Key Elements of Complete Personalization
If all of these four elements are in semi-decent shape, connecting the dots among them is the key for success. I’ve seen organizations where proper personalization is not being done even after excellent data environment and personas are developed and managed, because of fictitious barriers between departments and lack of a common platform to exchange results of data work with drivers of technologies for personalization.
For such cases, I would recommend a stepwise approach to build conduits among the key elements:
- Procure and install a commercial personalization engine (i.e., start with outbound email or inbound Web personalization, depending on channel strategies).
- Test the engine with simple segments (not necessarily model-based personas yet), or raw “trigger” data.
- Concurrently with Step 2, conduct a data audit to see if data sources are properly aligned on a personal level.
- Develop a 360-degree customer view, if it's not ready.
- Consolidate data around the 360-degree view, and convert transaction and even- level data into “descriptors” of individuals (refer to “Chicken or the Egg? Data or Analytics,” “It’s All About Ranking” and “Beyond RFM Data” for further details).
- Create personas in the order of importance based on marketing goals, channel strategy and product promotion schedules (refer to “No One Is One-Dimensional”).
- Map personas (and/or segments or trigger data) to proper content (e.g., match a "Wine Enthusiast” persona with contents for “wine”).
- Test a personalization engine with personas and tagged contents (similar to Step 2, but with model-based personas or model scores).
- Expand the practice to all channels (i.e., outbound, inbound-PII-known, inbound-PII-unknown, as described in “Personalization Framework”)
While there are other routes, there really is no shortcut in all of this. There is no magic bullet in complete personalization. Even this phased approach suggested here may not work for everyone. Just this morning, my team worked out a different set of steps to cover the basic four elements, as we were dealing with a highly matured analytical operation. But even such a company didn’t have all of the dots connected to handle inbound personalization properly, so we all had to step back and build a roadmap first.
The key is connecting all of the dots in the end. The order of operations may vary greatly, as we will all inevitably encounter different types of shortcomings in different areas. The simplest way is to identify the lowest-hanging fruit, then fully test it and check it off of the list. Challenging parts must also be examined from the beginning, as some groundwork may have to be done concurrently (e.g., start with data hygiene and tagging processes, while the content library is being developed).
Proper personalization happens only when all of these four elements work harmoniously. All of those sub-par personalized messages that we see in our inboxes, on mobile apps or on websites are the results of technology-driven efforts. It is time to bring some real data into the mix. With an organizational commitment, it isn’t that difficult to put it all together, taking one step at a time.
Collaborations among disparate departments and divisions can be fruitful in a short period of time, if there is a thoughtfully built technology-content-data-analytics roadmap. Without it, arduous arguments and ineffective division of labor are pretty much guaranteed, collectively heading to nowhere fast.
If you get to lead such meetings, please feel free to bring the chart that I shared here. When key elements for personalization are commonly understood, it becomes so much easier to prioritize tasks and share assignments among distinct teams.
Learn even more about the convergence of technology and branded content at the FUSE Enterprise summit. Artificial intelligence and personalization will be featured among many other techniques and technologies.
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 principal and chief product officer at BuyerGenomics. Previously, Yu was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, he 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.