Predictive analytics is a process that uses machine learning to analyze data and make predictions. It has been used for a long time, though the adoption has been low because of the complexity and costs.
With Big Data, both of these things are changing, as more affordable solutions are now available that can be used by companies of all sizes. Here are six benefits of predictive analytics for online retailers.
1. Predictive Search
A consumer’s interaction with a retailer’s site often starts with site search. If that search can be made intelligent to predict what the consumer is looking for, it will spur sales. Predictive search helps determine that by analyzing past click-through behavior, preferences, and history in real-time. BloomReach, a data analysis firm, does this well, as shown in the screenshot below. BloomReach captures the consumer’s intent, based on past click-through behavior, and analyzes the site content to show relevant product matches for the search term.
The front-end search interface looks easy to the consumer. But behind the scenes, BloomReach enables predictive search by running proprietary algorithms that continually analyze data based on machine learning to show the results to the consumer. This cloud-based solution is easy to deploy and can work with multiple ecommerce platforms.
2. Recommendations and Promotions
Despite the availability of several recommendation and event-driven promotion engines, it is still a challenge to determine the right product recommendation or promotion that will help close a sale. Predictive analytics makes this challenge easier by using machine learning to understand a consumer’s behavior, including the purchase history of that consumer and the performance of different products on the site, to determine relevant recommendations that have a higher probability of generating a sale.
Predictive analytics does the same thing with promotions to identify those that have worked in the past, and then offer the best promotions in real-time based on the consumer’s browsing pattern. The Shopify App Store, for example, contains an app from Canopy Labs that enables retailers to predict customer behavior and increase sales by recommending the right products. This app defines a unique predictive model for each online retailer based on its product type, customer base, and sales forecast.
3. Pricing Management
Predictive analytics analyzes pricing trends in correlation with sales information to determine the right prices at the right time to maximize revenue and profit. Pricing is managed using a predictive model that looks at historical data for products, sales, customers, and more. Based on this model, the price for a given product and customer can be predicted at any given time. Amazon is a heavy user of predictive pricing. You can see this in the image below as different prices for the same product are in different colors. Another solution, WisePricer, adjusts product pricing in real-time based on a pricing algorithm that looks at several inputs, like competitor pricing and product pricing trends.
4. Fraud Management
Fraud management and chargebacks are a retailer’s nightmare. Predictive analytics can lower credit card chargeback rates and reduce overall fraud by analyzing customer behavior and product sales and removing products from the assortment that are more susceptible to fraud. The fraud management predictive models identify potential fraud before the customer completes the purchase transaction, resulting in reduced chargebacks and also reduced labor and fees required to process the chargebacks. Predictive analytic solutions come with pre-built fraud models for a specific industry, such as retail, making it easy to deploy the solution. As an example, ecommerce vendor UltraCart has a predictive analysis solution for managing fraud and reducing chargebacks, called Chargeback Guardian.
5. Supply Chain Management
Predictive analytics helps understand consumer demand, to effectively manage the overall supply chain process. This includes planning and forecasting, sourcing, fulfillment, delivery, and returns. If a retailer can predict the revenue from a specific product — say in the next month — it results in better inventory management, optimized use of the available warehouse space, better use of cash flow, and avoiding out-of-stock items. Large retailers are realizing the benefits of predictive analytics for supply chain management, as evidenced by Walmart’s recent acquisition of Inkiru, a predictive analytics startup with models for supply chain optimization.
6. Business Intelligence
A better understanding of consumers leads to serving them better by offering the products they want at the price they want and with effective post-sales service. Predictive analytics makes this possible by capturing customer information, reviewing trends, and developing models that identify what a customer might like. At times, consumers may not be able to vocalize what they like. But predictive analytics can still recommend the right products. Intelligence gained through predictive analytics helps build a culture of better decision making, where any question that is raised can be modeled using the right data inputs. For example, an app from Custora produces insights to better understand customers and maximize customer lifetime value. It is available on multiple ecommerce platforms, including Shopify and Magento.
Predictive analytics can be a huge competitive advantage for a retailer, though the models have to be thoroughly tested before they are deployed for use on a site. Also, periodic human intervention and supervision is required to ensure the models have not gone awry; all models have some margin of error.