The Use of AI in eCommerce to Personalise User Experience

Gone are the days when questions around technologies like AI, ML and NLP were around the choice of adoption and extent of adoption. Today, given the volume of data that ecommerce businesses (or, for that matter, any business) have to deal with, they have no choice but to turn to these technologies to make sense of data, gain a competitive advantage, make informed business decisions, and record sustainable growth.  

While their adoption has spread across industries from healthcare to manufacturing, their potential in ecommerce focuses on eliminating data silos and creating a centralised source of information, extracting relevant data based on specific objectives and deriving data and insights into it, and enabling businesses to use these insights to deliver more personalised experiences to their customers.  

A report by InsightAce Analytics tells us that the global value of AI tools for ecommerce is set to touch USD 16.8 billion by 2030. More specifically, a McKinsey report indicates that AI adoption has increased significantly especially in the product/service development, sales and marketing, and operations division in ecommerce businesses.  

Let’s look at an AI use case with an example.  

North Face’s AI driven search engine 

The North Face is a US-based ecommerce retailer for sports-based apparel, footwear and accessories. Founded in 1966, the retailer has a presence in 125+ countries, and runs around 1300 own retail stores.  

Some time ago, the retailer decided to incorporate AI into its operations to deliver highly targeted and personalised experience to its web customers. It implemented Fluid’s AI software and IBM Watson’s cognitive computing technology to achieve this.  

For example, let’s say a customer is looking for running shoes on the website. He/she can share a voice input, following which the AI technology will ask successive questions like; what is your shoe size? And, are you a beginner or advanced runner? Where do you plan to run? And so on. Based on the inputs, IBM’s computing technology will search through the thousands of products listed on the site and show what is most relevant to the customer’s responses. 

Here’s the video featuring The North Face AI driven search engine. 

Today, businesses are ready to capitalise on every opportunity they can to stay ahead of the game, and technologies like AI have the potential to help them achieve this. Let’s look at the many ways AI can be used in ecommerce. 

Build Better Website and Search Engines 

In most cases, first-time customers visit eCommerce websites to search for something specific. So, how you design your homepage (what elements and messaging you keep), navigation system and search engine will make a huge difference in the way they engage with your site, and it can determine their future engagements with your brand as well.  

A Merit expert says, “AI, for example, can throw insights into which pages on your website your customers visited, and how much time they spent on each page or with each product. If your homepage, for example, is not performing well (that is, visitors are hardly spending time on your homepage), you can then find out why, and reorganise your site in a way that it meets your customer’s expectations.” 

Similarly, using autocomplete in your search bar can lead to higher conversions. Or, developing an algorithm that shows related products when a customer is viewing a particular product can result in higher cart value and conversion rates.  

Did you know? Ecommerce businesses are now using NLP to make their autocomplete features more widespread. For example, they’re training the algorithms to identify related words, phonetic differences and misspellings to still understand what the customer is searching for and show relevant results.  

A study shows that brands that built an autocomplete feature into their search engine saw a 24% increase in conversions. 

Deliver More Personalised User Experiences 

We’ve spoken about this enough but this still remains the best example of personalisation – Amazon’s homepage experience. When a customer has visited your website and searched for products, or purchased products from you in the past, using AI algorithms can show related products on the homepage the next time the user visits your website. It’s like building a personalised website for each customer based on their past preferences, purchase history or likely related interest.  

A study by Boston Consulting Group found that ecommerce businesses that invested in personalisation saw an incremental growth in revenue of 10% and more. 

Price Your Products Right 

Deep learning technologies can help businesses identify how much a customer would be willing to pay for a product or service based on various factors like demand for the product, economic conditions, competitor pricing and more.  

A very obvious example of using AI to predict pricing would be what airline companies do today. They use various algorithms to arrive at the right pricing for an airline seat – it depends on the weather, the date of travel, the demand for that flight, the price being charged by competitors, the economic conditions like cost of fuel and so on.  

The core of every pricing strategy lies in understanding who your customers are, who your competitors are, and how much your customers can afford to pay. 

Manage Your Inventory Better 

Did you know? Ecommerce businesses lose millions of dollars because of poor inventory planning and excessive inventory in warehouses.  

This is one department that businesses simply can’t leave to hunch or chance. Ecommerce businesses must take into account customers’ buying trends, seasonal changes, and demand and supply of stock when planning their inventory.  

In fact, inventory management is not just from the customer front. Depending on the likely demand, businesses also need to determine if their vendors can meet their demand requirements, and/or if they need to engage more suppliers to meet the demand.  

We recently published a blog on demand forecasting where we talked about the 4 types of forecasting that ecommerce companies must invest in, to prevent loss of revenue and plan their inventory better.  

Enhance Omnichannel Experience for Customers 

Just like The North Face, many businesses today acquire customers from multiple channels – through physical stores, websites, social media channels, emails and so on.  

AI can ensure that no matter which channel the customer chooses to engage with, they get an alignment in experience when they visit your store or your website. In fact, with AI, you can collect insights from across channels and use those insights to improve your performance across every platform you have a presence in. 

The future of AI holds a lot more potential for ecommerce businesses. We’re talking voice-based search, visual shopping experiences using 3D and virtual reality technologies, optimising operations using AI, more enhanced cross selling and upselling opportunities and more. Now is an exciting time for ecommerce to get one step closer to personalising customer experiences. The possibilities are endless. 

Merit’s Expertise in e-Commerce Data and Intelligence  

Our state-of-the-art eCommerce data harvesting engine collects raw data and provides actionable insights  

• Three to four times faster than standard scrapers  

• At lower cost  
• With Increased accuracy (up to 30% compared to standard scrapers)  

Our powerful, new scraper engine can gather massive data sets from multiple sites and geographies in real-time so you can stay informed on customer behaviours and market trends.  

Merit’s eCommerce data engine provides a high degree of confidence in insights generated from analytics – thanks to confidence in the data quality and access to enriched data.  

To know more, visit: https://www.meritdata-tech.com/service/data/retail-data/