Insight, Impact, Innovation
HomeNewsroomMaking it Easy - Customers don't like to be sold but love buying and welcome any help they can get

Making it Easy - Customers don't like to be sold but love buying and welcome any help they can get

Jun 30, 2014


Published By : Business Standard- The Strategist

Author: Srikanth Velamakanni, co-founder & CEO, Fractal Analytics

Good salespeople know this and focus on fulfilling customer's shopping mission by offering relevant recommendations. In the online world, analytics aims to substitute this

responsive salesperson with three important differences:

  • We can leverage a "perfect" memory and "infinite" computing power to find what is relevant to the customer at the moment
  • We can reconfigure the store layout and "show" customer only these relevant things
  • We can leverage reviews and buying behaviour of friends/peers

With every action customers are conveying information about their attitudes, preferences, life-stage and socio-economic status. If you regularly buy a full basket of groceries, but never buy meat, odds are higher that you might be vegetarian. What if this kind of customer understanding can be developed algorithmically at internet scale covering millions of customers, billions of transactions and interactions? We can create rich customer understanding across several dimensions. Companies have developed solutions to understand customers, decode their "genome" and use it to make better recommendations.

Once you understand your shopper, you must also learn what she is trying to do. What search terms did she use? What is the time of the day, day of the week and her geo-location? When you search "red roses" instead of "cheap flowers", Google results and Ads are very different. In the first instance, Google shows fewer ads than in the second instance where Google "knows" you are more likely to buy. Understanding customer context can dramatically improve the relevance of product recommendations.

Once we "know" the shopper and the context, we can hyper-personalise her experience of the store with relevant products and offers that meet her needs. This is a problem of plenty — analytical techniques discussed above can prune and prioritise these offers.

When you see a store having sections like "people who bought this also bought this" or "people who considered this product ultimately bought this", the recommendation engine is at work to personalise the page. The higher the relevance of recommendations, the bigger is the size of the shopping basket.

If relevant information about our friends is presented, it can increase conversion and help shoppers take the leap of faith required before high involvement purchases. Advice from other users is usually seen as credible, unbiased information and improves customer conversion and size of the customer's shopping basket.

Online stores frequently have limited time offers (for instance, additional 15 per cent off for three days only) and basket size-based offers (for instance, additional 10 per cent off if you spend more than Rs 5,000) to create time pressure and improve size of the customer’s shopping basket. Customers can get hooked to these tactics and stores might find it difficult to wean customers away from these expensive tactics.

Stores can experiment by understanding customer price elasticity and offering every customer a unique price at which they are willing to buy. So, a store can charge one customer a high price if she is price insensitive and offer another customer enough discount to make her buy. When such pricing is permitted by law, it can still lead to customer angst and loss of trust.