Predictive analytics is helping ecommerce businesses improve customer service, prevent fraud, and streamline processes.
A surprising fact is that predictive analytics in ecommerce is asking the same questions as market research analysts would do. Such predictions can include the maximum price a customer is ready to pay, the main pain points in the selling process, or the most popular products this coming Christmas season.
Alexander Marmuzevich, CTO of InData Labs, comments:
If there are any hidden patterns in massive datasets, predictive analytics methods are quite up to detecting those patterns and using them. Risk analysis, trend prediction, scoring of bank clients, targeted ads, and churn management – all of that is based on generated data and predictive analytics.
These insights would make a difference in the way companies make money and allocate their resources. Predictive analytics has the potential to make them more efficient and perform essential functions, which we discuss further.
Tailor-made recommendations and promotions
A recommendation is only efficient when it’s adequate, as even the best offer for a lipstick is unlikely to impress men over 40. Predictive analytics learns about the consumer’s behavior and combines information sources like previous purchases and current search requests to determine their future actions. Predictive analytics tools also work as aggregators, taking information from thousands of customers and computing their most likely preference.
The era of “one price fits all” is long gone. Two different people checking the same hotel booking site or flight booking app can get different prices based on their history or search queries. Even the same person, on the same website, could get different prices for the same product if the algorithms determine that there is an increased interest for the product.
Dynamic pricing means that online shops can dispose of their stocks at the best prices and make a profit when demand for a specific item is rising. Without predictive analytics, it could take months until a trend is identified, but with its help online stores can automatically modify prices within a given range, like a savvy manager would do in the old days.
Fraud prevention or minimization
Online fraud is hurting both retailers and customers. For ecommerce, fraudulent transactions can result in millions of lost revenue, while for customers the risks include lost time trying to get the money back or cancelling the transaction.
The best predictive models come with built-in fraud prevention capabilities. These are based on location identification, previous transaction history, and the buyer’s profile analysis that looks at buying patterns, usual payment methods, and preferred retailers. These algorithmic models act before the transaction takes place by asking for additional verifications and confirmations.
Fraud prevention mechanisms can also be customized by industry, with fine-tuned triggers that help fight false negatives like purchases from abroad for traveling customers.
Vertical value chain integrations
When your company already has the answer to the most critical questions, including the type of products in demand, the recommended price, and customers’ preferences, you can move confidently to the next steps, including sales forecasting, delivery, and post-sales services.
Predictive analytics adds value in multiple business areas, including stock management, sourcing, and warehouse management. This leads to more accurate cash flows and stock control. Integrating predictive analytics into the value chain results in increased automation of orders, fulfillment, and returns, making the process more efficient and cost-effective in the long run.
Business intelligence for fast accurate decisions
The most valuable selling point of predictive analytics is the ability to help decision-makers understand customer expectations and market trends in near real time. It can be the driver behind more conversions and sales. This is made possible through customized pricing and tailored promotions to suit customers’ profiles.
The BI tools of the future will no longer rely on the answers of the customers, but let their actions speak louder than words. Understanding a customer’s motivation can translate into better product placement and increased sales. This is what Steve Jobs was talking about when he said that the customers don’t know what they want until you show them.
Enhanced customer experience
Customers trade their privacy for time. They are willing to give retailers more details about their preferences in return for getting a customized experience as fast as possible. If you ask modern customers, they would like to see what they need as soon as they enter an online store.
Through predictive models, each customer can be directly targeted, especially if they are high-value customers who tend to spend more. A company that uses this approach is Harley Davidson, which increased its sales leads in a New York dealership by 2,900%, combining predictive analytics with direct contact by a representative.
Since all predictive analytics systems are built using machine learning, their functioning is very dependent on the quality of the input data used for training purposes.
Ideally, a sound system requires multiple data points about users’ behavior over an extended period. Some companies looking to adopt such solutions don’t have relevant past logs to build predictions on. This problem is referred to as sparse data, and one solution could be collaborative filtering.
Other potential problems include recommending items that are not in stock or have been already bought by a customer. To avoid this, the algorithm should always look only at available products and correlate the past purchases with future recommendations.
The good news is that the algorithms are learning faster than ever and that soon these tools will become more accessible in terms of their cost, so more businesses will be able to tap into the power of predictions.