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Customer Segmentation & Personalisation with Machine Learning

 

Customer segmentation is a method of dividing customers into groups based on their shared characteristics such as age, gender, interests, or purchase habits. This is important for allocating company resources and targeting clients effectively. Customer segmentation influences mainly sales and marketing decisions in organisations. It allows marketers to tailor their campaigns to different target groups for promotional, marketing, sales, and product development strategies. Personalised marketing and sales efforts that are based on well segmented customer groups have much higher conversion rates and therefore impact an increase in revenue. 

 

Clustering 

Even though customer segmentation is such a vital part of organisational decision-making and machine learning is advancing rapidly, very few companies are using automation processes to optimise their efforts in customer segmentation. Organisations that are using machine learning algorithms in their customer segmentation efforts often use “clustering”. 

Clustering is a machine learning technique for identifying and grouping similar data points (e.g. customers) together. The objective of clustering algorithms is to ensure that the data-points in the same group are very similar, and data-points in different groups are extremely dissimilar. This process is “the hard part” that  42% of marketers in the USA and UK do not complete due to lack of employees, time, and funding, which prevents them from reaching their personalisation goals.  

 

Once customers, stores, or locations have been segmented into groups, managers can make decisions about how to target each group most appropriately based on their differences. For example, specific segments may be optimal targets for inbound marketing campaigns such as cross-selling and others may be better suited for outbound campaigns such as email messaging. Thanks to machine learning models, companies no longer need to spend ages defining segments and allocating customers to them. Now, their time can be used for understanding who will be best served through which channel, with what message, and at what point in time. Companies can now focus on how to maximise the impact of current capabilities and resources such as available channels, agents, content, and offers 

 

In addition to simply automating the grouping process, machine learning can impact the “contacting” process. Machine learning models will learn (based on each customer interaction) whether the chosen communication channel, content, offer, and the point in time were “right” for a given customer. Based on the customer interaction with campaign touch points, machine learning models can automatically predict what engagement mode is most likely to delight each individual customer. The more data there is, the more accurate the models will be. Machine learning models do not even need to base their assessments on data from current campaigns – historical data from customer engagement with previous campaigns can be used to train the models.  

 

 

Other Benefits of Doing Customer Segmentation with Machine Learning 

 

Simpler & More Precise Segments  

These days customers cannot only be segmented based on their demographic or purchase behaviour data for effective segmentation results. These segments are simply too broad and deliver only suboptimal conversion rates. Organisations are now including more advanced additional criteria to ensure better segmented groups. Specific data from the customers’ sales journey is included such as whether they attended an event, clicked through to the company website, clicked on a product, and whether they bought the product or not. The desired criteria to include is always changing and often growing. If managed only by humans, it can quickly become unmanageable and inaccurate. Allowing machine learning models to do this process for you, ensures your customer segments are always optimised, saving employee time and reducing the organisation’s costs.   

 

Changing Segments 

Decisions to change the segmentation criteria to include different characteristics have perviously been a challenge. Manually reallocating customers from their initial segment to another requires constant monitoring and shifting. Machine learning models automate this process and update and shift customers from one group to a more relevant group in real-time. In essence, the customer segmentation process does not need to be time-consuming, expensive, and manually adjusted, thanks to the implementation of machine learning clustering in this process.

 

Concluding Thoughts 

There is no longer a need to manually allocate customers to segments. With machine learning, each customer is their own segment, defined by any chosen criteria that organisations deem best. When it comes to customer segmentation, machine learning algorithms are highly effective for personalising the customer experience and leading to conversions. 

 

If you want to learn more about how Machine Learning can best be applied in your organisation, contact us to find out you could be saving time, money, and resources.  

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