Customer Marketing as we know it is changing – here’s what to do about it

Personalisation, personalisation, personalisation. It’s a term that has been ringing in the ears of retail customer marketers for years now. And the evidence for its importance is there – effective, contextualised personalisation is key to increasing conversion rates, brand affinity, basket sizes, and customer retention.

Yet, despite marketers understanding the value of tailoring communications to each customer, it remains a major challenge for most brands – especially for those in retail.

The reality is that crafting even two different versions of a message, let alone hundreds for various customer segments, consumes precious time and strains the already limited resources of most marketing teams.

And, this is impacting effectiveness. In fact, research by Plinc of 200 senior marketing professionals reveals that nearly half of customer marketers describe their personalisation efforts as ‘unsophisticated.’ This leads to that generic marketing copy we receive often as customers, personalised with nothing but our names.

But, here’s how customer marketers can change tact.

The need for hyper-personalisation at scale

With heightened competition, engaging customers has become increasingly important – yet also challenging. As such, personalisation has become a lynchpin for success, with our results showing that implementing personalised recommendations can boost basket sizes by at least 20%.

However, up until now, resource constraints have hindered many retail marketing teams from achieving a sophisticated level of personalisation. Given the tough current economic climate, placing further strain on budgets and resources, it’s unlikely this will change any time soon.

The solution to this personalisation challenge lies in artificial intelligence (AI), which has sent shockwaves across all sectors of business. AI and Large Language Models (LLMs) promise to be a game changer. AI is fundamental to processing the data underpinning predictive models, whilst LLMs will support content creation at scale.

Together, they have the potential to use extensive troves of customer data (which many marketers already possess) to craft perfectly tailored content on a customer by customer basis. The technology is capable of using each customer’s preferences, purchase history, and current context to deliver hyper-personalised marketing at scale.

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Automation Overcomes the Barrier

Ultimately, generative AI enables marketing departments to automate content generation, eliminating the time-consuming obstacle. This technology assists teams in creating “banks” of segment-specific messages, drawing from ideas conceived by teams. These messages are then stored in content libraries, categorised based on various customer attributes, including demographics and personal preferences.

To effectively harness these content libraries, marketing teams can implement “trigger” processes. Simply put, when customers take specific actions, such as clicking on a banner or browsing a particular product category, the system can automatically select and overlay relevant messages onto the content the customer views. This dynamic personalisation can even extend to images, enhancing the customer experience further.

The Martech Revolution

With the advent of LLMs, the role of retail marketing teams stands on the brink of a revolution. With regards to content generation, customer marketers’ role will shift from creating to reviewing content. This transition promises to save marketing teams countless hours each week. Reviewers will play a crucial role in training AI, directing content accuracy, and maintaining brand identity and tone of voice.

However, only companies with well-structured content strategies and unified customer data will be able to harness this new way of delivering personalised communications. Marketing teams must establish clear data pipelines, connecting their data sources with their desired destinations. It’s also vital to identify priority use cases for integrating LLMs into existing processes to avoid unnecessary technological clutter.

Moving forward

Currently, for many businesses, generative AI models are hindered by open-source constraints, particularly when it comes to data privacy laws in regions like the UK.

However, the future holds the promise of AI capable of leveraging first-party data. Instead of relying solely on internet data, AI will tap into a user’s own data, including internal customer behaviour and demographic information. This transformation will revolutionise customer marketing in retail, helping teams achieve their personalisation goals and delivering an enhanced shopping experience.

Generative AI represents a transformative opportunity for customer marketers to finally unlock the full potential of personalisation at scale. Without AI, it’s nearly impossible for marketers constrained by time and resources.

By tapping into vast customer data and harnessing the power of generative AI, marketers can deliver personalised content tailored to each customer’s unique preferences, purchase history, and real-time context. As we look ahead, the promise of AI capable of leveraging first-party data holds the key to a seamless, highly personalised shopping experience.

Embracing generative AI is the path forward for personalisation, and those who adapt to this technological revolution stand to gain a significant competitive advantage in the market.

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