In our previous blog, we discussed the advantages of implementing AI for businesses. Now it’s time to discuss leveraging AI for Ecommerce marketing in various ways.
AI-driven marketing is a well-accepted mandate for businesses who are digital. According to SalesForce, 84% percent of marketers today are reported to be using AI, a 186% increase in adoption since 2018. So much that AI is being introduced in different forms at different stages of the marketing funnel. There is AI generated content for scaling content generation and SEO ranking as well as advanced AI-powered marketing analytics to target and retain customers .
The need for Hyper-Personalization in 2022
According to a report by McKinsey, over 70% of modern consumers want businesses to deliver personalized experiences to them, and personalization generates 40% more revenue for fast-growing businesses.
This is because customer churn rates, and acquisition rates are also incredibly higher for the businesses who haven’t leveraged AI analytics for enhanced consumer experience, thereby making a strong case for relevant product recommendations using Machine Learning capabilities.
This is where Amazon has excelled over all its competitors with their flagship ecommerce store that provides every user with a tailored experience the moment they enter the app or website (based on their interests and past shopping behavior). Plus, 35% of Amazon’s revenue is generated by its advanced recommendation engine.
No business has to be Amazon to make hyper-personalization work for them but it is crucial that they adopt modern analytics strategy and solutions to aid with fast growth and industry competition.
According to IMARC, a leading market research company, the global e-commerce market reached a value of US$ 13 Trillion in 2021. To penetrate faster and enjoy a good market share would require resolving the most depressing eCommerce challenges like conversions, high customer acquisition costs, high churn rates, among many others. Customer Retention will matter more than ever before.
Customer Sentiment Analysis
The first step is to be prepared, to know your target segment in and out. This helps with audience research, identifying opportunities and scope for improvement for all your products. Customer Sentiment Analysis uses Natural Language Processing (NLP) to scan across the internet and identify customer’s perception and sentiment towards the brand.
Brands use Customer Sentiment Analysis to build new products, improve existing services, audience research, and build better content around their audience. For a more in-depth understanding of how it works, you can read our blog on customer sentiment analysis, here.
AI-powered Recommendation System
We have reasons to believe that in 2023, the demand for real-time hyper personalization may grow exponentially. A result of increasing customer expectations and trend shifts in buying behavior. There are plenty of ways to implement real-time hyper personalization for your business.
The traditional recommendation systems that many e-commerce sites use still rely on collaborative filtering techniques, which use streams of historical customer data to deliver the recommendations. This process is however slowly moving out of trend and being replaced by real-time personalized recommendation engines thanks to the recent developments in the field of AI.
An AI chatbot is another good example of real-time hyper-personalization where AI enables real-time interaction with multiple users, providing them fast, and reliable communication from the brand. This results in increased customer satisfaction and retention.
Plenty of Ecommerce businesses exploit dynamic pricing techniques that align with their sales strategy. Hospitality, and transport sectors also take advantage of the same. Dynamic pricing is the concept of selling the same product at different price points in response to the shifting market conditions. The process enables businesses to instantly and continually change the prices of their products in real-time. This is why it is also called real-time pricing and is widely used in logistics, the industry most affected by volatile market conditions.
Dynamic pricing is used to strategically sell to different kinds of customers, especially by seasonal planning (holiday pricing, surge in demand pricing etc.), and by product life-cycle. This enables profit maximization and wider market access for the business.
There is a debate that simply refuses to die out. That robots and AI will be responsible for taking over this world. There is a long way to go to ascertain such theories, however, today, AI is somewhat responsible for slowly altering the changing consumer behavior trends taking place globally. Dynamic pricing, hyper-personalization by targeted offers, product recommendations, virtual assistants, chatbots, and other means are definitely working in favor of many organizations. It is in the best interest of e-commerce companies to invest in AI early and stay ahead of the fast-changing trends.