Using e-commerce analytics to improve your customer experience
The e-commerce industry has been growing rapidly in recent years, and as a result, customer expectations are constantly evolving. Customers today demand personalized, convenient, and frictionless experiences when shopping online. To meet these demands, e-commerce businesses are turning to AI, ML, and analytics to improve the customer experience (CX).
Here are a few key technologies and approaches that can be used to enhance e-commerce CX through analytics, along with relevant examples of how companies are leveraging these technologies to stay ahead of the competition.
Personalization
In the world of e-commerce, providing a personalized customer experience has become a critical component of success. Customers expect businesses to anticipate their needs and preferences, and to provide tailored recommendations, marketing messages, and shopping experiences. This is where artificial intelligence (AI), machine learning (ML), and analytics can be incredibly powerful tools for e-commerce businesses.
By leveraging these technologies, businesses can analyze customer data, detect patterns and trends, and use this information to provide personalized experiences that meet customers’ unique needs. For example, top e-commerce companies use machine learning algorithms to analyze customers’ purchase history, browsing behavior, and other data to provide personalized product recommendations.
Similarly, e-commerce businesses can use AI to create personalized landing pages for customers based on their search history, browsing behavior, and demographic data. This can help customers find products they are interested in more quickly and easily. Some of the possible use cases are as below:
1. Personalized product recommendations: One of the most common ways that e-commerce businesses use AI, ML, and analytics to personalize the customer experience is by providing personalized product recommendations. By analyzing customer data, businesses can identify products that are likely to be of interest to individual customers and recommend them in a targeted and personalized way.
For example, one can use a sophisticated recommendation engine that is based on machine learning algorithms to analyze customer data, such as browsing and purchase history, and uses this information to recommend products that are likely to be of interest to individual customers. The recommendation engine can also take into account a wide range of factors, including customer preferences, buying patterns, and product popularity.
2. Customized marketing messages: In addition to personalized product recommendations, e-commerce businesses can also use AI, ML, and analytics to provide customized marketing messages. By analyzing customer data, businesses can identify individual customer’s interests and preferences, and use this information to create targeted marketing campaigns. For example, Music and video streaming companies use machine learning algorithms to analyze customer data and provide customized marketing messages. One can use the customer’s preference and past behavior information to create personalized playlists, targeted ads, and other marketing campaigns tailored to individual users’ interests and preferences.
3. Tailored shopping experiences: E-commerce businesses can leverage user experience by providing tailored shopping experiences. By analyzing customer data, businesses can identify individual customers’ preferences and create customized shopping experiences that meet their unique needs. For e.g one can provide an online personal styling service, to create personalized styling recommendations for each customer. Another example of tailored shopping experiences comes from e-commerce grocery stores where instead of manually adding items to the cart, the e-commerce site builds a predefined basket/list of items in the cart for each user, based on past purchases and recommendations and the user needs to only edit or refine and simply check out.
4. Chatbots and virtual assistants: Chatbots and virtual assistants are becoming increasingly popular in e-commerce for customer support and service. These AI-powered tools can answer frequently asked questions, provide recommendations, and help customers find the products they are looking for. Chatbots and virtual assistants can help businesses provide 24/7 customer support, reduce response times, and improve customer satisfaction. There are several types of chatbots and virtual assistants that e-commerce businesses can use, including rule-based chatbots, AI-powered chatbots, and virtual assistants.
Rule-based chatbots are programmed to respond to specific keywords or phrases and can provide pre-defined responses. AI-powered chatbots use natural language processing and machine learning algorithms to understand customer queries and provide more accurate and personalized responses. Virtual assistants can provide more sophisticated assistance, such as helping customers complete transactions or process returns.
For example, H&M uses a chatbot to help customers find products and provide styling recommendations. The chatbot can also help customers track their orders and process returns. For example, a beauty retailer could use a virtual assistant to recommend products based on a customer’s skin type, age, and concerns. This can help customers find products that are tailored to their needs and preferences.
While personalization facilitates a great experience for the user, however, some of the analytics and approaches work in the background also provide a rich context to engage with the user and provide a hyper-personalized experience, some of the use cases related to the same are as below:
5. Targeted promotions: Another effective way to enhance the customer experience using e-commerce analytics is to provide targeted promotions. AI can help e-commerce businesses to identify customers who are at risk of churn and provide targeted promotions to encourage them to stay. For example, if a customer has not made a purchase in a while, an AI-powered system can automatically send them a personalized discount code or offer to encourage them to make a purchase.
Targeted promotions can be generated using a variety of techniques, including RFM analysis and customer segmentation. RFM analysis is a technique that involves analyzing a customer’s Recency, Frequency, and Monetary value to determine their likelihood of making a purchase. Customer segmentation involves dividing customers into groups based on their behavior or characteristics and providing targeted promotions to each group.
Optimizing the customer journey
The customer journey refers to the series of steps that a customer takes when interacting with an e-commerce business. While it is it’s important to understand the customer lifecycle in e-commerce, it’s even more important to Optimize the customer journey for enhancing the customer experience and increasing customer satisfaction. ML algorithms can be used to map the customer journey, identifying points of friction or opportunities for engagement. This can help e-commerce businesses to optimize the customer experience, reduce churn, and increase customer satisfaction.
ML algorithms can be used to analyze customer behavior data such as website navigation, search queries, and purchase history. This data can be used to identify patterns and anomalies in the customer journey, allowing businesses to optimize the customer experience. For example, if customers are consistently abandoning their carts at a certain point in the checkout process, an AI-powered system can identify the issue and suggest changes to reduce cart abandonment. Some of the other ways the customer journey can be optimized are:
1. Predicting churn: Churn refers to the percentage of customers who stop doing business with a company over a certain period. Predicting churn is essential for e-commerce businesses using analytics to improve CX, as it allows them to take proactive measures to retain customers. Predictive analytics can be used to identify customers who are likely to churn in the future, allowing businesses to take proactive measures to retain them.
For example, if a customer has consistently purchased a certain type of product but has not done so recently, an AI-powered system can automatically send them a reminder or offer related products. Predictive analytics as mentioned earlier can be generated using a variety of techniques, including machine learning algorithms such as decision trees, random forests, and logistic regression. These algorithms can be trained on customer data such as purchase history, browsing behavior, and demographic information to predict churn.
2. Customer feedback analysis: Customer feedback is essential for e-commerce businesses as it provides valuable insights into the customer experience. By analyzing customer feedback data from various sources such as social media, reviews, and surveys, e-commerce businesses can identify common issues.
3. Voice search: Voice search is becoming increasingly popular among customers, and AI can be used to improve the accuracy and relevance of voice search results. This can help customers find products more quickly and easily, which can improve the customer experience. For example, many online retailers use voice search on their websites and mobile app to help customers find products. The system can understand natural language and provide relevant search results based on customers’ queries.
4 Fraud detection: With the increasing prevalence of online fraud, e-commerce businesses need to take steps to protect their customers’ financial data. AI and ML can be used to detect fraudulent transactions, which can help prevent unauthorized access to customer data. For example, businesses can leverage learning algorithms to analyze transactions and identify suspicious activity. This helps protect its customers from fraud and increases their trust in the platform.
Learnings in fraud detection can also be used to improve the customer experience by reducing the risk of false declines. False declines occur when legitimate transactions are declined due to suspected fraud. This can be frustrating for customers and can lead to lost sales for businesses. By using AI and ML to improve fraud detection, e-commerce businesses can reduce the risk of false declines and improve the customer experience.
5. Dynamic pricing: Dynamic pricing is a pricing strategy where businesses adjust prices based on factors such as demand, inventory levels, and customer behavior. AI and analytics can be used to implement dynamic pricing strategies, which can help businesses maximize revenue while providing customers with fair pricing. For example, many online food and beverage companies use dynamic pricing to adjust their product prices based on demand as well as the availability of ingredients /raw materials based on factors such as demand, traffic & supply chain constraints.
Related reading: (Case study) Customer review analytics helps e-commerce marketplace enhance shopping experiences
Enhancing customer experience is not just about on-site or on-app experience but also about optimizing logistics and transportation by improving the efficiency and accuracy of delivery processes while reducing costs, and enhancing customer satisfaction through the fastest delivery. Here are some ways in which e-commerce companies are utilizing AI and ML in logistics and transportation:
Predictive delivery planning: AI and ML can help e-commerce businesses use analytics to improve CX to optimize their delivery routes by predicting which orders are likely to be placed, where they will be placed, and when they will be placed. By analyzing data from past orders, weather, traffic, and other factors, predictive algorithms can create optimized delivery routes and schedules that minimize travel time, reduce fuel consumption, and improve overall efficiency. Some of the ways the same can be achieved are as below:
1. Automated vehicle routing: Automated vehicle routing is another application of AI and ML in logistics and transportation. By using real-time data from GPS, traffic reports, and weather forecasts, AI algorithms can optimize the routes of delivery vehicles in real-time, reducing delivery times and costs. This can also help to reduce the carbon footprint of delivery fleets by reducing fuel consumption and emissions.
2. Intelligent fleet management: AI and ML can also be used to manage delivery fleets more intelligently. By monitoring the condition of delivery vehicles and predicting when maintenance will be required, businesses can reduce downtime and improve the efficiency of their fleet. Additionally, AI algorithms can optimize vehicle allocation to delivery routes, ensuring that the right vehicle is used for the right job and reducing unnecessary fuel consumption.
3. Real-time delivery tracking: Real-time delivery tracking is another way in which AI and ML can optimize logistics and transportation in e-commerce. By providing customers with real-time updates on the status of their delivery, businesses can enhance customer satisfaction and reduce the number of inquiries to customer service. This can be achieved by using GPS tracking, sensors, and other technologies to track the location of delivery vehicles in real-time and provide customers with accurate delivery time estimates.
Some of the other techniques used in e-commerce analytics for CX or customer experience are as below:
Demand forecasting: Demand forecasting is a critical component of supply chain optimization. By analyzing data from various sources, including sales history, customer behavior, and market trends, AI algorithms can predict future demand with greater accuracy. This can help businesses to optimize inventory levels, production schedules, and delivery routes, reducing costs and improving customer satisfaction.
Inventory optimization: AI and ML can also be used to optimize inventory levels in the supply chain. By analyzing data from multiple sources, including sales history, lead times, and delivery schedules, AI algorithms can predict the optimal level of inventory needed to meet future demand. This can help businesses to reduce inventory-carrying costs and avoid stockouts.
Warehouse optimization: Warehouse optimization is another area where AI and ML can be applied in the supply chain. for e-commerce analytics to improve CX. By analyzing data on inventory levels, order volume, and warehouse layout, AI algorithms can optimize the placement of inventory in the warehouse to minimize the time and distance required to pick and pack orders. This can help to reduce labor costs, improve order accuracy, and increase throughput.
Predictive maintenance: AI and ML can also be used to optimize the maintenance of equipment and vehicles in the logistics and transportation industry. By analyzing data from sensors and other sources, AI algorithms can predict when maintenance is needed before equipment fails, reducing downtime and maintenance costs.
Route optimization: Route optimization is another important application of AI and ML in logistics and transportation. AI algorithms can optimize delivery routes to minimize travel time and distance by analyzing traffic, weather, and delivery schedules. This can help to reduce fuel costs, improve delivery times, and enhance customer satisfaction.
In conclusion, AI and ML can provide significant benefits to e-commerce businesses by not only providing direct real-time personalization but by also optimizing customer life cycle, supply chain, logistics, and transportation processes. All the above-mentioned processes provide for win–win for e-commerce businesses, because they not only enhance customer experience but also internally improve efficiency, efficacy, and performance of the operational processes and reduce cost, effort, manpower, and wastage.
To know how Netscribes can help you gain a strategic edge over your competitors and expand your market presence through robust e-commerce analytics services, contact us.
Based on insights by Pavan Kumar V, Associate Vice President, Analytics
