Online cart abandonment rates remain as high as 70% on desktop and 80% on mobile. An abandoned cart happens when a customer adds products to their online shopping cart but leaves the site without completing the purchase. This is a huge unrealized revenue opportunity in e-commerce. Even a small percentage of recovery can lead to a significant revenue lift. Let’s understand how AI can help tackle the challenge of abandoned shopping carts. By using AI technologies, brands can identify the reasons behind cart abandonment and deploy strategies to encourage customers to complete their purchases.
AI systems can monitor customer behavior in real time and trigger a reminder if a customer adds a product to a cart but does not check out in the next few minutes. For example, Wendy adds a pair of shoes to her cart, but then stops browsing. AI can trigger an email to Wendy encouraging her to complete her purchase by giving an added incentive such as free shipping or a limited period discount.
Based on the customer’s past interactions, preferences, and items in the cart, AI can personalize the email reminder’s content. This includes product recommendations, personalized messages, or specific discounts tailored to the customer. The message can leverage scarcity techniques such as low stock, impending price increases, or expiring discounts. For example, Carol abandons a cart with an organic cotton sundress. AI can then send a reminder re-emphasizing the benefits of her chosen ecofriendly product and may even suggest an organic jute bag, offered at a discount to go with the dress. Generative AI can dynamically create email content that may resonate with the customer by crafting unique subject lines, compelling body text, and personalized product recommendations based on the user's browsing and purchase history.
AI can also help determine the best time to re-engage a customer and to send a reminder to urge her to complete her purchase. For instance, Diana is usually browses in the evening. However, while browsing, she abandoned a cart in the morning. But AI would determine and send a reminder to her in the evening when she is typically more active and may have a higher chance of completing the purchase.
AI can not only determine the time, but also the best channel to re-engage the customer. This could be via email, SMS, push notifications, or through social media ads. For example, Elle may receive a push notification urging her to complete the checkout of her sunflower tote bag before it runs out of stock.
AI can also be leveraged for feedback collection, initiating a feedback loop if a customer repeatedly abandons cart. It may send an email for feedback on their shopping experience, with questions geared towards uncovering barriers they face at checkout.
AI can be used to prevent abandoned carts in the first place. Using predictive analytics, AI can forecast the likelihood of cart abandonment by analyzing customer behavior patterns and engagement levels throughout the shopping process. This allows businesses to proactively intervene with targeted actions to reduce abandonment rates. AI can also help optimize and simplify the checkout process by analyzing data from numerous customer interactions to identify bottlenecks or pain points in the checkout process. AI can also dynamically offer promotions or discounts specifically targeted at customers who are at risk of abandoning their carts. This can be based on the customer's past behavior, the value of the cart, or other contextual data. Using AI, websites can detect when a user is about to leave the page and present them with a last-minute offer or a reminder to encourage them to complete the purchase. This could be through a popup or through AI-powered chatbots or virtual assistants who can ask if the customer needs help, offer assistance with questions, or provide incentives to complete the purchase.
AI-based trigger mechanisms for abandoned cart require integration of real-time data monitoring, behavioral analysis, and automated actions to re-engage customers who have left items in their shopping carts. Here are the steps involved:
1. Data Monitoring
At the core of the trigger mechanism is continuous monitoring of user actions on a website or application. AI systems track various activities such as items added to or removed from a cart, page navigation patterns, time spent on pages, inactivity periods etc. This data is collected in real-time, allowing the system to react promptly to user behaviors indicative of potential cart abandonment.
2. Behavioral Analysis
Using machine learning algorithms, the AI analyzes the collected data to identify patterns that typically lead to cart abandonment. This might include recognizing that a cart has been inactive for a preset amount of time, identifying exit patterns, such as moving the cursor towards the close button or switching tabs or analyzing the customer’s history of cart abandonment. The AI uses this analysis to predict when a cart is likely to be abandoned based on current and past user behaviors
3. Setting Trigger Conditions
Based on the behavioral analysis, specific conditions are set as triggers for the abandoned cart notifications. These conditions are predefined actions or inactions that, when detected, initiate an automated response. Examples of trigger conditions could be: A user has added items to the cart but hasn’t proceeded to checkout within 20 minutes or a user has visited the checkout page but left the site without completing the purchase.
4. Automated Actions
Once a trigger condition is met, the AI initiates an automated action aimed at re-engaging the customer, such as sending a personalized email or SMS, displaying a pop-up message to complete the purchase, or a push notification to the user’s device if they have an associated app installed.
5. Personalization
AI systems use customer data (previous purchases, browsing history, demographics) to tailor the message or offer in the reminder. Personalization can involve recommending similar products that might interest the customer, offering discounts on items the customer seems hesitant to purchase or tailoring the messaging tone and content to match the user’s profile.
6. Optimization and Learning
AI systems continuously learn from each interaction. They analyze the outcomes of triggered actions (e.g., whether the customer returned to complete the purchase) to refine the prediction models, adjust the trigger conditions, and improve the personalization of messages. This feedback loop helps in optimizing the abandoned cart recovery strategy over time, making it more effective.
Some of the commonly used AI models for predicting cart abandonment are:
Classification Models - These models predict whether a customer will abandon a cart based on past behavior and other attributes. Common approaches include logistic regression, decision trees, and random forests.
Clustering Models - Techniques like K-means or hierarchical clustering are used to segment customers based on their shopping behavior, which can help in tailoring interventions aimed at reducing cart abandonment.
Sequential Pattern Mining - This technique identifies common sequences or paths that lead to cart abandonment, helping marketers to intervene at the right moments.
Neural Networks - More complex AI models like deep learning are used to predict cart abandonment by analyzing vast amounts of data and identifying non-linear relationships that simpler models might miss.
Here is a quick word on how packages such as Salesforce and Adobe handle cart abandonment. In Salesforce ecosystem, Salesforce commerce cloud captures data on customer interactions and behaviors. It leverages Einstein AI for predicting abandonment and personalizing interactions. Then, Salesforce Marketing cloud is used to engage customers to complete their purchase. In Adobe ecosystem, the Adobe real time customer data Platform tracks cart additions and abandonments in real time. Adobe Sensei analyzes customer data and behavior patterns to predict abandonment. When cart abandonment is detected, personalized reminders are sent through Adobe Journey Orchestration or Adobe Campaign. Adobe Target is used to personalize web and mobile experience for cart recovery messaging.
Apart from these, there are several e-commerce platforms such as Shopify, WooCommerce, and Magento that have built-in features or plugins/add-ons specifically designed to handle cart abandonment. CRM systems can also track customer interactions and data to facilitate more personalized recovery strategies.
By leveraging AI and data-driven insights, e-commerce businesses can significantly increase the chances of recovering abandoned carts and turning potential sales into actual revenue. Timely and personalized efforts can drive recovery upto 20%. AI can help push that percentage even higher.
(Article originally published at: https://www.linkedin.com/pulse/cart-abandonment-using-ai-win-battle-against-unfinished-neha-verma-bvaqc/ )
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