Average Order Value Down? Endeca Could Be the Culprit
If you are seeing your Average Order Value (AOV) flattening or declining, and you’re still using Endeca, your search engine could be to blame.
AOV is total revenue divided by number of orders. If your AOV is dropping, that means your loyal customers are spending less and less with each purchase. That’s a real problem, says Richard Isaac, CEO RealDecoy, a leading enabler of ecommerce for brands like American Express, Samsung, Honeywell, Coach, and a long-time integrator of Endeca.
“In my experience, most retailers overinvest in customer acquisition while under-investing in growing average order value and conversion rates,” says Isaac. He estimated that the cost to acquire a new customer is 9x the cost of retaining an existing one.
“Top retailers agree that digital commerce search has a huge impact on their cost of service, average order value, and customer loyalty. But many of them struggle with obstacles like budgets and technology that does not (and may never) meet their needs. Most importantly, they lack a data-driven approach to making enhancements,” he explains.
Today’s ecommerce shoppers expect search to be personal and precise–to provide them with exactly the info they need, when they need it.
Customers are not really interested in your top 10 guesses about what they might want. They just want the top one or two items that fit exactly what they need. And when that doesn’t happen, customers get frustrated.
Today’s retailers try to meet this demand for a personal approach to ecommerce with more words. They provide catalogs with many thousands of products that have complex and specific descriptions, hoping that customers will have the patience to wade through it and find what they need. But what if they do not show such patience?
Endeca’s Keyword Search Is No Longer Enough
Endeca’s simple keyword search is no longer enough to keep up with the volume, velocity, and variety of products and words that retailers must manage if they don’t want to leave money on the table.
Most teams using Endeca know they must act smarter and faster, yet Oracle isn’t innovating the Endeca product and support is inadequate. The question isn’t “whether” to transition, but when and how. When do the benefits of switching outweigh the costs and potential for disruption? That time has come.
Average Order Value and Conversion Success
Leading retailers like Lenovo have recently upgraded their search capabilities and as Marc Desormeau, Lenovo’s Senior Manager of Digital Customer Experience put it, “Since the migration [to Fusion from Endeca] we’ve seen a 50-percent increase in conversion rates and other key success metrics for transactional revenue.”
So what do folks like Desormeau and Isaac look for in a modern solution? First off, they want newer and better search algorithms that allow customers to find what they need on the first try. Secondly, they seek a modern scalable architecture that allows them to do more with their data and handle changes in real-time. If they need a full re-index of their catalog and site content, they want it to take minutes not hours. Just like you, they want their customers find what they need, when they need it, however they describe it!
With retailers scrambling to try and find ways to improve the customer experience, don’t forget to look at site search analysis. The right analysis will help you figure out user intent.
So the right solution should learn from customer search behavior. As customers click on different results, those results should be boosted. If an individual customer trends towards certain types of products, they should automatically see more of what they’re interested in, without a merchandiser having to create a specific rule or customer segment.
Turning on and Configuring Signals
Finally, Endeca users have relied on thousands of difficult-to-maintain merchandising rules in order to personalize, customize and optimize the customer experience. Modern solutions use AI and machine learning techniques to do the bulk lifting. They save predictive marketing tools (including rules) to do the more specific tuning. This means tens or hundreds of rules instead of thousands. And it means a lot less drudgery for merchandising teams.
Predictive Merchandising in Fusion 4.2
Retailers who aren’t moving from Endeca quickly enough will not be able to take advantage of signals, relevancy tuning, and machine learning. They will be leaving money on the table. In fact, those that do move typically capture enough additional revenue to pay for the migration in as short as a few months.
Contact us today to learn how Lucidworks can help your team create powerful search and discovery applications for your customers and employees.