Mobile Search Personalization

Delight your mobile shoppers with personalized search and navigation. Mobile traffic is at an all time high, with over 60 to 70% of shoppers using their smartphones to browse even when they are at home. Most of them, however, are not satisfied with the mobile experience and prefer making the actual purchase on a desktop. Often, it’s hard to find relevant products on the mobile site or app and scrolling through category pages is not enjoyable.

Let’s see how journeys a specialty chain retailer is delivering a hyper personalized experience across e commerce touchpoints. As a first time visitor on the mobile site, I see a pop up encouraging me to sign up. As I start browsing the homepage, I see top sellers and popular products based on my context, my current location, popular products in my local stores, whether here in New York City, my device type, etc. And these are powered by RichRelevance(now Algonomy) out of the box strategies. I will focus on search now, let me do a search for generic term sneakers. There are over 800 results here. And these are sorted based on what’s popular with people and what people are clicking and buying. Using wisdom of the crowd.

Converse seems to be popular with shoppers right now. Now this isn’t what I am looking for. And it will be hard for me to find sneakers of my preference by going through all these result pages. How can we help the shopper find what they are looking for. I switch to the navigation bar here and select my preferred brand Adidas from the list. Now immediately a signal is sent to the system about my behavior, and we will see how the site responds to the signal. Now I am going to the product detail page for these athletic shoes. I start seeing other product recommendations from the same brand and also complete the look recommendations. For example, a black Adidas hoodie and a backpack. Now I switch to the red shoes, you would notice how the cross sell recommendations change. I now see a different sweatshirt that pairs well with my selected shoes.

Now let’s see if these interactions have changed the search results. A search for sneakers again has boosted Adidas brand and Converse shoes are pushed down. My preference for the Adidas brand has been captured in real time by the underlying user profile. Even though I am not logged into the site, and I did not specify the brand name in the search box. The preferences I showed on the category page have been captured automatically to personalize search results. And I see Adidas sneakers upfront. In this case journeys has chosen to give high weightage to personalization and affect the changes on various pages immediately.

Other retailers may choose to delay personalization and or give lower weightage to individual preferences. A key difference compared to other ecommerce sites is that the functionalities across the commerce site come together as a cohesive unit. Most retailers have this siloed meaning search recommendations and category pages do not talk to each other. And that makes the customer buying journey bumpy. At the same time, curating the pages can be a nightmare for the merchandiser who has to go to different dashboards and set up rules to align what a shopper sees.

Now, let me demonstrate what happens when I go back to the menu and browse to a different category. Let’s say backpacks and bags. Here RichRelevance(now Algonomy) is personalizing the category page based on my affinity and clearly the 48 products are sorted to boost Adidas products. Now, I want to show another cool complete the look example. I scroll down on the backpacks category. And I show interest in this checkerboard pattern vans backpack. And in addition to the similar product recommendations, RichRelevance(now Algonomy) advanced merchandising automatically displays this complete the look section. These delightful vans sneakers perfectly complement the backpack I just selected. If I select the other patchy backpack, you’ll see that the complete look changes to reflect the stylistic nuances of my currently selected backpack. This experience is extremely valuable to the shoppers as they don’t have to dig deep into the catalog and relevant products are instantly surfaced no matter how they choose to interact and browse. Before I wrap up, let me show you results for a more specific search query. We know that the more detailed a search term the stronger is the purchase intent, and hence the results should be spot on. Here I searched for yellow canvas sneakers and see relevant results from converse and vans. The important point to note is that search should understand any attribute that is specified in the query. It could be the brand, color, category, product name, fabric, gender, or something more granular.

To summarize, the value comes from connecting the shopper journey across every touchpoint and leveraging their behavioral data in real time, so they don’t have to start from scratch as they switch between search, navigation and product pages.