Customer Segmentation

Customer segmentation

What is customer segmentation?

Customer segmentation is the process of dividing customers into groups, based on some common attributes or characteristics they have. Within business-to-consumer (B2C) marketing, we typically use demographics such as age, gender or location in order to group customers into segments. However, we can also use data relating to behavioural and purchasing activity, for example, which email campaign a customer responded to or the time since their last purchase.

This helps us gain more clarity about their needs and preferences — ultimately which marketing messaging, offers, and customer experience — they may respond well to.

Why customer segmentation?

Once we have built our customer segments, we are able to send more specific marketing messaging effectively tailored to the customers’ needs. It is this personalised approach that provides a better, more relevant experience for the customer and ultimately will help drive higher levels of conversion and customer loyalty. From a sales perspective, we may even decide to market completely different services, products or offers to the customer: all based on the understanding we have of the customer segments they belong to.

As we build a better understanding of our customer segments and a more detailed historic view based on data, we are able to determine things like which groups are most profitable, which groups are most likely to become a repeat customer and which groups may be more interested in certain products or services. It is this understanding of the customer and their value — be that at a lifetime or an order level — that allows us to adjust paid media and campaign costs, helping us maximise the efficiency of marketing budgets.

Applications and benefits

So, what else can we do with our customer segments, and why should we use them?

The benefits include:

Targeted messaging which will provide greater traction.
Opportunity to increase conversion rates with more relevant offers and messaging.
Increase customer loyalty as customers feel valued.
Improve customer LTV and AOV by targeting customers with products they are more likely to purchase.
Optimise marketing spend by targeting high-value customers and reducing spend on customers unlikely to purchase.
Deliver an all-round better customer experience by displaying products and services customers are interested in.

By using the knowledge and understanding, we can target customers with the relevant marketing messaging and experience, therefore personalising our approach towards them. The power of personalisation not only assists conversion, but boosts customer loyalty and increases time spent on site — given the fact a tailored experience or message will gain greater traction with the target customer.

Cubed’s AI-driven segmentation

And yet, as with everything in digital, the concept of segmentation is ever evolving. So, how can segmentation work harder for us? At Cubed, we use data to enrich our real-time view of the customer, supplementing the segment information, and helping us to build a deeper understanding of what a customer is likely to do next.

Cubed’s native customer segmentation tool allows you to build customer segments within the platform itself, using purchasing and behaviour metrics at a lifetime, order and visit level. However, it is Cubed’s real-time propensity scoring (defined as the current likelihood a customer is going to convert for any given goal or event) that allows for more advanced use of customer segmentation. For instance, by using Cubed’s propensity scoring, we are able to segment and target customers who we predict are likely to not only purchase generally, but purchase a specific product category and critically, at what point.

We pull this segmentation data via Cubed’s API to the page in real-time, giving us the capability to personalise a customers’ on-page experience and optimise towards any given goal. We do this using propensity scoring — which is calculated based on the behaviours that any specific customer is showing at that given moment — as well as the historical data we have captured around their activity. This allows us to serve the target customer with what we deem to be our own next best action, giving us the very best opportunity for a personalised, relevant customer experience and display of content. This could be a discount code pop-up on screen, or the remerchandising of products on the next page load to show the most appropriate product categories. If a customer leaves the site without converting, we can even send an email with an offer relevant to the product we predict they are most likely to purchase, providing they surpassed a given propensity threshold.

The data within Cubed allows us to enhance customer segmentation and automate the creation of a personalised and tailored experience. The fact this is based on both real-time data and historical data gives us the most accurate understanding of the customer and allows us to deliver the most relevant marketing messaging or content, at any given moment.

With a common increase in CPA’s and CPC’s across the marketing landscape, personalisation is becoming increasingly important for brands as they look to maximise the value of their marketing budgets for new customers and increase the value of their existing customers. Machine learning based data and unique behavioural data delivering highly effective customer segments achieves this. To find out more on how you can implement this data capture and technology yourself, please contact Andrew Skinner at andrew.skinner@cubed.email.