Infinite Analytics


Your Ecommerce is now backed by data science. Know thy customer!

by Akash Bhatia, January 4, 2021

ecommerce backed by data science
Photo by Mark König on Unsplash

Conversational search and recommendations is one of the go-to AI tool for e-commerce as it tops conventional search and recommendation by allowing greater interaction between the user and the system. The system gets to engage in a natural dialogue with your customer directly and understand his or her needs better by asking a series of relevant and sequenced questions. If you are to buy a smartphone, the system may ask your budget, your preferred brand, and your preferred system specifications to narrow down its search, thereby saving you a lot of time and trouble of going through N number of brands. System Ask — User Respond (SAUR) paradigm for conversational search can be achieved via Multi-Memory Network (MMN) architecture, in which the system is fed with a wealth of user data in e-commerce. It conducts a search during the ongoing conversation and responds when it is confident of having the right or most accurate results.

Retargeting ensures sales and is done by placing ads on external websites to grab the attention of customers once they have exited your own online platform. It helps in creating multiple engagements with the customer, and leads to sales. Retargeting can be used a) To connect with existing customers wherein you could add their email addresses to send them specific ads via your retargeting ad network and b) To connect with probable customers or browsers who have shown an inclination towards your product. This is found via their browser cookies and you can retarget these browsers.

Retargeting can be divided into two: Social retargeting wherein the ads are displayed on customers’ social media networks such as Facebook, Instagram, Twitter etc and Search retargeting wherein ads are displayed on customers’ display network such as Google results, YouTube, etc.

Marketers are constantly on the lookout to ensure customers are shown products they’re most likely to purchase and Personalisation of Ads helps. AI helps immensely in product recommendation advertising for e-commerce. With endless data in the bag, AI predicts what the customer wants to buy and when. AI helps in making advertising highly relevant and contextual, thus it resonates with your customers even more. This is done by using the historical data sets in identifying customer buying and behavior patterns, their likes and dislikes etc. Thereafter, your ads are custom-made or personalized for each of your customers, and these would then deliver the required end result i.e. either a click or a purchase. Every positive response adds to the aforementioned data sets used, thereby, making them more efficient.

Targeting with geo-location intel in e-commerce is a sure shot formula to increasing sales as it allows you to tap on-the-go customers. By knowing their location, you can direct customers to your nearby shop, products/services, restaurant etc. This is done through their smartphones through which they share photos of a particular place they are in, as well as GPS enables apps. Once such data is obtained, real-time ad campaigns can be delivered to their smartphones. You are not only connecting to the right customer, but also at the right time. Google Ads contain a feature allowing advertisers to specify a location or a set of locations in which they want ads to be shown.

All these AI tools make the system and the user come closer, while marketers get the required sales, and human effort is minimized.

To know more,

You could also read about how AI Based location targeting can boost your marketing ROI multifold, here.

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