Key Elements of Complete Personalization
I have been writing about using “model-based” personas stemming from a 360-degree customer view for proper personalization for some time now. This time, I would like to cover basic elements other than data and analytics. Too often, even advanced data players have a hard time executing complete personalization due to shortcomings in other areas. If consumers do not get to see personalized messages in the end, what good is all that data and analytics work?
Last month, I introduced a personalization framework that separates outbound and inbound personalization. Then I divided the inbound part into two groups again, one for cases where the target’s identity is known, and the other where the identity remains unknown. Such division is necessary as we are all living with marketing divisions created based on marketing channels and it is nearly impossible to identify all targets (refer to “Personalization Framework”).
Now, let’s examine another set of checklists for complete personalization. When I say “complete,” I am counting both “reactionary” personalization that is popular in the tech community; and “planned” personalization based on past transaction, promotion and response history, as well as demographic data.
Regardless of channels or types of personalization in the mix, marketers would need to connect all of the following elements to get the job done right (i.e., target consumer actually gets to see customized content through their preferred channels).
- First, starting from top-left, we need technology that enables us to show different content to different targets. If it is about the website, the site must be modularized. If it is about email, we should be able to swap different content in and out easily. If it is about mobile apps, such content drivers should be built in. If it is about online chat, then customized scripts should be triggered at the right time. If it is about offline, well then, marketers must train their store employees to ingest information from terminals or hand-held devices and pamper customers accordingly.The bottom line is that we need some technology to drive customer engagement. But one should never treat this part as the end game. Too many marketers fell into that trap, considering the job done by setting some commercial personalization engine on an auto-pilot. That is the source of many “bad” personalization efforts: ones that are annoying, invasive, irrelevant and, ultimately, boring.
- Moving clockwise, at the risk of stating the obvious, marketers must have an ample amount of content to display. In the days when commercial use of digital images and CGI (Computer Generated Images) is widely available, creating a library of content should be a matter of commitment. But a great many marketers suffer from content shortage, or on the reverse side, content overload, necessitating a decent content management system. There is no complete personalization, even after procuring the latest personalization engine, if everyone gets to see the same old generic images or messages.
- Then of course there are data. I have been talking about this subject ad nauseam, so let me just reemphasize that data must be the primary driver for all customized messaging. Various types of data from disparate sources must be realigned to create a “customer-centric” view (or commonly known as a “360-degree view of customer”), as personalization should be about the person, not channel, division or products. Too many marketers get overwhelmed at this stage, and sheepishly resort back to the default setting of a commercial personalization engine with rudimentary segments based on some intuitive rules. That is a real shame in this age of abundant data.
- Speaking of abundant data, to drive a personalization engine in near real-time, all of these datasets must be “summarized” in forms of personas, segments or model scores. Each score is essentially a summary of hundreds of considered variables, and they are in the end just another set of “small” data feeding into personalization engines. In the age of Big Data, making data smaller and more digestible using modeling techniques is an essential activity, not an option. On top of that, such statistical work also improves targeting accuracy. Even the worst model outperforms rudimentary rule sets designed based on human intuition.
Now the question would be “Jeez, where do we start”? Unfortunately, the answer to that question is “It depends.” It depends on the state of available data, technology platform, content library, types of developed models and segments and, most importantly, commitment level of the marketing leaders in the company.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at firstname.lastname@example.org.