One good thing about talking to smart people is that they typically know other smart people. After my interview with Jerry Rackley he introduced me to Stephan Sorger. Stephan is the VP of Strategic Marketing at On Demand Advisors, and also on the marketing faculty at the University of San Francisco and U.C. Berkley, where he teaches marketing analytics.

Earlier this year Stephan released the  textbook “Marketing Analytics.” The book is aptly named—it’s just that with the current hype surrounding “big data,” most readers will probably expect a book purely on that topic. A better title would have been “Marketing Analytics: How to Measure Everything,” because Stephan’s book discusses all the metrics that marketers should concern themselves with, not just big data. Little data matter, too!

As with Jerry Rackley, Stephan Sorger and I had a lengthy conversation regarding marketing, branding, and metrics. Below are several nuggets from that call.

The Book and the Framework

What I needed was a textbook where people who are not math experts could learn about what analytics could do for them. The most important element is establishing a framework by which to tackle a subject like that. The framework itself is in six sections, going from strategic to tactical.

The first block, or strategic side, is market analysis, which includes models and metrics such as market sizing, SWOT analysis, Porter’s Five Forces, market terminology, market segmentation, targeting, and positioning.

The second block is competitive analysis. Now that you have understood one side of the coin, which is the demand side, you look at the supply side. You look at what competitors are doing.

The next block is strategy and operations. What are needed to get analytics done? This includes understanding market entry and exit decisions, forecasting, predictive analytics, data mining, and so forth.

Block number four is the marketing mix, the four P’s: product, price, place, and promotion. I talk about individual strategic decision models and metrics for each of those four categories.

Block five is sales and support. That’s looking at the tools that people can use to facilitate sales and support, like customer lifetime value, the net promoter score, and so forth.

And the sixth and final block is what I call analytics in action. How can you really start a revolution inside your company? What things should you be working on today?

Three Sales Funnels

We actually have three funnels. The first funnel is from contacts to marketing qualifying leads (MQLs). How many people do we need to know about before we can actually get some expressions of interest?

The next funnel is from MQLs to sales qualified leads (SQLs), where the MQLs have now been nurtured to where we know something about how they are qualified. By studying that funnel, I can know more about what it takes to go from one to the other, and just as importantly, the ratio of SQLs to MQLs.

And the third funnel goes from SQLs to opportunities, meaning these are good, bona fide sales opportunities. The kind of thing that you want to actually get sales involved with.

I did this type of analysis for a CEO I worked for long ago. First, I started off having these huge fifty-page PowerPoints with lots of data: “Here’s our brand equity; our social media efforts, and so forth.” I quickly came to realize that the CEO didn’t look at any of that, nor did he care about it. He actually thought that was a waste of his time.

So I replaced the PowerPoint with a half-page weekly email: metrics for MQLs; numbers we got from a trade show; numbers of SQLs; numbers of opportunities. The CEO said to me, “These are the only emails I read. I just want to be clear, these aren’t the only emails I read from you, but these are the only emails I read from anybody in the marketing department, ever. So keep up the good work.”

Using Analytics to Drive Sales

Macy’s started doing some data analysis of stores in resort locations, and they found that the shopping behavior changes depending on the perception of the location.

For example, Hawaii. Macy’s initially thought that people would buy Hawaiian shirts, sunglasses, shorts, and so on. But customers weren’t—they went on shopping sprees. They bought suits and other expensive attire, because they were on vacation. They wanted to spend money!

Macy’s analyzed the data and realized this. They found that any sort of suits and fancy dresses were always out of stock because people were buying them up as soon as they came in. But no one had ever bothered to analyze those data.

Macy’s changed their collections, and that actually generated quite a bit of revenue.

Using Analytics Post-Sale

I used to work at Oracle, and we were conducting a big study as to what is a reliable indicator that customers are actually happy. Customers were asked to fill out customer satisfaction surveys, and they would mark all fives across the satisfaction board. Everyone was happy, and then the next month the customer would leave us. It was my job to figure out why this was happening.

The first problem I found was that the regional reps were assigned to gathering the satisfaction information. But the sales reps’ bonus compensation plan was based on customer satisfaction. So lo and behold they all came out with all glowing reviews. Once I realized this, I started looking for any correlation between satisfaction and anything else that we record. And there was only one metric: days sales outstanding.

If customers pay their bill on time, chances are very, very excellent that they like you and will continue to do business with you. If customers pay their bill late, no matter what they tell you, they are about to leave.

Presenting and Leveraging Data

I had the privilege of being at an absolute train wreck of an operations review. We had the heads of each department—marketing, professional services engineering, and so on—present to the GM of the division to (a) tell him what and how they were doing, and (b) typically ask for more resources.

I saw the engineering department head come up and just completely miss his opportunity. He told the GM how all the engineers are overworked, how the department is putting in huge hours, how things are starting to slip behind schedule, how the morale is low. He went on and on talking about people getting burned out, and then at the end, he pitched for more people. And the GM said, “No.”

And I couldn’t blame the GM because at that moment I would have said “no” as well. I would have said, “You haven’t talked about anything I care about.” In this case the GM cared about revenue. It was sadly ironic that the engineering department had the biggest impact on revenue of any group in the organization, but the presentation hadn’t captured that.

The very next presentation was from the head of professional services who had a dinky little organization. But she actually said “Number 1, here is how we can contribute to revenue. Number 2, here is the correlation between headcount and revenue, and as we add headcount, how the revenue goes up. But we’re getting increased demands, and at some point we are going to stop being able to increase our revenue. We’re going to have to stop incremental revenue, and it’s going to occur on this date right here.” She actually showed a graph of when that was going to occur.

And the GM’s reaction was: “What can I do? How can I help? How can we hire more people? Can we get a team to hire more people?”

Getting Started Simply

Remember, the CEO doesn’t want everything, he only wants what’s relevant to him. Spend your time on identifying the two or three numbers that the CEO wants. The number one thing that you can do immediately is to start segmenting your sales process into the three funnels. If you can do that, you’ll gain a lot more insight. You’ll see results almost automatically.