Mario’s grill rusted through and he’s ready to upgrade to a more durable one. He begins with a web search engine and clicks through to a store whose brand he likes. On that site he uses faceted search to refine to a handful of models whose attributes might meet his needs. He scrolls through the user reviews on those grills to see which ones other buyers trust, then prints out the reviews for the two he likes best. He checks local inventory online and finds a store nearby that has both.
Mario uses his mobile phone to get directions to the store. Once there, he goes to the grill aisle and inspects them in person, reviews in hand. He discovers another model on clearance that he hadn't noticed online. He looks up the model on a store kiosk, which is similar to the website, but has no user reviews. He turns to his mobile phone instead and reads user reviews. Now he has a top pick. He uses his mobile phone one last time to sanity check the in-store price against that of a web-only superstore. Feeling good about his purchase, he drives home with a new grill.
Does your site offer this experience? Should you cover all bases? How and why?
Modes of Search
One model of search behavior places it on a continuum ranging from fact finding to discovery. And within that continuum, people use a variety of search features to support the different modes.
Pete will show you how, in fact finding, users know in advance what they’re looking for. They already know the specific brand and model of grill they want. You’ll see examples of search features that support fact finding including alpha-numeric string correction, auto-phrasing, and bar-code or QR code scanners.
You’ll learn about the other extreme of the continuum: discovery. Discovery happens where people don’t yet know what they want or how to describe it. They know they want a more durable grill, but don’t yet know the attributes that make a grill durable, or the brands they trust to build a reliable one. Pete’s examples of search features that support discovery include faceted search, user review search, and features of document search. This last example brings into play product information pieces like the buying guide, product info sheet, and demo video search.
Context of Channel
Your users’ expectations of search features vary according to the context of the channel. They expect mobile to be location-aware, and in-store kiosks to be inventory-aware. They expect online to be optimized for a big screen, and mobile for a small one. And they expect the store to know which channel they used. For example, the call center should be prepared with different return information for an online shopper than a brick-and-mortar one.
Although the multi-channel search experience is already common, it is still not a well-designed experience. People face unnecessary gaps across channels, often because the channels aren’t aware of each other.
The Multi-channel Search Experience
There’s a reason the experience is still poor. Retailers still aren’t organized to make multi-channel work. Different groups typically own the different channels, and they have no incentive, or even counter-incentives to cooperate. Even when groups do try to coordinate across channels, they still have difficulty knowing when a shopper crosses from one channel to the next, or even measuring how the channels affect each other.
Pete will share examples from well know organizations like Home Depot, Borders, and B&H Photo Video. He also shares his latest thinking on Facebook and its rise as a new channel. Multi-channel search experience design is still in its early stages, but we already have a good idea of how to go about it, which you’ll learn in this seminar.
As an experience design discipline, multi-channel search is still in its infancy, and investment in it lags actual user behavior. That means there are still no best practices. However, there are early adopters whose experimentations might point the way towards the future.
Be sure to save your team's spot for this important and insightful look at multi-channel search.