Predicting the Unpredictable: A Journey Through the Landscape of Advanced Purchase Models

Identifying future shopping trends is the key to developing a strategy that keeps you ahead of the market, and in this game, accurate data is your most powerful tool. Whether it's additional products in the shopping basket, the likelihood of a bank transaction, or the risk of a customer leaving a telecoms operator, understanding the 'propensity to...' has undoubtedly changed the rules of the game for customer attention and loyalty. This article explores the layers of this fascinating subject and guides you through the intricacies of predicting the seemingly unpredictable. 

In Search of Tomorrow: On the genesis of predicting propensity to buy 

Predicting purchase propensity is an evolution of commerce and technology that is unfolding in front of our eyes. In the analogue era, predicting customer needs relied on salespeople's intuition and simple research methods. With the advent of the digital age, especially after the rise of the internet and e-commerce, data began to flow in streams. Companies that first understood the value of this data quickly surpassed their competitors. 

Data analytics and predicting buying models are a natural evolution of this trend. The development of big data and machine learning technologies has made it possible to process huge data sets in real-time. Culturally, the adoption of this technology reflects a shift in the approach to the customer - from an anonymous buyer to an individually understood person with unique needs and desires. 

It's also important to remember that predicting propensity to buy has become a new currency, and companies that can extract and process it will gain a competitive advantage. Today's consumers expect brands to treat them as individuals, ensuring that each interaction is tailored to their personal preferences. The economic background that has given rise to this need for prediction is thus directly linked to the advancing direction of service and product personalization, as well as reflecting the dynamic history of commerce and its continuous adaptation to a changing world. 

Data that becomes fact 

Every click and scroll is now valuable information, making predictive buying a key element of business strategy. It is an analytical process that uses machine learning algorithms to analyse purchase data and user behaviour to predict their future actions. 

The system is based on the analysis of multi-dimensional data, focusing on user behaviour and preferences. The data analysed can include browsing history, app interactions, purchase history and many other aspects. Each element is processed into a predictive model that determines the likelihood of a customer purchasing with exceptional accuracy. This ability to 'predict the future' allows companies to not only react to market needs but to anticipate them. 

How Does It Work in Practice? 

A customer browses products in an online store, and propensity to buy algorithms assess which products are most appealing. It's a continuous and dynamic process - systems are constantly updated to reflect changing trends and consumer preferences. That’s why propensity to buy is not only a way to increase sales, but also to build deeper relationships with customers, where effectiveness is essential. 

Evolving Trends in Prediction Models: Key Elements to Watch 

Predictive modelling is still at the forefront of AI advancements, especially for solutions that can directly impact revenue generation. Trained on rich customer datasets, these require not only advanced software but also continuous oversight by AI and data analytics specialists. A provider of such functionality must therefore know these areas and be able to respond extremely quickly to market needs and customer challenges. 

The level of knowledge and sophistication of the AI department is key here. Why is this important? Because not every large company has a sophisticated AI and data science department. Therefore, the solution is to choose a prediction ecosystem that is fully automated and does not require client-side control by an expensive data science team. The results of the predictive analysis must be immediately available within the same ecosystem, so that they can be used immediately in other activities, such as targeting email campaigns, promotional activities or loyalty programmes. It's also worth noting the time it takes to perform the analysis; the ability to get results the same day, or within a few weeks for very advanced analysis, allows many configuration variants to be tested to find the best business solution. 

This is the kind of solution that the Synerise platform offers. In addition, customers avoid time-consuming problems with data preparation and transfer between systems, the maintenance of additional computing environments or specialist data science teams. The cost and time saved can easily be redirected to activities that generate direct profit. 

What else should you pay attention to when sifting through hundreds of predictive model solutions and platforms? Make sure they offer these 3 solutions: 

1. Replicating best customers with Lookalike modelling 

Innovations in data analytics continue to transform the marketing landscape, and lookalike modelling is a prime example. Known from platforms such as Meta, it allows companies to compare two customer segments and find those with similar characteristics. This can be used, for example, to target a marketing campaign at customers who have responded well to it in the past. Synerise provides this functionality, opening the door to targeted marketing campaigns for its clients. 

2. Predicting the next interaction with the Propensity to buy  

Propensity to buy is another model available on the Synerise platform that goes beyond standard analytics. It allows detailed purchase predictions to be made, enabling companies to find a group of customers who are most interested in a particular product, product category or brand. The study of purchase propensity is particularly important in the case of costly promotional campaigns or the implementation of additional customer contact channels, such as paid advertising. Wrongly selecting the target audience for such a campaign will result in high costs for the company with no certainty of profit. The use of the Synerise predictive model reduces this risk by selecting the right audience. What is also worth highlighting with this type of prediction is the additional possibility of filtering, e.g. focusing only on customers with a medium basket size. 

3. The Art of Personalisation with Custom Predictions

Custom Prediction is a feature that takes personalisation to a whole new level. They can be used to predict specific customer actions, such as making a purchase within a certain time frame, opening an email or clicking on a web push notification. This not only enables precise targeting but also optimises media costs by limiting communication to only those customers who show real interest. Custom predictions in Synerise also include churn prediction, which involves identifying customers who are likely to stop using a product or service shortly. This understanding can reduce the need to send costly marketing messages with large discounts by excluding segments of customers who would purchase without additional incentives or who are planning to stop using products and services. In this way, we protect the company's margin while not forgetting about customers who need more engagement. 

These innovative predictive methods are the new stars of marketing, enabling companies not only to understand but also to anticipate the needs of their customers, opening the way to more effective and profitable marketing campaigns. 

The role of predicting market needs 

In the digital age, the ability to predict a customer's next move, predicting propensities, serves as a lighthouse, guiding e-commerce giants, financial titans and telecom colossi through the fog of market uncertainty. Imagine knowing your customer's next purchase before they make it, predicting a bank customer's investment trend or a telecom user's preferences. This is now the reality of predictive modelling. 

Predictive solutions don't just work in the background, they actively shape the future of customer interactions, turning potential into profit and uncertainty into opportunity. 

Strong business thanks to forecasting 

The ultimate goal of predictive analytics is not sophisticated calculations, but concrete results. E-commerce platforms increase sales by matching products to customers' needs at the right time. Banks gain customer loyalty by tailoring their services. Meanwhile, telecoms companies are reducing churn by giving customers what they didn't know they needed. 

These scenarios are not distant dreams; they are real benefits derived from integrating predictive models into business strategies. However, what remains a dream for many companies is transitioning from predictive models based on numerical analysis to modern models based on AI engines. Prediction based on technologically advanced models analyses the buying propensities of each customer individually, changing over time, and responds to today's needs with the precision of a Swiss watch. 


To sum up our immersion in the world of predictive buying, we return to the undeniable truth: the future of business lies in anticipation. In the era of hyper-personalisation and predictive analytics, those who can predict and adapt to customer propensities will not only survive, they will thrive. Predicting propensities is not just a smart strategy; it's the foundation of future market success. Choose wisely. 


Product Marketing Manager, Karolina Staszak