When most people leave their jobs, they lose their salaries, benefits, and a desk. When Ronald Wayne left his job, he lost $280 billion. 

Ronald Wayne founded Apple along with Steve Jobs and Steve Wozniak, and when he left after just 12 days, he sold his 10% share in the company for $800 – a share worth hundreds of billions of dollars today. For context, no single person currently owns as much as 3% of the company. If Ronald Wayne had just kept back 16 cents worth of stock, he would own as much as current Apple CEO Tim Cook. 

If Ronald Wayne had a way to predict the future, he would have made different decisions. Unfortunately, there’s no crystal ball to predict the future, but there are tools that can give you a fighting chance. 

Predictive analytics redefines e-commerce by helping companies anticipate future needs and find the best path forward in uncertain times. 

Key Takeaways

• Optimize your digital spaces with predictive analytics. 
• Predictive analytics can help you easily qualify leads. 
• You can use predictive analytics to determine the best pricing strategy for your business. 
• Forecast demand using predictive analytics with Hypersonix. 

Overview of Analytics

While the broad idea of “analytics” frequently moonlights as an umbrella term for any data-driven process, analytics is a field of computer science that uses technology to find meaningful patterns in data. Within this broad idea of analytics, several subcategories exist, including descriptive, diagnostic, predictive, and prescriptive analytics. 

• Descriptive analytics looks to the past to describe past activities in data-driven terms. For example, descriptive analytics may answer questions like “When were our sales at their highest?” or “which template leads to the highest conversion rate?” 
• Diagnostic analytics similarly look into the past, but this time answer questions like “why were our sales so high in 2019″ or “why does this template lead to the highest conversion rate.” In comparison, descriptive analytics describes and diagnostic analytics diagnoses. 
• Predictive analytics, unfortunately, can’t use data from the future, so it does the next best thing. Predictive analytics uses data to make predictions about future trends. Predictive analytics answers questions like “which product will perform the best in this market?” and “what is the lifetime value of this customer?” 
• Prescriptive analytics goes hand in hand with predictive analytics rather than simply predicting future outcomes. Prescriptive analytics uses data to determine the best path to achieve intended results.

These categories of analytics are not discreet, meaning they overlap, work in tandem, and often describe different aspects of the same process. In particular, predictive and prescriptive analytics work hand in hand because questions like “how will this design change impact sales?” and “what’s the best design change to achieve a certain sales impact?” are really two different ways of describing the same kind of data. 

Space Optimization

The movie Interstellar introduces TARS, a sarcastic robot optimized for space. While predictive analytics could probably help with that, that isn’t the kind of space optimization most e-commerce platforms are interested in. 

In the world of brick-and-mortar retail, businesses use planograms to optimize their store layout. While this kind of visual merchandizing has a long history in physical storefronts, e-commerce retailers can apply the same tools to your online storefronts. 

Imagine that you are selling homemade candles. You have 40 different types of candles in different scents and sizes and a few accessories like matches and wax trimmers. You ostensibly have two categories of products: candles and accessories, but should you split your visual space 50/50 between these categories? 

Not only should candles rank higher on your visual hierarchy, but you should draw attention to specific scents and sizes as well. While some e-commerce platforms try to DIY this by simply displaying the most popular items first, predictive analytics helps the most successful e-commerce companies (like Amazon and Alibaba) determine which products should take precedence and when. 

Lead Qualification  

Your sales funnel is full of leads from a variety of sources. Unfortunately for e-commerce companies, not each of these leads will convert into a sale. Among the leads that do, not each converted customer will result in the same lifetime value. Not each sale will result in a brand advocate. Sifting through this bog of leads without predictive analytics can feel like flipping a coin with 10 tails and only 1 head. 

Predictive analytics redefines this e-commerce process by qualifying leads based on a complicated set of factors like geographical location, advertising history, purchasing history, psychographics, and more. The most sophisticated digital experience platforms can even track data as specific as where users click on your website, if they click repetitively (indicating frustration), if they click quickly (indicating impulsiveness), or if they leave items in their cart (indicating an opportunity for follow-up). 

While some of these patterns are opaque, some are so subtle that they would be invisible to even the most highly trained human eyes. Predictive analytics helps e-commerce companies qualify leads by using historical data to predict which kinds of leads will result in conversion and which of those converted customers will provide the greatest lifetime value. 

In tandem, prescriptive analytics can help your company identify how to achieve the most desirable outcomes. 

Pricing Strategy 

Some companies maintain extremely static prices (think of companies like dollar stores or retailers who have advertised 29.99 + shipping on tv for years), but other companies have very dynamic prices (think of hotels and airlines where prices change day-to-day or even minute-to-minute.) 

How often should you change your prices? How high is too high? How low is too low? How big of a change is too big? 

Astute businesspeople may develop an intuition for answering those questions, but predictive analytics can back up the answers with data. 

Pricing strategy is extremely complex and varies wildly from industry to industry. With no one-size-fits-all answer available to e-commerce companies, predictive analytics is the best way for you to determine the best strategy for your unique company and market. 

Demand Forecasting 

When Popeyes announced a new spicy chicken sandwich in 2019, they got more than they bargained for. Their new flagship product was completely sold out in a matter of two weeks, leaving many customers frustrated with long drive-through lines and no sandwiches. 

While no predictive analytics tool is powerful enough to account for every unknowable, Popeye’s inability to accurately forecast demand led to them missing sales and losing business during the biggest sales boom Popeyes had experienced. 

Demand forecasting applies to much more than chicken sandwiches. Every e-commerce platform struggles with the same dilemma: too much physical stock leads to storage and warehousing expenses (plus assets tied up in merchandise inventory), while too little stock leads to late delivery and unsatisfied customers. If you sell a service rather than a product, you face the same challenge: labor costs money, and too much demand can hurt as much as too little demand. 

Predictive analytics redefines e-commerce by helping companies forecast demand and adjust their strategies accordingly. 

Ecommerce with Hypersonix 

Are you ready to revolutionize your e-commerce? Excited about how predictive analytics can boost your bottom line? Unsure where to start? 

Hypersonix is here to help. 

Hypersonix uses AI to drive optimization for retail, e-commerce, and grocery companies. Hypersonix has the experts and tools to help you take your company to the next level if you’re ready to see your revenue grow and turn your data into meaningful insights. 

To see how Hypersonix can help, request a personalized demo today.