Why Customer Segmentation Data Is A Commodity To Be Harnessed

Data is one of the most critical aspects of any online strategy that goes hand in hand with segmentation. So in this aspect, it is the capacity to dissect data based on a set of data points such as age, gender, last visit etc. These segments can then be used for online and offline customer engagement. 

Many companies already carry out segmentation on their data, but a significant proportion still has not adopted this strategy. Those that do the benefit and do realise very quickly and for those that fail to segment their consumer data they are missing out on revenue opportunities. Data segmentation is an ever-changing area, especially with the advent of big data platforms and new data acquisition services, so this area is a key one to focus on.

Investing Time To Understand Customer Segmentation Data

Critical reasons for segmenting customer data are to:

  1. Target customer groups with greater accuracy
  2. Analyse customer data to find patterns and help identify new or evolving strategies
  3. Enable detailed specific insight

An example of this is a set of anonymous users visit your digital property but do not purchase any service or products; however of that group, a percentage visit regularly and some not very often. From an anonymous user perspective, you can define these as two distinct segments based on recency or frequency of visits. This may not be useful to interact with both groups but present different content offers or prices as it can be hypothesised that the more frequent users have a higher propensity to purchase.

Once the results of this targeting are tested and known, you can go one step further, knowing that a user has purchased, you can then link this data to the frequency of visits and to their characteristics of the location, age, gender etc. This is the foundation of segmentation usage.

Effective Segmenting Methods To Push Your Company Forward

There are two modes in which segmentation can take place, and both are highly effective, although not always practised.

The first and most common approach is to take the data and push it into an offline data store/warehouse where it can be segmented and then used for reporting or targeting purposes. In the targeting approach, users can be allocated into segments, which can then be used for engagement.

Once user segments are identified they can be extracted and then used either in marketing communications such as email (the most common approach) or segment matches can be fed back into the operational systems where interactions can be updated to allow engagement. For insight, segmentation can then inform predictive models and further propensity analysis to provide refined targeting (a topic for another day).

These segments can be based on previously created customer classifications or can be used to analyse detail customer dimensions to provide insight. The types of interactions can vary from changing price points on products and services to cross-selling items that are typically purchased together.

The second mode mentioned earlier and not well established yet is real-time segmentation. This is where user data segment dimensions are applied to users in real-time and if a match occurs an action takes place, for example, if a user is browsing certain content on a news site such as cars and travel they are of a certain demographic, they are instantly provided with a relevant offer as they conform to a defined segment.

The possibilities here are endless as this segment matching can also push app notifications or near real-time email communications. However, the key point here is that rather than waiting to process the segment offline and send out communications such as email that may or not be read, the engagement takes place immediately providing higher conversion opportunities and improves stickiness for the customer.

Whichever approach is undertaken the value of targeting based on segmentation is widely accepted as a mechanism to increase engagement and in turn growth of conversions.

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