Data points in retail - what they are and how to use them

5 min read

Data points in retail can offer valuable insights into your customers’ needs and behaviours, helping to strengthen your marketing activities. These data points guide you in making informed marketing decisions, which can align with your wider business goals. By analysing data points in retail, you can remove the guesswork around what your customers need and what their pain points are, improving your ROI and strengthening your customer relationships. After you've finished this article, you'll know all about data points in retail, what they are and how to use them, and how you can utilise them in your own business.

What are data points for retail?

Data points for retail refer to any statistics, facts or figures that can be collected about your retail business and used to improve it. This retail data comes in many forms, such as point of sale (POS) data, social media engagement data and loyalty card data. By analysing data, you can better understand your customers and make valuable changes to your marketing and overall business strategy. With retail analytics, you can identify trends and patterns that inform smarter, more effective marketing decisions.

The importance of data points in retail

Unfortunately, more than 13,000 retail stores closed in the UK in 2024. This shows that having a data-driven strategy is more important than ever. Data points in retail can help businesses stay competitive by offering key insights into customer behaviour, preferences and future demand too. Harnessing this data is vital for both short-term success and long-term growth.

Here are some of the key benefits of using data points in retail:

• Increased ROI: Retail analytics helps improve your ROI by offering insights into customer values, enabling you to track the success of marketing campaigns. By understanding which campaigns perform best, you can adjust your strategy for better outcomes.
• Greater personalisation: Analysing data points in retail allows you to tailor marketing strategies to individual customer segments. By utilising customer data, you can send personalised messages that resonate with specific needs, driving engagement and fostering customer loyalty.
• Capture new opportunities: Retail predictive analytics can help identify emerging trends before they become widespread. By analysing historical sales data and customer behaviour, you can spot opportunities early and position your business to capitalise on them.
• Allocate your budget: Retail analytics tools allow you to track which marketing channels most effectively reach your target audience. This lets you focus your budget on the most successful channels, such as social media or SEO.
• Improved customer experience: Data points can enhance your understanding of customer interactions, allowing you to create more personalised and positive experiences, which leads to better customer satisfaction and retention.

Types of data analytics in retail

There are several types of retail analytics that each provide valuable insights for retailers looking to improve their operations and marketing strategies.

1. Descriptive analytics
   Descriptive analytics helps answer the ‘what happened?’ question by interpreting historical data. It involves analysing past sales data to identify trends and relationships, offering retailers a clearer understanding of their business’ performance.
2. Diagnostic analytics
   This type of analysis aims to answer ‘why did it happen?’ It digs deeper into raw data to find correlations and uncover reasons behind trends. For example, you could use diagnostic analytics to identify the cause of a spike in social media engagement, factoring in external influences like weather or news events.
3. Predictive analytics
   Retail predictive analytics uses historical data combined with statistical modelling and machine learning techniques to predict future outcomes. By recognising patterns in past behaviour, predictive analytics can forecast future sales and demand trends, helping retailers prepare for shifts in customer behaviour. This allows retailers to predict future trends, ensuring they can stay ahead of the curve in a rapidly changing market.
4. Prescriptive analytics
   This type of analysis suggests the best course of action by combining insights from predictive analytics with available resources. It helps retailers respond effectively to forecasted trends and optimise strategies, from inventory management to marketing campaigns.

Examples of data points in retail

Retail businesses can leverage various types of data to refine their marketing, pricing and promotion strategies, and improve customer experience. Examples include:

• Business analytics
  By analysing historical business data, retailers can identify trends and make data-driven decisions to improve their marketing strategies, sales forecasting and customer relationship management.
• Sales forecasting
  Retailers can estimate future sales and demand based on historical sales data, helping them allocate resources efficiently and avoid running out of stock or overstocking.
• Demand forecasting
  Demand forecasting uses predictive analytics to predict future customer demand for products. This can help retailers adjust their marketing efforts and optimise inventory management systems.
• Customer experience analytics
  By analysing data from both online and offline sources, retailers can enhance the customer experience and tailor their offerings to better meet customer needs.

Future trends of data points in retail

As the retail industry evolves, the role of data points in retail continues to expand. Here’s a look at some market trends and some future trends that are likely to shape the use of data in the retail sector:

• Increased use of web analytics
  As online shopping continues to grow, web analytics will play an even more critical role in understanding customer behaviour. Retailers will increasingly rely on customer analytics to optimise their online presence, track user behaviour and personalise the shopping experience across digital platforms.
• Integration of machine learning
  Machine learning models will increasingly be used to analyse large datasets, helping retailers predict future trends and make more informed decisions. By leveraging advanced data analytics techniques, retailers can enhance sales forecasting and inventory management for greater efficiency.
• Real-time data for instant decision-making
  Real-time data will become more important, allowing businesses to respond immediately to customer needs, inventory levels and market conditions. This data can help predict future trends and optimise customer interactions in real-time, ensuring a seamless customer experience.
• Personalisation at scale
  As customer data continues to accumulate, retailers will be able to use customer analytics to offer highly personalised experiences at scale. The ability to predict customer behaviour and preferences will enhance loyalty and satisfaction, driving long-term success.

How to utilise data points in your business

Utilising data points in retail can unlock crucial insights that drive smarter decisions and improve customer loyalty. Here are several ways you can use data points in your business:

• Improve decision-making
  Data analysis tools provide valuable insights into your customers' behaviours, preferences and purchasing patterns, enabling you to make informed decisions rather than relying on guesswork.
• Refine operations
  Retail data analytics helps optimise business operations by predicting trends in demand, allowing for better resource allocation, inventory management and supply chain planning.
• Align your goals
  Data-driven insights help align marketing strategies with business objectives, allowing your teams to collaborate effectively and target the right customer segments with the right messages.

How Apteco can help you with data points for your business

Unlock the potential of your customer data with Apteco. You can harness the power of your transactional data to drive conversions and increase sales. You’ll also be able to tap into your loyalty and reward programme data, to incentivise your customers’ future purchases through targeted promotions. Apteco’s software and data analytics tools can give you a complete picture of your customer data, so you can better understand their needs and behaviours.

Take a look at how we can support your retail business, and when you’re ready book a demo today.