Data points in retail - what they are and how to use them
Data points can guide you in making informed marketing decisions, which can help you achieve wider business goals. They can remove the guesswork around what your customers need and desire, to improve your ROI and strengthen your customer relationships.
Here, we’ll dive into how data points within retail work, their benefits and how they can help you make stronger, more informed marketing decisions.
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, which you can then use to improve it. This retail data comes in many forms, such as point of sales data, social media engagement data and loyalty card data. This data can then be analysed for you to get to know your customers better and make valuable changes to your marketing and overall business strategy. You can study your retail data and identify trends and patterns to make stronger, more informed marketing decisions.
The importance of data points in retail
Over 17,000 retail stores in the UK closed throughout 2022 - a significant increase from the 11,459 store closures in 2021. With this in mind, it’s more important than ever to have a data-driven strategy in place for your marketing activities, so you can make educated decisions and place your brand in a strong position for growth. Harnessing your data is vital to the success of your business, both now and in the future.
Here are just some of the benefits of using data points in retail:
- Increased ROI: Data-driven marketing in retail can increase your ROI by offering key insights into your customers and their values, and allows you to track results. You can then monitor which campaigns are working and which need improvements, and then adjust your strategy accordingly
- Greater personalisation: Tapping into your data points can help you consider each customer’s individual preferences and needs. You can segment your audience into categories and identify trends amongst them, to target their specific needs and values with personalised messaging. This can help increase customer engagement and brand loyalty
- Capture new opportunities: In retail, strategies, channels and products are constantly evolving. A data-driven approach can help you spot trending opportunities before they become popular. Keep an eye out for how your products or services are performing over time and whether they change or transition, so you don’t miss out on new opportunities
- Allocate your budget: Data-driven marketing can help you allocate and spend your marketing budget most effectively. You’ll be able to spot which channels are most frequently used by your target audience and focus on spending on those channels. For example, you may see that TikTok helps you reach out to your target market more than Twitter, making it a better channel for you to invest time and money in
- Improved customer experience: You can use your data points in retail to understand your customers’ behaviour and appeal to their needs and values. Treat each interaction your customer has with your business as a data point, and use this data to create personalised messaging. In turn, you’ll make them feel valued and understood, which can encourage them to make purchases in the future
What are the types of data analytics in retail?
There are four types of data analytics in retail that each play an important part in providing you with key insights about your customers. Here, we’ll dive into the different types of retail analytics and their role:
Descriptive analytics aims to answer the question of ‘what happened?’. It helps retail marketers organise their data and tell a story with it. Descriptive analytics involves you interpreting historical data to then understand changes that happen within your business. It’s often seen as the simplest form of data analysis because it describes trends and relationships but doesn’t go beyond that.
As mentioned, descriptive analytics refers to the interpretation of historical data to improve your understanding of change. It involves you using historical data to draw comparisons with other reporting periods. From there, you can measure what has occurred during a set period of time.
A popular example of descriptive analytics is reporting. This involves taking raw data, such as the interactions users have with your website, or social media content, and comparing this data with historical data to identify trends. For example, let’s say you’re responsible for reporting on which social media channels drive the most traffic to the business’ website. By using descriptive analytics, you can analyse the website’s traffic data and identify the number of visitors from each source. You can then update your team on changes over time, such as the number of website visits increasing over the year.
Diagnostic analytics aims to answer the ‘why’ of specific problems that occur within your business. It involves you taking the same raw data identified in descriptive analytics and diving deeper into the data to find correlations and trends between data points. You could use statistical analysis, algorithms and even machine learning to do so. Diagnostic analytics can also open your eyes to potential anomalies or problems.
For example, if your latest marketing report shows more engagement with social media activity than usual, you can dive into your data to see whether there was a specific reason for this increase. You can also use diagnostic analytics to look at external data, such as weather patterns, the news or competitors’ activities, to see if other factors were responsible for this uplift.
Another type of data analysis in retail is predictive analytics. Predictive analytics is a more advanced form of data analysis that makes predictions about future outcomes by using historical data, combined with data mining techniques, statistical modelling and machine learning.
As a retail company, you can use predictive analytics to find patterns in your data and identify both risks and opportunities. Essentially, you’re using algorithms to spot patterns in your data, then make predictions about future events and their impact. In doing so, you can predict risks or pitfalls that may impact your business and make the effort to avoid them.
The final type of data analysis is prescriptive analytics. Prescriptive analytics suggests the best course of action to respond to the future trends identified in predictive analytics and improve business outcomes. Essentially, it aims to answer the question ‘What can be done?’ It involves using techniques such as graph analysis, complex event processing, heuristics and machine learning.
Prescriptive analytics takes information about potential situations, past and current performance and available resources to suggest a course of action or strategy. Essentially, it is the opposite of descriptive analytics which examines decisions and outcomes after they’ve happened.
Examples of data points in retail
As a marketer in retail, you can use data points to inform your marketing strategy, and in turn, improve customer experience and loyalty. Analysing your data can help you to understand your customer’s shopping habits and their purchase history, to then offer them personalised shopping experiences and improved customer service. Here, we’ll uncover some key examples of data points in retail.
Business analytics takes traditional data analytics and puts it into the context of your business. It involves taking in and processing historical business data, analysing said data to identify trends, and then make data-driven business decisions based on those findings.
Business analytics comes with several benefits including:
- Informed decisions: Say goodbye to off-the-cuff decisions. Instead, business analytics can help you make strategic decisions to improve your marketing strategy.
- Increased revenue: Business analytics can increase your ROI and overall financial returns.
- Streamlined operations: With business analytics, you should be able to anticipate operational issues before they become larger problems. You can use your data to foresee problems before they occur, which can save operational costs and keep your operations running smoothly and efficiently.
Forecasts are all about the future, so you’d be right in assuming sales forecasting is estimating future sales. Sales forecasting involves estimating future revenue by predicting the amount of product or services a sales unit (such as a salesperson, or even a company) will sell in the next week, month, quarter, or year. An accurate sales forecast can improve your team’s decision-making and focus your sales team on high-profit sales pipeline opportunities.
Like sales forecasting, demand forecasting involves predicting future outcomes. Demand forecasting uses predictive analysis of historical data to predict customers’ future demand for a product or service. This can help your business make well-informed supply decisions, as they can estimate the total sales and revenue for a future month or year. This can be beneficial for marketers, as you can align your marketing strategy with these insights. Let’s say it’s been predicted that a certain product will sell more during the upcoming month - with this in mind, you can tailor your marketing activities accordingly to increase sales.
As the name suggests, web analytics is the analysis of data generated by users interacting with and visiting your business’ website. Web analytics can be used to measure user behaviour, optimise the user experience of the website and help you meet business objectives, such as increased conversions and reduced cart abandonment rate.
Customer experience analytics
Customer experience analytics is the analysis, processing and evaluation of customer data to improve customer experience. Customer experience data can come from both offline and online sources, including customer surveys, social media interactions, loyalty programme sign-ups, cart abandonment rate and mobile app usage.
How to utilise data points in your business
Data points within retail can unlock key insights into your customers’ behaviour and buying habits, and help you make smarter marketing decisions. Here are just some of the ways you can use data points in your retail business to your advantage:
- Improve your decision-making: Using data points can help your team make better, more informed decisions. You’ll gain a clearer understanding of your customers and have data-driven insights about who they are, their preferences and behaviours, rather than guesswork.
- Refine your operations: Your operations can be refined based on the insight your data points provide. This can improve efficiency and delivery, making the process more streamlined.
- Align your goals: Transparency around business goals is vital so your marketing team can collaborate with other departments. You can use data and objectives to establish a strong pipeline. Then, you can use your data to categorise your customers, so you know who you’re looking to target as a business. To convert leads, your marketing team and sales team need to work together to drive the pipeline. Together, you can decide what constitutes a lead and how to categorise your customer data most effectively.
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 can give you a complete picture of your customer data, so you can better understand their needs and behaviours.