What is predictive marketing and how can it be used to anticipate consumer needs?

By Rita Hassani Idrissi

Published: 5 November 2024

At a time when digitalisation and technological evolution have propelled companies into a world of constant change, predicting the act of buying has become an essential element in accelerating consumer conversion. But how can we anticipate the needs and behaviour of Internet users when faced with such an overload of information?

That's where predictive marketing and behavioural data come in. These cutting-edge techniques make it possible to analyse all our personal data (messages, transactions, GPS signals, internet searches, etc.) and offer ultra-precise recommendations for products or services.

But how do these predictive systems work? What tools can be used to deploy this kind of marketing system? How does predictive marketing work in practice? And what is its relationship with Big Data ? Find out in this article.

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What is predictive marketing?

Definition

Predictive marketing is a method of anticipating and predicting the most likely buying behaviour of consumers. It is made up of a set of techniques based on data analysis , which enables companies to define tailored and personalised marketing strategies and actions.

The main objectives of predictive marketing are to:

to collect all data relating to consumers (internet searches, social networks, e-commerce sites, etc.),
to predict a consumer's buying intentions and future needs,
to propose a personalised offer to each consumer in advance.

How it works

Predictive marketing works primarily through Big Data. It is the fuel of predictive systems: without it, analyses would be inconclusive and irrelevant, if not impossible.

👉 Big Data is all the data that concerns us, such as our messages, transactions, the weather, GPS signals, our web searches, our "likes" on social networks, etc.

All this information is then analysed by algorithms to predict consumers' different purchasing intentions. Predictive marketing is therefore based on machine learning or scoring tools. These tools are programmed to send automatic alerts when they observe a combination of criteria defined in your database.

The tools used

The tools used for predictive marketing are software and platforms capable of storing a large amount of data and analysing it. The solution may be an integral part of your CRM or independent of the rest of your business tools.

The software and solutions used to collect and analyse consumer data are mainly Customer Data Platforms (CDP)

👉 CDPs make it possible to unify all the personal data (contact details, consents, transactions, cookies, etc.) of customers and prospects and thus optimise sales and marketing actions. These predictive analysis tools are made up of several functionalities, including :

Datasmart
Machine learning
and Predictive Targeting.

⚠️ Data collection must respect the privacy of Internet users. Companies need to adopt appropriate processing solutions and software with RGPD certification.

What are the challenges of predictive marketing?

Collecting and analysing data has become a real performance driver for businesses. 90% of world leaders have become aware of the importance of data since the start of the pandemic (Kiss the Bride, 2020). The challenges and benefits of predictive marketing have therefore never been so widely recognised.

Predictive systems offer the following advantages:

Strengthen your customer relationships

By developing your knowledge of the customer, you can considerably improve the relevance and success of your marketing strategies, particularly relationship marketing. You can offer them deals and discounts that match their expectations, your exchanges are more personalised and your recommendations are perfectly tailored.

Improve the consumer buying journey

You can improve your customers' buying journey and consumer experience by offering them tailor-made deals that are most likely to interest them.

👉 Predictive marketing also gives you the opportunity to automate certain tasks through marketing automation (such as reminders of events, birthdays, etc.), to encourage more personalisation and contact.

Create more complete and ambitious sales tunnels

Your sales tunnels and processes will be more comprehensive, particularly through cross-selling and up-selling. These two techniques are renowned in the marketing world for being highly personalised and relevant.

👉 They enable you to make concrete proposals that are tailored to the consumer's needs, offering products or services that are totally in line with their previous purchases.

Gain a competitive edge

Predictive marketing enables you to anticipate your users' needs and reach them before your competitors do. By predicting the offer that will be best suited to your customers and prospects, you can optimise your spending, which will improve your return on investment, and therefore your revenues.

3 emblematic examples of predictive marketing

Netflix

Netflix has always used predictive systems to offer its users a unique customer experience. In fact, recommendations are designed entirely from the videos you've watched previously.

👉 Because you've watched X, you'll like watching Y.

Thanks to an algorithm, the platform makes suggestions based on the types of content you watch and your preferences.

💡 According to a report published by the brand, predictive marketing has enabled it to reduce its churn rate and increase the average length of its subscription. It also claims that this method has saved it more than a billion dollars a year.

Amazon

The e-commerce market leader also uses predictive marketing to build customer loyalty. Like Netflix and others, the platform suggests products based on a number of criteria:

the pages you have viewed
the actual time spent per page viewed
your previous purchases
items you've put in your shopping basket but not bought...

Like Netflix, Amazon uses a highly advanced algorithm that compares the buying behaviour of one user to another, with the aim of finding potentially common preferences.

SFR

Thanks to predictive marketing, SFR is able to identify what it calls 'churners' - customers who are considering cancelling their subscription. Its predictive tools analyse the web for:

the number of pages consulted by their customers
the length of each visit
the keywords used in search engines.

This analysis enables them to identify the vast majority of customers who wish to cancel their contracts. As a result, the company can establish a strategy for building customer loyalty and recovering customers before they unsubscribe.

The 3 essential steps for optimal predictive marketing

Thanks to Big Data and artificial intelligence, companies are implementing new strategies by transforming masses of data into effective tools for guiding their marketing campaigns. To achieve their goals, they are using a well-defined process:

1 - Data collection

Predictive marketing is based on data mining. This is a data-gathering technique that is now easier to access thanks to the development and democratisation of the Internet. There are many ways of collecting information:

cookies
forms
questionnaires, etc.

2 - Data analysis

Once this data has been collected, an algorithm is set up by the company or by specialised software (Customer Data Platform), which analyses and processes it according to a number of criteria specific to each company. This behavioural data may relate to: the time spent on each product, the number of abandoned shopping baskets, etc.

3 - Improving the customer experience

This is where machine learning will play an important role. Through predictive analysis, it will make it possible to recommend a personalised offer tailored to the customer's needs based on their searches and the behaviour that has been detected during data collection and analysis.

👉 This anticipation will enable the company to implement optimal marketing actions to improve the customer experience and, in the long term, boost its conversion and retention rates.