Predictive Analytics: The Game Changer of E-commerce

The E-commerce field is prone to constant change, the evolution in technologies is happening by the second, and the businesses are battling it out in the market to stay ahead. Consumer expectations are now higher than ever, and it’s a challenge for businesses to keep up with the changing trends and meet their needs.

A fairly new solution to staying in the race to become the best is Predictive analytics. Predictive analysis is the process of using current and historical data to predict the future. Businesses use it to improve their operations and manage their inventories better. It can be used by all the different industries alike, be it Manufacturing, Banking and Financial Institutions, Oil and Gas Industry, Retail or Health industry, Energy and Social Media.

CVM AND CEM

Predictive analytics is not a one-sided boon; It benefits the customer more because the data that the company yields is used to improve the customer experience. Predictive analytics takes a holistic approach to maintain and improve their customer base, such as Customer Value Management & Customer Experience Management. 

Customer Value Management is an important aspect that drives personalized marketing by analyzing and understanding the customer buying pattern through effective communication. Its ultimate aim is to improve customer loyalty.    

Customer Experience Management is another approach that the businesses use to track, oversee, and organize every interaction between the company and the customers. The objective of CEM is to continually optimize and provide superior customer experience.

Be it the growing trends of AR and VR being used in websites to help the customers enjoy the benefit of trying on a product at home, or the e-commerce websites showing the videos of their products, every new feature today is designed around customer experience.

Predictive Analytics uses a technique called RFM modeling to understand the value of their customers.

  • Recency: This factor considers the last time the customer interacted with the brand, It is believed that the more recent the encounter, the better as it enables the brand to put across effective communication.
  • Frequency:  How often does the customer shop from the brand’s website or shop? The frequent the visits and interactions, the better the customer’s loyalty; it indicates that the customer finds value in the brand’s products.
  • Monetary: As the name suggests, monetary indicates the amount of money that a customer spends with a brand. The customers are filtered based on their spendings and customers who spend more are treated differently than the others who do not.
RFM Modelling

The customers are rated based on the criteria mentioned above, and an RFM score is arrived at as a result. It indicates how important a customer is and what measures must be taken to make the customer stay loyal.

All the measures to retain the customers do lead to an increase in the Average Basket Size of the customer; Average Basket Size is the average monetary value spent per customer per transaction in your store. Predictive analytics, and its related tools encourage the customer to purchase more than they would otherwise.

Predictive analytics uses techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze the existing data and forecast the future. The reason why Data analytics should be used more frequently is that according to Forbes, the companies today use only around 0.5% of all the Big data with them, which means a massive chunk of these data are untouched and not utilized at all, using them would have been difficult in the past, but now because of the significant advancement in technology, companies are recognizing the need of predictive data analytics.

WHY IS IT IMPORTANT?

CUSTOMER EXPERIENCE

45% of Companies use Predictive analytics for customer satisfaction.

Today an average online consumer is smart, he/she visits multiple e-commerce websites, compares the prices and features, reads plenty of trusted reviews or may even wait till the price drops to make the move of actually purchasing the product, or he might make the purchase offline to leverage the benefit of personally checking out the products, most commonly known as ROBO i.e., Research Online Buy Offline, keeping track of all these interactions is next to impossible for a human analyst, but it’s of almost no difficulty for an intelligent algorithm, By scrutinizing all the past behaviors and keeping track of all the sites visited by the consumer, The system can provide meaningful recommendations based on everything the system knows about the consumer. According to Forbes, companies who adopted Predictive analysis saw a 40.38% influence in the revenue after 36 months of adoption.

OPTIMIZING OPERATION

43%  of companies currently use predictive analytics for product recommendations

At this point in time, companies cannot just decide on a random product to develop and sell; instead, they need to have proper data as on what products will “click” in the market and what products are required by the customers. Generally, if you want to pull such information, you will pay hefty sums to the consulting firms, but with the use of data analytics, you can get these very intricate pieces of information for a fraction of that cost, all you have to do is interpret the data. Inculcating data analytics in business will be beneficial to the companies, in the long run. In this way, you can win over new customers and maintain existing customers.

PRICING STRATEGY

According to a study at Harvard, a 1% improvement in pricing can improve your revenue by 11%

Pricing is the key to the success of an e-commerce company; hence fixing your pricing strategy is pivotal in its future course. Being alert to the different pricing trends that arise during festivals and at the times of high customer traffic will help forecast future trends. The predictive analysis does all this and more, i.e., even keeps a tab on the customers’ response to past pricing trends and what techniques the competitors are adopting, all of which can be used to build an optimized pricing strategy for the product.

DETECT FRAUD

Companies expect to lose roughly 5 % of revenue to fraud each year.

A rise in the digital environment does impose a threat of fraudulent activities, but data analytics keeps a close watch on the customers’ behavior, it monitors all activities on a network in real-time. As soon as the system encounters anything out of the ordinary, it detects it, and counteractions are performed.

Predictive analytics is the future of customer engagement and sales conversion. If adopted at the right time, the possibilities of business development are endless. So connect to NDZ today – at aiswarya.cy@ndimensionz.com , and let us help you in transforming your business.

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