Many retailers allow site customers to sort site search, category and sub-category results by price, average customer review, top sellers, new arrivals to name a few. While this is great for usability, it’s also an opportunity to glean information about your customer which you can apply to merchandising and personalization on home pages, product pages, promotional banners and even email campaigns.
Amazon is a classic example of a retailer that uses merchandising zones on its home page. Featured products change as the visitor expresses intent, whether searching or browsing:
This history is even saved and used in future sessions whether the customer is logged in or browsing as a guest provided cookies are not deleted.
Imagine this scenario. Tuesday evening, customer is checking out these categories on a site but does not purchase that evening:
- Children’s learning toys, sorts by average customer review
- TiVo/DVRs, sorts by price low to high
- Customer looks at business books, sorts by best-selling
Returning Thursday evening to the home page, customer sees the following merchandising zones on the home page:
- “Top Rated Learning Toys”
- “Our Lowest Prices on TiVo/DVRs”
- “Best Sellers in Business and Investing”
When the customer intent is recognized, addressed and reinforced it increases the relevance and appeal of suggested items.
Just imagine the improved attach rates you could get if you showed “Most popular backpacks,” “Customer favorites” or “Our best backpack deals” according to how the customer sorted category results instead of “you might also like” or “customers who viewed this also viewed.”
Think about the 4 buying modalities of Future Now’s Persuasion Architecture: Competitive, Spontaneous, Humanistic and Methodical. (If this is new vocabulary for you, read this explanation). What sort-by filters could you use to determine which buying modality a customer falls into?
One’s personality might be dominant in one of the 4 categories but he or she could behave differently for different buying decisions. For example, I consider myself more methodical – I researched a portable GPS purchase for months reading as many reviews as I could. I have also made toy/gift purchases based on top selling/highest average customer review (humanistic) and bought clothing online with less thought because I expected the item to sell out fast (competitive).
If you understand the customer’s purchase-specific modality you can present copy that will address that can speak to that customer more effectively. You might even deliver a different page layout (a featured review, reviews on top, less emphasis on blowout sale messaging etc).
Invitation to Live Chat
If a customer sorts by average customer review and lingers long on the page, they’re a better candidate for a live chat invite than someone who sorted by low price and bounces around between pages. This can save significant costs on live chat.
Also, a sort by price high to low might indicate that your filtered navigation is not specific enough. For example, one might sort diamond jewelry high to low to find highest carat weight items, or a GPS navigation section to see newer models or more feature rich products. If you go through live chat logs for customers flagged “sort high to low” and discover their questions to customer service or subsequent search queries, you could understand what new filters could enhance the findability of products.
Should you push a BOGO (buy one, get one) offer free shipping above $100? What headline should you use — “20 Beauty Finds Under $20″ or “As Seen on Oprah: Such-and-Such Eye Cream!” ?
Imagine collecting information about a customer’s browsing history and sort-by actions and delivering more personalized subject lines, headlines/banners or offers in your email campaigns.
Really, we’re only scratching the surface.
In a blog post titled Optimizing Landing Pages to Match Customer Motivation I wrote:
“It’s near impossible if not completely impossible to predict an individual visitor’s purchase role and customer personality (with technologies ever improving, we might be able to shortly). But you can optimize your landing pages to “cover all the bases” if you understand what different customer types respond to.”
I still don’t see ESP applications on the market that will read your customer’s mind upon arrival, but think about the clues your site visitors leave you to help you personalize to them more effectively. It’s not limited to sort-by.