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

By Rita Hassani Idrissi

Published: November 5, 2024

At a time when digitalization and technological evolution have propelled companies into a world of constant change, predicting a purchasing act has become an indispensable element in accelerating consumer conversion. But how can we anticipate the needs and behaviors of Internet users in the face of such information overload?

That's where predictive marketing and behavioral data come in. These cutting-edge techniques make it possible to analyze all our personal data (messages, transactions, GPS signals, Internet searches...) 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 device? How does predictive marketing work in practice? And how does it relate to Big Data? Answers 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 behavior of consumers. It is made up of a set of techniques based on data analysis , enabling companies to set appropriate, personalized marketing strategies and actions.

The main aims of predictive marketing are to:

collect all consumer data (internet searches, social networks, e-commerce sites, etc.),
to predict a consumer's purchasing intentions and future needs,
anticipate and propose a personalized offer to each consumer.

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, our transactions, the weather, GPS signals, our web searches, our "likes" on social networks and so on.

All this information is then analyzed by algorithms to predict consumers' various purchasing intentions. Predictive marketing therefore relies on machine learning and 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 large amounts of data and analyzing it. The solution can be an integral part of your CRM or independent of the rest of your business tools.

The software and solutions used to collect and analyze 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 optimize sales and marketing actions. These predictive analysis tools are composed 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?

Data collection and analysis have become a real performance lever for companies. 90% of global executives have become aware of the importance of data since the start of the pandemic (Kiss the Bride, 2020). The stakes and benefits of predictive marketing have therefore never been so widely recognized.

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 propose offers and discounts that match their expectations, your exchanges are more personalized and the recommendations perfectly adapted.

Improve the consumer buying experience

You can improve your customers' buying journey and consumer experience by proposing tailor-made offers that have the best chance of appealing to them.

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

Create more comprehensive and ambitious sales tunnels

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

👉 They enable you to make concrete proposals 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 right offer for your customers and prospects, you can optimize your spending, improving 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 offers 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 over 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 several criteria:

the pages you've viewed,
actual time spent per page viewed,
previous purchases,
items you've put in your shopping cart but haven't purchased...

Like Netflix, Amazon uses a highly advanced algorithm that compares the purchasing behavior 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. SFR's predictive tools analyze the web for:

the number of pages consulted by their customers,
the duration of each visit,
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 brand can establish a strategy to retain and recapture customers before they churn.

The 3 essential steps to optimal predictive marketing

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

1 - Data collection

Predictive marketing is based on datamining. It's a data-gathering technique that's now easier to access thanks to the evolution and democratization 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 specialized software (Customer Data Platform), which will analyze and process it according to several criteria, specific to each company. Behavioral data can include: time spent on each product, number of abandoned shopping baskets, etc.

3 - Improving the customer experience

This is where machine learning comes into its own. Through predictive analysis, it will enable us to recommend a personalized offer tailored to the customer's needs, based on their searches and the behavior 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.