Generative AI Can Help Save Brands as Hyper-Personalized Experiences, Heightened Demand Wins Consumers

Today’s leading companies need to market, plan, and forecast with extreme precision. Generative AI can help.

Major changes in today’s consumer landscape – including more buying channels, new habits and shifting wealth distribution – mean that consumer-facing brands should consider changing their marketing and product strategies. By leveraging data, machine learning, and AI, these organizations have an opportunity to better know each individual customer, their likes, dislikes, what motivates them to purchase, and more. According to Deloitte’s research on personalized CX, 69% of consumers said they’re more likely to purchase from a brand that personalizes experiences. Consider some recent examples of how brands are leveraging data to create demand and give consumers what they want. Earlier this year, we saw a viral Valentine’s Day Cup create a craze among consumers that led to quickly sold-out products, a social media frenzy, and mass feelings of FOMO. Now, experts are predicting that this wasn’t just an isolated event, but rather a glimpse into the future of what brands can do to expand products and profits.

In many ways, this is exemplary of how brand loyalty has evolved. Factors like inflation and economic turbulence make simply having a popular product no longer good enough – consumers are becoming choosier and more willing to let go of even staple brands if they no longer feel seen or valued by them, or if they don’t exemplify values that are important to them (for instance, environmentally-friendly products/companies.) If brands want to gain and retain consumer spend, they need to put experience at the center.

However, a memorable interaction can mean many different things depending on who is experiencing it. This is where Generative AI (GenAI) comes in. New GenAI technology can help brands not only understand what their target audience needs to feel connected, but also inform where there are specific audience trends, the places they’re choosing to fulfill those needs, and how often they’re going. This information can make or break how a brand is positioned to its audience. There are also a few ways brands need to think about how they can use GenAI tools to ensure they’re creating a holistic approach to meet the needs of their audience and build lasting loyalty. The two biggest factors are targeting/marketing and demand planning.

Become a master marketer

To effectively use GenAI as a marketer, practitioners need to first understand the shift away from mass targeting with broad campaigns to individualized micro touchpoints for each of their customers. Key factors that are driving this shift and ultimately, the rise in personalization, include the reality of many firsts in the U.S. market, including:

• Women are projected to control more wealth than men (from 49% in 2019 to 65% by 2040)¹
• The U.S. population will include more people over age 65 than under 18², and the most diverse generation in history is coming of age.³

This “Mass to Micro” approach researched by Deloitte’s ConvergeCONSUMER team shows that moving away from mass, manual, and reactive decision-making to a more dynamic model that is continuous, automated, and predictive can help bring brands’ marketing and targeting strategies into the future.

So, what constitutes a micro touchpoint? Tactics to reach a consumer can include several hyper-personalized marketing strategies such as connecting through social media, streaming services, influencers, blogs, and more. The most innovative retailers are exploring applications of propensity models to help shape social media impressions and selecting the channel that their most desirable customers gravitate to. But that’s just the medium – the data behind those touchpoints is even more critical to get right. Insights that show who, where, how, and why brands need to target specific audiences have historically been difficult to pull, especially on such a small scale. But now, GenAI is making getting that granular data a lot easier.

By using GenAI to analyze data on consumers, brands can target very niche audience members across platforms – allowing them to build marketing experiences that resonate closely with that group. For example, AI can tell brands that Amanda in Indianapolis is likely to be buying three brand-name yoga sets online on the morning of Friday, March 15 after signing up for a new gym membership. Brands can then serve her a personalized ad on the news site she’s reading as well as a fitness-related post from her favorite social media influencer.

GenAI is also redefining what it means to know your existing customer base. While most organizations believe they have a view of the segments they serve, many use simplistic views of their customers based on simple demographics. Organizations that embrace the era of GenAI are using a more nuanced way of grouping together like-minded customers by blending their first party information with third party signals, propensity models, lifetime value models and churn models to create a truly comprehensive customer file. They then process that enriched customer file to identify the actual number of cohorts in the data.  Freed from the constraints of simplistic partitions of age, gender, or where they live, machine learning is enabling us to discover the non-obvious connections among groups that many would consider totally unrelated. GenAI comes into play in explaining these cohorts in terms we can comprehend after the sophisticated mathematic have partitioned them out. Furthermore, GenAI provides natural language elucidation of unknown trends and insights within cohorts, while highlighting variations across cohorts in a way that even the best-intentioned human marketers could never do alone.

GenAI can create 360-degree touchpoints for marketers in areas that were once challenging, and the tech holds great promise in this business – but implementing it into operations will require long-term transformation. Plus, it may take time for organizations to learn that even though the concept behind the “mass to micro approach” increases complexity, it can ultimately create a more hands-off method for brands coupled with the use of GenAI. This shift signifies a departure from traditional strategies, ushering in an era of data-driven, real-time adaptability.

Plan with precision

GenAI’s potential goes full-funnel, and its ability to problem solve doesn’t stop after marketing and personalized targeting. Once the hyper-personalized marketing tactics have worked their magic to stir brand buzz, GenAI can support even further by helping organizations demand plan and forecast how much of each product they’ll need and where – down to the exact location.

This is helpful for a few reasons, one being that for essential brands that rely on having inventory in stores to keep up with constant consumer demand (like grocery, food, and CPG brands), these tools can help them predict and pivot during major supply chain disruptions. Another is that for brands whose products are non-essential, this data can help predict demand from a macro and micro level – helping inform inventory strategy.

A strategic outcome may be that GenAI analyzes data and suggests intentionally keeping inventory low in high-demand markets to increase interest. This way, if there is limited inventory that’s smaller than a brand’s audience base in certain markets, consumers who did get the product feel like they’re part of a special brand experience. This is a great example of how GenAI is a powerful tool that marketers can keep in their back pockets not only to refine creative solutions but to also spark them in nontraditional ways.

GenAI’s potential is still being discovered

GenAI is still in its infancy, but we’ve already discovered hundreds of ways we can use it to refine processes in all kinds of industries. But, there’s still a lot to learn.

While we already know it can help organizations understand consumers and their own internal processes better, there are countless ways it will push the boundaries of what’s possible in marketing. Ultimately, the potential it holds is to take data out of the back-office functions and incorporate it into front-office functions, engineering an overall more streamlined organization.

Organizations looking to get started using GenAI should first make sure they have a clear view of the quality and governance of their data. Without this strong foundation there is a greater risk of exponentially amplified bad insights, so investing in a scalable data management solution and professionals who can help get your data in order will be critical.

GenAI shouldn’t be something to fear. Instead, leaders should be excited about the potential of GenAI to unlock additional value in their marketing operations.