Clustering is key to proper data-driven segmentation
The first things marketers do when planning a market strategy is often to use clustering and segment the market into distinct groupings of prospects that have similar profiles and therefore are likely to respond to the same marketing tactics — think for instance of “soccer moms” or “tech enthusiasts”.
In the consumer world, marketers and advertisers will quickly think in terms of demographics: what is the age, income, education, marital status of my prospects? In the B2B world, marketers will instead think of firmographics, i.e. revenue and employee count, or industries. How large is this company? Which industry is it from? Tech, automotive, hospitality?
The problem with segmenting using demographics or firmographics is that it often has very little bearing on actual buying behavior. Think of the classic Prince Charles vs. Ozzy Osbourne meme above. Same demographics, very different buying behavior! So, what is the answer? The answer to this is: (i) use a lot more data, including behavioral data, usage data, and any other attributes that are relevant, then (ii) apply a clustering algorithm to your data to identify relevant clusters, or groupings, of customers.
The four elements of a good clustering scheme
How do I know if my clustering approach is effective? It needs to produce segments with the following characteristics:
- Distinct. Are your clusters clearly separated or is there so much overlap between them that you can’t tell the overlap between two clusters?
- Sizable. Are your clusters large enough to be worth your time and energy? Or are they so niche that they do not represent an interesting market opportunity?
- Identifiable. Can you define and identify the prospects that belong to each cluster? This particular task is becoming increasingly easier as production models have the ability to tag prospects as records get created.
- Meaningful. Finally, are the different clusters meaningful from the perspective of your business audience? Do they align with your own experience targeting your customers, can you relate to them?
Good clustering results can be accomplished using a variety of clustering algorithms. The most commonly used approach relies on a combination of principal component analysis and K-Means clustering. Note that we cover commonly used clustering algorithms and how to implement clustering in practice in our comprehensive guide on clustering model building.
Benefits of using segments
Once you have developed a good set of segments using clustering you can identify typical buyer profiles (personas) for each segment, yielding the following benefits:
- Clearer understanding of who your buyers are
- Improved value proposition
- Improved conversion metrics (higher rates, higher quality leads, shorter sales cycles)
- Improved prospects and sales volumes
How can we help?
Do you need better predictive analytics? Want to learn more? Feel free to check us out at https://analyzr.ai or contact us below!