Steven Shaw has been cited by Dave Taylor, Bob Stone, Mona Goldstein and others as the biggest back room boy in the direct marketing business in Canada. No this is not an article on politics. What they mean is that he is never in the front but pushes from behind. He is the guy that develops marketing analytics strategy for most of the major direct agencies in Toronto and they all thank him very much for the privilege.
Charles: Good morning. What do you have to say for yourself?
Steven: I was up late last night and I’m still waiting for your coffee.
Charles: Sorry about that. I’ll fix it right now.
Charles: Is that better?
Charles: You and my partner Brian Salter have played in the technology business together several times. What’s the connection between technology and marketing analytics?
Steven: To be honest not much. I think technology just gets in the way of clear thinking. Success in marketing analytics rests on having a point of view, clean data, clear objectives and a testing plan. Manipulation of the data into a segmentation, for example, is easy and can be done on a desktop, mainframe or an excel spreadsheet if needed.
Charles: So why is analytics shrouded in mystery? It all seems so complicated.
Steven: Well that’s how the big guys charge so much money for so little. In my world a robust monthly report suite that includes data migration, strategy, design and automation is a $5K to $20K proposition each month even on large data sets. It shouldn’t be more.
Charles: I now see why the agencies love you.
Charles: what do you think of the state of marketing analytics today vs. when you started out in the 70s?
Steven: Access to information captured and made manageable by data management systems today is becoming the business equivalent of the scientific breakthrough. In my day it was the Business Intelligence department that everyone went to. People like me were largely ignored. It wasn’t until about 10 years ago that a real interest started in what the customer was doing at a segment level.
Charles: So what do you do with all this data?
Steven: That answer hasn’t changed in the past 20 years and won’t change in the future is my bet. We calculate customer value. We build loyalty. We build models to predict future business and to identify potential attrition. We target to increase response and reduce cost of sale.
Charles: And the challenges?
Steven: That hasn’t change much either…companies still think about products not customers, capturing customer data still seems difficult, systems don’t talk with each other, a standard of customer value is elusive, customer lifecycle strategy is largely unknown outside the direct marketing community, marketers are bad at analytics and analyst don’t get marketing. Is that comprehensive enough?
Charles: Whew! So what do you think are ‘good’ analytics?
Steven: Interesting question. Good analytics are objectives based. In other words they are driven by the business leader not by IT. Success is based on building a strategy that guides process change and investment and, most importantly, sticking to it. Technology is just the enabler of the business vision. Even so it more often than not gets in the way of a successful outcome.
Charles: So what drives the data strategy?
Steven: The data strategy must be driven by an understanding of how information can enable or improve a business process. For example, increasing cross-sales (the business value) requires data about your current customers and the products they own (the data). Establishing some early, visible benefits is important to launching the data strategy and giving it momentum and credibility.
Charles: what is “good” data? Is all data “good”?
Steven: There are many answers to that question. Here’s mine…Not all data is business critical. Data that is critical to the business typically has two characteristics: (1) Association with something of long-term value to the business, e.g. the customer. (2) Relevant across multiple systems, processes and stakeholders. Marketing uses an end-to-end view of the customer loyalty ladder the marketing touch points required to make the sales funnel effective. These touch points and the process around them reveal critical data assets and associated attributes that are segmented, ranked and prioritized by statistical manipulation to optimize sales and engagement. If this information is siloed and inconsistent, customers will get inconsistent messages and service, process owners will have difficulty measuring their effectiveness, analyses will not reconcile, and implementing new controls or improvements will require changes within each process step. Improvements to these critical data assets will yield important business benefits. By identifying and improving critical data assets tens of millions of dollars in benefit will result that will justify millions of dollars of investment in implementing a data strategy. I’ve run a little off topic here.
Charles: All good stuff. How can we avoid confusion? How can we focus?
Steven. It’s important to keep the set of critical data assets as small as possible. That’s tough and needs a strict manager to oversee.
Charles: Can we go back to how data is managed?
Steven: Sure…Flows of data across systems and processes must be organized in a coherent way. It’s business architecture, not technology architecture that must define core data capabilities.
Charles: Are you saying that the business must lead?
Steven: No question, yes.
Charles: Has this been true in your experience?
Steven: Yes, and No. I’m a bit selfish and simply won’t work on a project where the business isn’t setting requirements.
Charles: What is the business actually defining? What is the business responsible for?
Steven: Here’s my list…To organize technology platforms and business processes based on their function in the ecosystem, to capture and create data cleansing and organization, to mine business insights from it, and to use those insights to drive intelligent actions in the business. By capturing data that measures the outcomes of our actions, we create a closed loop that allows companies to use their data to test, learn, and improve their processes.
Charles: As you know I’m on board and that’s what we’ve been focused on at Salter, de Gruchy.
Steven: You’re right but it’s not the main stream. Most marketing leads just haven’t made it there.
Charles: I seem to be onto questions associated with ‘lists’ but I would like to ask what is the checklist you would follow that would tell you an organization has a high quality enterprise information strategy?
Steven: Standard data management capabilities such as data sourcing and integration, quality and metadata management, data modeling and data governance. Insight capabilities including tools, data, and processes for management reporting and advanced analytics. Action capabilities provision data and business intelligence to applications, business processes and business partners, and capture responses to interactions.
Charles: We are just about out of time. One more question…What is data governance best practice?
Steven: Because data is so ‘present’, the governance structure must be collaborative, with a central governing body addressing most of the important and common data, and most of the data managed locally in the lines of business. Company policy, staffing structure and resources need to be aligned behind this to ensure success.
Charles: Steven, a big thanks for doing this.
Steven: My pleasure
Charles: See you in email