Applied to personalized marketing, artificial intelligence transforms raw data into actionable profiles.

Quizzes, tests, or games with rewards effectively allow for the collection of diverse first party data: closed ended responses, verbatims, images… Through semantic and behavioral analysis, these signals fuel fine grained segmentation that subsequently boosts CRM performance and enables brands to better convert and retain their audience.

Here are 3 concrete ways to leverage this data to personalize future marketing campaigns with precision.

SUMMARY

• Analyzing quiz responses to better personalize campaigns
• How to dynamically fuel audience segmentation
• How to leverage AI to transform visual content into actionable marketing insights
• Mini glossary

How to use AI to personalize marketing campaigns ? Brands can start by sharing multiple choice tests (Quizzes, personality tests, etc.) with their audience. These interactive activities make it possible to qualify a contact based on their preferences, motivations, or needs, using actionable behavioral data.

1. Each response is associated with a specific value (or weighting), determined beforehand according to the brand’s objectives.
2. With the help of AI, these weightings can be interpreted and a score can be calculated for each participant.
3. This score makes it possible to automatically associate a typical profile with an individual, based on their cumulative responses.

Note: this processing relies both on rules defined by the brand (for example: “if a user selects answer A, they are probably price sensitive”) and on predictive models capable of identifying finer correlations between the responses given and future behaviors (such as interest in a product, the probability of purchase, or churn).

Result: intelligent CRM segmentation. Indeed, it is based on declarative signals, easy to link to marketing personalization scenarios:

• product recommendation,
• promotional message,
• highlighting a relevant service, etc.

This type of analysis integrates natively into a personalized marketing strategy, with a high ROI.

Examples of marketing personalization based on quiz profiles

In the energy sector, a quiz can question an audience about their consumption habits or ecological sensitivity. By analyzing the responses, AI can group users into three segments:

• a “thrifty” profile, looking for discounts and bill optimizations;
• an “environmentalist” profile, interested in green offers;
• and a “classic” profile, sensitive to simplicity and stability.

Each group can then receive a personalized message, a different product, or a targeted demonstration.

In retail, a marketing game centered on purchasing habits makes it possible to qualify profiles based on their primary motivation. The semantic analysis of choices helps isolate:

• a “deal hunter” profile, for whom discounts are a trigger,
• a “loyal” profile, attached to a brand,
• or a “curious” profile, always in search of new products.

Thanks to the power of IA and personalized marketing, these campaigns can trigger more relevant activations, leading to a better conversion rate.

Marketing games featuring free response fields (quizzes or personality tests with open ended questions, forms, surveys, etc.) are powerful data collection tools. Unlike predefined choices, verbatims reflect the actual language, expectations, or objections of the participants.

Semantic analysis, applied by AI, makes it possible to automatically extract the key information contained in these responses: recurring words, expressed intentions, signals of interest or disinterest, and barriers to purchase. The algorithm then classifies and groups these elements according to themes and intensity levels.

The benefits for marketing are multifaceted. This personalization through AI makes it psosible to refine automated customer profiling. It enriches the CRM with qualitative signals, which can be leveraged to adapt content, offers, or activation drivers.

Even better: this contextual intelligence makes it possible to identify weak signals or uncover unexpected customer segments. It also improves AI lead scoring by integrating an emotional or intentional dimension.

Examples of marketing personalization based on verbatims

How to achieve dynamic segmentation with AI? Let’s take the tourism sector as an example.

During a contest, participants are invited to answer the question:

“What is your dream destination and why?”

AI identifies quickly trends: “nature,” “disconnection,” “budget,” “with family,” “off the beaten track”… These elements then guide the dynamic segmentation of the CRM: “adventurer,” “slow traveler,” or “planning family” profiles.

Marketing teams can then activate tailor made campaigns, adapted to these new targets, with a tone, a promise, and visuals aligned with their deepest expectations.

Let’s take the example of the “slow traveler” profile identified via semantic analysis of verbatims. To meet the expectations of this segment, marketing campaigns will integrate:

• A soothing tone, centered on experience and reconnection.
  E.g.: “Take the time to discover [the destination] differently”
• A targeted promise: itineraries off the tourist circuits, responsible consumption.
  E.g.: “7 days by train through the little known landscapes of Italy”
• Impactful visuals: contemplative atmosphere, people alone or in small groups, far from the crowds.
  E.g.: sunset over a lake, forest cabin, remote village

These elements create a more relevant message, consistent with the segment’s aspirations. Result: a better engagement rate and an optimized marketing ROI.

Gamified campaigns that invite participants to upload an image (meal photo contests, favorite outfits, interior design, etc.) are a powerful source of behavioral data. Thanks to AI image analysis, this content can be leveraged for automated customer profiling.

AI recognizes objects, scenes, moods, or even emotions present in the image. It then maps these elements to typical profiles or probable purchase intentions.

Result: unprecedented CRM enrichment. These visual signals create a new level of segmentation, complementary to declarative data. AI lead scoring is thus much more refined and the personalization of marketing campaigns more relevant.

Example of marketing personalization based on UGC

Let’s take a fictional example. In a food marketing campaigna contest invites participants to share a photo of their favorite homemade dish. We are talking here about UGC, or content organically generated by users. AI can then analyze the visual composition: fresh products, careful plating, semi-professional utensils. It classifies this profile as a “passionate gourmet.”

This signal enriches the CRM and makes it possible to target this audience with campaigns centered on premium products, cooking workshops, or exclusive content (recipes, food documentaries, etc.). Data collection from images thus boosts marketing personalization with a message, an offer, and visuals in total alignment with the customer’s needs and expectations.

To better segment your customers, exploit the full potential of your gamified campaigns. With LeadSense, automatically analyze the data collected through interactive games, identify the most promising profiles, and adapt your campaigns in real time. You thus better feed your CRM, gaining efficiency and responsiveness. No more need to manually analyze your first party data; AI handles it for you!

• Intelligent CRM segmentation: a technique for grouping contacts based on their behaviors, preferences, or intentions in order to activate personalized marketing campaigns.
• AI lead scoring: automatic evaluation of a prospect’s potential to convert, based on their actions, responses, or intent signals.
• Semantic analysis: Automated processing of free-form responses to identify keywords, emotions, or intentions expressed by the user.
• Behavioral data (or first party data): set of data originating from a user’s interactions (clicks, choices, submissions) revealing their preferences or needs.
• Marketing personalization: Adaptation of messages, content, or offers based on the profile or behavior of a prospect or customer.

A study carried out by Harvard Business School reveals that 95% of products launched on the market fail to find their audience because of poor segmentation.

Brands understand the importance of personalising their offering. But also the way in which they communicate with consumers. That’s right, each buyer has unique preferences and expects companies to address their needs effectively.

Before setting up a campaign or launching a new product, brands need to understand their audience and segment it into groups. In this article, we will look at the issues involved in segmentation and the techniques for segmenting your customer base and boost the campaigns ROI.

SUMMARY

• Segmentation in marketing: what is it?
• What are the different types of marketing segments?
• Why use segmentation in marketing?
• Gamification as a marketing segmentation tool

Segmentation in marketing consists of dividing your market (i.e. consumers interested in a product or service) into sub-groups. Within these groups, we find buyers with common characteristics that are relevant to the brand. These may be demographic or behavioural profiles.

By identifying and targeting consumer segments, companies can tailor their products, services and marketing efforts to meet the needs and preferences of their audience.

For example, segmentation in marketing has enabled the men’s skincare brand Old Spice to identify a group of customers it had not thought to address: women who buy this type of product for their partner.

1) Demographic segmentation (the who). This strategy consists of segmenting your audience on the basis of characteristics such as age, gender, education, income, profession (in the B2B sector) and so on. The advantage of this approach is that it is easy to implement (the data is simpler to collect).

2) Psychographic segmentation (the why). Here, the brand groups its prospects and customers according to their interests, values, aspirations or personality traits. Given that these characteristics are subjective, this is a more difficult strategy to implement. It is more effective because it strengthens understanding of the market.

3) Geographical segmentation (where). Une marque présente à l’international peut segmenter ses clients en fonction de leur pays de résidence. Elle adaptera la langue qu’elle utilise, mais aussi la tonalité des communications en fonction des codes spécifiques aux pays.

4) Behavioural segmentation (the how). The brand groups buyers according to common behaviours (the way they interact with companies). This can include their consumption habits, online browsing, brand loyalty, etc. This data is easy to collect (on its website or social networks) and makes it possible to personalise the customer experience.

Segmentation in marketing requires effort and resources. Nevertheless, it is a strategy to better understand its audience and personalise its campaigns, boosting their ROI.

The advantages of segmenting your customer database include :

• Better use of resources. Segmentation enables you to focus your marketing efforts and budget on consumers who are likely to want to buy. It is essential to target its marketing or emailing campaigns and make the message more powerful and more likely to convert.

• A stronger brand image. Une entreprise qui comprend à qui elle s’adresse et adopte des messages alignés avec les valeurs de ses clients aura plus de chances de fédérer une communauté. C’est aussi un facteur de différenciation. Il donne aux entreprises un avantage concurrentiel sur les autres marques de son secteur.

• Optimised profitability. Companies have seen their acquisition costs soar. The ROI of marketing campaigns (particularly Ads campaigns) has become a key factor in maintaining profitability and viability. Segmentation in marketing makes it possible to target the audience for digital advertising and convert more with the same budget.

Before you can create customer segments, you need to collect qualitative data. Data collection is an increasingly complex area of marketing activity. The announced end of third-party cookies proves that it is essential for brands to ‘own their data’ (by collecting first-party data). Elles ne dépendront plus d’autres acteurs pour refine customer knowledge.

To maximise these third-party data collection levers, brands need to use tools that will enable them to build up enriched, reliable and relevant CRM databases. Gamification is one of these tools, as animations and competitions offer a number of advantages:

• They enable a wider audience to be reached: 1.5x more users are prepared to share their data via a gamified experience.

• They are more engaging and captivating. En moyenne, les expériences interactives gamifiées génèrent 40x more engagement in terms of time spent with the brand.

Brands can therefore use gamification in two ways to collect data:

• By asking participants in a game to fill in a collection form (before or after the experiment).

• By choosing a game mechanic that collects data by analysing the behaviour of participants. Here, the prospect does not necessarily fill in a form, but shares data via a Quiz or product preference collection mechanisms such as Swiper.

Segment the data collected via an interactive experience

A tool like Segment (combined with a Playable Marketing platform) facilitates this work. The data is processed directly via the tool, which can then be used to automatically create up to 30 customised segments. Brands can then adapt their communications to ensure they deliver the right message to the right person, via the right channel.

Companies will also be able to A/B test their segmentation strategy The tool offers greater visibility over the performance of each campaign, with detailed reports enabling the impact of each action to be monitored in real time.

Conclusion