Segmentation and personalisation: combining brand expertise and AI for better performing personalised promotions

5 min read

Personalisation has long since been a necessity for retailers and brands looking to stay ahead of the competition. According to research as far back as 2018, 80% of consumers are more likely to make a purchase when brands offer personalised experiences. But while that’s a useful generalisation, the true picture is more nuanced, and it doesn’t necessarily address the goals of consumer brands, especially with regard to promotions. Promotions are not always about selling more stuff to reduce surplus stock or to increase customer loyalty.

Consumer brands launch promotions to support a wider variety of objectives, including to promote brand awareness, to attract new customers, to win back lost customers, or to promote a new product launch. Each campaign has its own objective, and personalisation of the promotions in that campaign can help reach the objective more efficiently but the technologies used to build and target the campaigns are often hindered by a one-size-fits-all approach that isn’t as effective for all campaign objectives. For example, proposing an on-the-shelf segment to achieve the objective of winning back lost customers (ex. “people that have stopped buying my brand over the past X months”), can work for some product categories but not for others such as seasonal products. 

Traditional targeting Features that propose generic and standardised objectives and segments often fail to capture the intricacies of consumer behaviour across different product categories so brands often fall short when tasked with more nuanced objectives, such as winning back lost customers or attracting new customers to a category.

Forward-thinking retailers are now turning to bespoke segmentation strategies that leverage the deep understanding brands have of their customers to perform tailored segmentation that personalisation algorithms can leverage for more effective campaigns.

The Limitations of Current Personalisation Technologies

Current personalisation approaches, although powerful, are not without their constraints. They rely on standardised segmentation models that hinder the ability of the personalisation algorithms to support diverse campaign objectives. These models typically analyse customer data based on past purchase behaviour, demographics, and location to tailor promotions. While effective in driving repeat purchases and fostering loyalty among existing customers, they often fall short in addressing more nuanced objectives. While these technologies excel in driving loyalty among existing customers within specific product categories, they struggle to provide the level of customisation needed to effectively target different consumer segments across various product types.

For example, when targeting promotions for milk, a staple household item with a broad consumer base, a personalised marketing approach may focus on factors such as family size, dietary preferences, and frequency of purchase. However, the same segmentation model applied to eggs, another commonly bought grocery item, will likely yield irrelevant or ineffective results. This is because there are big differences in purchase behaviours and a different competitive environment from one category to another.

So the problem lies not in the utilisation of customer data to match promotions with customers but rather in the application of a single segmentation approach that fails to account for the unique characteristics and consumer behaviours associated with different product categories.

The Need for Customisation and Brand Expertise

The answer? Expert human knowhow. Retailers and agencies are increasingly recognising the importance of leveraging the expertise that brands bring.  No one knows their target audience better than the consumer brands themselves and retailers are looking to use that understanding of the customer to enrich and improve the AI-generated segments used.

This collaborative approach is one we’ve adopted here at relevanC. It allows nuanced consumer behaviours, preferences, and trends - that are not captured by algorithms alone - to be taken into account in the model. This customisation empowers our brands and retailer partners to address the evolving needs and preferences of their customers in real time.

By continuously refining and adapting their segmentation strategies based on a combination of AI insights and brand expertise, we can ensure that their promotional efforts remain relevant and impactful while creating more meaningful and engaging experiences for shoppers.

Introducing New Features to Empower Brands

In our ongoing commitment to empowering brands, we are excited to unveil new features that enhance our personalised promotions platform, RCDMP (RelevanC Data Marketing Platform). These developments signify a significant step forward in our endeavour to assist our brand and retailer partners in delivering tailored experiences to their customers, capitalising on their expertise to drive meaningful results.

A cornerstone of these advancements is the introduction of a feature that empowers brands to individually define brand and objective-specific segments. With this new self-service capability, brands can leverage their expertise to craft highly targeted segments that align precisely with their customers' profiles and preferences. Bespoke segments will serve as the foundation for our matching algorithm, enabling brands to effectively reach and engage their target audience for each campaign objective with personalised promotions.

Brands are continually striving to refine their marketing efforts and achieve optimal outcomes. With our platform, brands can now conduct rigorous testing of different discount mechanisms, messaging strategies, and promotional formats to find the most effective approach for each segment of their customer base. This iterative process empowers brands to continuously enhance their promotional strategies and maximise their impact. With the introduction of these new features, brands can anticipate several potential improvements in their promotional efforts. By leveraging AI-driven personalisation and custom segmentation, brands can unlock new opportunities to enhance the effectiveness of their marketing campaigns.

One of the key benefits is the expected increase of at least 30% in burn rate or the rate at which promotions are redeemed. Additionally, brands can expect to see a rise of at least +10% in coupon usage as a result of more effective personalisation. When promotions are tailored to match customers' preferences and behaviours, they are more likely to resonate with recipients and prompt them to take action. As a result, brands can anticipate higher redemption rates and increased customer satisfaction. Last, brands will be able to test and learn: for example, are clients reacting well to promotion made for a new product? If promotion proves to be effective for recruitment, what mechanisms are performing the best?

Not just a trend

Objective-based personalisation is paramount in a retail landscape where brands strive to deliver tailored experiences that resonate with their customers. The introduction of new features combining AI-driven technologies with customised segmentation represents a significant step forward in achieving this goal.

At Casino Group, we’re embracing a future where personalised marketing needs to evolve even further to take into account the seemingly unstoppable trend towards Local. Smaller inventory spaces and franchise business models add yet another level of complexity to personalisation. As technology continues to evolve and customer expectations evolve with it, brands and retailers must remain agile and innovative in their approach to personalisation. By embracing objective-based personalisation and leveraging advanced technologies, brands can stay ahead of the curve and learn faster which promotion strategy works well while improving customer experience.

We believe that objective-based personalisation is not just a trend; it's a strategic imperative for brands looking to thrive in an increasingly competitive market. By prioritising personalisation and investing in the right technologies, brands can build stronger relationships with their customers and drive sustainable growth for their business.

Camila Ramirez

Senior Product Manager Data Marketing Platform (Personalized Promotions)