Enlisting recommendation engines to lead buyers through the retail discovery and purchasing process has gone from nice-to-have to an online strategy no-brainer. Shoppers click on one shirt and are immediately presented with “might like” apparel and accessories. They search for nutritional supplements and end up adding an entire exercise routine to their cart.
For electronics retail, however, the story is very different. Far from simply suggesting a matching style or a complementary purchase, consumer electronics marketers have to deal with more demanding product databases and catalogs. That is, an apparel retailer might recommend a skirt that a shopper does not particularly like, and that is ok - no harm done. The shopper can just move on to other items that she likes better. For an electronics retailer, however, the story is different: complementary items have less to do with “taste” or “preference,” and much more to do with whether they are compatible or not.
For consumer electronics, it’s about what fits, and less about what looks right. What is compatible with a new HDTV or iPhone 6 is a binary equation – a group of accessories and complementary items either fit the main product, or they don’t. When it comes to product recommendations for consumer electronics products, accuracy is of monumental importance, and a far more important consideration than taste or preference.
Best practices for choosing the right consumer electronics recommendation engine.
Products for electronics are “normalized,” or “standardized,” meaning a Nikon D90 camera is a universal commodity, with the same SKU#, irrespective of the store a shopper purchases it. Complementary electronics products are generally normalized as well, all with standardized IDs. Good recommendation engines utilize master electronics catalogs in order to keep recommendations in-line with the steady stream of new electronics and compatible accessories entering the market, thereby maximizing opportunities to make the most revenue.
First and foremost, product recommendation tools have to be able to understand product catalogs intelligently to ensure the compatibility of products recommended. Nothing is more frustating than spending time considering a surround sound system or an HDMI cable for an HDTV that is already in the shopping cart, just to learn that the products recommended are not compatible with that HDTV. Product elimination is essential. Recommendations engines also must be capable of making compatible/complementary recommendations that have the greatest opportunity to increase profit margin for the retailer. Why offer surround sounds system A when B is better, is the same price, BUT it has a 10% higher margin for the retailer than A does?
Don’t Downsell Unless Customers Request It
“You might also consider this” is very useful for upsell but only if used correctly. Price is one consideration. Recommendation engines should never recommend a similar item that costs less than the original item searched, unless the shopper makes a specific request for that. If a customer is looking at the Nikon D90 for $899, for example, recommendation engines should not automatically suggest cameras for $799.
The same goes for what kind of products are recommended based on the customer’s original search. If the customer is looking at 3D TVs, the recommendations should all be 3D TVs also.
Best practices for engagement by shopper type.
Consumer electronics retailers have to be ready to engage both the less savvy shopper requiring a little bit of handholding, as well as the educated buyer, who knows what he wants.
The Top-of-the-Funnel Shopper: Provide an Awareness Journey
Be ready to guide them through the buying process.
- Technical jargon can be confusing to someone just beginning her search. Leverage user experience solutions that ask scenario-based questions (like who is this for? what is your main reason for buying?) alongside intelligent analytics engines to help shoppers find the right solution for their specific needs.
- Keep the site experience simple and engaging. Everyone likes to shop in an uncluttered store, but white space, simple graphics and clean visual lines can be especially helpful to keep someone who is “just looking” on your site longer.
- Be ready to answer those one-off questions that aren’t already in your FAQ or answerable with other self-help features. Recommendation engines are helping some retailers anticipate and respond to consumer questions quickly—enabling one-page FAQs to become increasingly dynamic and responsive in nature.
The Bottom-of-the-Funnel Shopper: Get to the Hard Data
Show the data that matters: Specs, prices and side-by-side comparisons
- Show select products side-by-side using standardized data for product comparison within the same product line or across different manufacturers. Include a snapshot of main specifications only (like model, price, memory and screen size in the case of a laptop for example) and include an opportunity to click and “show more.” Note: audiences should not have to scroll to see anything in this specification snapshot.
- Provide a “show more” option that allows interested audiences to open up a product’s full list of specifications. Unlimited scrolling is acceptable here. A clickable option to “show more” should be available as well.
- Create links to relative pages as often as possible within specifications to simplify the experience, keep shoppers on site longer and shorten decision-making time.
True, consumer electronics retailers have a little more to worry about than those in other segments. But that doesn’t mean they have to all be rocket scientists, either. The right recommendation engine can be that for them.
If you’d like to learn more about how Strands Retail is helping online consumer electronics retailers increase the profitability of the customer buying journey for all of their shoppers: