How AI’s Predictive Customer Analytics Helps Make the Most of Your Marketing

by Grant Coleman Emarsys

Today’s retail marketers face an impossible battle. With so many brands out there constantly fighting for share of voice, they face the unenviable task of trying to react to customer interactions, events, and behaviours in real-time to try and retain their market share. Despite the popularity of this reactive approach, it’s now becoming clear that it’s not consistent with listening to customers’ needs and desires and delivering them positive user experiences.

The good news is, marketers are not facing this challenge alone. Artificial Intelligence (AI) has the potential to fundamentally change the way retail marketers interact with their customers through the intelligent analysis of CRM, and the generation of insights on customer behaviour, taste, and expectations. When applied correctly, AI can drive value for brands by getting the right content in front of the right customers at the right time.

But what are the benefits of implementing AI? What are the challenges to adoption that lie ahead, and how can marketers overcome them?

Retail Marketing Transformed

Many brands invest a huge amount of time and effort in tools and strategies that rely on reacting to customer interactions. It makes it difficult to inform their marketing efforts with real-time, data-driven predictions of customers’ purchasing behaviours. This leads to customers receiving marketing content that is irrelevant to their preferences and purchasing habits. Ultimately, this can lead to missed revenue opportunities, misplaced resources, and a lack of return on investment.

Without intelligent analysis of customer behaviours, it’s next to impossible to predict their likelihood to make a purchase, take advantage of a specific offer, or establish their projected value to a brand over time. This creates a ‘best guess’ mentality when it comes to campaign execution, ultimately offering customers a lacklustre experience. It also means brands and retailers are unable to create a coherent marketing strategy that anticipates customer needs and delivers unique, individual experiences.

Current tools do not offer retailers a bang for their buck – they need platforms that ensure every customer interaction enriches their marketing strategy.

Theory into Practice

Machine learning patterns and AI are transforming how retailers and marketers interact with customers. Indeed, studies show that 85% of marketers believe AI will have a significant impact on the marketing industry as a whole. Much of this belief comes from previous promises that advances in technology would allow marketers to segment or ‘personalise’ content for customers based on a limited understanding of their purchasing habits.

Personalisation on this basis, however, is not enough. Marketers must consider solutions that help them ‘individualise’ their communications, increase customer loyalty, and maintain market share in an era of increasingly tough competition. AI can help deliver individualised experiences by identifying where they should allocate marketing budgets, anticipating how customer behaviours may impact existing marketing and business models, and implementing tactics in advance to increase the effectiveness of their campaigns.

AI can help achieve this thanks to data analytics tools that create projections of metrics such as purchase probability, customer loyalty, affinity, or estimated transaction value. Marketers can use these projections to inform the execution of their key campaigns, including integrating tactics such as next-best offers, and individualised incentives into their communications.

The advantages of AI are clear from a business perspective: it helps marketers predict the value of individual customers and the revenue potential of customer segments. This will allow them to allocate marketing budgets more efficiently and better project the value of marketing activities to the business

As with every new technology however, results can’t be expected overnight. There are a number of considerations marketers need to bear in mind before AI can have a real impact on the marketing function.

Clearing the Way for Fulfilled Potential

Despite the clear business case for implementing AI in the marketing function, just 37% of retail organisations are investigating potential use cases for AI. Many more also don’t yet understand how AI can help them be more proactive in reaching out to customers.

This is due to a number of challenges, including the significant cultural change required to underpin the adoption of AI. Brands have always been reactive in the past, and some may not feel ready to shift their strategy to avoid disrupting the status quo. On top of this, many brand marketers are strapped for time and under intense pressure to implement marketing campaigns that drive business results.

Implementing AI for the right use cases can therefore appear daunting. And that’s without mentioning the giant martech stacks that brands may have accumulated over months or years, which make integrating a variety of disparate systems an operational or IT risk.

Brands must view AI not as a disrupter, but as an enabler, to overcome these challenges. This means taking time to develop a robust AI strategy across their marketing stack, rather than implement it on a siloed, or channel-by-channel approach, as this risks diluting and hindering its impact.

When deployed correctly, with marketing objectives interwoven within its DNA, AI will enable brands to make informed recommendations on the tactics required to deliver business value. Customers don’t have time for poorly tailored marketing content – brands need to take the next step and invest in AI to get ahead of their competitors and ensure they remain competitive.

Grant Coleman