Segmentation: Separating the barracuda from the barramundi

Jean-Luc Ambrosi

Jean-Luc Ambrosi is an award winning marketer and recognised expert in branding and customer relationship management. He is the author of the new book, Branding to Differ, a strategic and practical guide on how to build and manage a successful brand.

Customer segmentation has become a key area of focus for marketing managers. No longer just a novel idea, it is increasingly becoming entrenched in the modus operandi of organisations.

Segmentation provides an added level of sophistication to increase promotional activities and customer management’s effectiveness, be it for acquisition, retention, cross-sell or upsell purposes. Despite this, it is often poorly understood and underutilised. Of course segmentation is not the ultimate panacea to increase results either, and can at times prove as unreliable as a weather pattern. But as a tool, as a mechanism, and perhaps more importantly as a strategic approach, it can provide the foundation on which to improve marketing and sales effectiveness.

Take the finance industry for example. On the one hand, it has been an early adopter and on the other hand, a laggard. Data rich environments like credit cards have shown the way on how to leverage this tool with great effectiveness. Yet other areas of finance, such as asset management and banking, have often been slow to adopt, missing opportunities especially from a cross-selling point of view.

There is little doubt this is changing, and segmentation could prove the next battleground in the search for increased return on investments. The recent availability of big data in providing richer sources of information will steer organisations even further in this direction. Business leaders, managers, strategists, analysts and marketers must seriously think about including this competence in their beach bags.

Segmentation is not new and emerged with the advent of technology, statistical analysis and data mining techniques in the late 1950s and early 1960s, by enabling the use of statistical techniques to be applied in the business to consumer world. Jump to the 1980s and segmentation for marketing and sales started to provide organisations with a material edge. Direct marketing in particular led the way as it needed to lift response rates, and gain a better understanding of how to boost activity effectiveness. Remember the introduction of AB testing, personalised fields and the new world of database marketing?

Segmentation became a pivotal tool for increasing response rates alongside systematic testing. Today the same concepts apply to the online world which utilises similar strategies, approaches and mechanisms to traditional direct marketing. The ability to reach particular audiences linked to the ability to measure response and steer activities, messages and offers accordingly is a fundamental direct marketing process.

Schools of fish and schools of thought

The marketing conundrum resides in the fact that different groups of customers have different needs and display different behaviours, requiring organisations to treat them differently. Targeting all customers the same way based on the average customer profile will only deliver average results. Treating every customer differently is also rarely practical or economical.

In essence, segmentation is a method to group individuals via certain common attributes to target them with increased efficacy. This applies both upwards and downwards. Upwards is when many individuals need to be grouped as individual targeting is not possible. This will apply, for example, when a customer base grows exponentially and customers need to be differentiated according to certain criterion (such as monetary value to the organisation, geography and lifecycle stage). This then enables marketers to formulate strategies to service those customers more efficiently or acquire more individuals with similar profiles.

Downwards applies to the need to break down a larger population into smaller units to increase customisation and cost efficiency. For example, a financial institution selling investment products will not target the entire population as the cost would be exorbitant and provide a large variability of returns (as a portion of the population would not be able to invest in the product, and not all customers will be profitable). It will therefore need to define the ‘ideal customer groups’ according to a number of variables to be able to target them via the right channels, the right communication and the right offers.

Grouping customers according to certain common characteristics is like defining schools of fish in the ocean. It allows you to link specific individuals into segments (through their common characteristics) while targeting the number of individuals making up the segment as one entity. Doing so allows you to analyse and define the potential response of these segments to the different elements of the marketing mix. The behaviour of a school of barracudas will be easier to understand and target than if you try to look at each individual barracuda among all the fish in the ocean.

To build efficient segmentation models, the two most commonly used techniques are: The use of behavioural analysis, or who and how customers behave in real environments; and attitudinal analysis, looking at what they think, and how are they predisposed toward your brand or product category. Rather than one multipurpose segmentation model, we often need to think in terms of juxtaposed models working in conjunction with each other.

Through the use of data mining, defining the 'who' is about defining variables (individual pieces of data) which point to a higher propensity to behave in a certain way. This could be propensity to purchase for example, to defect, or to remain loyal. On an existing database, the grouping of these variables into behavioural segments allows you to pinpoint individual customers more likely to take up specific offers.

Once the school of barramundi has been identified, located and its patterns determined, it needs to be captured with the right nets at the right sites. The use of attitudinal segmentation provides a basic understanding of segments by revealing the motivational drivers or in other words the 'why'. This understanding facilitates the creation of value propositions customised for the target audience. This enables organisations to build targeted messages and offers that are more like likely to be adopted by the target audience.

While the two segmentation models can be used separately (as is often the case), integration provides valuable input and can help define the targeting approach, or the 'how and where'. However, the undeniable complexity and real 'art' consists in integrating the two effectively. The planning of the two must therefore be made concurrently to provide a full picture and actionable model.

The sophistication of segmentation can drive major investments in tools and qualified resources, but simple and effective approaches can also be implemented. While the use of data mining is ideal for resource rich organisations, internal knowledge can be used as a starting point to segment markets and customer groups and should not be dismissed. This source of knowledge is used in some segmentation techniques to start and steer the clustering process (grouping of variables to create segments).

The reason for this is that segmentation models must before all be implementable. Their aim is to provide better targeting. If not, they are just an intellectual exercise with questionable value. What matters most in the build of these models is that they be adapted to the internal and external realities of the organisation.

Overarching pre-conceived models claiming to be all things to all people will rarely deliver on their promises. Purpose built ones might provide a better alternative and ensure you are catching barramundis - not barracudas or even sharks.

Tags: data-driven marketing, customer insights, segmentation

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