Predictive analytics – the secret weapon of Retail

  • By glider
  • Published: 23 Oct 2019
  • 4 Minutes Read

“Tell me who your friends are, I will tell who you are” is the old saying. “Tell me your Facebook ID and email address, I will tell who you are” is the modern one. One would really be shocked at how much information a third person can find about oneself from internet without even needing a password or any sort of authentication. We leave a bit of ourselves in the form of data, in the never-ending-available-for-all internet log, whenever we do an online transaction or any business deals. Retailers are the early adapters who successfully use these data to increase its sales and customer base.

With the changing consumer buying behaviour to online retail websites, smartphones, online money transfer interactions such as PayPal, retailers collect all sorts of information about their customers as they use their smartphones & Tablets, buy online, withdrawal cash from the bank and post on social media as “Big Data”. Retailers extract value from the Big Data in order to better understand customers, meet customer demands, boost sales, improve margins and become a stronger competitor in the market. For them, more data means a better understanding of customer, thought process, market insights and risk assessment. All of these insights can be used to make informed business decisions if interpreted correctly using predictive analysis technology and tools.

By Segmenting data retailers can form different customer groups which helps them identify the right target group to develop acquisition marketing campaigns to convert prospects to customer. It also helps them to identify the right audience for a new product to, create targeted marketing campaigns, and optimize the sales-channel mix.

Cross sell and upsell models help retailers identify the best cross sell opportunities in convincing existing customers to make more purchases across a broader set of product categories. In simple words this is the reason why we end up spending more money than what we initially allotted for any particular item.

Next product recommendation helps to promote additional products to existing customers when the time is right. Based on the existing products the customer owns, companies determine which of the items should be offered to a customer.

Customer lifetime value (CLV) models are used to design programs to appreciate and reward valuable customers. By calculating CLV, retailers gain numerous benefits such as a way to track sales and growth, the ability to evaluate profitability of marketing strategies and whether a specific customer is profitable.

Voice of the Customer (VoC) are the data comes in the form of unstructured free text captured from sources like call centre records, blogs, social media, customer surveys and emails. They are used to understand customer feedback and predict customer behavior.

Fraud analytics is applied through predictive models Key Performance Indicators (KPIs) and what-if scenarios to detect patterns of unusual activity and prevent attacks on data by predicting compromised areas. Just like how we get an alert mail from bank when it sees any suspicious activities through our debit/credit card.

This ultimate secret weapon along with a broader set of technologies being introduced into the retail landscape, there is an imminent interaction of social and economic trends which is creating the future of retail. Retailers are looking to take on a holistic type of approach that integrates components such as shopper experience, omni-channel strategy, e-commerce, m-commerce etc., because they understand that we, the connected consumers are evolving.

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