Last week I had the privilege of participating on the Advisory Board for the Retail Marketing Analytics Program (ReMAP) at the University of Minnesota, Duluth (UMD). Perhaps the best part of these sessions is the opportunity to meet with the students, many of which will be tomorrow’s marketing scientists.
During a few conversations on this visit, I was asked how to secure an entry-level position that would involve lots of cool predictive analytics. I want to focus on one of the answers I shared — don’t tell anyone you’re doing predictive analytics. What do I mean? Imagine you’re a freshly minted analyst in the following situation:
Your manager asks you to quickly evaluate who responded to a promotion.
You have many factors to investigate (because you have lots of data).
You have very limited time to find a great answer and build a deliverable.
The required deliverable needs to be simple and free of analytic jargon.
I remember my first day at high school. Yikes it was scary. The older kids were BIG! The teachers were BIG (the phys ed teacher was even a little mean), the school was BIG . . . Everything felt so BIG! But as the year ticked by, l became familiar and comfortable with my classmates, teachers, and the school -- the place shrunk to a more comforting size.
Today marketers feel about data as I did about my first day at big school -- it’s BIG. There is lots of it, and it’s coming at them from many directions and in many forms. But data does not feel so big and daunting to the marketer who recognizes their customers buried in the fog of big data. The fact is, customer recognition is the key for marketers to make sense of big data; and it is at the heart of all effective marketing activities. I write about this in my most recent report: “Customer Recognition: The CI Keystone.”
So what is customer recognition?
Recognition associates interactions with individuals or segments across time and interactions. The strength of recognition is gauged on its ability to associate interactions to anything from individuals to a broad segment; and to persist those associations across different touchpoints over time.
Keys are needed for recognition at touchpoints. There are many types of keys, ranging from IP addresses, to cookie-based TPIKs, to phone numbers and customer account numbers. At Forrester we call them touchpoint interaction keys (TPIKs)
You don’t need to be a fine woodworker to sit in a chair. An inability to precisely construct an angled mortise and tenon joint does not preclude you from resting your feet. Similarly the time is rapidly approaching where you won’t need to be a marketing scientist to deploy analytics. Ignorance of neural networks will no longer impede your ability to use them to improve a campaign. The democratization of predictive modeling or other trends involving the intersection of customer analytics and marketing technology is much of what I will cover for Forrester Research.
In my new role as a senior analyst I look forward to helping Customer Insight professionals increase marketing and business returns through becoming more intelligent enterprises. This might involve guiding clients on technology decisions, organizational strategy, or benchmarking to their peers. What topics would you like to see me cover?
Tag management tools are much more than the management of tags. Strategic use can:
give control of digital marketing campaigns to marketers – relieving significant IT burden,
significantly reduce digital marketing implementation and operational costs,
garner support for digital marketing programs – even in highly regulated firms – by offering detailed multi-stakeholder visibility and control of scripts and digital data,
reduce the “stickiness” and dependence on digital technology vendors, and
enable digital data syndication, which in turn drives dynamic segmentation and bottom-up attribution programs.
Forrester is currently assessing the tag management capabilities of top global brands, advising on their strategies and guiding them with their digital marketing road maps. Also; tag management research is ongoing with a few papers due for release later this year.
SAP today announced plans to acquire KXEN, a provider of predictive analytics technology. The terms of the deal are not known. This is an interesting development for both companies and highlights the focus on the democratization of predictive analytics, especially for marketers. The proposed deal puts the spotlight on two shifts in the analytics landscape:
Expert user to casual user. Our research shows that finding top analytics talent is a key inhibitor to greater customer analytics adoption. As a result, users expect analytical tools to cater to nontechnical, nonstatistician business and marketing users.
Yesterday, Acxiom, one of the world's largest data brokers and a key player in the marketing services ecosystem, launched an important new consumer service (still in Beta) called "About The Data." It's an initiative to show consumers some of the data that Acxiom has compiled about them, to provide education around how certain types of data are sourced and used, and to let users correct and/or suppress the use of these datapoints for marketing purposes.
This is a big deal. Why? Because it's pushing Acxiom (and, frankly, the entire third-party data industry) way out of its comfort zone on a few levels.
First, this is not a company that is used to dealing with consumers on a mass scale. Acxiom's DNA is fundamentally B2B; learning how to communicate to, and design tools for, individual consumers is a massive undertaking, and it shows in the UI. For example, when I attempted to register my address with a "#" preceding my apartment number, the format was rejected without any indication that symbols were disallowed in that field. As a tech-savant, it only took me one more attempt to figure that out, but not all consumers are so savvy. Similarly, clicking the "Home" button on the navigation bar logs users out without any notice or warning.