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Predictive analysis is a strategy that is becoming more and more essential for businesses. Utilizing machine learning to analyze the data that a company has gathered can now be used to make more accurate predictions regarding the future. While it has been used in many industries for longer than many of us might realize, adoption of this process has typically been quite low due to the high complexity and cost. However, big data and the growing range of tools available are changing that rapidly. Today, businesses have access to a wider range of affordable and accessible solutions that any company of any size can use to make more accurate predictions and subsequently better business decisions as a result. Here are some of the main ways that predictive analytics can benefit online retailers, and how to get the most from using this process in your business.
The search function may often be one of the first interactions that a customer will have with a sales website. Predictive analysis of user interaction with your website can make it possible to make searches more intelligent, better predicting what your viewer might be looking for through analyzing past searches and search terms. Predictive search is a feature that works by analyzing real-time action and preferences, click-through behavior, and visitor history to analyze the content of the site and ensure that the most relevant product matches certain search terms are shown.
Today, one of the most powerful tools that you can use when improving the user experience on your retail website is personalization. Predictive analysis is one of the best ways to achieve a high level of personalization on your website that will earn the respect of your users. It can sometimes be challenging to recommend the right products to customers in order to help close sales; however, predictive analysis can make it easier for you to do this on a user-by-user basis by using machine learning to find out more about the preferences and behavioral traits of a certain individual user. This information is then put to use when suggesting products that they might be interested in based on their past browsing habits, purchases, searches, and more.
Predictive analysis can make it easier for you to determine the right prices for products and services at the right time, ensuring that your profits and revenue are maximized. Analyzing pricing trends along with sales information allows you to better manage to price using a predictive model that will include historical sales data and information about customers, products, and any other relevant factors. As a result, it is possible to accurately predict the best pricing strategy for any given product or customer at any time. Amazon, for example, is a company that will use predictive analysis in this way, with product prices fluctuating a lot based on analytics data collected by this retailer.
Retail businesses can also use predictive analytics to gain a better understanding of the customer demand levels, making it easier for them to manage the overall supply chain process. This involves a range of different factors such as planning and forecasting, order fulfillment, the delivery process, and customer returns. Predictive analysis for supply chain management will often involve predictive reviews of specific products over certain time periods, which can lead to better optimization of space, improved inventory management, better cash flow utilization, and a lower risk of items going out of stock sooner than expected. The process can be so successful that it is now a key aspect of running many large retail businesses such as Walmart.
Predictive analytics can also be used to provide your retail business with a boost by determining which types of promotions and deals are likely to work best based on the results that you have had in the past. Using predictive analysis when coming up with new promotional deals or flash sales can help you gain more success from the event by analyzing past consumer behavior, browsing patterns, shopping habits, and more. This gives companies a better chance of conversions from promotional events by offering a combination of product recommendations and promotions that users are likely to be interested in.
Predictive analysis can be used to build, maintain and strengthen customer relationships, leading to a more loyal customer base and more repeat orders. This allows retail companies to offer a higher standard of customer service, since spending time analyzing data that allows them to get to know their customers better gives them a chance to offer the products, services, content, prices, and promotions that customers are likely to want and be interested in. In addition, predictive analysis can help a company determine the best type of post-sale customer service to offer based on what's worked in the past to bring customers back for more.
Fraud and chargebacks can often be a total nightmare for online retailers, but the good news is that you can use predictive analysis to reduce chargeback rates from credit cards and minimize the risk of fraud. You can do this by analyzing consumer behavior in relation to product sales and removing products that tend to be more susceptible to fraudulent activity. Fraud management predictive analysis models can help you identify any potential fraud risks before a transaction is completed, resulting in fewer chargebacks and avoiding related fees and the additional labor cost required to deal with the situation.
Marketing is another key area where retail businesses can put predictive analytics to use. By analyzing marking campaign data from the past and using predictive analysis to determine how you can expect your target audience to react in response to various marketing strategies and campaigns, it's easier for retail businesses to make better decisions in regard to the marketing methods that they choose and how they approach their target audience to get a better response in the future.
While there's a lot of focus on gaining new customers, retaining current customers is one of the main aims for retail businesses when it comes to success. Keeping satisfied customers coming back for more should always be the top goal of any retail business, and predictive analysis can help you achieve this. By learning more about your customers and their preferences from the start, you can make better customer service decisions and provide a customer experience that they will be satisfied with.
There are many examples of how predictive analytics is used in large companies on a daily basis. However, it can be used by businesses of any size in order to achieve all of the above benefits. Some of the main strategies that you can use to improve results across the board in your retail business using predictive analysis include:
This is a particularly useful application for eCommerce businesses since they can use past data to make sense of which products are likely to be the most popular options at different times of the year. For example, a shopping site that sells a range of gift products might find that certain products will sell out over the holiday period, but sales are significantly reduced throughout the rest of the year. This information can then be used to determine major factors such as pricing, promotions, and more during the period when the product in question is likely to be in higher demand.
Predictive analysis can help your business figure out a wide range of 'what if' types of scenarios, helping you determine what is most likely to happen in a range of situations based on the responses in the past. For example, you can use predictive analysis to find out more about the products that your customers are most likely to turn to as an alternative if a product that they are looking for is out of stock, or if customers tend to spend less or more when purchasing an alternative product to the one that they were looking for. By gathering and analyzing the data related to these types of situations, retail companies can subsequently make better decisions regarding which products will be the most profitable to market in certain situations.
Predictive analysis methods can be put in place by retail businesses to ensure that the right customers are being shown the products that they are more likely to be interested in and spend their money on. It can provide you with more information regarding which customers are more likely to spend more money on high-end products, and which customers you're going to have more luck targeting with budget alternatives, for example.
Retaining customers is one of the main priorities for any retail business. Not only is it cheaper compared to constantly acquiring new customers, but repeat customers will often have a much higher lifetime value to the company when they are loyal and additionally spread the word about your brand through word-of-mouth marketing. Predictive analysis of your customer service and aftercare systems can help you get a better idea of the steps that lead to customers coming back for more, or only making one purchase. For example, you can determine the marketing strategies that are more likely to lead to repeat customers in the future compared to one-time buyers, analyze the type of content that your repeat customers are more likely to engage with compared to others, and how their purchases from you might affect their future purchasing habits, such as buying a product then coming back to purchase the same product in a different color or an add-on product.
Predictive analysis can be used to study data collected from your marketing campaigns in the past, making it easier for customer acquisition in the future. You can analyze your previous marketing activity to find out more about the types of advertisements or content that typically attracted more users to your website, led to more conversions, or created more customer engagement to determine what's likely to work well again when repeated in the future. Emerson College's Master of Arts in Digital Marketing and Data Analytics online program is an ideal program to consider if you want to learn more about how predictive analytics and data analytics, in general, can have a huge impact on marketing success today.
Finally, prioritizing the customer experience is key to getting the best results from predictive analysis strategies in your business. When you make your customers a top priority, you will be able to learn even more about them that you can put to use for making decisions and coming up with new strategies in almost every aspect of your business, but especially when it comes to pricing, personalization, online content, marketing, and customer service. Using predictive analysis in your retail business means that the customer should always be at the forefront of any decisions, strategies, and campaigns that are put in place. As a result, brands who employ this tactic can often enjoy several benefits including more positive online reviews, a higher number of repeat customers, more customer loyalty, better word-of-mouth marketing and brand referrals, increased customer engagement, and better customer relationships.
Today, predictive analysis is a huge part of running any retail business. From the predictive analysis of visitor behavior on your website so that you can show them more relevant product suggestions, to predicting how customers respond to certain types of marketing over the long term so that you can keep customer retention in mind when coming up with new marketing campaigns, predictive analysis has become more important than ever for retail businesses of any size. And the wide range of predictive and data analysis tools that are now available has made it easier for even the smallest of businesses to make use of the data that they gather to enjoy more conversions and sales, higher profits, and better relationships with their customers.
