Retail analytics refers to the process of synthesizing business data on sales, inventory, and consumer behavior into valuable insights. Merchants can use it to make more strategic decisions about their management and operations.
In 2023, the Southeast Asian retail industry is expected to reach $53 billion in market value, with an estimated user base of 398.8 million by 2026. Because of this, retailers are facing a growing need to optimize their operations and retail data analysis, in order to meet rising consumer demand effectively.
Why is retail analytics important?
Although it’s possible to run businesses based on common sense and guesswork, that approach is neither profitable nor sustainable on a larger scale. Here are some ways retail analytics services can benefit retailers.
Optimized inventory management
In the past, retailers had to rely entirely on past orders to figure out which products they should restock. Even then, they had to calculate the order quantities themselves in order to avoid understocking or overstocking.
Thanks to predictive retail analytics, merchants can now adjust their inventory according to customer purchasing trends. Not only does this allow them to focus on more important tasks, it also boosts customer retention because their desired products are always available.
Better targeted marketing
There is nothing worse than doing shot-in-the-dark marketing because you don’t understand consumer behavior. Ideally, marketing campaigns should target individual consumer groups based on their shopping habits.
With in-store retail analytics, retailers can tailor their marketing campaigns to these groups more effectively based on sales and traffic data. This will ensure that customers respond better to your marketing, allowing you to divert business resources elsewhere.
Advantage over competitors
There are two things retail merchants must do to stand out among their competitors. First, they must identify growing trends and capitalize on them quickly. Second, they must identify flaws in their operations and fix the root cause of those problems.
Both these things can be accomplished with retail analytics software. When retailers can get real-time insights on business KPIs, consumer demand, and more, they can adapt better and faster. This gives them an advantage over competitors who rely exclusively on past performance to make their business decisions.
Types of retail analytics
The field of retail analytics is further divided into four different areas. Each area plays a unique role in business operations and provides different key insights:
- Descriptive analytics
- Diagnostic analytics
- Prescriptive analytics
- Predictive analytics
Descriptive analytics
This is the most common form of retail analytics. In fact, all retailers use descriptive analytics to some degree, because it tells them what is currently happening in their stores. This allows them to make future decisions using past performance data.
Retail analytic tools like the in-store customer tracking software from Comeby leverage descriptive analytics by using data from multiple sources, sometimes simultaneously. For instance, they could combine POS system data with e-commerce data to determine the store’s best-selling products.
Diagnostic analytics
Diagnostic analytics is one of the more advanced forms of retail analytics solutions. It uses the data provided by descriptive analytics to help retailers figure out why something is happening with their business.
Advanced diagnostic analytics services use Machine Learning (ML) or Artificial Intelligence (AI) to produce business insights. This is especially useful for large retailers, who must rely on big data analytics to sift through raw data.
Predictive analytics
While descriptive and diagnostic analytics use past trends to provide useful insights, predictive retail analytics takes it a step further by predicting future trends.
For example, retail analytics trends may predict that consumers respond better to personalized offers. This will prompt retailers to study their customers’ demographics and tailor their marketing campaigns accordingly.
One aspect that benefits the most from predictive analytics is sales forecasting. In the past, retailers used existing sales data or market research to make predictions for future sales. However, with the rise of AI algorithms, retail merchants can no longer keep up using traditional methods.
Prescriptive analytics
Unlike the other three forms of retail analytics, prescriptive analytics uses real-time data to make a retailer’s business strategy. It does this by not only predicting future trends but also prescribing methods to capitalize on them.
With most retail analytics solutions, these trends are predicted using advanced AI and machine learning technology. This makes it the most complex form of in-store retail analytics.
Examples of traditional retail analytics
Cross-selling
Retailer A, who owns a convenience store, wants to sell more soft drinks. By observing his store’s foot traffic, he discovers that most customers stop by at night to buy snacks before going to the cinema next door. With this information, Retailer A decides to cross-sell soft drinks to people who buy snacks.
After looking through his sales data spreadsheet, Retailer A realizes that selling two products together at a lower price is a good tactic to increase revenue. So, he introduces a bundle discount for customers who buy both a snack and a drink.
Identifying lost sales
Retailer B, who owns a shoe store, notices that her store failed to hit her sales KPI. She decides to use diagnostic analytics to find out the root cause of this failure.
After watching customers enter and leave without buying anything, Retailer B investigates further by looking through her store’s customer reviews. When she finds out that most of her lost sales stem from a lack of larger shoe sizes, Retailer B decides to stock larger-sized shoes in order to meet customer demand.
How big data is changing the retail industry?
Currently, big data is revolutionizing the retail industry and giving tech-savvy retailers an edge over their competitors. Those who leverage big data have a better understanding of their customers and operations. Big data analytics allows them to fine-tune their business decisions to maximize profit while also minimizing costs.
For example, AI technology in retail analytics services could run multiple simulations to test for the best pricing/marketing strategy.
Theoretically, one simulation might involve cheaper product pricing, while another may upsell certain products to specific consumer groups. This lets retail merchants see what happens if specific changes are implemented, without the consequences of testing them in real life.
How is data analysis used in retail?
The retail industry generates a gold mine of big data for companies to work with. Common key data sets such as sales figures are analyzed and used to improve their businesses.
Market data
Knowing your competitors is almost as important as understanding your own business. That’s why retail analytics software usually analyzes data on market share, competitor pricing, and the demographics of their customers. This helps you gauge your store’s performance according to industry benchmarks.
Customer data
All customers should be treated equally, but not all are equal. Retail analytics can identify your most profitable customers based on their number of sales, total sales value, etc. and successfully target them with marketing campaigns.
Supply chain data
Understanding supply and demand is key to running a successful retail business. In-store retail analytics can pinpoint profitable products depending on location, time of year, and even demographic. With this information, you can stock up on popular products in advance and capitalize on growing trends.
Read more on “How retail analytics help improve sales?”
What are the benefits of predictive analytics in retail?
Predictive retail analytics revolves around distilling huge amounts of data into actionable insights. However, there are an infinite amount of factors that may affect a business. As such, depending on the retail analytics tools used, merchants will receive different insights. Here are several examples of predictive analytics benefitting retailers.
Personalized customer experience
When a customer’s experience is tailor-made to their preferences, that’s when a business boosts its customer retention rate. Amazon is the most notable example since they use predictive analytics and customers’ browsing data to personalize product recommendations.
Improved customer journey
It’s one thing to give customers good experiences, it’s another to provide them consistently.
Predictive analytics in retail help cultivate long-term relationships with individual shoppers, allowing retailers to win over customers even after they leave the store.
Flexible pricing
One way that retailers profit from trends is to raise prices for popular products. With predictive analytics, you can adapt to these trends more easily and adjust the pricing accordingly.
Optimized marketing
Dynamic pricing isn’t the only advantage that retail analytics platforms offer. With insight into factors like customer profile or web session duration, retailers can craft promotions that will tempt customers into buying more.
Reduced stock wastage
Having too much or too little stock is every merchant’s nightmare. Predictive analytics takes all the guesswork out of inventory management and gives them more peace of mind.
Less customer churn
Dissatisfied customers call for different tactics to avoid customer churn. Retailers can use predictive analytics to identify risk factors in advance, and then offer incentives for customers to continue buying from their stores.
Conclusion
With demand for the retail industry growing every year, merchants need to reinvent their businesses and adapt to the times. Otherwise, they might risk falling behind their competitors.
The best tool a retailer can have is a retail analytics service like ComeBy. Contact us for more information on pricing, and choose the best plan for your business today.
