In this competitive retail landscape, retailers can’t rely on just a single strategy to succeed. Of course, marketing their products and selling online are excellent means to expand their customer base – but that’s just the tip of what’s possible.
62% of retailers reported that the use of retail analytics gave them a competitive advantage in their industry. And if you thought that retail analytics is limited to only analysing the supply and demand of products, you’re mistaken.
It extends towards understanding your customers’ needs, ensuring that your marketing and sales strategies are in the best interest of your clients and the company and helping you stay ahead of any competition.
What Is Retail Analytics?
Retail analytics is the use of analytical data and tools that help businesses analyse trends, performance and patterns to make data-driven decisions regarding marketing, supply chain management or any critical operation.
Retail analytics help businesses gain a broader understanding of their overall performance and health and to make better choices to run the business more efficiently. Generally, retail analytics collects information from the following sources:
- POS systems (date and time of purchase, item sold, and credit / debit card or check details)
- Sensors (collect customer footfall, dwell time, and store heatmap)
- RFID tags (prevent burglary and track frequently moved items)
- Wi-Fi infrastructure (sends out promotional messages and collects approximate location data)
- Mobile GPS (sends out promotional messages and collects precise location data)
- Video cameras (formulate store heatmap and prevents burglary)
- Weather reports (forecast supply and demand to optimize inventory)
- People counter systems (counts the footfall or number of customer visits)
- Staff attendance tracking system (tracks the log time of employees)
Types of Retail Analytics
1. Descriptive Analytics
Descriptive analytics answers the “what happened?” aspect of business activities. For example, when running a promotional campaign, descriptive analytics can be used to determine the overall ROI of it.
Descriptive analytics can also be used to view daily / monthly / weekly sales transactions or for preparing financial reports such as profit and loss statements. This type of analytics collects historical data such as transactional history, inventory changes, or promotional success metrics to give a detailed summary of the business activity in question.
2. Diagnostic Analytics
Diagnostic analytics answers the “why did it happen?” aspect of your business activities. For example, suppose you A/B test a social media promo campaign and saw that one went unnoticed, while the other brought in new customers. Then diagnostic analytics will compare the datasets of the two campaigns and determine why one failed and the other succeeded.
3. Predictive Analytics
Predictive analytics answers the “what will happen?” aspect of business activities. It doesn’t predict any event per se but forecasts the probability of an event to taking place. It allows retailers to predict consumer behaviour, and determine the upcoming trends, aiding them to modify their supply chain processes accordingly.
Predictive analytics can also help brands reduce churn rates. By identifying customers who are about to churn, retailers can offer specialised discounts to retain them.
4. Prescriptive Analytics
Prescriptive analytics answers the “how to make it happen?” aspect of business activities. It looks into “what happened, why it happened, and what will happen” elements of an operation to determine the best actions to perform.
For example, prescriptive analytics can help retailers with price optimization by determining the optimum price ranges that users are willing to pay.
Price optimization is performed by taking into account and modeling data points such as production cost, supply and demand, historical sales data, competitors’ pricing, customer survey data, and demographics.
How Can Retailers Use Data Analytics?
1. Inventory Management
In short, retail analytics can be used in inventory management to keep the shelves full, but not too full as in the US alone, 10% of the grocery store food items are wasted. By tapping into multiple data sets such as changing consumer preferences, weather patterns, and upcoming trends, retailers can foresee demands and stock up accordingly.
With predictive analytics, retailers can fine-tune their promotional campaigns and use historical data to predict the outcome of each campaign.
For example, while running a paid social media campaign, marketers can use predictive analytics to define the target groups that are more likely to engage and convert, thereby reducing its costs.
3. Fraud Detection
Online stores are known for their liberal return policy – which is often used by consumers to scam the retailers. E-commerce giants like Amazon and Best Buy now use big data analytics to differentiate genuine customers from scammers who are wrongfully benefiting from such loopholes.
Companies look into the return history of a particular customer, their purchase habits and the review and ratings of a product (along with many other parameters) to identify these scammers. Retailers also use analytics to minimize payment frauds.
4. Recommendation Engines
Retailers use recommendation engines to recommend the most relevant items to customers. A combination of purchase history, consumer demographic and behaviour, and search history is used for this. If customers are entirely new to a website, best selling or trending products will be recommended to start with.
5. Price Optimization
As previously mentioned, price optimization allows retailers to offer competitive pricing. But lowering the price thoughtlessly will reduce the profit per unit. The key is to find a sweet spot that maintains appreciable profitability against the customers’ willingness to pay – hence the use of analytics.
By gathering and analysing multiple data sets, analytics tools suggest the best pricing strategy for each product. And the data collected may include,
- Historical sales data
- Inventories (supply and demand)
- Customer survey and behaviour data
- Weather report
- Operating costs
- Macroeconomic variables
- Demographic and psychographic data
However, price optimization may not always mean lowering the price. Depending on the product or service uniqueness, availability, and demographics of customers, prices can be increased too.
6. Customer Sentiment Analysis
Customer sentiment analysis is the use of analytics to detect the polarity (positive, negative or neutral) within texts, images or any visual medium. It is especially useful in social media marketing to understand customers’ emotions towards a particular product or marketing campaign.
Sentiment analysis allows retailers to understand the sentiment of their customers in real-time, thereby helping in tweaking their products, store layouts, online presence and branding.
7. Customer Lifetime Value Prediction
For marketers, predicting the lifetime value of customers is critical to ensure they’re targeting the right group. If the cost of acquiring shoppers is more than their lifetime value or if they churn too soon, then the acquisition efforts become futile.
Retailers desire high-value customers, and retail analytics can make them easily identifiable. However, acquiring high-value customers alone won’t do the trick. Instead, they must be retained and by monitoring the spending habits and purchase history of customers, marketers can send specialised discounts or incentives.
Another way a customer can bring more value to a retailer is through word-of-mouth promotion. Customers who are more likely to post user-generated content (UGC) on social media networks will help in bringing more awareness to the brand and ultimately, sales.
8. Location of New Stores
For retailers, establishing brick and mortar stores means investing a considerable amount of money. As the location is among the top factors that determine a store’s success or failure, investors can leverage analytics to make better decisions.
By considering the demographics of an ideal customer, access to public transportation, post code, crime rates, competitor analysis and foot traffic of the area, retailers can make data-driven decisions for higher success rates.
Importance of Retail Analytics
1. It Optimizes Entire Operations
Retail analytics can help businesses analyse foot traffic trends and determine the right number of floor associates they require at specific locations and timings. Retailers can also benefit from demand forecasting, supply chain optimization and promotional analysis, to name a few others.
2. It Helps in Finding the Most Valuable Customers
Retaining customers that bring in the most value is critical and retail analytics makes that possible. By taking into account parameters such as shopping and spending history, in-store visits and referrals, retail analytics can shed light on valuable customers and help businesses streamline their promotional activities.
3. It Helps With Waste Reduction
By analysing the rate of sale and shelf life of each product, retailers can identify the optimal quantity each store location requires at a particular time – all while avoiding wastage due to expiration or mishandling.
4. Identify Profitable Product Ranges
As consumer interests change continually, predictive analytics can enable retailers to identify upcoming trends such as new designs in a jewellery line and optimize their supply chain processes accordingly.
Retailers can also discover undesirable aspects of a specific product and tweak them accordingly. For example, they can look into customer survey data, social media discussions and purchase trends of a product to see whether it is accepted or needs further modifications.
5. Employee Performance Evaluation
For retailers having thousands of stores with hundreds of employees in each, tracking the performance of each individual can be tedious, especially if they rely on traditional techniques. With retail analytics, employers can look into the sales performance of each employee, along with other parameters such as log time, customer satisfaction and many other factors.
Retail Analytics Use Cases
1. ALDO Group
With more than 2000 stores, spread across 95 countries, the ALDO Group, famous for their fashion range, would have had a hard time without retail analytics. The company now uses a mobile reporting app to access analytics, which was made possible by the mobile business intelligence platform SAP BusinessObjects Roambi.
Before employing the mobile solution, stakeholders in the company had a hard time tracking the sales, store traffic and customer data. They used to rely on KPI reports sent out as multiple PDFs.
By the time these reports made it to the district managers, they were outdated. With the addition of retail analytics into the system, managers could monitor the key performance indicators in real-time, and that too in a visually pleasing way.
The ALDO group also uses big data analytics for monitoring payment and billing and for fraud detection, especially during days like Black Friday and Cyber Monday.
Founded in 2010, BaubleBar is an e-commerce fashion store that offers affordable jewellery. The company’s marketing team extensively uses retail analytics to attract new customers, all while keeping the existing customers loyal to the brand. Here’s how:
The company has an internally-built software program that allows marketers to gather customer metrics such as their age, colour choices, geographical location, and most importantly, their preferences (what they like and dislike).
Additionally, the team also utilises data collected from social media networks to see what the customers are discussing. By analysing all these data sets, BaubleBar can predict upcoming trends in jewellery and incorporate the findings into their supply chain – allowing them to come up with products before they hit the mainstream.
This way, the company can stay relevant and cater to the growing needs of its customers. Predictive analytics is critical for the company as the supply chain process of a new product will take around four weeks.
3. Zookies Cookies
Zookies Cookies sells flavoured dog cookies and mixes both online and in-store. During their initial days, they weren’t sure about which flavours to produce the most. To make sure they were manufacturing the right quantity of each flavour, they relied on retail analytics.
With the help of Shopify and Google Analytics, the company was able to collect critical customer data such as their past purchases and geographical location, and understand who its customers are. The company also looked at the number of returning customers and also measured the average time a customer visits their online store before making a purchase.
With analytics, Zookies Cookies was able to understand the flavour their customers were interested in the most, thereby optimizing their supply chain process.
Through analytics, Zookies Cookies also found that many of their customers are existing customers of Nationwide Pet Insurance. This helped them run highly targeted marketing campaigns, which resulted in better sales.
4. Demand Forecasting
A merchandise retailer with thousands of stores around the world had issues with determining the stock-keeping unit level (SKU, which refers to the specific items stored at a particular store or location).
The goal of the analytics solution provider was to help the retailer’s store operation team to reduce the inaccuracies with demand forecasting and predict with less than 5% error for at least 95% of the stores.
The analytics solution provider started with setting up a proper system that could fetch the in-store sales data along with tools to measure the changes in the data collected. After that, predictive analytics was used to improve the precision of forecasting, along with statistical methods such as regression analysis and mixed modelling.
The implementation of the solution helped the retailer to initiate a weekly process that would forecast the demand with reasonable accuracy, with a majority of the stores brought under 5% error. The retailer was also able to pinpoint stores that needed manual intervention to boost productivity.
Amazon extensively uses retail analytics across all its product and service range. On a global scale, Amazon can be considered as a leader when it comes to collecting, storing, processing and analysing customer data, to determine customer preferences and spending habits. Here are some of the ways Amazon does it.
Personalized Recommendation System
Amazon uses a collaborative filtering engine (CFE) to analyse your
- Previously purchased items
- Items present in the online cart
- Products which you rated or reviewed
- Product browsing history
By analysing such pieces of information, the personalized recommendation system is capable of coming up with relevant product recommendations. By doing so, Amazon generates 35% more sales annually.
Anticipatory Shipping Model
Amazon’s (patented) anticipatory shipping model extensively uses big data analytics to predict the products you’re likely to purchase. This method is also capable of predicting when and where you’ll need a specific product.
The items are then shipped to a local warehouse or distribution centre so that they’ll be ready to be shipped at the very moment you order them. With the help of predictive analytics, Amazon can reduce shipping costs and delivery time.
With price optimization, Amazon attracts more customers and experiences an increase in annual profits by an average of 25%. Optimization is performed by taking into account
- Your browsing activity, order history and preferences
- Product availability
- Competitors’ pricing
- Expected profit margin (and many other factors)
The product prices are usually updated every 10 minutes as the data is updated and analysed. This way, Amazon can optimize its profits, even on less-popular products.
Supply Chain Optimization
To reduce the delivery time and shipping costs, Amazon uses supply chain optimization to find the closest warehouses or vendors, with respect to your location. This helps Amazon reduce shipping costs by 10 to 40%. Amazon also uses big data analytics for delivery route optimization and product groupings to minimise delivery time and costs.
Book Recommendations From Kindle Highlighting
Amazon analyses the highlighted words and notes that Kindle readers share with their peers. By doing so, Amazon is able to tap into the reading preferences of a customer, which helps in sending out relevant e-book recommendations.
The French cosmetics brand Sephora wanted to analyse the foot traffic to their stores, in order to optimize their online marketing, in-store sales promotion and employee management strategies.
For that, a retail analytics company combined data from POS (point of sale) systems, video cameras and people counting systems to analyse the trends in footfalls.
The resultant was a retail video analytics solution that gives store managers actionable insights on footfall statistics, peak timings and consumer trends with more than 95% accuracy. The solution also helps in determining the correlation between the frequently-visited product sections and the most-sold product categories.
The worldwide retail analytics market is expected to be valued at US$9.5 billion by 2025, with a growth rate of 18% in the next five years. With extensive adoption of technologies such as IoT, RFID tags and Wi-Fi positioning, retailers will have more data to analyse – which will help in making better, profitable and customer-oriented decisions.