How Predictive Analytics Helps Increase E-Commerce Transaction Volumes

Reading Time: 3 mins

cart-adbstract.jpg

The field of analytics is rapidly evolving as e-commerce professionals search for new ways to increase transaction volumes.  

Predictive analytics in particular are playing a big part in better understanding consumer behavior by giving the opportunity to study future events through existing data.  

Streaming giant Netflix studies user ratings across dozens of channels to better understand what type of original content may be popular among subscribers in the future.  Amazon uses a similar predictive algorithm to understand why seemingly unrelated products are often purchased together.  

These types of insights help e-commerce leaders increase transaction volumes through greatly enhanced customer relationships in many important areas of operation.

Personalized experiences

One of the most important aspects of predictive analytics is having the ability to use big data to better understand each of your customers on an individual basis. 

Surf outfitter O’Neill built an AI learning tool that could use predictive analytics to mimic in-store recommendations from employees.  The algorithm would study each consumer’s browsing habits for things like color, texture and style to better understand what mattered.

It also considered how often certain items were purchased by each consumer before generating recommendations.  For instance, most of their customers only bought one lightweight jacket per season, so there was little reason to display similar products if one was recently purchased.

The results were encouraging. Page views were up by 62 percent with a 26 percent higher conversion rate and a 17 percent increase in transaction volumes.  That same data also helped O’Neill optimize further by discovering different types of shopping habits based on device usage. 

This allowed them to fine-tune the customer experience even further when customers connected from a smartphone, tablet or PC.  None of these discoveries would be possible without predictive analytics.

Cross-selling

Predictive analytics can also be used effectively to drive logical cross-selling recommendations for related products and services.  

Using analytics a leading retailer discovered on Friday evenings male shoppers that purchased diapers also had a strong tendency to buy beer.  Armed with this insight, the retailer moved their baby section right across from the beer aisle. Alcohol sales soared.  

Whether or not this story is 100 percent true, it illustrates how predictive analytics can drive effective cross-selling and increased transactions. The raw data provides insights into the user’s digital journey from the first moment they make contact until the time they leave.  

This information is essential to better understand what customers are looking for and what drives them to purchase recommended cross-sell products within the same transaction.

Supply chain optimization

Nothing drives a customer away from an e-commerce site faster than seeing that dreaded “Out of Stock” label where the “Buy” button should be.  

According to The Jibe, 63 percent of shoppers have encountered a “Not in Stock” scenario online and nearly two-thirds of this group will choose to shop elsewhere.

The one place that predictive analytics truly shines is identifying problems within your supply chain and allowing AI to automate many aspects of inventory management based on customer buying habits.  

Companies like Walmart and Amazon have invested heavily in this technology in recent years to sell products online that haven’t even arrived to their warehouses yet.  By tracking the efficiency of their vendors, transit companies and millions of SKUs, they are able to maximize their supply chain while also getting a better handle on fraud, returns and mismanaged inventory.

Enhance customer loyalty

A great example of bringing predictive analytics full circle is a story told by LifeProfit founder Dustin Garis in a recent TED Talk.  

After calling into Zappos for clarification on a particular shoe, the customer service rep sent someone to physically check a nearby warehouse.  During that waiting period, the rep pulled up Dustin’s search history on Zappos.com and saw he was interested in distance running.  She then found a fun-looking mud run in Dustin’s area and convinced him to sign up, which created an amazing memorable experience.  

Even though Garis did not purchase shoes that day, he has since made dozens of purchases from Zappos and shared this experience with TED’s audiences across the world.

A key benefit delivered by predictive analytics is gaining a much deeper understanding of your customer base in real time.  Not only can that information empower operational staff with important customer insights, it can also guide your company to build strong relationships with your customers and lead to increased transactions.


About the Author

Keith Koons is a B2B technology writer for Six Vertical with over 14 years experience writing about business technologies and marketing. He lives in the foothills of South Carolina and enjoys everything the great outdoors has to offer.


More from our blog