AI-managed data will help retailers avoid another sales slump in 2025

After the "golden quarter" of October to December 2024 proved disappointing for many retailers, brands will be keen to avoid a repeat this year. There are reasons to be optimistic, however.

While high street sales may have flatlined over this peak shopping period, online sales did see growth – and this will have provided brands with quality customer data that can inform marketing activities throughout the year. The growing availability of AI tools to take action based on that data is also creating an opportunity for consumer businesses. This will enable brands to tailor experiences to meet their customers' individual preferences.  

To win in 2025, brands need to be able to go "beyond the campaign" and wield data better. They need rapid access to quality data assets that enable their teams to find the right customers - and then plan and act as an omnichannel operation. This is what's required to drive double digit improvements in performance. 

The "supply chain" of data – from integrating and unifying data to understanding and acting on customer information – has transformed. What used to be a 12–18-month long process is now being compressed into hours and days. Any brands sitting on legacy technology and processes must now evolve, or risk being left behind. 

There Are Silver Linings 
What's clear from sales figures in that last quarter of 2024 is that not every retail sector suffered. The rising cost of living did alter household spending, but statistics recently shared by Barclays show it was a mixed story of feast and famine for UK retail in 2024, with certain categories and channels benefitting from changing consumer behaviour.

While non-essential retail spending increased just 1.5%, some categories, such as entertainment, have been growing nearly 6%. Online sales have also outpaced in-person sales – with ecommerce up 2.6%, while bricks and mortar was down -1.7%. This shift should be prompting retailers to ask if their customer understanding is mature enough to enable engagement across channels. Do they know who their best retail customers are and can they quickly react to driving them online if they aren't returning to the store? 

If marketers are fast enough to identify these changes in spending habits, it will put them in a great position to adapt and take advantage of the trends. AI tools and modern cloud data architectures are allowing them to do just that faster than ever before, by helping them to put the right offerings in front of their customers, in the right channel and at the right times. 

Data is Messy
In order to do this successfully, however, brands need clean, well-organised customer data that AI tools can work with. This does pose a challenge, as customer data tends to be very messy, siloed and inaccurate. 

To build an accurate view of a customer, information has to be brought together from multiple sources – in-store sales, ecommerce platforms, email campaigns, customer surveys, CRM systems and more. To ensure brands can rely on these customer profiles, they need to be capable of updating this data in real-time.

While this creates complications, AI is helping retailers to overcome this and take four actions that will lead to increased sales:

1. Cleaning Customer Data 
One of the biggest problems businesses face when pulling customer data together is duplicate identities. Multiple points of contact, whether they are online or offline, allow customers to use different 'identifiers'. For example, customers often use different email addresses and will present their names in different ways, using abbreviations or nicknames. The more frequently a customer interacts with the brand, the messier their data becomes. 

This means a retailer's best customers are often the least understood because they sit invisibly hidden in the data. It can also result in the creation of various profiles that actually relate to the same person.

In order to personalise individual experiences, and do this at scale, brands need to avoid the prospect of sending conflicting messages to the same person. With AI-powered identity resolution, however, they gain the ability to identify duplicate profiles and stitch them together. 

At one retailer, analysis showed that more than 30% of their top customers were using multiple identifiers. When they deployed AI, however, they were able to identify +24% more high value customers than with their legacy approach. Even with the most conservative of conversion rates, this will translate into tremendous improvements in incremental revenue and profit - with the retailer now able to better serve and drive incremental business from this group of previously hidden customers.   

2. Democratising Access to Data 
With clean data, AI can then be used to empower teams across the businesses by providing rapid access to customer insights. GenAI solutions, for instance, are allowing non-technical users to ask questions of their customer data platform using natural language.

This is democratising access to customer data and providing insights that will not just support marketing teams, but also help media, product, merchandising and analytics teams to quickly adapt their offerings and optimise their investments – providing retailers with a newfound agility to keep pace with the current trends. 

3. Predicting Behaviour 
With a strong data foundation, businesses can then build a better understanding of who their future high-value customers are and what customers are likely to buy next. This ability to anticipate behaviour has now become so essential to brands that predictive AI is fast becoming a fundamental element of any quality customer data platform.
It is enabling brands to concentrate their energies on the right customers. At the same time, it is also helping them to create omnichannel media campaigns that target match profiles and deliver messaging that is most likely to resonate - helping to drive new acquisitions and deliver a greater ROI from advertising.

4. Personalising Experiences at Scale 
When businesses have highly accurate data that is always up to date, AI can help them to respond to customer requests in real time, and in a very personalised way. This will allow brands to give each customer VIP treatment. These experiences will help to build loyalty, encourage greater spending and lead to more referrals.

If brands and retailers are going to maximise the opportunities that AI is opening up in 2025, however, they need to start with that initial customer data foundation. Even if their sales were unimpressive during late 2024, the data gathered during this period still holds huge value.

Once cleaned and unified, this data can unlock the potential of AI by allowing businesses to understand their customers, identify shifting behaviours and tailor experiences accordingly.