According to Harvard Business Review, companies using Artificial Intelligence in sales have led to an increase in leads and appointments of more than 50%, cost reduction of
40%-60%, and call time reduction of 60%-70%. Further allowing human employees to utilize their time closing deals.
Recent years have seen an acceleration in the use of AI and the functions it performs. Gartner says AI Augmentation will create $2.9 trillion of business value in 2021. With such huge numbers not using some sort of AI, in 10 years time will be equivalent to not being on the internet today, as Kaza Razat, PWC panel expert and AI developer, puts it.
That brings us to the question: what is Artificial Intelligence? AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Machine Learning is a subset of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so by reading historical data.
Machine Learning and AI in retail specifically use predictive analysis to tailor recommendations to the customer giving them dynamic content for a unique experience, demand forecasting, churn predictions, optimize pricing, visual search, and recommendation as well as using natural language processing to aid customers through conversational commerce. These innovations are giving retailers of all sizes the same tools as company giants like Amazon. Digital transformation increases speed, efficiency, and accuracy using predictive analytics systems.
This article will outline the application of Machine Learning in eCommerce development by discussing applications of eCommerce marketing AI, product discovery, personalization, forecasting, and optimization. How these factor in increasing sales, customer engagement, and customer’s journey. Finally talking about digital transformation in bricks-and-mortar retail and what’s next in the world of machine learning and retail.
How is Machine Learning being used in Ecommerce?
Machine learning is the unsung hero of successful businesses like Amazon, North Face, eBay, and many others. Using trends like dynamic personalization, visual search, and intelligent assistants you can enhance your customer experience and overall engagement.
Recommendation engines entice you into buying something by utilizing sales or product data that includes most popular products, discounted products, and similar products and through customer activity, by analyzing your purchase or browser history. Machine learning algorithms make connections and identify patterns to make recommendations that are relevant to the customer’s needs. This introduces the customer to products they may not have found on their own.
Example: Amazon recommendations show you related products, best-selling, new items, and “customers who viewed this item also viewed” that are tailored to your unique shopping experience and search history.
Visual Search and Visual Recommendation
This function is similar to the above with one difference that recommendations are made of relevant products based on visual similarity. It allows retailers to group similar products in their recommendations based on patterns, style, color, or shape which are hard to express in words but are easier to express through images. This increases the chance of the customer making a satisfying purchase.
Visual search aids in visual recommendations by allowing the users to take pictures from the offline world, camera roll, or even a magazine to search. Gartner says brands redesigning their website to include visual search will increase digital commerce revenue by 30%.
Conversational Commerce is using chatbots and automated assistants to interact with online shoppers adding value to their customer journey whether it is through something as simple as answering a query. These bots use natural language processing to have fluid, natural interactions with customers mimicking human speech to complete any queries instantaneously, adding to a positive user experience.
Dynamic content presents products differently in context. Depending on what the user previously browsed, recently, or regularly purchased. Pop-ups also utilize the user’s location and stage in the customer’s journey. This helps your site be more relevant for the user by personalizing their experience.
How can you incorporate dynamic content in your business? Using dynamic content in emails for conversions, personalizing messages for change in pricing of an item in the cart, or just promoting relevant products are all effective ways.
This includes incorporating personalized elements through chatbots and automated assistants. This helps in forming a one-to-one relationship with your customers. Customers reported that they were 110% more likely to add additional goods to their baskets and 40% likely to spend more than expected, according to BCG. By designing the algorithm so it teaches itself and keeps up-to-date with current trends and styles, retailers like Zalando and AFC have benefitted hugely through personalized assistance.
Forecasting and Optimization
Demand forecasting uses statistical and machine learning models for analyzing huge amounts of data to see the impact of pricing, promotions, holiday sales, and seasonality. It then converts these into decisions for you to take about stocks and pricing. It also helps you anticipate demand for certain products and help you make decisions about stocking strategically.
Ecommerce AI through churn prediction techniques helps pinpoint which segment of a retailer’s customer base is most likely to churn, giving them specialized attention and potentially retaining them. By predicting the potentiality for a particular customer to churn, you can offer them pre-emptive offers or rewards as ‘thank you’. Calculate which customers are valuable enough to reward with multiple discounts versus ones that aren’t worth the resources.
Machine learning’s predictive models identify patterns in areas like marketing positioning, distribution costs, product availability, competition, and time of the year. Using these patterns it creates dynamic pricing which is adjusted in real-time.
Sentiment Analysis & Customer Feedback
AI can analyze texts on social media posts, customer feedback, or chatbot conversations by using natural language processing and machine learning to identify the thoughts and feelings of customers towards a particular product and the features responsible for it. This function helps provide context for shift in sales, churn and demand statistics.
How is offline AI changing bricks-and-mortar retail?
Before the Coronavirus pandemic, 90% of the goods purchased globally were in a physical store according to StoreFront. So it’s clear that people like shopping in physical stores. The question is how can retailers add to customer experience using Artificial Intelligence and Machine Learning?
Facial Recognition is making new headway. In China, apps like Alipay and Wechat pay have incorporated facial recognition to allow customers to make easy and quick payments anywhere. Boutique sweet shop Lolli&Pops demonstrated an opt-in facial recognition program that activated in-store cameras to recognize shoppers and associate them with data from the loyalty program of the company, enabling shop assistants to better serve shoppers according to their preferences and past purchases.
Augmented Reality or Smart Mirrors are using machine learning, computer vision, and pattern recognition to give users a futuristic experience. These allow the customer to virtually try on clothing, accessories and cosmetics in-store by experimenting with different styles, colors, and fits. Sephora in Milan introduced this to positive feedback.
Offline AI can be of use in inventory and merchandising like using trend analysis to inform retailers about choices of stock, product launches, and promotions. By analyzing trends of the past 5 years machine learning can determine the best time to launch promotions, seasonal updates, or marketing campaigns. Live streams consumer feedback and sentiment through blogs and social media also help to determine if further investment would be beneficial or not.
Trends can affect purchasing and by monitoring these trends AI can predict any inventory updates required. One of the worst things for business, after all, is someone who comes to your store looking for something and you don’t have it in stock. To forecast consumer demand at a granular level and predict when a given item will go out of stock, NextOrbit uses predictive analytics, reportedly reducing out-of-stock by 25% and contributing to a revenue rise of 2% to 4%.
Machine learning helps businesses with:
- Real-time business decision making
- Simplifies product messaging and prevents incorrect sales forecasts.
- Easy spam identification
- Boost predictive repair performance
- Recommend right products
- Enhance network and security quality
Atlas SoftWeb helps you do just that by using best-in-class emerging technologies for eCommerce website development. Our machine learning developers help your business by conducting data classification, data mining and analysis, and use machine learning algorithms to build predictive website and application models. Ranked among the top providers of machine learning technologies. We help businesses like yours manage personalized marketing strategies while offering product optimization. Hire AI developers and Machine Learning experts at Atlas SoftWeb for your business!