Amazon seems to read our minds, often predicting what we want even before we even know it. Predicting customers’ orders is vital to increasing sales while reducing inventory and supply chain costs. Simply put, that is what predictive analysis for e-commerce is all about. Can you risk being wrong-footed when others in the industry are getting it right?
Technology has placed a dilemma in front of most businesses; there is too much data and information available. Organizing and understanding this information is complicated and micro-level predictions were just not possible before. It has all changed now, with Big Data and Predictive Analytics tools becoming more affordable. E-commerce companies are using these tools to understand the patterns of online user behavior to create the personalized experience that is driving sales up.
Here are the 5 big ways that Predictive Analytics is changing the face of online retail:
1. Making Customers Brand Loyal Through Personalization
Gaining a new customer is much more expensive than retaining an existing customer. While this is a known fact, getting a one-time shopper to become a brand loyalist is not as difficult now as it once was. Even one sale can generate enough data to create automated, actionable insights that convert these customers into followers. Invesp, a consulting company specializing in conversion rate optimization, reports that 45% of online shoppers are more likely to shop on a site when they get personalized recommendations. The massive e-commerce giant’s marketing strategy is based on this.
Today, smaller retailers are realizing that this is an investment worth making, helping them to create a different shopping experience for each customer. Deals, products and personalized offers follow the customer where ever they go, enticing them back to the site and showing results through reduced shopping cart abandonment.
2. Making Better Pricing Decisions
Many retailers dealing with hundreds of products still haven’t gone beyond Bandit Testing to set prices. Settling on the optimum price is difficult when dealing with many products, especially when prices are set manually; too low and net profit will suffer, too high and inventory might stockpile. Instead, predictive analytics along with Artificial Intelligence can track inventory levels, compare competitor pricing and check the demand levels to determine what the product price should be.
Retailers need no longer wait for traditional sales times. E-commerce predictive analytics is being used to find the best times to reduce or push prices up. Gradual price changes are found to be more effective than massive price fluctuations that will impact revenue negatively.
3. Predictive Inventory Mobilization
As a customer, there are few things more frustrating than wanting to buy something and seeing it is out of stock. Situations such as this one lead to one more sale lost and, more distressing, an unhappy customer. This is more often the truth when it comes to online buying. For the retailer, it could also be a costly problem to be left with a container of stuff that is no longer in demand. Smart retailers are using predictive analytics to focus on items that can have a high demand–picking up on emerging sales trends and optimizing delivery–thus making better use of their warehouse space. Just how important this factor is can be seen through Walmart’s acquisition of Inkiru, a predictive analytics start-up that focuses on supply chain optimization.
4. Improving Customer Experience
It is not all about the product and pricing; customer service can be a big differentiator between online e-commerce retailers. A report by NewVoiceMedia revealed that 67% of customers could be labelled “serial switchers.” This term is used to describe customers who could be driven to competitors due to a poor customer service experience. These bad experiences are costing businesses around $75 billion a year. Big data is providing metrics to track the customer service experience through understanding shoppers’ complaint categories, how easy it is for them to contact customer service, and finding a resolution or answers to their questions. Predictive analytics can use these metrics to alert e-commerce retailers of potential problems so that they can resolve them even before the customer has to face them.
5. Reducing Fraud and Making Online Payments Safer
Loss from fraudulent transactions has long been written off as a risk of the trade by many online retailers. Predictive analytics software for fraud management is used widely by insurance companies and is just as valuable in e-commerce. These predictive analytics models work by identifying potential fraud even before the online purchase transaction is completed; it is that fast. This software analyzes browsing patterns, purchasing methods and payment methods to create rules that can easily differentiate between a normal customer and a potential fraudster. Some companies use specialized e-commerce predictive analytics tools especially for fraud detection, showing how important it is to tackle this problem.
Predictive analytics provides many benefits to the e-commerce industry through increased personalization, informed pricing decisions, better customer service experiences and fraud reduction, among others. If you have not yet employed predictive analytics in your business strategy, it might be time now to speak to a professional to understand better how it can streamline your operations. At SevenTablets, we specialize in custom mobile app development and predictive analytics. Our predictive analytics tool, Sertics, can help your business identify inefficiencies and improve your bottom line.
Reach out to our team today!
VK studied computer science at Jawaharlal Nehru Technological University in Hyderabad, India and earned a Master’s Degree in computer science at George Mason University.
Latest posts by Venkatesh Kalluru (see all)
- The Top 4 Mobile App Development Trends of 2019-2020 - February 13, 2019
- Enterprise Software Development Best Practices You Should Know - February 5, 2019
- The Best Artificial Intelligence Apps for Business - January 25, 2019