A Complete Guide to Enable Artificial Intelligence in Retail

Understanding Consumer Behaviour in the Retail Sector

Consumer behaviour is the analysis of customers and the mechanisms they use to choose, use (consume), and dispose of products and services, including emotional, media, and behavioural responses from consumers.

As the number of TouchPoints grows, it is increasingly challenging to ensure consistency of customer communication. AI in Retail market can analyze multiple touchpoints using Statistics, Machine learning, Deep Learning, and NLP and represent it through Data visualization tools. This helps visitors to get the required products at the right time and in a highly personalized manner.

To understand customers and to build a 360-degree view of the customers in Artificial Intelligence in Retail, there is a need to analyze the customer journey, buying pattern of the customer, sales analysis, customer behavioural analysis using Online Shopping, Product, and Brand datasets generated from E-Commerce websites.

What are the common factors that influence consumer behaviour?

The various factors that influence consumer behaviour for AI in e-commerce are listed below:

1. Personal factors: an individual's interests and opinions that can be affected by demographics (age, gender, culture, etc.).

2. Psychological factors: an individual's response to a marketing message will depend on their perceptions and attitudes.

3. Social factors: family, friends, social media, income, they all influence consumers' behaviour.

How AI is transforming Retail Industry?

Here is how Artificial Intelligence modernizing the retail industry:

1. Personalized Product Recommendations: AI can find patterns in customer behaviour from prior purchases, demographics, preferences, etc. AI can be used for customized marketing and sales offers, rating based recommendation, location-based recommendations, recommendations based on recent/past activity, Cart recommendation, recommendations based on real-time operation, and matching people with interests, similar products, popular products. AI in retail helps to increase cash flow and sales, Increase in Return on marketing investment (ROMI) as well as an increase in customer engagement and conversion rate.

 

PERSONALIZED PRODUCT RECOMMENDATIONS

 

As shown in the dashboard, the end goal is to recommend products to the customer according to its taste. Artificial Intelligence will analyze the previous history of that particular visitor, analyze their purchasing behaviour like if it finds that specific customer previously purchased items related to the clothing of any particular brand. AI will recommend similar products of similar brands only. This will help retailers to increase customer satisfaction by 80 % by delivering relevant information.

2. Pricing Optimization: Modern business moves at a speed where multiple sales channels are involved. Retailers always want to find the perfect balance of profit, value, and desire. To serve customers in the best manner, there is a need to analyze every aspect, such as seasonality, price, inventory levels, and competitors. AI-based price optimization models help customers and retailers to get the best rates and Price Prediction. For this, AI systems will do analyses in terms of Cost-based pricing, demand-based pricing, competition-based pricing, break-even analysis which allows them to increase demand and maximize profits.

 

PRICING OPTIMIZATION

 

As shown in the dashboard, the end goal is to get the minimum price estimation of a product that has a maximum rating and top-selling. For instance, Customer is searching for the cost of catwalk heels, then Artificial Intelligence in Retail will check Price of those catwalk heels all over the competitors' sites and demand for these heels in the market, then show the price of catwalk heels. AI will also recommend that Amazon have a minimum amount on the top with a maximum rating. In India, it is showing a minimum price on 1st March 2020.

3. Next Best Offer (NBO): Nowadays, Businesses are engaged with customers on their terms as customers interact via preferred channels and at a preferred time and want high-level services and recommendations on relevant offerings. AI-based Next best offer model analyzes the customer information and sales data and gives suggestions regarding products and services offered to the visitor. AI models will understand the sophisticated features of the product and map them with a customer persona (profile), which takes into account features such as purchasing frequency, type, and nature of customer and usage patterns. AI in retail gives the best recommendations to the customer, which increases the accuracy of the sales.

 

NEXT BEST OFFER (NBO)

 

For this, AI-based next best offer system will analyze personal information of users like location, social media used, purchasing frequency through previous purchasing behaviour and give relevant insights. For instance, in the above dashboard, the AI system is showing that in February month, the customer made the last purchase of forever 21 top and no interaction with the webpage after that.  So, accordingly, retailers can plan to send mail to customers along with some relevant offer on forever 21 tops. This will show customer awareness and increase revenue by 10%.

"AI will be accessed as part of advanced applications that enable merchandising processes rather than generic AI platforms."
Source- AI Will Transform Retail Merchandising

 4. Customer Service Optimization: As the trend of online shopping increases, there is also an increase in customer inquiries such as tracking packages, answering queries related to returns, and exchanges. Customers expect immediate responses; otherwise, it might be a possibility that they can move to some other platform. Artificial Intelligence in Retail can automate customer service by integrating chatbots with the website that can help businesses to automate the chat process. Chatbots can resolve 60% queries automatically in less than a minute. Also, forwarding remaining conversations to the right agent.

 

CUSTOMER SERVICE OPTIMIZATION

 

From an analytical perspective, retailers can analyze the behaviour of customers interacting with a chatbot. AI-based chatbot analytical models give insights related to the sentiments of customers while interacting, along with the customer lifetime and how frequently that customer is interacting to know about a particular product.

5. Product Search: Most people do research online before buying any product, which means consumers abandon service providers if their product-related search results displayed are irrelevant. AI in Retail can understand its customer needs by analyzing customer behaviour, interest, and intent and personalize the work accordingly to the person.

 

PRODUCT SEARCH

 

The goal of the retailer is to give the best search result according to the customer's choice. AI-based product search systems analyze the customer based on its interest, brand preference and orders history for a particular customer. These analytical models estimate which product or service has the highest chance of being relevant for the customer. Like in the above visualization, AI will get to know that customers are mainly interested in the heels of this particular brand. So, Artificial Intelligence in Retail will give search results for heels only that will consume customer's time and increase average order rate and a number of purchase by 20% through satisfactory results.

6. Customer Segmentation: By targeting specific groups of people with particular interests, retailers can cross-sell and up-sell their products in a better way. Sending personalized content to a customer will increase the probability of buying the product as well as developing higher brand loyalty as the customer feels more appreciated and valued. AI in Retail can create new segments based on parameters such as New or returning customer or visitor, Past purchase behaviour, Search behaviour, time spent on website or engagement with email campaigns, Purchasing behaviour Benefits sought, Customer journey stage, Occasion or timing, Customer satisfaction, Interest, Usage, Viewed content, Viewing platform (mobile vs desktop), Location, Demographics, Average order value, Likes and preferences which would not be readily apparent to humans when reviewing the same customer data.

 

CUSTOMER SEGMENTATION

 

AI-based systems will analyze the customer in terms of his search behaviour, a past purchase, campaign source, device, location, time spent on a website and do segmentation and further give a recommendation. As shown above, Artificial Intelligence in Retail a particular customer is from the USA and using an iPad to buy clothes and reached the portal via Facebook. Using all this information, AI will segregate and do segmentation of customers to send personalized content. It will increase the probability of buying the product as well as developing higher brand loyalty as the customer more appreciated and valued.

7. Customer Journey Path Identification: Analysis of customer journey will provide valuable information to the business. AI-based customer journey analytics helps to identify the visitor by correlating their activities and attributes across the channels, multi-channel and devices. AI in Retail will gather and analyze visitor journey data such as device ID, browsing, app usage, call logs, locations, check-ins, social media, reviews etc. The same visitor will be identified and tracked through the journey, and the path will be formulated via the identity graph.

 

CUSTOMER JOURNEY PATH IDENTIFICATION

 

The goal of the retailer is to track the user journey to know whether a user is existing or new. AI-based systems will analyze the user journey based on various parameters such as campaign name, campaign source, device, location, UTM medium used, browser. For instance, in the decomposition tree above, i.e., identity graph, AI-enabled system is tracking information about the user. , i.e., it is from India and using three different devices to reach the purchase stage website. Through a push_brand campaign on Facebook, they arrived. Artificial Intelligence in Retail concluded from that, this user is new so, to convert it to the customer retailer can give flat 300 off on their first purchase and free shipping.

8. PREDICT CUSTOMER LIFETIME VALUE(CLV): By predicting Lifetime value of a customer, and retailers can retain highly valuable customers easily. Based on some parameters such as average purchase value, average purchase frequency rate, average customer lifespan, AI model can predict the customer lifetime value. By knowing the customer CLV customer lifetime value, retailers can work on retention of existing customers via email, SMS or social media marketing.

 

PREDICT CUSTOMER LIFETIME VALUE(CLV)

 

As shown in the dashboard, the main motive of retailers is to increase customer lifetime value. To do this, AI-based CLV predictor models will search for lifespan value, purchase value, the frequency rate of customers and predict customer lifetime value. Through this retailer get to know that actions need to be performed for retention of returning through the mail, campaign source and SMS so that they can increase revenue and have a better understanding of customer requirements.

"An algorithm can solve the whole function on its own, without the need of human intervention, such as model swapping or new data marking"
Source- End to End Machine Learning

Use cases of AI in retail

• Product Categorization: To help customers to choose the product from the correct category, e-Commerce companies need to automate product categorization. Sometimes many products could belong to multiple classes; the goal is that customers can input some title or short description of the product, and the system can automatically choose the correct category for you. AI in Retail can automate the categorization of products based on attributes, images and video automatically. This makes the content-related search easier for business and product-related search more accessible for customers.

• Intelligent Demand Forecasting: Forecasting demand allows companies to improve efficiency for supply chain, manufacturing, and operations. By analyzing parameters like Glance views, Unique visitors, the Total number of customer reviews per item, The average customer review ranking per item, The classification of the item's page unit sale and sales amount compared with other things, LBB (Lost Buy Box), Rep OOS, Sellable and unsellable on-hand units. Artificial Intelligence in Retail allows to analyze a much more significant amount of data around these parameters and Improve shopping cart size and sales, Reduce inventory cost, Increase customer satisfaction, Order optimization with much more accuracy as compared to humans.

A Holistic Strategy

To learn more about Artificial Intelligence in Retail Industry, we advise following the below steps: