which segment to go for. One you’ve done that, everything else that you do in marketing is done with a clear target in mind.

Check out our guide to segmentation, targeting and positioning for more on this. Or contact us if you need help building your own market attractiveness model. 

Photo credits

Coins spilled from jar : Photo by Josh Appel on Unsplash

Chess board : Photo by Randy Fath on Unsplash

Calculator : Photo by Ray Reyes on Unsplash

Man looking at ceiling (adapted) : Photo by Anton Danilov on Unsplash

You got this : Photo by Ava Sol on Unsplash

Target : Photo by NeONBRAND on Unsplash

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