The golden quarter (October -December) is the most important part of the retail calendar.
It's a period that typically sees retailers generate 30–50 per cent of their annual revenue, while accounting for up to half of their annual profits. For retailers in sectors such as toys, jewellery and technology it can contribute an even greater share.
In fact, research from Epsilon estimates that UK consumers will spend £17.9 billion on gifts and personal purchases during this year's golden quarter, equating to an average of £334 per adult.
For retailers looking to maximise revenue and profitability during this crucial period making the most of their AI capabilities is essential. AI can play a pivotal role in analysing customer data in real-time, generating valuable insights that can enhance personalisation, wider communications and CRM activity. This way retailers in an increasingly competitive marketplace can differentiate themselves and build stronger connections with customers.
A strong data foundation is key for AI success
For retailers who want to maximise their use of AI during the golden quarter their biggest challenge isn't deploying the technology itself, but ensuring they have high quality, reliable customer data to power the AI.
AI is only as effective as the data that underpins it. When AI systems rely on inaccurate or incomplete data they can produce hallucinations and unreliable outputs, leading to ineffective automation, poor personalisation and inaccurate recommendations. This can undermine customer confidence and erode trust.
An estimated 94 per cent of organisations experience data quality issues with inaccurate, duplicate or incomplete data affecting their day-to-day operations. These challenges are often caused by poor data capture at the customer onboarding stage, and ongoing data decay, which creates significant barriers to the successful implementation of and effectiveness of AI.
Customer contact data can decay by around 25 per cent each year as people move home, pass away or get divorced. At the same time, around 20 per cent of addresses entered online contain errors, from spelling mistakes and incorrect house numbers to inaccurate postcodes. This means data teams can spend a significant proportion of their time cleaning and structuring data for analysis, rather than using it to generate valuable insights. Those that don't clean their data risk poor outcomes from their AI activity.
To tackle inaccurate customer contact data retailers should put robust verification processes in place both at the point of data capture and when cleansing existing data in batches.
In many cases, improving data quality doesn't require significant investment. Simple, cost effective changes to existing processes can make a meaningful difference.
Use address lookup or autocomplete
Employing an address autocomplete or lookup service during customer onboarding is an effective way to improve address accuracy. These services provide accurate, correctly formatted address data in real-time as prospective customers begin entering their details. They also reduce the number of keystrokes required by up to 81 per cent. This not only streamlines the onboarding process, but can also help reduce the likelihood of customers abandoning their purchase. It's also important to apply this approach to the first point of contact verification for email addresses and phone numbers, to ensure these valuable communications channels are verified in real time.
Remove duplicate customer records
Data duplication is a significant challenge for retailers, with many seeing duplicate rates of 10 – 30 per cent on their customer databases. This can occur when data from different departments are combined, or when inconsistencies and errors are introduced as customer information is captured across multiple touchpoints. Duplicate data can confuse AI applications, while also increasing the time and cost involved in customer communications. Sending duplicate messages can further frustrate customers and potentially damage the sender's reputation.
An advanced fuzzy matching tool can identify, merge and remove even the most difficult duplicate records, helping to create a 'single user record' that supports the delivery of a more accurate 'single customer view' (SCV) from which AI can make learnings.
Implement data cleansing
Data suppression, or cleansing, are also essential elements of an effective data quality strategy and therefore in supporting AI initiatives. These services can help identify customers who have moved or are no longer at the address on file. As well as removing incorrect addresses these tools can provide deceased flagging which helps organisations to avoid sending mail and other communications to people who have passed away, which can cause unnecessary distress to their friends and relatives.
Implementing effective suppression strategies can help retailers reduce costs, protect their reputation, mitigate fraud and enhance the quality of the data that underpins their AI initiatives.
Enrich customer data
It's equally important to augment customer data with demographic, firmographic, geographic, social media and property attributes, while also filling gaps in email and telephone data. Doing so creates a more complete view of each customer, helping to strengthen AI-driven analytics, personalisation and omnichannel marketing. With access to a broader range of data points, AI can identify patterns more effectively, anticipate customer needs and make more accurate predictions. This enables retailers to deliver more relevant recommendations and communications.
Make data machine-readable
Finally, AI agents need access to high quality, API or machine-readable data. AI systems rely on data that can be accessed, interpreted and processed rapidly at scale, without the need for manual intervention. Well-structured, consistently formatted machine-readable data reduces ambiguity and makes it easier for AI models to understand and use information accurately. This can accelerate decision making, allowing AI to analyse and act on machine-readable data far more quickly than humans. Machine-readable data also supports seamless integration, enabling information to flow efficiently between different systems, applications and AI tools.
Therefore, using well-labelled data is essential for maintaining accuracy across mission critical AI applications, particularly as AI systems scale.
In summary
With the golden quarter fast approaching now is the time for retailers to ensure their customer data is accurate, enriched and machine-readable. This will ensure their AI efforts are maximised, resulting in a profitable end to the quarter and year.