I’ve been writing for weeks about customer buying processes, including how the steps that a buyer goes through can change because the product, pricing, market conditions, and buying influencers differ for each product being sold.
I hope this discussion has gotten you thinking about what steps your customers go through in making the decision to buy (or not buy) from your business, because that process is essential to understanding how to optimize Web marketing for results that matter.
The next, tricky step in planning how to analyze customer data (yes, we are still talking about putting together a strategy for what to analyze!) is to acknowledge that even within a common framework, customers are still individuals — humans — and will choose to approach the purchase process in individual and personal ways.
Effective salespeople intuitively understand this fact of human nature and are always shifting and fine-tuning the sales experience so that the selling method is appropriate to the individual buyer. Whereas one customer may be totally data-driven, another may make decisions intuitively. One may actively seek third-party opinions from trusted sources, while another may prefer to base decisions on personal experience. Some of us respond well to visual clues, choosing to learn from demos and comparison charts, while others respond more strongly to verbal pitches or emotional cues.
The overall buying process can follow a fairly predictable format, but the individual process will always be personal. (This is equally true in the B2B world. The bill may be paid by a company, but there are still humans in the process, and all of them have their own way of making decisions.)
Your Web site is most effective when each customer can accomplish information-gathering and decision-making goals in his or her own way, handle the transaction the way he or she would prefer, and communicate any post-sale support needs in a way that personally suits him or her. Great Web sites, like great salespeople, allow customers the option to do business the way the customer wants to, not just the one way the site expects.
This makes data analysis tricky, because you won’t know which behaviors to measure until you have some sense of the various ways prospects and customers choose to navigate your site.
It’s easy to assume that an abandoned shopping cart means the prospect was not really interested or, as some have assumed, that Net security was the stopping point. But if the prospect signed off right after checking your shipping page, you may have an indication that the problem was shipping cost, or the expected time of arrival, or the knowledge that you don’t use environmentally friendly packaging materials. If the departure occurs at the credit information page, there may be a security concern, or the shopper is impatient with having to retype a card number or irritated at having to sign in again when the password is not recalled. Unless you are looking for specifics, your findings may not be terribly useful.
To get the most out of customer data, we need to put ourselves into the customer’s mindset, think as broadly as possible about what various behaviors might mean, and then test those assumptions with real people.
Only when we are certain that we’ve covered most of the likely behavior patterns can we begin to finalize which customer data makes sense to track and measure. And, even then, we have to stay open to the possibility of change — because customer behaviors change as readily as the weather.
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