Data is a very powerful tool and as long as it’s used responsibly with disclaimers for users to know what they are sharing, data can enhance their experience massively.

I love shoes, mostly sneakers and do tend to splurge quite a bit on them. It was my birthday recently and my wife decided to pamper me. Quite obviously, I opened up my Wishlist on Myntra and there they were — almost 50 odd pairs of carefully curated shoes I must have shortlisted over so many months and occasions. All I had to do was filter for my size, and voila! Here I’m, writing this article wearing my cool new Onitsuka Tiger.

Data on the buying behaviour of users is a blessing for E-commerce. It is a very powerful tool and as long as it’s used responsibly with disclaimers for users to know what they are sharing, data can enhance their experience massively. Let’s understand the how’s of this phenomenon-

1. Website Personalisation: According to an Accenture report, nearly 40% of e-commerce customers have abandoned a website because they found the choices too overwhelming. While a brand would want to create a broad portfolio to cater to maximum customers, a personalised user experience basis an individual’s past browsing patterns reduces the confusion caused due to too many choices and leads to higher conversion. How? Once a consumer logs in, brands can collect their browsing, wish-list and purchase patterns to understand their preferences about favourite products, size, colour and frequency of purchase to roll out personalised experiences at scale.

2. Personalised Communication: Since I buy (and obviously read) a lot, there are more than 30 odd books in my Amazon cart at any given point in time. Whenever the prices for these books change, I get an email from Amazon intimating me on the change of price incentivising me to pick it up. Personalisation of communication can lead to more than 30% increase in monthly e-commerce revenues with very little incremental costs. While on the other hand, it can reduce the need of carpet bombing the same communication to the entire user base leading to high spamming. 48% of consumers spend more when their experience is personalised.

3. Building Loyalty: It’s much more expensive to bring new customers to an e-commerce website than reaching out to existing customers and improving their lifetime value through enhanced experience. Higher customer lifetime value can lead to better unit economics for the brand. Customer loyalty is not just about giving redeemable points for higher spends on the website (it definitely is a necessary ingredient) but providing a better experience on the website through sales previews, personalised service offers, special schemes and creating opportunities to buy more frequently. Brands that have great loyalty programs have seen lower customer attrition, higher lifetime value realisation and better overall unit economics. KPMG’s 19-20 report on customer loyalty suggested that when a customer is loyal to a brand, 86% will recommend a brand to friends and family, 66% are likely to write a positive online review after a good experience.

4. Better User Flow: As a customer browses a website, there are several drop-off points where the customer decides to abandon their journey without a transaction. From the path to checkout page, lack of information on product details page, not being able to redeem points at checkout to inability to find the product easily, it could be anything. All these items are addressable issues. Collection of data for each of these can help tweak important customer touch points on a brand’s website including product search and filtering, shopper education, customer reviews availability, cleaner menu divisions leading to better retention of users on your website and higher conversion rates overall.

5. Higher Average Order Value: A brand’s average cost of bringing a customer to its website is decided by its media targeting strategy. The margins made by every successful customer purchase has to be higher than the amount spent in bringing enough customers to the website so that one of them will end up buying something. This is how unit economics operate. However, once customers are on the website, there is a high chance for brands to increase average order value — by an intuitive recommendation engine. If I am about to buy a book from a website — it can ideally recommend me other books from the same author or books from other authors in the same genre. A lot of websites have great recommendation engines that are based on past behaviour and overall correlations. A good recommendation engine improves the chances of higher overall order value (leading to better unit economics) & provides better consumer experience. As suggested by Mindtree’s white-paper on personalisation, 24% customers who clicked on recommendations have higher AOV, 66% converted better, 21.3% spent more time on the website.

As per McKinsey 2020 consumer data report, 72% of consumers only engage with personalised marketing messages & 80% of self-classified frequent shoppers will only shop with brands who personalise their experience. It’s interesting to observe that, women are 9% more likely to give personal information than men so as to receive a personalised experience.

Data is a powerful tool but a lot depends on how brands collect and deploy it. E-commerce can be intimidating for many users especially when they are browsing a 50,000 sq. ft store worth of merchandise on a 5 inch screen. But with personalisation, brands can actually do their customers a huge favour.

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house