Our clients are always looking for a way to get a quick return on investment on their segmentation programme. Tagging your database with segments is a great place to start. Not only can this help to improve your messaging effectiveness and targeting in the near future, but it shines a light on the customer journeys already in play for different groups and how data is being leveraged.

In order to segment your database, certain data points need to be captured and associated with individual contacts and there are a wide number of ways in which you might segment your current audience.

Three use cases for Database Segmentation

  1. Advertising – Once you’ve applied segments to your database, you should have a much clearer idea on the product areas, spending habits and interests of your customers. You can then use this data to create lookalike audiences to target through advertising.
  2. Focuses sales efforts – Whilst we often think about segmentation as a tool for focusing our sales and marketing efforts, its also important in establishing less relevant leads and prospects. There are significant time and cost savings associated with narrowing your targeting but often these figures are not factored in when thinking about the impact that insight has.
  3. Map out customer lifecycles – Segments should never be thought of as static entities. People should move between them as their lifestyles change and so too do their needs. Understanding the relationship between different segments can help you to identify key moments to intervene. The shopping habits of a newly married couple and one that have started a family may be wildly different, but how you prompt them to choose your brand when they make that change?

Attributes in which to segment your database

Ideal Customer Profile (ICP)

Typically, when organisations think about their “ideal customer”, it will be the ones that represent the largest profit margin, the shortest buying cycle, or those which require the least amount of support. An ICP can help to identify common characteristics amongst your customer base, but you should be careful in how this is used.

Let’s say for example that you discover that your highest paying customers are all 40+, is it their age that is attracting them to your brand or is it that you are spending more money on a channel which is likely to have an audience of this demographic? Could it simply be that as these people are older, they have more spending power? While data can give us a useful picture of the customers more likely to convert and grow, it doesn’t give us a strategy. If we already had a healthy segment of 40+ customers, perhaps it would be better to try and concentrate on the 30+ crowd and by growing this segment, we will in time, grow our prime segment.

It is also important to consider the size of your ICP when compared to the rest of your database. It is not uncommon for most organisations to have an 80:20 split where 80% of their profit will come from 20% of their customer base. There is always an inherent danger in chasing ideal clients which will typically take more time and resources, and in the meantime may alienate the bulk of your audience. Bonamy Finch would advise that applying ICP to your current database is not necessarily the most useful way in which to segment your CRM, but identifying these clients is key in retention schemes.


Source Channel, Content or Campaign

Another way in which you might consider segmenting your CRM is from which source they came in from, and which content/campaign brought them there. It is becoming easier to attribute actions to understand whether a customer came from organic search, referral, paid adverts etc. While technologies like HubSpot, SharpSpring and Lead Forensics can help track website and social activity, they are far from flawless and this only gives you the online picture. In today’s cross-channel and multi-layered sales funnel, its extremely likely that your customer will have interacted with you on multiple fronts including offline and so true attribution can be difficult, and even where you may have that data, it’s increasingly difficult to quantify buying behaviour.

Understanding what has worked before can help shape future activity and budget, but these actions still take place within a silo of your own channels and don’t help you to understand what brought that consumer into the category in the first place. Understanding the channel and source that a customer has interacted with can help to personalise their communication though.

Needs, Interests and Hobbies

While many organisations have long held data of how customers interact with their brand, they have not been able to contextualise this with external data about how that customer may interact with other brands. (outside of market research) Thanks to advances in technology, it is now easier to merge third party data, which enables a clearer picture of your customers. Understanding their likes, interests and hobbies may enable you to think of additional channels/placements which may not be immediately obvious. Social media data has enabled this on a large scale, but many would criticise the level of data available. As an example for nearly every twitter account, the number one interest of followers is “dogs” and whilst our canine companions may be popular, this won’t help many brands with targeting and highlights the value of additional qualitative research for better context.


Buying Personas

Where an ICP focuses on an ideal customer, buying personas are representations of groups of customers. They are based on market research and encompass multiple forms of segmentation including demographics, behaviour patterns, motivations, and goals. In order to create detailed buying personas which can enact practical change, we combine customer data, survey data and third-party marketing data to create a single view of the consumer that results in the right business decisions being taken to both acquire and retain customers.

Playing 'database tag' is often a good place to start when it comes to the application of your segments. If you are looking to squeeze more out of your data and have it feed into the tools your teams actually use, then whey not talk to the team at Bonamy Finch? Get in touch

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