Creating Loyal Customers
August 29, 2014
Smart marketers and retailers know it’s more affordable – and more profitable – to retain customers than to find new ones. Personalizing the online shopping experience is an excellent way to create deeper relationships with customers in order to retain their loyalty over time.
However, when it comes to choosing the right technology to enable these personalized customer connections, it’s important to realize that not all recommendation solutions are created equal. Online retailers need to choose their technology carefully because even small weaknesses or limitations can become magnified on the website, under the critical eye of discerning customers.
Here are seven traits offered by the best product recommendation engines. Use them to find the right solution for your business:
Multiple scenario management. From first-time visitors to high-value registered customers, sophisticated algorithms are needed to optimize product recommendations based on a wide range of factors so that the right offer is presented in every situation. These factors should include the customer’s current shopping interests, search queries and complete behavior history, along with the business rules governing recommendations.
Real-time recommendations. The engine should continually learn, adapt and dynamically deliver recommendations as users click through the site. A customer’s in-session actions and historical profile data should be combined with the collective wisdom garnered from all site visitors to deliver the best content.
Seamless integration with a customer database. It should connect in real time with a data-rich customer database that has tracked a user’s online activity both in-session and over time.
Easy customization. The engine should have an intuitive interface that lets non-technical users modify business rules governing recommendations, providing flexibility and full control over a range of scenarios.
Flexible A/B testing. The solution needs to give marketers a platform to plan and test multiple recommendation sets and select the most effective products, brands, segments, terminology and placement of recommendations on the web page.
Analytics and competitive benchmarking. It must include robust analytics and ad hoc reporting to continuously measure and improve performance.
Integration with email marketing. The solution should easily integrate with an advanced email marketing solution to create deeper customer engagements and retarget abandoners.
See how the world’s largest online retailers are increasing sales by 10 percent or more by optimizing merchandising experiences for their customers. And get an in-depth look at the most important characteristics of a successful recommendations engine, as well as practical tips to boost sales, increase revenue and build lasting customer loyalty.