Avnish Anand on why personalization has become critical now and post Covid-19 for businesses

In this Research NXT Interview, Avnish Anand, the Co-Founder and Online Head at CaratLane, talks about his journey being the owner of digital product management in CaratLane and how his team strives to create seamless and integrated customer experience across traditional and digital channels through the usage of data science and ML-powered marketing tools.

Key takeaways from this Research NXT interview:

  • How machine learning-powered marketing approaches identify escalation prevention mechanisms through sentiment studies for CaratLane customers.
  • Why personalization needs to become even more critical now post the Covid-19 business era and how CaratLane already has the first-mover advantage.
  • How data science is being used by CaratLane in maintaining a lean inventory model and to understand customer’s preferences and start building back the inventory at different stores accordingly.

Here are some extracts from the insightful conversation we had with Avnish.

Could you tell us a little about your background and how you came to be the Co-Founder at CaratLane and your role as the leader of digital product management in CaratLane?

Avnish: I became a part of this company in the initial stages way back in 2007 when the business was formed by just the two founders Gopal and Mithun. The company was started based on the idea that we could sell solitaires and then diamond jewellery online. I came on board without any background in digital business. We built the company slowly and put all the other things in place and took up various roles as required. I was there for three and a half years and then left to join Times Internet. However, in 2016, I came back here, and we started expanding into new things. I started managing more core functions like product management, digital marketing, sales, and so on. While these responsibilities have been kind of organically added, I truly enjoyed every bit of it.

Unlike other industries, the customer journey and the conversion time in the jewellery business is long and complicated. Hence, data science plays a vital role here.

From 13 stores in 2016 to 92 now, this is an excellent progression to being India’s leading omnichannel jewellery brand. How does CaratLane create seamless and integrated customer experience across traditional and digital channels?

Avnish: For us, omnichannel is not the end goal. It is a way of life at CaratLane. What we care about strongly is how do we solve our customer problems in a better way. Our overall business funnel has two critical aspects; the first part is demand generation, which is getting people to the website and lead them into the buying journey. The next part is the buying journey itself which includes browsing, discovering, and selecting products, until it becomes a conversion. So, over the years, from these customer journeys, we identified triggers for purchases and the issues in the buying process. The learnings led us to start the physical stores as well, which instilled more trust and credibility in our online marketplace. The stores were introduced as a destination for “Try and Buy” convenience. Moreover, the stores also cater to a lot of other operational and service delivery requirements. So, whatever solutions or channels get built is always done by keeping the customer as the focus.

Additionally, at CaratLane, we look at customer segments in slightly different ways. A critical section for us is that is it for self-purchase or is it for someone else. Secondly, there are patterns around cities when it comes to try and buy jewellery. Thirdly, very obviously, we have the genders as a segment. We also segment customers based on the frequency of purchase and even occasion-based buying. To sum it up, our customer segments are created based on the various attributes and use cases of the buyers.  

However, to ensure that the customer experience is seamless and very personalized for each customer, we need to find and collect the buyer signals from as many sources as possible. And this is where technology comes into play. I believe complete personalization is still a hopeful dream mainly because standard solutions available in the market don’t work well for us. Secondly, unlike world leaders like Amazon, we don’t have massive data where just correlation itself can be excellent insights. So, I think we are on this journey we will soon be using AI-led personalization once we are at a strong position with the data collecting process and then building the right technology and experiences to create it at scale.

Online Jewellery business primarily runs on a very lean inventory model to ensure the price advantage. Do you use any AI-led data mining for making smarter decisions around pricing and promotions across channels, and even supply chain and inventory management?

Avnish: Absolutely. With the right approach and the right kind of data, we have been able to do inventory planning and saw a lift in business. Even in terms of supply chain and category management, we start by understanding the customer’s preferences and start building back the inventory at different stores accordingly.

Post the Covid-19 phase, the planning and execution of personalization needs to happen in a much better manner as it becomes even more important now.

 The role of artificial intelligence (AI) is and will be crucial for the personalization of services, making smarter decisions around dynamic pricing and offers. Are you leveraging AI-led decision making to deliver omnichannel experiences for your shoppers?

Avnish: Yes, one of the use cases where we have already implemented machine learning tools is performance marketing. All marketing campaigns along with dynamic pricing based on customer attributes, and then the whole remarketing part is being carried out by tools equipped with ML. Another use case is that we try to understand the customer journey with various data points like intent types, and then we accordingly serve the customer with relevant offers.