What is retail data analysis? 

As the name may suggest, retail data analysis is the process of gathering then analysing your retail data to inform your business decisions. This analysis covers a broad range of data types and can be used for numerous purposes, from customer demographics and inventory levels to click-through rates and social media engagement. 

By collecting and interpreting your data, you can better understand your customers’ habits and decision-making, to optimise your product offering and marketing strategy. 

Some of the key components of retail data analysis include:

• Sales data: Analysing the transactions your customers make can help you identify trends, popular products and any seasonal variations. This insight can help you set prices and know when to promote or discount certain products.
• Inventory data: Keeping track of your inventory levels and turnover rate can help you maintain optimal stock levels and manage supply chain efficiency, so your customers can get the products they want, when they want them.
• Customer data: Retail data analysis can help you understand your customers’ buying patterns and decisions. This allows you to tailor your marketing efforts accordingly. 
• Marketing data: You can evaluate the success of your marketing efforts through metrics such as conversion rates, click-through rates and return on investment (ROI) to measure the effectiveness. With this knowledge, you can tailor your marketing strategies to be more effective. 

How is retail analytics data collected?

The first step in retail data analysis is deciding the best method to collect your data. There’s no one-size-fits-all approach when it comes to retail data collection, as no two businesses are the same. In fact, there are numerous methods you can adopt to get to know your customers’ preferences.

Here are just some key methods you can use to obtain valuable data:

• POS systems: Point-of-sale (POS) systems can capture detailed information about each sale you make, including the time and date of the purchase, the payment method used and the product details. You can identify trends and patterns in your customers’ purchase history, such as certain products rising in popularity at certain times of the year, and then optimise your marketing campaigns to reflect this trend. 
• CRM systems: CRM systems can offer more than just automation - they’re an invaluable data tool that can help you track the interactions you have with your customers. You can gain insight into their preferences, purchase history and previous communications (emails, chat logs, calls etc). This retail analytics data can inform your marketing efforts and boost customer retention long term.
• Web analytics: If you’re not using web analytics, what are you waiting for? Website analytics tools are designed to track, measure and report on website activity including site traffic, click-through rate and conversions. 
• Social media analytics: If you’re investing time and budget into your social media activity, you’ll want to know your efforts are paying off. By diving into your social media analytics, you can measure the growth of your social media channels, and the engagement your posts are getting and also collect customer reviews.
• Loyalty schemes: Loyalty schemes are designed to reward your loyal customers - but they can help you gather data on their preferences and buying habits too. You’ll gain insight into their preferred products, participation in promotions and when they earn and redeem loyalty points, if you offer them. 
• Inventory management systems: Ensuring optimal inventory levels is a priority for retailers, and inventory management systems can help you monitor your stock and turnover rates. You’ll be able to keep an eye on supplier performance, rectify issues such as overstock and ensure efficient supply chain management. 

How can a business make use of retail data analysis? 

As we’ve mentioned, harnessing the power of retail data analysis allows you to make strategic, data-driven decisions that can benefit your business, and in turn, your loyal customers. By analysing your data, you can identify customer preferences, seasonal trends and your top-selling products. With this insight, you can personalise your marketing efforts to appeal to different customer segments and encourage customer loyalty through tailored promotions and communications. 

Below are just some of the ways you can use retail data analysis to benefit your business.

Predicting spend

Retail data analysis can be a powerful tool for predicting spend, allowing you to forecast future sales and tailor your marketing strategy to maximise revenue. 

You may choose to analyse past sales data to predict future spending. For example, you may spot certain products that are more popular at certain points in the year than others, allowing you to predict future sales. You can also use recency, frequency and monetary (RFM) analysis to evaluate a customer’s value, by analysing their previous purchases, how regularly they buy from you and how much they spend on average. From there, you can predict future spending and identify which customers generate the most revenue for your business. 

Forecasting demand

Retail data analysis can help you accurately forecast demand, so you can manage your inventory and supply chains effectively. To forecast demand successfully, you’ll need to dive into your historical sales data and identify past sales trends, patterns and seasonality, to help you predict future demand. For example, you may spot that certain products spike in sales during certain holidays, or back-to-school seasons. Recognising these patterns can help you plan ahead, prepare for peak demand periods and avoid any stock issues. 

Providing offers

You can improve the popularity and effectiveness of your promotional offers by tailoring them to the unique needs and preferences of your customers. By diving into your customer data, you can segment your customers into different groups, based on their shared characteristics, such as their shopping preferences or demographics. By understanding their preferences, you can tailor offers to resonate with each group and increase the chances of them making a purchase. You can also use your customer data to identify customers’ preferred products or price ranges. You can then create personalised offers that complement your customers’ tastes, such as offering them a discount on a product they’ve previously viewed, or on items that are similar to their previous purchases.  

Better customer journey analysis

Your retail data analysis can play a pivotal role in both understanding and bettering the retail customer journey, to ensure you create memorable experiences that encourage your customers to return to you in the future. 

First things first, you’ll need to map out the various touchpoints and interactions a customer has with your brand, from where they initially found you, to any interactions they make after they’ve purchased. Remember to include both online interactions (website visits, email communications) and offline interactions (phone calls, direct mail, in-store visits). Once you’ve got a list of these multiple touchpoints, you can collect data from each of them. For example, you can look at your mobile app to gather data on app usage and in-app purchases, and web analytics to track how your users interact with your website. By analysing the data from each touchpoint, you can understand your customers’ behaviours and preferences, and identify any pain points or blockers in their customer journey. 

Better channel targeting

You can use your retail data to optimise your marketing strategies, by identifying the most effective channels to reach and engage your customers. As mentioned, you’ll first need to collect data from all potential customer touch points, including website analytics and social media channels. From there, you can analyse the performance of each channel to identify which is the most effective at engaging different types of customers. You may evaluate the traffic and engagement of a particular channel, or return on investment (ROI) - the revenue generated when compared to the marketing costs of each channel. 

Collecting and analysing your retail data across different touchpoints allows you to identify the most effective channel to target different customer segments. You can then personalise and optimise your marketing efforts to appeal to these different customer segments, wherever they are in their customer journey, and whatever channel they favour. This not only ensures you prioritise the right channels for your customers but enhances the customer experience too. 

What are the key benefits of retail data analysis? 

As we hope you’ve come to realise, retail data analysis offers a variety of benefits that can help strengthen your customer relationships and enhance your marketing strategy. 

Here are just some of these key advantages:

• Enhanced customer experience: By getting to grips with your data, you can understand your customers’ unique behaviours and preferences. You can use this insight to personalise their shopping experience and target your marketing campaigns to their needs.
• Improved inventory management: Keeping on top of your inventory can be a challenge. By analysing your sales data, you can maintain optimal stock levels and reduce the chance of overstock or stockouts. This ensures your most popular products are always available, and minimises the unnecessary costs of overstock.
• Competitive advantage: Recognising and harnessing the power of your data can give you a competitive edge in your industry. By knowing your customers, you can adapt to changing conditions quickly and stay ahead of your competitors. 
• Increased sales: You’ll be able to identify your best-selling products or services, as well as the most effective promotions and any seasonality trends. This insight can help your business optimise its sales strategies and boost revenue. 
• Increased customer retention: Your customer data is a valuable tool to help you understand what drives loyalty to your business and encourages customers to purchase from you again in the future. By knowing your customer’s needs and preferences, you can enhance retention rates and build loyal customer relationships for the long haul. 

What types of software can help with retail data analysis? 

There are numerous types of software to support your retail data analysis. Each platform offers an array of unique features and capabilities to help retailers like you analyse and interpret data, and make informed, data-driven decisions. 

Some key types of software that can aid your retail data analysis include:

• CRM software: Customer relationship management (CRM) software can offer valuable insight into your customer interactions, behaviour and preferences. Many CRM systems have built-in analytics, so you can monitor various metrics such as bounce rates, demographic data and conversions. 
• Retail analytics platforms: You’re sure to stumble across several retail analytics platforms on your search. These specialised platforms offer in-depth analysis of your retail data, including sales trends and seasonality, and tools for demand forecasting. 
• Marketing automation tools: As a marketer, it’s likely you’ve heard of the likes of Mailchimp and HubSpot. These types of software can help you to automate your marketing campaigns, with valuable insights into campaign performance, engagement metrics and customer segmentation. 
• Customer feedback tools: While not as advanced as others, you can’t deny the value of surveys and customer feedback tools. These tools give your customers a voice to share their opinions and preferences and can help you identify any pain points and areas for improvement.