Artificial intelligence is transforming the ways in which businesses can understand their customers. Today, AI allows businesses to collect and analyse vast quantities of qualitative and quantitative data, distilling critical insights on consumer behaviour. However, it is the businesses that effectively act upon those insights that can unlock competitive advantage.

“Just making data and insight available doesn’t make you data-driven,” Sona Abaryan, partner and global real and luxury sector lead at tech-enabled data science consultancy Ekimetrics, said on stage last week at the BoF Professional Summit: New Frontiers: AI, Digital Culture and Virtual Worlds.

“Companies need to think about how they need to evolve their decision-making processes, the incentives that they provide, their organisational structure, to make a real impact and go from using data to improving the status quo to being really transformative with it.”

Founded in 2006, Ekimetrics has pioneered the use of AI and advanced data science, helping brands optimise their marketing dollars and leverage knowledge of their customers’ preferences and behaviours to put the customer at the centre of business strategy. Ekimetrics works with the likes of Estée Lauder Companies, Nestlé and over 50 luxury houses and brands, and gathers insights from partnering platforms like Amazon, Meta, TikTok and Google.

For one, consumer expectations are increasingly focused on a personalised experience. However, in trying to achieve this, many businesses have lost sight of the strategy and have become solely focused on software as a service (SaaS), which has flattened the efficacy of the services’ approaches to personalisation in a competitive environment.

“Everyone leverages the same platforms and methods, so you end up evening out the impact and need to look elsewhere to drive a competitive edge,” Abaryan added.

Brands that examine customer centricity from all aspects of business operations — from brand marketing and communications through to product design, merchandising and planning, even in their financial operations and forecasting — are extracting great success with reaching their target consumer. This approach is enabled through access to and analysis of more primary data on customers, allowing executives to better identify which areas of business to optimise to meet and grow long-term business health KPIs such as lifetime value (LTV), which is addressed in Ekimetrics’ white paper, The Role of Customer Analytics Throughout the Value Chain.

Now, BoF shares key insights from the talk at the BoF Professional Summit with Ekimetrics’ partner and global retail and luxury sector lead, Sona Abaryan, on how business can place customer data at the heart of decision-making and leverage insights for increased short and long-term return on investment (ROI).

What does it mean to be customer-centric today?

In its simplest terms, [companies] are trying to learn from their customers. It’s hard to think of any other industry where you have so much competition as in fashion — the way to stay ahead of, or keep up with the competition is to keep the pulse on how your customer is evolving.

To be clear, when we talk about being customer-centric, it is much more than personalising emails or the online catalogue. In fact, this relentless ‘SaaS-ification’ of personalisation that we have seen over the last decade has created a disconnect between strategy and tactic — everyone leverages the same platforms and methods, so you end up evening out the impact and need to look elsewhere to drive a competitive edge.

How challenging is it to use data well today?

We see a lot of enthusiasm and ambition for being data-driven at the C-suite level and while every company is different, some of them don’t know where to start. Some of them don’t know where the value is, or will look at single use-cases rather than what it takes end-to-end to drive adoption and make an impact. Moreover, executives need to recognise that making data and insights available doesn’t actually make you data-driven.

Companies need to think about how they need to evolve their decision-making processes, the incentives that they provide, their organisational structure, to make a real impact and go from using data to improving the status quo to being really transformative with it.

To give you an example, we work with L’Oréal on a product called Consumer Loop, which uses artificial intelligence to analyse ratings and reviews at scale after a product has launched. Consumer Loop makes that information available for product teams to enrich their product, research and development strategy. [Now,] what was previously a time-consuming and expensive process is accessible to thousands of their stakeholders globally, enabling them to make faster decisions about products.

How can businesses optimise data insights for improved customer lifetime value?

Acquisition of customers can be an expensive process if you don’t acquire the right customers and they don’t end up repeating. We see customer lifetime value as being an important metric in terms of business. How do you maximise the first-party data signals that you have to be able to predict early on whether the customer is going to be high value or not?

Understanding how your customer evolves is critical for longevity. If you do that well, maybe you don’t need to have a brand comeback every decade or two.

How are you thinking about your data capture strategy? You need to think about how you promote logged-in experiences online so that you can have a lot of that pre-purchase signal and the in-between signals to understand the value of that customer.

How can you leverage these customer insights to grow your business?

In terms of retention, it is really about being able to understand the potential of a customer early on – a key piece of information for clienteling. This is what gives the customer relationship management teams the confidence to invest early on in a customer that they know has high potential. So, that means you can use some of your more expensive channels because you know the value is going to be realised.

Whether it’s sending them that lookbook or inviting them to the event and making sure that that value is realised, when it comes to acquisition from a tactical perspective, media platforms provide a number of ways that you can discover and target your high value customer lookalikes. You can look to things like marketing mix modelling to understand, at a more aggregate level, what the marketing levers are that help you acquire a high value customer versus a low value customer.

How else will organisations benefit from adopting a more customer-centric approach?

Let’s start with product strategy — historically, the industry has seen a data-driven or customer-centric vision as being at odds with a design and product-driven vision. But whether you are a trendsetter in terms of your brand, whether you capitalise on trends or you are a mix of the two, understanding how your customer evolves is critical for longevity. If you do that well, maybe you don’t need to have a brand comeback every decade or two.

To give you an example, we worked with a fashion retailer who wanted to understand style from the perspective of their customers and not something that was predetermined by their product team. So, we looked at the combination of materials, colours, fits and lengths that customers were buying. Then, once you overlay LTV on top of that, it becomes really powerful in helping you identify what product ranges to extend or minimise because they are attracting and keeping a high-value customer.

How can AI help measure brand marketing’s effectiveness or return on investment?

Brand marketing, [including] things like sponsorship, are harder to track or certainly don’t have the kind of trackability of ROI [to which] CFOs and CMOs have become very accustomed with performance marketing strategies.

The end of cookies has certainly shaken up the measurement space, but the biggest challenge was that attribution-based methods were being used to make budget allocation decisions that they had no business to make. So now, if you’ve trained your CFO on seemingly high ROIs, how do you take that figure back to what you need from brand investment, with incremental ROIs that are going to seem like they are lower?

Think about it in a holistic way when you need to make trade-offs of big budgets. It is important to have one language of performance to keep a level-playing field of that decision-making.

The challenge isn’t just that for CMOs — they have a lot of silos in the business these days and a lot of tools giving them conflicting recommendations. You have attributions saying “pump all your money into performance media” and at the same time, you have all these brand and customer goals to achieve and no real measurement to do that.

Sponsorship is a great example — the industry is aligning behind sports sponsorship at the moment. But what is the ROI of that beyond some merchandise that you have sold? What has it done for your brand? What has it done for your sales? Sponsorship, without the right metrics and measurement, is one of the biggest blind spots in brand investment these days.

What advice would you offer executives looking to implement AI-driven insights into their business?

I would say firstly, focusing on incremental ROI and educating your CFO on that is key. We talk about measuring against multiple KPIs, not just what your short-term performance and marketing plan is. Executives should be thinking: how does that drive your brand equity? And how does that drive your strategic customer goals?

Finally, think about it in a holistic way when you need to make trade-offs of big budgets. It is important to have one language of performance — to keep with the sports sponsorship analogy — to keep a level-playing field of that decision-making.

This is a sponsored feature paid for by Ekimetrics as part of a BoF partnership.