The challenges of predictive marketing in 2024

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What is predictive analysis?

The goal of predictive marketing is to answer the question:

"How can I anticipate and meet my prospects'/customers' future needs even before they express them?"

In concrete terms, this covers techniques for processing and modeling customer behavior, enabling us to anticipate their future actions on the basis of present and past behavior.

This approach is based on the exploitation and analysis of large quantities of data... there it is, the famous "Big Data"! 😊 This data comes from a variety of sources, including the company's website.

Predictive marketing enables companies to personalize their offers by providing the right product or service, at the right time, to the right customer.

That's all there is to it! Long gone are the days when you spoke to all your visitors in the same way, and now you need to be able to focus on one strategy: creating a unique customer experience.

Predictive marketing: how to make the most of 2024?

Imagine this: you know exactly which customers are ready to buy, and which are likely to leave your brand. For the former, you send a personalized gift to thank them for their loyalty and encourage them to take action. For the latter, you offer a special promo code to convince them to return. At the same time, you analyze conversations with your customer service team to pinpoint the moments when frustration mounts, so you can immediately reassure them with an adapted, personalized response.

That's what predictive marketing is all about. It's no longer just about reacting to customers' past behavior, but anticipating their future actions and personalizing every interaction accordingly. While this concept is nothing new in 2024, the real novelty lies in the democratization of machine learning (ML) tools and large language models (LLMs).

1 - Predictive targeting: identify, anticipate and act :

Once the preserve of large companies with specialized technical teams, these technologies are now within the reach of all businesses, even those with limited resources. This accessibility enables a greater number of brands to implement effective predictive marketing strategies without the need for advanced data science skills.

Solutions such as Google Analytics 4 (GA4) and BigQuery ML bring this technology within reach of all businesses.

You may not know it, but GA4 offers 4 predictive audiences free of charge. They automatically identify users with high purchasing potential or those at risk of churn. Media campaigns and onsite targeting can then be adjusted accordingly, offering targeted promotions to at-risk users or personalized offers to potential buyers, for example.

BigQuery ML, meanwhile, lets you create customized predictive models based on your internal data, for even more refined segments, without having to dive into the complexity of algorithms. These consumer tools therefore enable you to exploit predictive segments and proactively adjust your marketing strategies, without requiring machine learning skills.

Another tool democratizing predictive targeting is AB Tasty's Emotion AI, which analyzes users' emotions in real time, adapting messages and offers according to their emotional state. This approach enhances campaign personalization, optimizing engagement based on visitors' immediate feelings.

2 - Personalizing content: understanding and anticipating needs

In our view, the second 2024 challenge of predictive marketing concerns content personalization. Here, it's not just a question of knowing when and to whom to send a message, but also how to personalize content in a relevant and contextual way. This is where LLMs (large language models) come in.

Analyzing emotions and needs with LLMs

LLMs like GPT-4 can analyze large quantities of text, whether it's customer feedback, conversations with customer service, or even comments on social networks. These models are able to identify not only the intentions but also the emotions underlying each interaction.

Let's take an example: you analyze transcripts of customer service interactions to detect signals of frustration or dissatisfaction. From there, you can adjust recommendations or the tone of your communications to reassure disgruntled customers and offer them personalized solutions, thereby boosting their satisfaction and loyalty. At Welyft, we're already using these data models to analyze user test transcripts and extract emotional insights to enrich our understanding of customer journeys.

Hyper-personalized recommendations with conversational agents

Where LLMs particularly shine is in the generation of truly 1to1 experiences where the user can interact with your brand in a hyper-natural and personalized way.

Thanks to this ability to generate content, LLMs enable us to create unique customer journeys where each product or content proposition is based on a deep understanding of the user's needs and emotions. We're no longer simply in the behavioral segmentation business, but in a complete, real-time understanding of each customer, which maximizes the chances of conversion and retention.

CRO players such as Dynamic Yield, for example, have addressed this issue with Shopping Muse, using these models to offer product recommendations that adapt in real time to user preferences.

Technical, strategic and ethical issues

While predictive marketing offers incredible opportunities, it still faces a number of challenges that must be taken into account to guarantee its effectiveness.

1 - Technical issues: data quality first and foremost

Data quality is crucial to any predictive model. Without clean, consistent and well-structured data, machine learning algorithms and LLMs will be less effective. So make sure your tracking plan is well established, standardized, with clear conventions and rigorous real-time data monitoring. This ensures that models have accurate information on which to base their predictions.

But for these predictions to be truly effective, clean, structured data is essential. Tools like GA4 facilitate real-time data collection, but for their predictive algorithms to work properly, the tracking plan must be rigorously standardized. The nomenclature of events and variables must be clear and consistent, so that the algorithms can interpret the information correctly. Without a well-defined tracking plan, predictions risk being biased and unreliable.

2 - Strategic issues: defining clear objectives

Predictive marketing, however powerful, cannot succeed without clear objectives. You need to know what you're trying to optimize: is it your conversion rate? Loyalty? Churn? Each use case requires a specific strategy and appropriate actions. The point is not to use predictions for their own sake, but to integrate them into a global vision that meets specific needs.

3 - Ethical issues: respecting confidentiality and customer expectations

Finally, a central issue remains the protection of personal data and respect for confidentiality. By using predictive technologies that rely on user data, companies must remain transparent about how this information is collected and used. It's essential to comply with regulations such as the RGPD and never compromise user trust.

Why is it so complicated?

We're not talking about witchcraft here, but too few brands are working on these issues. It's a real challenge that can be complex to set up without resources and skills.

Some tools can help fill this gap, and that can be a great help, but the lack of resources can make itself felt here too.

This is one of our favorite recommendations, to start small before accelerating by structuring yourself. This advice is particularly valid for predictive marketing, where the first milestones are essential!

Are you working on these projects in your company? Don't hesitate to contact me, I'd love to talk to you about it!

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