Top 10 Advantages of Using Data Analytics in the Retail Industry

Retail industry is witnessing a rapid change from the last decade.

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Ecommerce portals are replacing brick and mortar shops as the preferred place for shopping. However, this does not mean that brick and mortar retail shops are on the verge of extinction. According to Google’s Zero Moment of Truth research, 70% of consumers research online before purchasing in-store. This proves that there are customers who still prefer to buy from brick and mortar shops.

However, there is a change in the way retail industry functions – both offline and online.

Until few years ago, retail industry focused only on marketing and customer service. Now, the focus is on collecting data, analyzing it, and then using the conclusions attained from the analysis to improve the marketing and customer service strategies.

There are several advantages of using Data Analytics. Let us look at them in detail.

1. Provides targeted communication to customers

We live in an era where personalization of services differentiates businesses from competition. It is noted that personalized marketing is 20% more effective than traditional marketing. This is possible because of data analysis. Retail companies track data at all stages of the buying process. They also capture the past purchases of the customer. This data helps them to target the customer with personalised communication. So, the next time you see an ad of the shoes you had checked few days ago in an unrelated website, you know what is the reason behind seeing it.

2. Predicts demand and managing inventory

Data analytics helps retail companies to understand the customers’ buying needs and focus on areas that have high demand. The conclusion derived from data helps the companies to forecast the demand and accordingly manage the inventory.

3. Optimizes the price

A  US-based retail shop named Stage stores performed some experiments to understand the rise and fall of demand of certain products. They found out that when the price of a product is gradually reduced from the time the demand subsides, the demand increases again.

This is contrary to the popular practice of bringing down the price after the buying season ends and the demand is diminishing. Predictive analysis helped a great deal in determining the rise and fall of demand. Walmart has also built the world’s largest private cloud network to track millions of transactions real-time on a daily basis.

4. Enhances customer experience

Data analytics help retailers in analyzing how customers shop and use this data to produce a seamless customer experience. From choosing a product to buying it, data analytics focuses on providing personalized attention to each customer. This experience goes a long way in gaining customer loyalty.

For example, if you have noticed, when you order a product from an online store, details such as your address and payment method is already available as options in your account. All you need to do is, select the option and buy the selected product. You save time on entering the details. These small methods go a long way in retaining customers.

Another way in which data analytics enhances customer experience is through analyzing the products that customers buy together and giving them suggestions to buy a bundle of things at a discounted price.  For example, if a person buys a mobile online, the portal through data analytics gives suggestions to the customer to buy scratch guard and mobile cover too. This helps retailers to increase their sales through cross selling, and enhances customer satisfaction.

5. Predicts Trends in the market

Amazon has a sale before every major festive season. They have these sales because they have data that proves the worthiness of having it. Marketers use the technique called sentiment analysis that helps them to analyze the sentiments of the market.

For example, marketers know that festive seasons are the time when people buy the most. Similarly, sophisticated machine learning algorithms are used to determine the context and the data gathered is then used to predict the top selling products in a specific category.

6. Finds opportunities with high ROI

Data analytics help retail industry to find opportunities that have a high ROI. For example, retailers use predictive analysis to measure the response of people to marketing campaigns and to understand their willingness to buy a product.

7. Retains customers

A dissatisfied or disinterested customer can give sleepless nights to every retailer. Bringing back a disengaged customer especially when there are several options at their disposal is a huge challenge. However, it is not an impossible task. Data analysis helps in identifying the customer who is not engaging with your brand, the one who could be a long-term customer and the one who will be a frequent buyer. This helps the retailer to introduce offers and incentives that can engage and retain the customers.

8. Determines location for new outlets

This is especially advantageous for retailers who wish to open a new outlet in a particular location. Data analytics helps the retailer to find out places where maximum people spend most of their time. Analytics also provide information on the demographics, their spending power, and the market conditions. This helps retailers to select areas, which they think are apt for operating their retail outlet and get maximum customers.

9. Finds innovative ways to engage with customers

This is a derivative of point no 4. It is a known fact that customers always prefer personalized services and this gives businesses an edge over competition. It is observed that retailers who analyze the data based on the current trends in the market can resonate well with potential and existing customers. For instance, a retail giant found out through analytics that millennials did not visit stores.

So, in order to garner their attention, the retail store opened a below basement area in their New York flagship store where there were selfie walls and options to get customized 3D-printed smartphone cases. This increased the footfall in the stores, and in turn helped in increasing the sales in the stores.

10. Aids strategic decisions

There is a popular quote by W. Edward Deming that goes – in god we trust, all others must bring data. Retail industry is dependent on data to take strategic decisions. Companies rely on data to take informed business decisions using a single and trusted source of information about products and customers.

In conclusion

Retail industry faces challenges such as lack of security and data privacy. Additionally, there is also an issue of lack of skilled team to decipher data and the inability of the companies to implementing the insights gained from analytics into their business.

However, with right skills and correct inference from retailers, data analytics can help in increasing customer loyalty and boost their brand image by delivering customer satisfaction. With technology taking a centre stage in retail industry, it can safely be deduced that data analytics will remain a crucial part of it.

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