Not every customer or prospect is ready to buy. This means that when you engage you must do so in the context of the buyer’s frame of reference, not solely as a seller ready to make a deal.
Like many of you, I do a lot of research before I buy. For example, I recently purchased a new washing machine. Part of that process was researching products and comparing relative brand prices. Being an “old school” kind of guy, I first stopped at a local big box retailer one evening to see what was available (my last washing machine purchase was way back in the 20th century) and, after coming to terms with sticker shock and the vast array of products now available (they were all white way back when), I took a few pictures—especially model numbers—and went home to do more research online.
I started with a few websites of brands I had identified in the store, then reviewed consumer ratings and comparison of similar products on independent sites. I also looked at a few retailers’ sites to see which appliances they featured—that’s where the fun began.
I was in research mode, but most of the retailers assumed that I was in buying mode. Offers of discounts to “buy now” popped up, but I just wanted to review the specs of the product, and perhaps get a closer look at the items. I eventually did, but was interrupted by another offer to buy a washer/dryer combination at a discount for no interest for 24 months—if I opened a store credit card. What a setup!
There are three things the retailer didn’t take into account here:
- I wasn’t looking at dryers
- I already have a store credit card
- I planned to pay cash
This is where customer context matters. My browsing history would have given the retailer a hint that I was “just looking” and that context could have guided my online experience in a much more appropriate manner.
Big data and analytics can drive the right mode of interaction by understanding where the customer is in their “journey” (research, compare, recommend, buy) and then using analytics to power the follow-up interaction with the knowledge, understanding and prediction of what the buyer is trying to accomplish. Inputs like behavior changes based on time of day, day of week, mood, location and even input from shopping companions (a shopping experience with the mother-in-law tends to flow a little differently than one with, say, a college buddy) should be part of the analytics that drive the best interaction possible.
Knowing your customer’s context is important. Without this context, you create a higher path of resistance to the sale, which is an opportunity for a competitor to steal the show. With big data and analytics, you can better seize the moment with your potential customer and react in a way that helps drive them towards what they are ultimately looking for: your product.
Learn more about context for engaging customers at Insight 2014 this October, and register today with code SOCIAL100 to save $100 when you sign up.