https://clearaction.com/market-customer-intelligence-autodesk/

Autodesk: Creating a Unique Differentiator through Mktg & Customer IntelligenceYesterday Jack Androvich, Senior Director of Global Sales and Marketing Operations at Autodesk, shared on the newly-launched Marketing Operations Leaders talk show why and how his company established its market and customer intelligence function.

Jack’s successes provide marketers with very helpful guidance on how to enable greater agility in organizations through a combination of Marketing IT, data, analytics and strong Marketing Operations (MO) working cross-functionally to drive change.  The beauty of combining MO with the enterprise quest for agility is it aligns CMOs and marketing organizations with a challenge CEOs clearly lose lots of sleep over each night. Here’s a summary of Jack’s interview:

How did you decide to actually start a marketing and customer intelligence practice?
It started with a positive, perfect storm about 5 years ago. We had studied the work of Tom Davenport and Jeanne Harris captured in the book Competing on Analytics. A key takeaway for us is if you really get good at data quality and analysis of the data, you can separate yourself in the marketplace from your peer companies in business and financial performance. So we engaged two really bright MBAs who were both very keen on applied intelligence and how it could affect business.  We started by putting an intelligence practice in place for our European theatre that would look at the size of the market as a starting point to inform the sales planning process.  At the same time, a new leader came aboard in marketing who was a data hound and was really keen on putting this to work. At that point, we decided to formalize market intelligence which is essentially comprised of both the market intelligence, meaning market size, and customer intelligence, which answers questions like who are our customers, what are they buying, how big are they, etc.

What were some of the challenges that you were experiencing at the time?
We didn’t know what business questions we wanted to ask, and there was no single source of truth about the view into market or customers. The first challenge was key because we needed to identify what business questions we could answer that data or analytics would inform. So we end making a list across the company of about 300 business questions we thought we wanted to answer, which we boiled down with the help of a consulting firm to 35 core questions such as:

  • How big is the market we’re in?
  • How big are the segments we’re in?
  • How large are the customers?
  • What sort of share of wallet do we have?
  • How can we gain a better trusted advisorship with them to gain more wallet share?

The second challenge involved socializing the concept of a market-sizing model, which we eventually took globally, and surveying our customers to better categorize them by industry, size, quantity of purchase, etc.

How did you actually bring this competency to other parts of the organization?
This is a contentious area. For example, with respect to market sizing data, sales organizations typically use historical revenue data or they use other third-partyconsultants to apply predictive models to this historical data to project the future from the past. We took a different approach. We hired a firm with knowledge of the market we played in who were able to construct a theoretical model that not only included our past sales but our future potential sales and those of our competitors. When people we were trying to enroll first saw the data their response was “oh, this isn’t my data. This data isn’t right. It can’t possibly be good, so I’m not going to use it.” So instead of beating them over the head with the data, we essentially invited them to help us change the data in a way that they could have it as their data. In Europe this took about a fullyear-and-a-half because, you can imagine, it’s a long cycle to get these data articulated, collected and then analyzed. But we were finally able to finally get the sales people on board to say “you know, I believe this is what I’m seeing in Germany for a certain segment or in my particular part of the world. Once they came on board in Europe, we then decided to take the process global. We had the consultancy start building the global model and then we supported the process by creating a governance board that looked over these data and then owned the model for the market sizing as a product. So we ultimately had a group of about 7–10 people around the company, who really have a vested interest in the data being right.

The other thing we did coming out of that was to create addiction through adoption of the data. Once people got a taste of it, they could really start talking and singing from the same book about the size of their market. People really responded to hearing that we had a lot of adoption of the model and finally people became addicted to it. That led eventually to the use of the model not only by sales, but also by marketing and also our finance group who is very keen on looking at this as a way of thinking about the future.

What improvements or impact have you seen from it?
By understanding the customer opportunity as an ecosystem, we were able to find a multiplier in share-of-wallet potential that extended beyond the customer to the supply chain that surrounds the customer. For example, in Asia a customer that was designing an airplane needed to use the kind of software we sell but also their suppliers needed to use the same software or at the very least exchange data in the same format. Historically, the sales team looked to the finance team to provide this kind of opportunity insight, but they weren’t really equipped to provide the answers. Our ability to fill this need enabled us to get the sales peoples’ attention in a way that nobody else had. Also, in the last 7–8 months, we’ve been able to avoid a lot of expenditures on customer primary research that we normally would have done. By using our own data instead, we can already find the answers without additional surveying. People can come to the analytical team with a few good questions and get the answers they need. Finally, I would say we’ve uncovered a few billion dollars worth of addressable,account-basedshare-of-walletin the past 7–8 months that was sitting right in front of the sales team. .. they just couldn’t see it because of the lens they were using.

For others that would like to achieve similar results, do you have any advice?

  • Data and data quality are foundational.There’s a lot of ways to go wrong in this space.  Don’t ignore data and data quality or everything else will go wrong. It’s like a house that you build on a bad foundation and the problems live with you all the way to the roof. Ask yourself:
    • What’s the state of our data that would enable a market view or a customer view or both?
    • How good is that data?
    • Is it good enough to at least start bringing the data together in a way that exposes the flaws in the data so you can fix it and so you can start learning about what you’re seeing from it?
  • As you’re building an analytics team, don’t ignore having a really good data team behind them. If you’re having all your analysts spend and waste their time cleaning up data you are wasting resources. Good analysts are hard to find.
  • Co-create datasets with your stakeholders. This way the dataset ends up being owned by the whole group, so it’s not your data or my data, it’s not us vs. them thing.
  • Start just creating addiction through value. I’d say aim your efforts at the sales team. These people are out beating the street every day, quite often looking a million places for what they want or need. By just shining a light into their accounts, you give them a chance to find more business with less work; that is really creating addiction through value.
  • Go after quick wins. Don’t start too big. My motto is “think big, start small, and deliver early and often.” The idea of trying to build some big system or technology approach is really not where it’s at.  Get some good data. Get some good analysis. Get some good business questions that you want to answer. The better your business questions are, the better the data gets and the better the data gets, the better the business questions get. It’s a virtual never-ending cycle of improvement because the questions inform the data and visa versa. All these things are important for anyone that wants to go after this and succeed in fairly short order.
  • Don’t go it alone.  We had a really good partnership with our IT department and a really good partnership with a group that handles our corporate dataset (the core account info such as who the company is, what they own, what their licenses look like). We weren’t flying solo on this but took the leadership initiative for the company for creating in marketing a leading intelligence practice.

by Gary M. Katz, Chairman & Chief Strategy Officer at Marketing Operations Partners

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