In the past, we have seen how AI & ML can be used in the automotive aftermarket to perform product classification tasks at scale including use of image recognition. In this article, we will read about other general usages of AI and ML briefly that can be applied to the aftermarket segment as well.

Personalized Content Recommendation

When a customer browses a vendor’s website or proceeds to make a purchase, they leave their browsing history data and purchase data. This data collected from many such customer’s behavior and history is used by artificial intelligence models and algorithms to increase sales by producing personalized and tailored products and content recommendation to a customer.

Cross Selling and Upselling AI Engines

Techniques like cross selling (offering products complementary to product customer looking at – like Amazon’s Frequently bought together, Customers who bought this Item also bought) and upselling (upgrade on existing product being looked at in terms of features, brand) are powered by AI&ML engines. These techniques powered by AI can significantly increase sales as it is reported 35% of Amazon revenue comes from such marketing based on customer behavior analytics. Studies on e-retail sector have shown that these techniques can on average easily increase sales by 5% and up to 10%. The homepage of a customer becomes highly personalized as a result of these AI&ML techniques.

Sales and Personalized shopping

When a new customer visits, the geolocation data can be used to tailor the page based on past history of customers from that geolocation. For example, if customers visiting from a particular geolocation have purchased tyres, brake shoes, suspensions due to bad roads in that area, these AI systems use this history to predict a new customer visiting from that location will be more likely interested in buying these products, the home page gets tailored to include these products. Such personalization makes the customer product search easier and leaves good impression due to the ease and quickness of transaction and thereby increasing chances of customer retention apart from increasing sales from cross selling in homepage in the case customer is looking for a different product. A study among 2000 US, UK consumers found 73% preferred to do business with e-retailers offering personalized experiences. This shows the importance of personalized shopping experience in achieving high sales.

AI powered engines are also used to make recommendations based on recent activities of the customer. Hence, we see AI&ML powered engines are at the heart of growth in conversion rates, order value, and loyalty of customer.

Use of AI in reducing product returns

AI systems are also used to analyze product returns data including their geolocation, a particular customer product return history to reduce instances of product return or identify cases of customer misuse. This not only helps increase sales but also saves on product return logistics.

Effect of AI on marketing

The AI systems also power marketing based on customer activities. These engines recommend customers likely to be influenced by relatively expensive retargeting ads. They also play an increasingly important role in email marketing campaigns. AI based on customer activity, browsing time not only personalizes the email but also optimizes the subject line to attract the customer to open the email. The AI systems also send the email at the optimum time it has predicted the customer is likely to view his email and subsequently browse products on the channel. 

NLP sentiment analysis is used in social media marketing and for picking the review and feedback chatter of a product from these medias.

AI powered Customer Service

The AI systems are increasingly used in the customer service arena in the form of AI powered chatbots. The chatbots are trained for common product enquiry questions, complaints and return requests and continue to learn from previous customer interactions and hence do well on predefined tasks. This along with customer behavior, browsing and purchase history is used to make it a highly personalized and successful experience for the customer. They are also trained to redirect the customer to the right person in cases where the task required human intervention. Maturing chatbots are expected to handle 75% to 90% of queries successfully in some sectors. It is estimated that chatbots will save up to $11 billion globally in just health, banking, and retail sectors. It is also estimated that consumer retail spend via chatbots worldwide will reach $142 billion—up from just $2.8 billion in 2019. This shows the growing importance and need of AI powered chatbots in e-retailing. Apart from chatbots AI also powers email bots and voice call bots.  

The AI is also used to monitor the customer review section to weed out fakes and promote verified customer reviews.

Apart from these customer interaction cases, AI is also used in general eCommerce for content management, providing effective product search engine to the customer, price optimization, customer segmentation and warehouse and logistics management through demand forecasting at product level allowing for optimum inventory management.