icon-groupBefore any real personalization can happen, a retailer needs to first ask, how well do I know this shopper?  In most cases, the shopper will be an unknown or anonymous visitor.  These are users that you have never seen before or that you do not recognize. Using in the moment context and intent is the best approach in this situation.  The Baynote personalization engine understands intent by looking at anonymous user behavior and search terms. The engine compares the anonymous visitor to observed patterns of behavior from prior visitors and then delivers content that other visitors have found useful.   This gives your unknown shopper a better reason to engage with content and products on your site, but at the same time, avoiding creepy segmentation.

The next step up is shoppers that you recognize and have seen before, but are still anonymous.  We call these known users.  With these shoppers, start by leveraging current intent and then look at what you may know from past experiences. For example, a user arrives on the homepage by entering the URL directly in their browser.  Baynote personalization knows what they were looking at the last time they were on the site and uses that information to show relevant products. If the visitor then navigates to a different area or the site or searches for something else our technology instantly recognizes the change in patters and starts looking at their current intent instead of what they were looking at last time. So start by welcoming the shopper back by letting them know where they left off, and then follow them through their current shopping trip to see what is different and personalize accordingly.

E-Commerce Personalization Has a Name

Finally, we hit the mother lode – the named visitor.  This is someone who may actually need to log in, has a profile with you and some purchase history.  The key here is to entice that second purchase (or third, or fourth – but the second is the hardest!) Personalization for these shoppers starts by first using current intent and then leveraging insight about their segment membership, past purchases or user attributes provided by a CRM or marketing automation system. Past purchases can be used to engage the visitor in a dialog around complementary products for their recent purchase or to show them new arrivals that are similar to past purchases. Baynote also uses segments and attributes to filter results. For example, a user is identified as a high value purchaser who typically spends more than $500. Using this information the Baynote system can filter results that are returned to show higher value items.  For the named visitor, you must treat them as familiar, but be careful not to cross the line.  No one wants to feel stalked or followed.  Keep it light, keep it contextual and keep it appropriate.

 See Post 3 of 3: Why Bother?