E-commerce sites need to understand buyer behavior, of course, but careful data collection and analysis can allow them to know much more. It's essential for any reseller (online or offline) to know something about customer buying habits if they are to avoid making hit-or-miss decisions when selecting product, setting up displays and promotions, and communicating with past customers and new prospects. The more you know about how the various segments of the buying public tend to behave, the better you can plan your marketing and merchandising efforts.
The resellers who are best at tracking behaviors by audience segment, or even by individual, have a huge advantage in retaining customers, upselling former buyers, and tapping additional opportunities to sell.
Remember the days of the local merchant who knew you by name, remembered your favorite brands, and called you at home when new stock arrived that matched your previous buying habits? Folks in my parents' generation recall everyone from the butcher to the newsagent operating that way. I've had far less experience with that level of personal service (but I'm loyal as can be to those businesses that pull it off); some readers may not have much of a sense of it at all.
Long before we had e-commerce, the best of the catalogers were working to replicate that almost-forgotten experience; businesses such as Lands' End and L.L. Bean seemed to know you when you called, knew what products were likely to interest you, and reminded you when they had a relevant offer. Of course, the telesales people manning hundreds of phone lines didn't really know us as the butcher had known our grandmothers, but great computer databases allowed the replication of that personal and customized service.
It's no surprise, then, that some of the e-commerce sites that best understand how to collect, interpret, and make use of customer data are those with catalog-business roots. But some pure-play commerce sites are doing it well, too, though many are not.
What does a site measure and act on to get the service equation right? The answer, as in last week's example, is to watch behaviors, look for patterns, then do a reality check of those perceived patterns against real customer behaviors. Ideally, that means talking to your customers, but at a minimum, it means working to put yourself in their shoes to test whether the patterns your data is revealing map to any predictable buying behaviors.
Everyone reading this knows what it feels like when the commerce site seems to know us well and puts exactly the right offers in front of us. Odds of purchase go way up when the interaction is personal and relevant, and anyone attempting to design a measurement and data-analysis effort ought to become a student of these sites by shopping there regularly. Even without having inside views of proprietary data-tracking programs, we can learn a lot about what a site is measuring just by how it communicates with us as customers.
What may be less obvious are those online merchants who are missing that personal-relationship feeling by either not tracking behavior at all or, more likely, tracking the wrong things. Usually the latter is a result of not getting granular enough, and as a consequence, every customer is treated the same way despite very different purchase habits.
An Aunt's View
A good example (without naming names) is a children's site I visit from time to time. This site has me on a list of previous shoppers, so I get mailings all the time. But somehow someone's neglected to pay attention to how I shop, so the mailings are never on target. Hint: I have many nieces and nephews, and I send gifts for birthdays, holidays, and special events.
This site obviously has my file included among repeat shoppers, because I regularly receive "privileged customer" email offers. But these folks have apparently never paid attention to the fact that none of my purchases goes to my billing address -- that they go to four different (regularly repeated) addresses around the country. And no one's noticed that everything I purchase is gift-type stuff -- toys and games, new-baby equipment, shower gifts. And I always order the gift wrap (big clue) and a card.
So why do I get offers for great deals on such staples as diapers and baby medicine? Aunties don't buy them, parents do. If they were tracking and watching for certain behaviors, not only would they know that I am not a prospect for parent-type purchases, but they'd know almost exactly which of my siblings' offspring had birthdays or events on what dates, and with a little more careful watching, they could surmise what age and perhaps gender each of them is.
Now, wouldn't a reminder about an upcoming birthday for an eight-year-old boy, citing gifts in categories I've bought or searched before, be more effective than a generic email pitching car safety for infants?
You get the idea. Consumers give us all kinds of clues, but our servers can't draw conclusions from that data unless we've set things up to look for certain patterns and to attach particular meanings (or trigger specific actions) when those patterns appear.
Anyone else have examples to share of measurement used well, or poorly, from a consumer's point of view? Please share your examples.
Reprinted from ClickZ.