by Stuart Russell, Chief Strategy Officer at Plinc
AI has revolutionised the world of business over the past few years, transforming almost every industry, sector, and practice in its path. Yet, despite this transformation, many marketing teams are struggling to leverage the new technology.
According to research commissioned by Plinc, a significant 43% of senior marketing professionals working in B2C don’t consider AI effective in their line of work. That’s a worrying statistic, given that customer marketing is, perhaps, one of the areas of business that could most benefit from AI. Drawing insights from data to make automated decisions at speed can be a game-changer. Hyper-personalised customer experiences at scale. At last.
But this scepticism for AI isn’t due to a lack of ambition. Marketing teams want to create effective customer experiences. In fact, they’re desperate to build more personalised programs. The problem is that, for too long, most have lacked a strong foundation of unified and accessible data. As such, AI (and any other new MarTech development that follows) will have difficulties reaching its full potential.
However, unifying and consolidating this customer data could help make marketeers ease their scepticism, embrace AI’s benefits and deliver meaningful, hyper-personalised marketing.
A trust gap with data
At the heart of this AI scepticism is marketeer’s relationship with data. Many businesses lack the resources to unify and analyse it, leaving marketing teams struggling to gain actionable insights and create more dynamic customer experiences.
With this in mind, it’s unsurprising that customer marketing professionals are increasingly disillusioned with the efficacy of AI and martetch in general. In fact, only 23% of businesses are confident that their marketing teams are working off a fully realised view of their customer. Almost half of customer marketing professionals are not using real-time omnichannel data.
Yet, implementing and embracing AI may seem easier said than done. Trying to integrate AI into unsophisticated data systems would be sending marketeers down a rabbit hole. Without a solid data foundation to draw from, AI-backed martech will be unable to materialise accurate, full-formed insights, and will ultimately become just another piece of tech gathering dust.
When we consider these foundational disparities, it’s easier to understand why marketing teams may have reservations about their data and the potential of AI to help them.
Unifying data into a ‘single customer view’
To overcome this lack of conviction and unlock AI’s promise of personalisation at scale, businesses first need to unify their customer data into a single source. A single customer view (SCV).
That means that marketing teams, as well as the AI-powered tools they use, can access a 360-degree view of their individual customers whenever they want (over a third of marketing professionals admitted that they did not have this). This data should include online and offline activity, and come from a wide range of first-party sources, including transactions, web behaviour, reviews, email, footfall, and customer service.
Using this solution, all the information relating to individual customers is held in the same place – ready for AI to leverage. In this way, marketing teams can then activate the data and its insights, targeting customers across channels with ultra-personalised communications that align with their interests and behavioural patterns of customers.
Just imagine using AI to respond to customer activity as it happens – seeking, finding, and using their data to communicate specific, personalised messages to them.
Among retail businesses, there are some marketing teams that are implementing these changes and realising the potential of AI to inform their communications. Marks & Spencer International is one of these.
The business serves customers across 100 markets, relying heavily on eCommerce and digital marketing to reach people across the world. Yet, they wanted to take their offer strategy to the next level, testing different promotions for each of their customer segments. The objective was determining which specific offers drove the highest incremental value amongst its customer base.
To achieve this, they partnered with Plinc to deploy Future Value Modelling, a methodology that uses machine learning to predict the future value of any given customer. Crucially, the model had access to a range of unified, accessible data – from customer behaviour to purchase history and demographics -, which enabled the technology to equip the marketing team with future value scores for each customer.
This allowed M&S International to focus marketing efforts on certain customers who held the most future value to the business. Similarly, it allowed them to predict how campaigns could affect their long-term value.
So, despite marketing professional’s initial disillusionment, it is clear that AI does have the power to transform their practice. But, businesses need to adjust some of their data collection and processing methods in preparation first. An SCV is critical in that respect.
Once businesses have done that, like M&S International, a new form of advanced, hyper-personalised marketing will become possible. And the 43% of marketing professionals who believe that their personalisation efforts are ‘unsophisticated’, might be tempted to change their minds.