data quality

Big Data In Retail: Common Benefits And 4 Real-Life Examples

Big Data In Retail: Common Benefits And 4 Real-Life Examples

Melissa IN Team | India, Retail | , , ,

From defining prices to forecasting demand and deciding what would be the best time to offer customers a discount, big data is revolutionizing the retail space. Did you know that product recommendation algorithms are responsible for 35% of Amazon’s sales? They’re not alone.

Both offline and online retailers are adopting data-first strategies to understand customer profiles, map their needs to products and devise marketing strategies that increase sales and profits. Let’s take a look at some of the key benefits retail companies can experience by utilizing big data.

1. 360-Degree Customer Profiles

Companies using big data are not limited to data entered by customers while signing up and sales history to create customer profiles. They source data from the IP addresses being used, social media, the type of credit card being used for transactions, local and macro events, response to marketing campaigns, etc.

As their data sources expand so does their comprehension of customer needs, likes, dislikes, etc. For example, a customer living in south Bombay is likely to fall into a higher income bracket as compared to a customer living in the suburbs.

Target mined big data and developed a model to identify women who were expecting. Based on their orders for products like unscented lotion and supplements for calcium, zinc and magnesium, Target gave women shoppers a pregnancy prediction score. They then used this profiling to send customers targeted ads for baby products.

2. Demand Prediction

Big data gives retailers a big advantage when it comes to pricing and demand forecasting. By monitoring relevant search words and taking inputs from third-party data sources, they can generate insights on customer behavior and, in turn, anticipate what products are likely to sell more and at what price. It also helps with product design and development.

One of the most visible uses of big data for demand prediction was the result of a collaboration between the Weather Channel, Pantene and Walgreens.

Pantene created advertisements for how their products eased hair issues caused by high humidity levels. They scheduled these advertisements according to data about humidity levels in the air as provided by the Weather Channel.

The advertisement directed customers to local Walgreens stores where they could buy these products. The result was a 10% increase in Pantene sales at Walgreens for July and August and a 4% overall increase in sales of haircare products at Walgreens.

3. Operational Efficiency

Retailers need to make sure they have enough product to meet customer needs without having too much stock sitting in the warehouse. A survey found that 31% of shoppers will switch to a competitor if a product is out of stock. If this happens for a second time, 50% will switch preferences to another retailer.

Big data helps retailers bring together and analyze data from different departments to monitor real-time demand. By comparing this data with the average lead time required to get products from warehouses to retail outlets, you can place fulfillment orders at the right time and ensure best sellers are always in stock.

Walmart uses data from in-store and online purchases, social media trending topics, local events and weather deviations to predict inventory needs. Their suppliers use a real-time vendor inventory management system to reduce inventory for products with low sales forecasts and redirect funds to stock products with higher forecasted demand and greater profit margins.

4. Improve The Shopping Experience

Online and offline stores both have realized the need to focus on improving customer experiences to maintain a loyal audience. Retailers look for insights into customer behavior from all kinds of data. Analyzing customer reviews can help retailers notify customers about garments that run large or small. Similarly, in-store video footage can help identify areas where customers gravitate and thus, influence product placement.

Sephora is a good example of how big data can be used to give customers a seamless omnichannel shopping experience.  Seeing how many customers compared prices on their phones while in a Sephora store and completed their shopping journeys across different channels, Sephora worked on improving their mobile website, app and subscription box.

They gathered data from customers’ shopping history, social media, etc to recommend products based on the customer’s past purchases, skin type, beauty regimen, etc. 

Maximizing The Benefits Of Big Data For Retail

When you’re dealing with big data, the volume, types, uses, applications, contexts, etc. all vary. Thus, meeting data quality standards is a big challenge. Left unaddressed, these big data quality issues can lead to errors in algorithms, make the inferences less reliable and even cause compliance issues. The only way to avoid this is by working on keeping your database clean and updated by verifying customer information like customer address, phone and email.  

Small errors can have big implications on the overall data quality. The system may flag a phone number as invalid because it is automated to accept 10-digit numbers and the customer entered his mobile number preceded by a ‘0’.

Or, let’s say you have incorrect pin codes for customer addresses. This would skew your analysis of orders and affect the accuracy of your demand forecasting. This inaccuracy would make itself visible much later when the retailer either runs out of a product or finds too much of it stocked in the warehouse. In turn, this would lower the trust your team has on data-driven decisions and make them turn to their own biases for future decisions.

The Bottom Line

The good news is that there are tools you can use to automate data verification. Data from CRM records are compared against reliable third-party databases to ensure that it is correct and valid. If additional information is available, you can use it to enrich the CRM records and fill in the blanks. Verification tools also work on standardizing the format of each field. This minimizes the risk of having duplicate records in your database.

When you have high-quality data, the analysis and inferences drawn from it will be reliable. Thus, your retail brand will be able to experience the full potential of using big data.

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