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6 Ways to Increase Revenue with AI in E-commerce

The goal of any online business or business that offers products online is to convert product views into sales. How many times have you gone online and added something into the online shopping cart, just to abandon it despite countless email reminders that “you forgot something in your bag”? Cart abandonment rates are at an all time high with over 75% of individuals abandoning their shopping carts. This article will provide you with six ways data analytics and artificial intelligence can decrease your cart abandonment rate and increase overall sales.

AI is a huge part of ecommerce and it is used for anything from purchase recommendations to targeted advertisements. Gartner has forecasted that by 2023 most organizations that have incorporated AI into their ecommerce platforms will improve customer satisfaction by approximately 25%. - resulting in increased revenue in addition to improved margins. In an earlier study, Gartner has also stated that companies who incorporate AI into their ecommerce platforms will improve customer conversion rates by up to 30%.

At a fundamental level, every business decision is made with the intention and goal of improving sales and increasing profits. When it comes to ecommerce, an effective and proven way to do this is to incorporate AI into marketing, customer experience, and product deployment. In this article we are going to provide six proven and effective techniques to increase conversions and revenue easily with AI.

1. Founding a User Experience Around Data

The user experience (UX) is exactly what it sounds like, it refers to a person’s emotions and attitude about a product, system or service. So as you have probably already concluded, the UX is crucial for any ecommerce business, even ones where online sales are not their primary revenue source.

Having a great UX design normally translates to a strong online customer experience and a high customer follow through rate. Whereas a poor UX design can have severe implications not only for sales but for a companies brand image as a whole. These are the very reasons why ecommerce businesses are trying to find efficient and impactful ways to improve their UX design.

So what exactly does creating a user experience around data even mean? It's not just about looking at conversion rates, product favourites, and return customers. Using interaction data from user navigations though ecommerce platforms can aid in the identification of areas of disengagement. You can track at which point through the customer journey you lose customer engagement. Is it in a select customer groupings? Across many? Analyzing this data can inform designers on how to improve the UX.

These disengagements can be caused by a deteriorated usability as well as poor general aesthetics. It only takes about 0.05 seconds for a user to form an opinion (positive or negative on your platform), and it has been reported that 38% of people will stop engaging if they deem the content or layout unappealing.

Aside from finding areas within your site that cause a decreased customer experience and result in diminishing revenue, AI can be used to optimize website testing. As opposed to traditional A/B testing, there are plenty of resources that offer multiple testing procedures simultaneously.

2. Usage of Chatbots

Intelligent chatbots that are able to use NLP effectively to communicate with customers are able to personalize selections and increase sales by more than 4 times. There are two important caveats to this: the first being that the sales bot is able to simulate human language effectively and the second being that it is able to generate a personalized portfolio.

For the first caveat, Forbes has reported that chatbots increased sales by 67% on average, but 74% of consumers prefer human interaction over a chatbot. This is due to the limitations behind dialogue with the chatbot. As NLP improves the two will begin to become indistinguishable to the degree necessary for gaining personalized customer portfolios.

This personalization created from simple user probes greatly increases the probability of sales. For instance, only 7% of the clicked products are from recommendations. However, on average this 7% of clicks creates 24% of total sales, and 26% of total revenue. As personalization increases, so will this recommendation click rate and therefore total revenue.

3. Product Keyword Optimization

A common issue consumers face when it comes to ecommerce is trying to translate their desired good into an appropriate search query. To illustrate this concept, a study completed by Weblink found that users who are able to locate desired searches are 216% more likely to become to regular users. Aside from generating return users, consumers who are able to quickly and easily locate search products spend on average 21% more on products.

Having a websites search option capable of understanding human input and translating that to desired products and suggestions is becoming more and more important within ecommerce. By using NLP and machine learning it is possible to assign a variety of potential keywords and tags to products to ensure descriptors can account for every aspect of the product. Using AI, you can analyze your inventory and assign a variety of keywords, attributes and descriptors based on the product appearance and description. Machine learning based, this method will allow you to analyze customer searches and product selection habits to continuously improve customer experience and increase search to sales revenue.

4. Pricing Optimization

Product pricing, product discounts and shipping costs are the most influential factors for consumers purchasing one product over another. This is precisely why product pricing strategies are a major part of any business and not just ecommerce. Conducting a fluid competitive price analysis, dynamically pricing your products, and measuring consumers willingness to pay are all feasible and important additions to your businesses operations.

Competitive pricing analysis can inform businesses on how similar or substitute products are demanded across regions, their price ranges, as well as how demand and pricing fluctuates over time. This continuous analysis enables the ability to include dynamic pricing into your business. Knowing the demand and price point of similar products allows for the introduction of targeted product discounts price increases to maximize revenue. Lastly, willingness to pay also follows suit with competitive pricing analysis because you are able to understand how a similar product sells for a higher price and even look further into why it is selling. Is it because there are product shortages? Is the competitor offering something you are not? Incorporating this type of analytics not only has the potential to increase sales but also increase the understanding of your competitive space as a whole - so get started today!

5. Inventory Optimization

Inventory optimization is similar to pricing optimization except it is much more focused on internal factors. Inventory is a major concern for ecommerce businesses, as there is a constant battle between not overstocking and not overselling. Positioning yourself in the happy medium where you can always supply demand but do not waste money on excess and possible dead stocked product.

The major aspects of inventory management are understanding the demand within product categories, forecasting future demand based on historical sales, setting minimal viable stock levels, seasonality, and knowledge of ABC analysis. All of these aspects have their own challenges but investing in an inventory management solution will make incorporating these very important business tracking metrics into your business easier and impactful.

6. Mapping the Customer Journey

This section is strongly linked with understanding your customers user experience when shopping with your business. With decreasing attention span and increasing ease of product acquirement, experiences on an ecommerce site are becoming more important than the products themselves. Understanding the customer journey helps to decrease triggers that cause reduced sales and ultimately lower the activation threshold of purchases.

A lot of the concepts of understanding the user experience happens here, including looking for trends in different customer segmentations (first time buyers, returning customers, lost customers). How do these various segments move through the website, and how do they engage with various touch points throughout their journey. The end goal of mapping a customer journey is to relieve pain points and improve areas of increased or decreased engagements. The data generated through interactions with your business provides the perfect information necessary to reach this goal.


Here we have looked at only six of the many ways in which AI and data can increase your ecommerce sales. AI can help make the customer experience more enjoyable, more engaging, more personalized, among many other things. In conclusion, these six AI practices can all increase conversions, sales, and therefore profit in the ecommerce space.

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06 de dez. de 2021

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