Predictive analysis is an increasingly popular process that utilizes machine learning in order to analyze data and make accurate predictions. Although it has been used for a long time, adoption has traditionally been low due to the cost and complexity of the process. However, big data has led to big changes in terms of cost and complexity, and there are now a wide variety of more affordable solutions that can be put to use by companies of all sizes. We’ve explored some of the main benefits of predictive analytics for online sellers.
Often, the first interaction that a customer has with a retail website is based on a search. Searches that can be made intelligent in order to better predict what the customer is looking for will help to boost sales. Predictive search works by analyzing past click-through behavior, visitor history, and preferences in real-time to analyze the site content and show the most relevant product matches for search terms.
Predictive analytics can make it easier for retailers to determine the right prices at the right time to ensure that revenue and profit are maximized. This is done by analyzing pricing trends in correlation with sales information. Pricing can be managed using a predictive model that takes into account historical data for sales, products, customers, and more factors. This model can be used to most accurately predict the best price for a given product and customer at any given time. One example of a company that uses predictive analytics for pricing management well is Amazon, where product prices tend to change quite a lot based on analytics data.
One of the main things that modern customers want more of when it comes to their shopping experience is personalization, and the best way to achieve this is through the use of predictive analytics. It can often be challenging to recommend the right products to customers in order to help close a sale, but predictive analytics can make the challenge easier to deal with by utilizing machine learning to better understand the behavior and preferences of a certain customer. This includes their past purchase history and browsing history, combined with the performance of various products on the site to determine the most relevant product recommendations that are likely to be the biggest success.
In addition to product recommendations, you can also use predictive analytics to determine which promotions have worked best in the past for customers, allowing companies to offer the best promotions in real-time based on customer data and browsing patterns. Being able to predict customer behavior based on their past patterns and preferences puts customers in with the best chance of closing deals by offering promotions and product recommendations that have worked well in the past.
Supply Chain Management
Businesses are also putting predictive analytics to use in order to gain a deeper understanding of customer demand and make it easier to manage the supply chain process overall. This includes a range of factors including forecasting and planning, fulfillment, delivery, and customer returns. Analysis that allows a company to predict review from a specific product over a certain period of time results in improved inventory management, space optimization, better utilization of cash flow, and avoiding items going out of stock. Many larger retailers such as Walmart are realizing the many benefits of using predictive analytics to manage their supply chains.
Fraud and chargebacks are every retailer’s nightmare. Thankfully, predictive analytics can be used to lower chargeback rates from credit cards and reduce the overall occurrence of fraud by analyzing customer behavior in relation to product sales and removing any products that are more susceptible to fraud. In addition, fraud management models of predictive analytics can identify potential fraud before the transaction is completed, which results in a lower number of chargebacks and the ability to avoid fees to process chargebacks and additional labor costs to deal with the situation. Many predictive analytics solutions that are designed for the retail industry come with built-in fraud prevention features that are easy to implement and use.
Companies can use predictive analytics to gain a better understanding of their customers. In turn, this leads to stronger customer relationships and allows companies to better serve their customers by offering the products and the prices that they want, along with providing a highly-effective customer service post-sale. Predictive analytics makes all of this possible by capturing a range of customer information, reviewing trends, and putting together models for understanding the customer persona and identity. Predictive analytics can come in very useful in situations where a customer might struggle to effectively vocalize what they want; the data gathered can be analyzed to ensure that the right products are recommended and that the customer’s needs are met.
Improved Marketing Campaigns
Along with improving the customer journey, predictive analytics can be put into use by retail companies to turn audience members into paying customers and drive conversions and sales. Predictive analytics is growing in importance when it comes to marketing, with more and more companies collecting data on social media and elsewhere to find out more about their audience and determine which marketing strategies are more likely to work in terms of maximizing sales and turning followers into customers. Predictive analytics can benefit marketing campaigns in a variety of different ways including the option to create more precise, personalized, and targeted content that will spark the interest of followers. Click here to learn more about the use of predictive analytics in marketing.
Finally, one of the main goals for retail companies is to keep satisfied customers coming back for more, and predictive analytics can make it easier for your business to make this happen. Thanks to the role of predictive analytics in getting to know and understand your customers better, you can determine why loyal customers stay around and compare it to the patterns of customers who didn’t come back.
Predictive analytics is now becoming increasingly popular in many business industries for getting to know customers better, boosting marketing results, and building strong relationships through personalized service.