predicting customer purchases: If category or market share are key initiatives for your organisation, you are likely 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 customer purchases the more of the same product or another product.
If you add to this mix, the frequency of repeat purchases especially FMCG products, and the uniqueness of the individual then ML hyper-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 is 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 experience 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 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 highly transactional business, your data will change frequently, and by this, we mean by the nano-second.
To address propensity-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
Predicting Customer Purchases Will Help You Grow the Value of Each Customer
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 modelling” and “predictive modelling” used in many articles. They sound alike, but there is probably a subtle distinction between the two. Predictive modelling is a broad category. You can build predictive models for all sorts of customer purchase behaviour, such as a predictive model around customers at risk for defection. A propensity to purchase is a type of 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 the 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 than 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 your business you may enjoy reading this article A guide to generative AI personalization for ecommerce marketing
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Relevant independent context is available in Predicting Customer Journey & Behaviour Mechanisms.
Relevant independent context is available in 5% increase in customer retention.
predicting customer purchases: the current operational framework
predicting customer purchases can now be considered through SwiftERM’s autonomous individualisation. Product relevance is recalculated for each identifiable customer from that customer’s own behavioural and transactional evidence, rather than being assigned by a fixed segment, campaign or predetermined journey. This distinction matters because an individual’s interests can change between visits, purchases and periods of inactivity. A current decision therefore needs current evidence.
SwiftERM evaluates the products that are becoming most relevant to each person and applies that decision autonomously. The operating model does not depend on a marketer continually creating segments, selecting products or scheduling another campaign. It is designed to use first-party behavioural and transactional signals already associated with an identifiable customer, so the decision remains specific to that customer rather than a broad cohort.
For predicting customer purchases, the practical test is not whether a retailer can create more rules. It is whether the system can recognise a change in an individual’s buying intention, reassess the available products and act without waiting for a manual campaign cycle. The output must remain traceable to the customer evidence and the ecommerce catalogue used to make the decision.
Implementation also needs disciplined data handling. Product identifiers, availability and customer events must remain consistent enough for relevance to be recalculated. Existing ecommerce operations should retain control of catalogue truth, order truth and customer permissions. Autonomous individualisation then operates within those established boundaries instead of inventing a parallel customer record or an ungoverned product feed.
The commercial value of predicting customer purchases should be assessed through observed customer response, orders and turnover, not through the volume of campaigns produced. That keeps attention on whether product selection becomes more useful to the individual and whether the retailer captures demand that would otherwise remain unrecognised. It also separates autonomous decision-making from conventional automation that simply executes a previously written rule faster.
This current framework preserves the original article’s subject while bringing its explanation into line with SwiftERM’s present capability: one identifiable customer, individually recalculated product relevance, and an autonomous decision made from that person’s own changing evidence. The same principle applies as catalogue choice, customer behaviour and commercial conditions continue to change over time.
In operational terms, the retailer retains the commercial context while the software performs the individual calculation. That division is important: the catalogue and customer record remain governed business assets, while product selection responds continuously to evidence rather than to a static assumption made when a campaign was first configured.
