Amazon.com is one of the oldest players in the digital personalization space and they are still one of my favorites. I visit Amazon from time to time, just to see what recommendations the site has for me.
In the last 10 years, personalization tools and techniques have spread to many sites in the B2C ecommerce world. Sites with advanced personalization now guide us in other areas such as what movies to watch, what songs to listen to, and what apps to download. But there is an interesting dead end in most personalization engines today.
For sites like Amazon, Netflix, and Pandora, the personalization engine is designed to cater to your most immediate need. Consider the following example:
A visitor to Amazon.com decides they want to begin to learn about web analytics. The visitor goes to the site's search function and finds relevant titles within the subject area and on their own, figures out which titles are best for beginners. As weeks and months go by, the visitor makes another purchase of a web analytics title.
The next time the visitor returns to Amazon.com and searches for another web analytics title, the personalization engine hasn't really learned more about the visitor. In fact, the engine will likely give equal promotion in areas like search results or category listings to books the visitor has already purchased. The personalization engine doesn't realize that the visitor has progressed from a beginner in the subject area to a more advanced user.
Here's a simpler example: If a visitor to Amazon.com purchases the title Fifty Shades Darker, on their next visit the site will recommend books one and three of the series. Shouldn't the engine assume that the visitor has already read book one?
Of course, it seems an insurmountable challenge for an ecommerce site to know which books are for beginners and which books are for experts in every subject. But there is likely a progression of topics and titles that web analytics could detect in most subject areas and/or titles.
Personalized banner ads, emails, and remarketing tools take the personalization one step further. If a visitor has demonstrated their intent, possibly by visiting a specific product page on a site, the personalized banners and emails will do their best to bring the visitor back for more engagement and hopefully a purchase decision.
These techniques are still fixated on getting the visitor to perform an immediate buying action. There is good reason for this emphasis on immediate action in personalization tools. These platforms are focusing on getting the visitor to make a quick buying decision. But how do these personalization platforms help when the buying cycle is complex and the buying decision cannot be made quickly?
Let's consider a first time home buyer's potential journey:
Most websites would identify the visitor in step 1 or 2 as someone potentially interested in mortgage products and would likely show them promotions, banners, and emails about mortgage rates from steps 1 through 8.
This type of personalization is like a friend that re-introduces themselves every time you see them, even though you always remember their name.
Now let's consider a common buyers' journey in the B2B technology space:
It's not uncommon for B2B technology purchases similar to the one above to take 3-12 months and involve many levels of education, engagement, and decision-making. Each step can be tracked between web analytics, marketing automation platforms, and the CRM. Current personalization engines do not track or target buy flows with this level of complexity.
We can see the market is in need of some powerful, progressive personalization tools. Progressive personalization would need to focus on understanding where a visitor is in a visitor flow and when the visitor is ready to progress to the next step.
This is distinctly different from onboarding methodologies and typical sales force/marketing automation platforms, in that the progressive personalization would not be focused on pushing visitors to the next step in the process. Rather, progressive personalization would focus on helping the visitor to complete each step in the process successfully.
Getting there won't be easy. Current personalization engines already endure a high degree of complexity in taking data from systems such as ad networks, remarketing platforms, and the ecommerce engine and using that data to deliver targeted imagery and content.
Enabling personalized marketing and sales to target visitors in a complex buy flow will require a heightened understanding of how prospects identify needs, educated themselves about solutions, and make buying decisions. Implementing a dynamic personalization engine that automates messaging in a complex buy flow will require new tools to be developed within the CMS platform and email marketing platform, as well as new levels of integration with CRM systems.
In today's market, the personalization engines focus on the needs of the simplest sales cycles. As these engines become more mainstream, we can assume they will start to focus on more complex user behaviors. They will begin to offer targeted messaging, imagery, and functionality to the sales cycles that require a higher degree of product education, extended engagement, and interactive interactions with solution providers.
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Mark leads the analyst team to develop ROI goals, data strategies, digital channel reporting, and establish processes for data analysis for EXTRACTABLE clients. Since joining EXTRACTBLE 14 years ago, he has worked on numerous high-profile websites including Yahoo, DirecTV, Visa, FedEx, and HTC. The most trafficked web page that he's ever worked on received 15 million unique visitors in one day, he has run analytics analysis on over 150 sites, and the biggest ROI he's ever seen on a corporate website redesign was > 800 percent. He is an active member of the Digital Analytics Association and has contributed to the DAA Education Committee for over five years.
December 12, 2013
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