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Predicting each customer's next purchase

Predicting each customer’s next purchase

If category or market share are key initiatives for your organisation, it’s likely that you are trying to determine your most promising sources of new revenue. Companies can no longer devise plans for the year (based on traditional approaches such as segmenting and clustering) and then sit back and relax.

Today, businesses must seek to maximise the potential of these strategies by determining each customer’s propensity to buy – the products each individual customer is most likely to purchase more of the same product or another product. If you add to this mix, frequency of repeat purchases especially FMCG products, and uniqueness to the individual then ML personalisation becomes the order of the day. Imagine an autonomous solution that uses unique data for each communication with your consumer. It builds loyalty by your knowing them as an individual and presenting product selections they are uniquely interested in, at the very moment in time for them. All this available through your first party data.

It’s widely accepted that it is both easier and more lucrative to sell to existing customers. Application of each consumer’s unique buying propensity, lying within their purchase history, navigation and online data, focusses and maximises far greater appreciation of consumers to previously unheard of levels of return. Indeed the amount of blank expressions we see, having no experiences and appreciation of this discipline, reflects the malaise on this matter. Early adopters, of course, flourish as they always have.

Therefore focus your initial data analytics on building models to help maximize your share of your existing customers’  business (share of wallet). Cross selling and up selling are natural applications for these kinds of models which leverage predictive analytics, because an organisation generally knows far more about current customers than it could possibly find out about external prospects.

You need to know how your data elements relate to each other, to help you see a pattern and understand, and impact something in the “real” world. The effectiveness is only as good as your captured data. It is dynamic, the more dynamic the market you’re in, the more dynamic your application needs to be. If you’re in a business that is highly transactional, your data will change frequently, and by this we mean by the nano-second.

To address prospensity-to-buy and share of wallet, many organisations would need to know the following:
• which customers are most likely to purchase a specific product (propensity to buy models)
• what is the next product a customer is likely to buy (next most likely product model)
• which customers are likely to defect or reduce expenditures (attrition predictor models

Answering each of these questions requires data.  Data cleansing, transformation, initial and ongoing validation etc

Propensity-to-Purchase Data Help You Grow the Value of your Existing Customers

As with all data solutions, the key challenges are defining a customer, extracting quality data, and analytics. The advent of AI and specifically machine learning (ML) within that, means this is becoming increasingly becoming of huge significance.

We see the terms “propensity modeling” and “predictive modeling” used in many articles. They sound alike, but there is probably a subtle distinction between the two. Predictive modeling is a broad category. You can build predictive models for all sorts of customer behaviour, such as a predictive model around customers at risk for defection. A propensity to purchase is a type of a predictive behaviour model.

The purpose of a propensity to purchase model is to understand the likelihood a customer will be predisposed to purchasing a product based on purchases they’ve already made at some point in time. Traditional propensity-to-buy models score customers based on their similarity to past purchases. Autonomous marketing solutions, like SwiftERM, also include first party data captured from each consumer’s navigation and other individual habits when shopping.

Historical data and measuring past performance of your enterprise enables regard to future offerings and customer activities so that you can effectively deploy cross-selling and up-selling techniques. Cross selling used to be interpreted as persuading existing customers to purchase additional services. Today it is essential this be appreciated as ensuring you do no more that ensuring you are maximising what that individual wants and needs when they want. In other words, you respond to their demands, and not try and manipulate or cajole them to yours.

To know more about artificial intelligence and machine learning to enable you business you may enjoy reading this article A guide to generative AI personalization for ecommerce marketing


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