Several years ago, I was speaking with a marketing leader at a Fortune 500 enterprise who had implemented the full marketing technology stack to create a multi-channel personalization program.
This included a 360-degree view of the customer, decisioning algorithms to power messaging, a big data environment for processing data, and the list goes on and on.
We’re talking tens of millions of dollars over several years. However, when I voiced my admiration for my colleague’s considerable accomplishments, he voiced doubts of his own. He said something to the effect of:
“While we’ve checked all the innovation boxes and implemented every best-of-breed marketing solution on the market, when it comes time to deliver ROI for our business—I’m unsure that it actually will.”
See, he had built the solution on the implicit assumption that personalized experiences are the “ideal” marketing strategy and must deliver a higher ROI because it was better for the customer.
In retrospect, there were a number of problems with this assumption:
- There was (and is) very little publicly available data that demonstrate the ROI of these programs.
- It is unclear as to what elements of a personalization strategies produce the most return. For example, is the 360 degree-customer view the most important thing? Are there particular channels that drive more results? Hint: yes.
- The additional resource investment required to support a global personalization initiative is high. Unfortunately, it’s often not included in the program forecast, and now, your personalization lift must overcome the additional overhead that comes with aligning all these channels.
Hence, I decided to join the machine learning revolution after nearly two decades in digital agencies working with global enterprises.
So, why did I make this career switch?
- It is impossible for humans to manage in real-time the complexity of a 360-degree customer view with the multi-channel complexity of personalization at scale.
- Building potentially thousands of rules-based customer journeys is prohibitively time-intensive and complicated.
- The combinatorics (this is actually a real concept) of channel, offer, day/time, customer attribute are too much too analyze and act upon by people and marketing technology tools.
- You can’t deliver positive ROI without a platform that can both discover, learn, and make decisions on 1000’s of nano-segments. Yes, I work for a platform company that already does this, but this will become standard across the industry within five years.
- Lastly, without connecting your machine learning programs to your business KPI’s then personalization can be a real financial and operational drag on the business. Too many programs get built with the assumption that personalization will pay off, but without the real data.
About Mike Zell
As Senior Vice President of Customer Success at Amplero, Mike Zell works with Fortune 500 companies to leverage machine learning to continuously optimize every customer interaction.
Prior to joining Amplero, Mike led teams that have delivered innovative marketing programs and results for a wide range of companies, including Nike, Microsoft, Disney, and Toyota. Most recently, he has served as VP of Services & Strategy for Pointmarc, a Merkle company. He also held leadership positions spanning digital media and optimization at Razorfish and Fabric Worldwide.
Contact him at firstname.lastname@example.org.
Amplero is a digital intelligence platform that enables marketers to achieve what's not humanly possible by leveraging machine learning and multi-armed bandit experimentation to automatically optimize every customer interaction to maximize customer lifetime value and loyalty.
Using Amplero, marketers in telecom, banking and finance, gaming, and software-as-a-service have seen more than three percent incremental growth in customer revenue and five times retention benefit, often touching their customers less frequently while delivering great omni-channel customer experiences.
To learn more or schedule a demo, contact us today.