Replace search on Lenovo.com. With products that span B2C, SMB, and B2B businesses and serve customers in 180 markets speaking over 60 different languages, supporting Lenovo.com is a broad mandate.
Move Lenovo.com search onto Fusion’s open source technology. With Fusion’s machine learning capabilities and out-of-the-box tools like rules editor, personalize Lenovo customers’ search results and improve relevancy.
After the first full year Fusion was deployed on Lenovo.com, the annual revenue contribution through search was increased by 95%. With Fusion signals turned on for just a few months, relevancy improved by 55%.
Lenovo is the number one PC manufacturer in the world, but that’s just the beginning of its product offerings. It’s also the number one provider of supercomputers, it sells phones, it provides large data center solutions, and sells transformational Internet of Things (IoT) solutions. With B2C and B2B business spanning over 180 markets in more than 60 different languages, Lenovo’s customer base is as diverse as the products.
So what leads a graphic designer in London to the exact ThinkPad laptop she needs? How does a business in North Carolina find the right infrastructure to support its internal systems? No matter what language the customer is using or what they’re saying, search is the universal language.
When Global Search Lead Marc Desormeau took over the search team for Lenovo.com, he was confronted with a question, “Why is search not optimizing as much as we want it to?”
“We index half a million records every 12 hours. Everything from data feed and product information, pricing, catalogs, things we sell. We also index information about jobs at Lenovo, about our environmental policies, our technologies, and customer use cases so it’s quite the range. It’s a pretty broad mandate to support Lenovo.com,” said Desormeau.
Just prior to Desormeau joining the team, Lenovo had set out to replace the current search engine, a FAST-based solution that was quickly sunsetting. Reaching out to Gartner and Forrester for vetted possibilities, the search team learned about Lucidworks Fusion as an option. During an 18-month extended RFP process, multiple vendors were assessed. Meanwhile, the mandate of the desired search engine shifted from simple transactional support to enacting a full digital transformation at Lenovo.
“Ultimately based on Lucidworks’ vision around AI, their future plans for machine learning and more sophisticated search implementations, and really looking for a search platform that can take us into the future, the decision was made to move to Lucidworks Fusion.”
Fusion, built with open source Apache Solr and Apache Spark, changed Lenovo’s search experience. Fusion’s out-of-the-box tools allow flexibility and customization so results can be tailored specifically to a product line, location, language, user, and more.
As the new, flexible search platform proved it could be leveraged to help meet different departments’ goals, the Lenovo community became more engaged with search.
Fusion tools like a business rules editor and query pipelines helped the search team get more buy-in from across the company.
“We’re using data captured from our search engagements to inform some of our investments in SEM by looking at customers who are coming to us organically. How are they then engaging with our own site search? How can we start joining some of that data to ultimately present a better experience? So both in terms of attention and awareness that the search platform is more than just a little box on the screen, it’s actually providing a lot of insights into our customers,” says Desormeau. “People are starting to understand there’s some real data here that’s valuable, and we can use that to present our customers with a better experience.”
The team launched Fusion-powered search on Lenovo.com mid-year 2017. 2018, the first year all search traffic passed through Fusion, saw annual revenue contribution through search increase by 95%. On Lenovo’s customer support site, clickthrough rates and bounce rates have shown dramatic improvement with Fusion, proving that customers are more quickly finding the content they’re looking for.
More recently, Lenovo has implemented Fusion signals to track user behaviors like click, add to cart, and purchase. Combining these user signals with machine learning, the search team was able to automate search result ranking for the vast amount of data in their knowledgebase. Relevancy, measured by how often customers click on the first result versus any subsequent result, has improved by over 55% in the span of just a few months since launching signals.
“We don’t have to go in and validate that results are good, our customers are telling us the results are good. We’ve had some dramatic growth. The results that we’ve had with Fusion are nothing short of astounding,” says Desormeau.
