Sonesh Prakash

6:22 PM, 10th Mar 2018

4 ways retailers can boost revenues with retail analytics


With the advent of e-commerce, the dynamics of the retail industry are changing dramatically. The rise of the omni-channel shopping experience has heightened customer expectations for attractive and consistent discounts. This is leading to pricing and promotional challenges for most retail players. 

The customer seeks instant gratification while purchasing through their mobile devices and in some cases is willing to pay extra to get the product/service delivered quicker. He has ready access to all the relevant buying information necessary for the sought after product. If only retailers could predict customer behaviour, it would help them customise their offerings and prices to suit each customer’s fancies. 

In such a scenario, how can a retailer transform his business using retail analytics? 

Retail industry staff including the likes of business executives, merchandising personnel, category managers, marketing and promotion strategists can use retail software solutions to make better decisions. Decision making abilities on pricing, marketing promotions, planning merchandising etc can be greatly optimised through these solutions. 

Here are some ways these solutions can help: 

1. Customisation 

Customers engage with retailers through various touchpoints and at times inexplicable ways. For e.g. a customer may completely abandon his shopping basket while purchasing online. However, the same customer may end up buying a plethora of goods at the company’s offline retail outlet. Retailers have to monitor this behaviour consistently to provide appropriate incentives to activate transactions online thus increasing profits. 

2. Real Time Pricing 

Based on demand supply dynamics, real time pricing can end up making a significant difference in the profits of retail firms. To tackle competition, retailers need to adhere to dynamic pricing in order to remain aggressively competitive and in sync with category wise pricing trends. Such kind of pricing takes into consideration a number of variables including competition prices, product sales, specific regional preferences and consumer actions to determine the appropriate price for closing the sale of a particular product. 

3. Customer Centric Support 

This very function can act as a major differentiator for a retail entity. Big Data analytics ends up giving the customer support representatives a holistic view of each customer’s interactions and transactions with their business. 

For example, consider an irate customer who has used various online and offline channels to complain about an unpleasant experience with your company. If the customer has filled an online complaint form and also spewed venom on twitter and Facebook, the right data analytics solution can update your customer service of this activity in real time. If the customer calls your support number, he will automatically be escalated to the right representative who can handle him better. This will result in better chances of query resolution and ultimately customer retention, thus increasing lifetime value of the customer. 

4. Keeping a tab on fraudulent activities: 

Monitoring suspicious transactions and buyer behaviour online becomes easier through data analytics. For example, in some cases, customers return merchandise after using it or claim that the product was not delivered and sell it through other channels. To combat such malpractices, retailers can leverage data analytics to process their sales transactions against known patterns of fraud, to detect it in real time. 

The data for monitoring the above activities can become unmanageable when retailers start recording millions of transactions on a daily basis. The sheer volume can get overwhelming when initiatives like loyalty programs are added and become ubiquitous. In such an ecosystem, data analysis through the right retail software solution can prove to be an Oracle of sorts to make intelligent and informed business decisions. 

However, the real challenge lies in asking the right questions to uncover the right answers to further business objectives.The future of data analytics would enable solutions to take care of the “right” questions as well, thus preempting those concerns :)
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