With more people shopping online than ever before, E-Commerce stores today have both many opportunities to attract potential customers and many challenges to overcome in order to generate more purchases and retain buyers.
In early 2021, e-commerce sales reached nearly $5 billion, and by 2024, experts claim they will jump to over $6 billion per year. It’s not surprising, as customers seek high availability and ease of ordering, which is exactly what they get as e-commerce stores grow and evolve.
As time goes by, stores and customers become smarter, and the shopping experience improves with them. This is where AI tools can be useful for E-Commerce stores to enhance and contribute to every aspect of e-commerce, from inventory management to customer service.
According to Accenture, artificial intelligence can improve the business productivity of e-commerce stores by up to 40%, and 87% of the leading global organizations in the E-Commerce field believe that artificial intelligence will give them a significant competitive advantage.
How Do They Use AI for E-Commerce?
You may be wondering what makes AI capabilities meaningful and powerful in the digital commerce world. In short, the ability to collect and analyze vast amounts of data to find patterns and then act accordingly.
Thanks to machine learning algorithms, AI tools also become ‘smarter’ as they engage more with customers and have more data to process. Through data, one can learn customer profiles and present them with offers and messages that resonate with them to improve conversion rates. Based on sentiment analysis, customer service can also be improved to provide a more personalized experience. Even with prices, adjustments can be made to tailor them to the buyer’s profile.
Examples of Using Data Collected by AI to Improve Customer Experience:
1. Personalized Recommendations
With personalized product recommendations, E-Commerce stores can provide more relevant shopping experiences that drive higher conversion rates, larger shopping carts, and customer retention by easily finding what they need.
⚡ How Does Netflix Do It?
The Netflix Recommendation Engine (NRE), powered by AI, is worth billions of dollars annually.
NRE uses algorithms to filter content based on each user’s profile. NRE can filter over 3,000 titles at a time to find the exact content the user may be interested in.
By analyzing data from the user’s behavior, the recommendation engine learns the user’s preferences and creates personalized recommendations for the profile. No matter how many customers there are, the engine can instantly tailor the right products or offers to a specific customer.
2. Dynamic Pricing Optimization
Setting the right price for your product depends on many factors, such as competitor prices, production costs, customer demand, and more. Exploring all this takes time, especially if there are many products.
What if artificial intelligence could do it on its own and automatically change pricing based on its data? Amazing? Excellent because it can!
With AI, products can be priced optimally at any given moment, taking into account costs and competitor prices through big data. In addition, AI tools can predict when to raise product prices and when to launch a sale if they have access to the right data. AI can also change the prices of hundreds or thousands of products in the store, saving time on manual adjustments.
For example, raising prices when consumers want a product immediately but it is not available on the competitor’s site, it is reasonable to assume they would be willing to pay a higher price if they know they can get it immediately.
⚡ How Does Amazon Do It?
Amazon is a giant store that uses dynamic pricing strategy. Whenever its competitors offer promotions or discounts, they constantly adjust prices with up to a 20% discount.
They do this gradually to ensure maximum profitability based on sales forecasts. This allows them to remain the cheapest while maintaining their profit margins.
3. Sales and Demand Forecasting
E-commerce companies constantly use forecasts to manage inventory, plan logistics, warehouse space, and set pricing strategies. However, accurate forecasting of demand is becoming more challenging as historical sales data are no longer sufficient and market changes are rapid and influenced by more factors.
This is where artificial intelligence comes into play. Instead of relying solely on historical data, AI makes sales and demand forecasts using real-time data, including demographic data, weather, comparison of similar items, reviews, and monitoring social media chatter.
⚡ How Does Danone Do It?
Among the companies that have embedded a machine learning system to improve demand forecasting is Danone. In addition to creating more accurate estimates for products with short shelf lives, their AI system also improved planning among different departments – sales, supply chain, finance, and marketing. The AI system enhanced efficiency, inventory balance, and helped Danone improve its service levels in various channels and stores.
4. Voice Search
Voice search is becoming increasingly popular and changing the way people shop online. According to a Perficient survey, 55% of consumers use voice search to research products, while 44% use it to add items to their shopping lists.
The forecast is that by 2023, voice commerce will reach $80 billion worldwide. To benefit from this trend, E-Commerce companies will need to ensure that websites and product lists are voice search optimized, including optimization for key phrases and clear and concise answers to common questions.
Moreover, as voice search technology continues to evolve, there may be opportunities to integrate voice assistants into the shopping experience, such as providing personalized recommendations or allowing customers to place voice orders.
⚡ How Does Sephora Do It?
A good example is the voice search offered by Sephora. In 2017, Sephora launched one of the first activities with Google Assistant, allowing users to order services and listen to beauty podcasts.
Google Assistant users can shop at Sephora and Google Home users can use Sephora’s Skincare Advisor tool to find nearby stores, get skincare tips, and determine their skin type. Users can also ask Google Assistant for makeup guidance with Sephora products through Google Home.
5. Visual Search
Visual search is another new capability that many e-commerce platforms are gradually integrating. It involves using artificial intelligence to allow shoppers to search the internet based on images rather than text or keywords.
For users who are not sure exactly what they are looking for or are typing in imprecise search terms, this capability is very useful to help find relevant products quickly and easily.
⚡ How Does Pinterest Do It?
The Pinterest Lens is an excellent example of this. With it, users can search for items in images they took on their phones or upload existing images to find products. In 2020, the visual search engine registered 459 million monthly active users.
Summary
Artificial intelligence is becoming increasingly important for E-Commerce. AI technology can provide benefits and solutions that enhance the shopping experience and productivity of the company – saving time on various tasks and strengthening the customer experience beyond expectations. Add to that the ability to deal with fraud, improve internal logistics, change product prices on the fly, and what else is there to ask for..
So if you are not yet using AI capabilities to empower E-Commerce activities, now might be a good time to start 😁
