This article is part of our E-commerce & Retail Business resource section
A Harvard Business Review and Snowflake Computing study found that businesses that use data to make decisions are more likely to succeed. Big data is not a new concept but its adoption rate is increasing because of the benefits it provides to retailers. If you haven’t done so already, it is time to get onboard because big data is the key to success. All top retailers such as Netflix and Amazon are using big data as a tool to learn more about their customers and applying strategies to boost sales and revenue. Today, the retail industry is changing at a rapid rate and without big data, any business can fail.
The evolution of e-commerce has made the amount of information available to retailers much larger. Online searches, purchases, product views, abandoned carts, customer feedback, inventory movements, and marketing interactions can all provide useful signals. Physical stores generate their own information through point-of-sale transactions, loyalty programs, inventory systems, and other operational tools.
The challenge is no longer simply collecting data. Modern retailers need to determine which information is useful, make sure it is accurate, and turn it into decisions that can improve customer experience, inventory, pricing, and profitability.
Table of Contents
How You Can Reap Benefits By Using Retail Data
Even if you have the best insights in the world from big data there is no point if you are unable to use it to make changes to how you do business. Here are some ways you can use retail data to attract new customers, retain existing customers, and increase overall sales.
Retail data can come from many different sources. Ecommerce stores provide information about online behavior, while physical stores generate transaction and inventory data. Marketing platforms provide information about campaigns, and customer service systems reveal common complaints and questions.
The increasing use of connected technologies is also creating more sources of retail information. The top 7 retail technology trends transforming ecommerce show how technologies such as AI, IoT, analytics, and other digital systems are becoming increasingly relevant to modern retail operations.
Personalize User Experience
You can get insights about your customers from big data and you should use it to better serve your customers by personalizing their experience. According to a study by SmarterHQ, over 70% of customers engage with brands that provide personalized messaging. A study by Adobe revealed that nearly 70% of customers say they won’t buy from a brand that doesn’t offer them a personalized experience. So yes, use big data to personalize your customer’s experience. Take the example of Amazon which has stated that nearly 30% of its total sales are because of personalized recommendations.
Personalization can take many forms. A retailer can recommend products based on previous purchases, display relevant products according to browsing behavior, send targeted offers, or customize communications according to a customer’s interests.
However, personalization works best when the underlying data is reliable. If customer records are incomplete or inaccurate, recommendations can become irrelevant and may actually make the shopping experience worse.
Retailers should also consider the difference between useful personalization and excessive personalization. Customers may appreciate relevant recommendations, but businesses should handle personal information responsibly and provide appropriate transparency about how data is used.
Optimize Pricing
For 60% of people, price is the most important factor in their buying decision. Price is important. Big data reveals information that can enable you to optimize your pricing. You cannot expect success by just being a retailer who has the lowest price because it doesn’t work that way anymore. You need to optimize your pricing so you and your customers are happy. This can be tough but big data can help you find that sweet spot where you can earn a good margin and your customers are willing to spend that much for the product.
Pricing data can include historical sales, competitor prices, inventory levels, demand patterns, promotional performance, and seasonal changes. Looking at these signals together can provide a better basis for pricing decisions than relying solely on intuition.
AI can take this further by processing large volumes of pricing and demand information. Retailers interested in AI for smarter pricing in retail can see how data can support dynamic pricing, promotions, inventory decisions, and other pricing strategies.
The objective should not always be to offer the lowest price. A retailer may instead want to protect margins, clear excess inventory, remain competitive in a particular location, or encourage sales of complementary products.
Use Data To Forecast Demand
When products go out of stock, your customers can go to your competitors if it is a product they really need urgently. In such a case, big data can help because it can efficiently forecast demand. This enables you to keep enough stock so even in the case of surplus demand you have sufficient supply to meet your customer’s needs. You can find out what your customers are searching for more frequently and this can help you get an idea about demand too. Big data reveals information about what the future trends can be and this can help you understand the demand and how certain factors can play a role in your product’s demand.
Demand forecasting becomes particularly useful when retailers have large product catalogs or multiple locations. A product that sells quickly in one store may have weaker demand in another location. Treating all stores or customers in the same way can therefore result in unnecessary stock shortages or excess inventory.
Modern retail analytics can combine historical sales with factors such as seasonality, promotions, pricing, inventory levels, and local demand patterns.
The goal is to get the right products in the right quantities to the right locations at the right time.
Launch New Products And Services
When customers show interest in a particular product and there is no dip in demand, then it can point to their continued requirement of the product. This is the power of big data because it can help you understand how much your products are valued. This can help you understand which types of products are constantly needed and bought. Based on this, you can launch new products which are improved versions of the products you are currently selling. Big data empowers you to understand your customers’ needs completely.
Data can also help retailers identify gaps in their product assortment. If customers frequently search for a product that the store does not carry, that information may justify further research into whether the product should be added.
Similarly, product combinations can reveal opportunities for new bundles. If customers frequently purchase two products together, a retailer may be able to create a bundle that makes purchasing easier while increasing the average order value.
Before launching a new product, however, retailers should combine data with other information such as product costs, supplier availability, competition, and expected demand.
Get The Timing Right
Yes, there is a best time for everything. And instead of just relying on guesswork, big data can enable you to know when the best time is for you to go things. Which products were bought most and when? Which customers shopped for a particular category of products and at which time? Do your customers shop on particular days more often? Questions like these can be answered by big data which can reveal the best time for your marketing strategies to work. This can help cut down costs yet target your old customers and new ones precisely.
Timing can affect everything from advertising and email campaigns to staffing and inventory planning.
For example, if sales consistently increase at a particular time of the week, the retailer can prepare inventory and employees in advance. If customers tend to respond better to promotions during a specific period, marketing budgets can be concentrated around that period instead of being distributed equally throughout the month.
This approach can make marketing more efficient because decisions are based on observed customer behavior rather than assumptions.
Learn About Competitors
A study has found that over 80% of shoppers spend time comparing products before they make a purchase. With the help of big data, you can find out which products are only offered by you and which ones your competitor is successfully selling. You can find out about assortment planning and other details. When you are aware of your competitors you are able to make better decisions to stand out and make decisions that give you a competitive edge. For instance, product matching can help you find out similar products being sold by competitors.
Competitor information can be particularly useful when combined with internal sales data. Knowing that a competitor has reduced the price of a product is useful, but knowing how your own customers respond to that change is even more valuable.
Retailers can monitor changes in assortment, prices, promotions, product availability, and customer interest. These insights can help businesses decide whether they need to change their own offering or focus on a different competitive advantage.
The goal is not to copy every competitor action. Instead, competitive data should help retailers understand the market and make informed decisions.
Provide Better Customer Service
A study by Walker concluded that 86% of customers are willing to pay more if they have a good customer experience. Another research found that 96% of buyers are loyal to brands that offer great customer service. Big data can help you improve customer service which can help retain existing customers and encourage new customers to become loyal. You should use big data to find out what customers like and what they dislike about your brand. This can help you make improvements in various parts of your business.
Customer service data can reveal recurring problems that may not be visible through sales figures alone. If many customers contact support about the same product issue, delivery problem, or checkout difficulty, that information can be used to identify the underlying cause.
Combining customer service information with purchasing behavior can also help retailers understand whether particular problems are affecting repeat purchases or customer retention.
Connecting Data Across Retail Channels
Modern retail is increasingly omnichannel. Customers may discover a product through social media, research it online, visit a physical store, and complete the purchase through a mobile device.
This creates a challenge for retailers because information can become fragmented across different systems.
Embracing e-commerce and omnichannel strategies requires businesses to think about how customer, sales, inventory, and product information can be connected across channels.
A customer should ideally experience the business as one organization rather than as several disconnected systems.
For example, if an item is available online but unavailable at a nearby store, the business should have a clear way of communicating that information. Similarly, if a customer buys an item in a physical store and later interacts with the business online, useful information should not necessarily disappear simply because the channel changed.
A unified view of data can make these interactions easier to manage.
Data From POS Systems
Point-of-sale systems are one of the most valuable sources of retail data because they capture information about completed transactions.
A POS system can help increase retail sales by providing information about products, transaction values, sales patterns, and other activities.
Retailers can use POS data to identify their best-selling products, understand sales by time period, monitor average transaction values, and compare performance between locations.
The type of system required depends on the business. A retailer can also consider the features available in a POS system for a restaurant or retail business when deciding how transaction data should be collected and managed.
The most important point is that POS data should not remain trapped inside the transaction system. It becomes more valuable when it can contribute to inventory, marketing, customer, and financial analysis.
Retail Analytics Turns Data Into Decisions
Collecting data is only the first step. Retailers need analytical processes that help them understand what the information means.
Retail analytics can be used to examine areas such as sales performance, inventory, customer behavior, pricing, promotions, and operations.
There are different levels of analysis. Descriptive analysis explains what happened. Diagnostic analysis investigates why it happened. Predictive analysis estimates what may happen next, while prescriptive approaches can help identify possible actions.
For example, a retailer might first notice that sales of a particular product declined. Further analysis may show that the decline occurred only in certain locations. A deeper investigation might reveal that those stores experienced stock availability problems.
Without analyzing the data beyond the headline sales figure, the retailer could incorrectly conclude that customer demand had fallen.
3 Examples Of Retailers Who Have Harnessed The Power Of Data
There are examples galore of retailers who have used big data to make decisions that have been quite beneficial to them. The following are examples of how some brands have used big data and benefited from it.
Target
Target made use of big data in 2012 by identifying patterns of buying behavior that revealed which women were pregnant or are about to give birth. They used this information to target women to buy at Target so they would continue being loyal to the brand after the baby was born. Target used insights from big data to create personalized strategies that worked wonders and helped increase its revenue manifolds.
The example illustrates the potential of combining different purchasing signals to identify customer needs. It also highlights why retailers need to consider how customer data is collected and used.
Data-driven personalization should provide value to customers while respecting appropriate privacy expectations. Retailers should have clear policies governing which information can be used and how it can be used.
Starbucks
Big data caused Starbucks to innovate and it is the reason why it is as popular today as it was before. Using big data, Starbucks was able to get information such as buyer behavior, traffic, and location to see if it had the potential to earn a profit. This enabled Starbucks to open various branches in close locations while earning well. Moreover, it provided personalized customer experience which helped it grow further.
Location and customer behavior data can be particularly valuable for physical retailers because performance can vary significantly from one area to another.
A store location that performs well for one customer segment may not produce the same results elsewhere. Data can therefore help retailers make decisions about locations, product assortments, promotions, and customer engagement.
The Weather Channel
An interesting example of big data use is when Walgreens, Pantene, and the Weather Channel partnered together. The Weather Channel used big data to provide information such as when the level of humidity will be high. Walgreens and Pantene used that information to target women to buy anti-frizz products at their stores during those times. This led to an increase in sales of 10% and 4% for Pantene and Walgreens respectively.
The example demonstrates how external information can become useful when combined with retail data. Weather, local events, holidays, economic conditions, and other external signals can affect customer behavior.
Retailers can therefore benefit from looking beyond their own transaction history when forecasting demand or planning promotions.
Data Quality Is More Important Than Data Volume
There is no benefit in collecting millions of records if the underlying information is inaccurate, duplicated, incomplete, or difficult to interpret.
Retailers should establish processes for maintaining data quality. Product names should be consistent, inventory information should be updated, customer records should be handled appropriately, and sales data should be reconciled across systems.
Poor data can produce poor decisions. For example, an incorrect inventory count could lead a retailer to believe that a product is overstocked when the actual stock is much lower.
Before investing heavily in advanced analytics or AI, businesses should therefore make sure that their basic data foundation is reliable.
Using Computer Vision to Generate More Retail Data
Physical stores can generate information beyond traditional sales transactions.
Computer vision in retail businesses can potentially provide information about shelves, products, store activity, and other physical conditions.
For example, a retailer may be able to identify whether shelves are properly stocked or whether particular areas of a store receive more customer attention.
These signals can complement POS information. Sales data can tell a retailer what was purchased, while other store-level information may help explain why customers behaved in a particular way.
As with any customer-related technology, businesses need to consider privacy, security, transparency, and appropriate use of information.
Personalization Goes Beyond Product Recommendations
Personalization is often associated with recommending products, but data can support many other aspects of the customer experience.
Retailers can personalize communications, promotions, content, product assortments, and customer service interactions.
The triumph of hyper-personalization in retail demonstrates how retailers are moving toward experiences that are increasingly tailored to individual customer needs and behavior.
However, personalization should not become excessive. Customers should receive information that is relevant rather than a constant stream of irrelevant promotions.
The best personalization uses data to reduce friction and make shopping easier.
IoT Creates More Real-Time Retail Data
The Internet of Things can provide another source of information for retailers.
The transformation of retail business with IoT shows how connected devices can become part of modern retail environments.
IoT devices can potentially monitor inventory conditions, equipment, store environments, and other physical processes. This creates a bridge between the physical store and digital analytics systems.
For example, connected equipment could provide information that helps identify operational problems before they become major disruptions.
The value of IoT comes from connecting the information to an action. Collecting sensor data without a clear purpose simply creates another data source to maintain.
Data and AI Work Better Together
Artificial intelligence can process large volumes of information and identify patterns that may be difficult for people to detect manually.
Retailers can use AI to support demand forecasting, pricing, customer segmentation, recommendations, inventory planning, and other activities. Current retail AI systems are increasingly designed to combine information about sales, inventory, pricing, promotions, customers, orders, demand, and channels.
But AI does not eliminate the need for good data. If the information is incomplete or biased, the resulting recommendations may also be unreliable.
Retailers should therefore see AI as another layer on top of a strong data foundation rather than as a replacement for data management.
Using Data for Inventory Management
Inventory is one of the areas where retail data can have an immediate business impact.
Too much inventory ties up capital and may eventually require markdowns. Too little inventory can result in lost sales and disappointed customers.
By analyzing historical demand, current sales, promotions, seasonality, and inventory levels, retailers can make more informed replenishment decisions. Modern retail forecasting systems can use these signals to adjust forecasts as demand patterns change.
Retailers with multiple stores can also use data to determine where products should be allocated. A product that is slow-moving in one location may be in high demand somewhere else.
This can help businesses use existing inventory more effectively before ordering additional stock.
Data Can Improve Marketing Efficiency
Marketing campaigns generate another valuable source of retail information.
Retailers can track which campaigns produce website visits, product views, purchases, repeat purchases, and other desired actions.
This makes it possible to compare marketing channels and allocate budgets more intelligently.
Instead of simply asking how many people saw an advertisement, retailers can investigate what happened after those customers interacted with it.
For example, a campaign may generate a large amount of traffic but very few purchases. Another campaign may generate fewer visitors but a much higher conversion rate.
Data helps reveal that difference and allows the retailer to make better decisions about future spending.
Protecting Customer Data
The increased use of retail data also creates responsibility.
Customer information should be handled securely and used for legitimate business purposes. Retailers need appropriate controls around access, storage, sharing, and retention.
Businesses should also be careful when combining information from multiple sources. Creating a highly detailed customer profile can provide useful insights, but it also increases the importance of responsible data governance.
Trust is an important part of the customer relationship. A retailer that uses data responsibly can build confidence, while poor handling of customer information can have long-term consequences.
Starting Small With Retail Data
Retailers do not need a massive data platform before they can begin making better decisions.
A practical starting point is to choose one business problem.
For example:
- Reduce stockouts.
- Improve pricing.
- Increase repeat purchases.
- Improve marketing conversion.
- Identify slow-moving products.
- Improve customer service.
- Increase average transaction value.
Retail businesses can also use data from operational systems and retailer portals to manage day-to-day activities. For example, retailers looking for information about the APSBCL retailer login can use the relevant portal information as part of their broader retail technology workflow.
The retailer can then identify the data needed to answer that specific question.
Once the process is working, the same approach can be extended to other areas.
Starting with a clearly defined business objective makes it easier to measure the value of data and prevents businesses from collecting information simply because it is available.
Bringing Retail Data Into Daily Decisions
The most successful data strategy is one that becomes part of everyday decision-making.
Store managers may use sales information to adjust inventory. Marketing teams may use customer behavior to improve campaigns. Merchandising teams may use demand forecasts to plan product assortments. Pricing teams may use competitor and demand information to adjust prices.
Data should therefore be accessible to the people who make decisions rather than being limited to a specialist analytics department.
Dashboards and reporting systems can help by presenting important information in a format that business users can understand quickly.
There is no escaping big data in retail
There is no escaping big data in retail. It was used in the past, is currently being used in the present, and will definitely be used by more retailers in the future. Big data is the ultimate answer to winning in retail. But remember that the application of big data is what makes a world of difference.
As you have seen in the examples above, those retailers who cleverly used big data were able to increase sales and connect to customers. But how can you get started on big data that will give you valuable insights? You can try Intelligence Node solutions as it offers a range of solutions that can help you better your business and make it more successful.
The most important lesson is that collecting data alone does not create an advantage. The advantage comes from using reliable information to make better decisions.
Retailers can start with basic sales and inventory reporting and gradually move toward more advanced analytics, predictive forecasting, personalization, AI, and connected technologies.
The future of retail will likely involve increasingly connected sources of information. POS systems, ecommerce platforms, mobile applications, customer databases, inventory systems, IoT devices, and other technologies can contribute to a more complete view of the business.
When these systems are connected properly, retailers can move from reacting to what already happened toward anticipating what customers may need next.
Data can help a retailer understand what customers bought, why certain products performed well, where inventory is needed, which promotions worked, and which parts of the customer experience need improvement.
That is ultimately the value of data in retail: not having more information for its own sake, but having better information available at the moment a business needs to make an important decision.
