Predictive analytics is a very exciting and hot topic in the online world, especially for ecommerce. When analyzed correctly, you can predict a slew of ideas: from what products will be most popular with customers before they are even launched, to the maximum price customers will spend on an item, to optimizing customer services before concerns become issues. Here are some of the greatest benefits of big data for online retailers:

1. Customer Engagement and Revenue Increases

Your customers engage with your site in different ways – some prefer a newsletter, others will click on a promotion, and still others might prefer liking and following a social media post back to your site. Predictive analytics helps you look at all the variables connected to generating engagement from your customers to complete that conversion, and get them involved.

Several vendors, such as Alteryx and Lattice, offer ecommerce sites models that will track and understand customer behavior. As your site grows, these models can be shifted along with your objectives. Often the easiest way to help grow with your customer is to track what they are buying, how they rate it, comparing their purchases against similar purchases by other customers, and see what items they put in their carts in real time. If you have a past history for customers, you can better predict what they might also consider purchasing – or what new customers might enjoy, based on their current cart.

As you increase your predictive analytics abilities along with generating customer engagement, you will certainly find a mirrored increase in revenue.

2. Pricing Management

Predictive analytics can analyze price trends along with sales information to help you determine the right prices at the right time to maximize profit and revenue. Previously, retailers used the A/B testing to set prices for different products and attempt to find the perfect price points.

The new approach uses various sources to support real-time pricing, such as customer activity, competitor pricing, desired margins, historic product pricing, and more. Based on this model, the price for a given product and customer can be predicted at any time. Remember that pricing management should be monitored closely, as it is an ongoing process and could suffer from automated price changes.

3. Better Targeted Promotional Launches

Without getting the word out, no customers will know about your blow out sales. Unfortunately, it is not always easy to get promotions right – until predictive analytics. Segmentation and targeting are important for online merchandising, according to 98 percent of fast-growing merchants, so it is important that you have the right tools. Predictive analytics takes data from multiple sources to figure out a personalized promotional plan, for use either with individual or segments of your audience.

Some examples of successful promotions include Macy’s, which used a solution from SAP that helped target registered online users of the merchant’s website. Within 3 months, they saw an increase of 8 to 12 percent in their online sales by combining browsing behavior within product categories and sending targeted emails for customers in each segment.

4. Fraud Minimization

A reality of business, especially ecommerce, is fraud. Luckily, predictive analytics can help reduce losses from fraud. Solutions such as IBM’s SPSS suite let you analyze browsing patterns, purchasing patterns and payment methods to spot fraud. This technology has a long way to go, as 84 percent of predictive analytics suite owners do not use this as a primary use; however, anything that can help reduce fraud, can help give you peace of mind.

5. Better Customer Service

Your customer service levels can shine with the addition of predictive analytics. Oftentimes retailers have more questions than answers on this subject, such as whether to offer both phone and email lines, how many representatives are needed, how to prioritize a high-value customer, etc. Using a model built specific to the needs of your company help you answer these questions and more. After a certain amount of time, the model will be shaped, and able to give you better predictions – without blowing the bank on more expensive auditors.

6. Supply Chain Management

Predictive analytics helps understand customer demand, to more effectively manage the overall supply chain. This includes planning and forecasting, sourcing, fulfillment, delivery, and returns. If a retailer can better predict the revenue from a specific product – say in the next month – it results in better inventory management, optimized use of the available warehouse or storage space, better use of cash flow, and avoiding out-of-stock notices for items.

Keep in mind that predictive analytics tools will not do the work for you! Without a question to answer, you will just be plugging data in and receiving no insights. These tools can increase your revenue dramatically – so when in doubt, do your research. Predictive analytics technology is crucial for success in today’s ecommerce world.