Why Customer Analytics in E-Commerce
When a customer walks into a retail store he will be treated as a king. The shopkeeper will see to that his customer is comfortable with the services they offer. Similarly a visitor who lingers into e-commerce store expects same level of comfort and service. E-Commerce has grown almost three times in a span of four years which is tremendous! In spite of this, there is a lag between customer expectations and what they actually receive.
Most stores offer same content to all their customers. This is because E-commerce companies normally generate leads from social websites or collect data from purchase histories of their wide range of customers. In fact it is quite difficult to tackle huge bundles of data and marketers fail to apply differentiated approaches for individual customers and end up sending ads for all products they offer. Such ads turn out to be irrelevant to the customers or at times they miss out on updating important offers to prospective customers. The probability of losing valuable customers in such instances is quite high.
According to a survey, 74% of online shoppers are frustrated with the fact that the content that they are served online has nothing to do with their interests and 89% of the customers switched brands after a poor customer experience. On the similar lines 20 % of annual percentage revenue losses are due to poor customer experience.
This is where analytics plays its commendable role in addressing these specific concerns. Analytics crafts sophisticated visions by taking entire data of the customer into account at gritty levels. These visions facilitate to construct business rules or “best decisions” that recommend the service or product that is appealing to the customer.
Role of Customer Analytics
In ecommerce, a firm may only be interested in customers who signed up or who made a purchase within a particular period. Analytics segments these set of customers using RFM analysis (based on their recency, frequency and monetary values). Besides transactional parameters of the RFM, analytics can spread its scope in analyzing the sentiments and behavioral patterns using Cohort Analysis. Analyzing behaviors could be subjective most of the time – for example, customers who buy more during seasonal sales or attractive discounts, customers who choose to buy because of the ease and comfort of payments and shipping methods and so on. Analytics resolves this type of data by observing timely and repeated behaviors to further define patterns with empirical data and further, generate insights for marketers on their campaigns and advertising.
The next level of analytics uses customer equity analysis which helps firms to prioritize their customers. At different stages, data identifies related customers and uncovers specific insights. This helps to derive the uniqueness of customers and then deliver communications, offers, and online experiences that reverberates customer vision. This helps us to understand their similarity and difference .Their individuality is exactly what makes personalization so powerful to develop customers’ comfort.