We ask a Founder of Adobe Analytics Consulting Group for insights on how to be a more data-driven marketer.
#TwelveDaysofISDI: Career advice, interviewing tips, and Silicon Valley secrets to make your 2018 the best it can be.
Our 12 days of giving back to the community continues with Academic Board member Adam Greco. Adam is a longstanding member of the web analytics community who has consulted with hundreds of clients across nearly every industry. As one of the founders of the Omniture (now Adobe) Consulting group, Adam managed accounts large and small and helped clients maximize their use of Adobe Analytics technologies directly and indirectly through his extensive blogging. He is a senior partner at Analytics Demystified, which helps companies maximize their use of web analytics technologies.
He’s also an author! In 2012, in partnership with Adobe, Adam published the first-ever book on Adobe Analytics — The Adobe SiteCatalyst Handbook: An Insider’s Guide. You can tell by his approach and thoughtfulness to every answer below, he’s someone we all can learn from — more importantly, someone we’d want to learn from. Adam will be leading the Data Analytics module for the Masters in Internet Business (MIB) program here at ISDI, for more information, visit us today.
We know you have a background with Adobe Analytics. Could you share with us your story and an interesting lesson you learned?
For the last 20 years, I have focused on CRM, marketing and digital analytics. When I ran the website for the Chicago Mercantile Exchange, I couldn’t figure out why we even had a website, so I started playing around with web analytics tools to set some goals for the website and measure how we were performing against those goals. Once I started using analytics tools, I got hooked and ended up working for the analytics tool vendor in its consulting group. I traveled the world helping its largest clients make website improvements via data.
After having helped hundreds of companies, I would say that the biggest lesson I have learned related to the digital analytics field is that there are two fundamentally different ways to approach analytics. I call these two approaches the “thermometer” approach vs. the “thermostat” approach, borrowing a quote from one of Seth Godin’s books.
Those who use digital analytics to report upon what is happening right now or what happened in the past, are “thermometers.” They are happy to tell you what is happening or what happened in the past, but they are not influencing the future in a significant manner. They are “report monkeys” who simply crunch numbers all day long.
Conversely, there are other organizations who use data to identify problems, ideate on those problems, identify potential solutions and test out their hypotheses. Those organizations are “thermostats” in that they use data to change the future.
I have found that the most successful companies (when it comes to digital analytics) are those that spend the majority of their time being “thermostats” vs. “thermometers.” In addition, those who work for companies that are “thermostats” tend to be happier and stay longer, because they can see the impact of their work on the bottom line of the business. They are a profit center helping achieve ROI, whereas the “thermometer” folks are more of a cost to the organization.
What is the best interview question you’ve ever been asked? What’s your favorite question to ask?
I would say that the best interview questions I have received are ones that ask about what I see myself doing a few years in the future or what my ideal job would be. I like these questions because they force you to think about where you want to go and give the interviewer a window into your long-term plans.
On the flip side, I like to ask interviewees questions that help me understand how they think critically. For the digital analytics field, I may also throw some math/logic questions into the mix. For example, I might ask someone the following question:
“If you had a retail website, and sometimes products are out of stock, and you want to quantify how much money was potentially being lost due to products being out of stock, how would you quantify that?”
In this case, many people would reply that they would multiply the value of the products that were out of stock by how often an “out of stock” message was shown on the product page. However, that is not the case, because it assumes that the product would have been purchased 100 percent of the time it was viewed! It would be more realistic to identify the historical product view to order conversion rate and then apply that to the value that was potentially lost due to products being out of stock. This kind of exercise allows you to see how candidates view the bigger picture and think about data critically.
Another favorite of mine is to ask interviewees to solve the “Monty Hall Problem.” This is a great logic test that allows you to test both math and resilience traits at the same time. For those not familiar with it, the problem goes like this:
“You are on a gameshow, and there are three doors. Behind one door is a car, and behind the other two are goats. You pick one door, and then the host opens one of the doors that has a goat. Now you have the choice of sticking with your door or switching to the other remaining door. Which choice is the better of the two? Should you keep your door or switch, and can you explain why you made your choice?”
While it is interesting to see if the interviewee correctly answers the question (there is a correct answer!), what is more important is how they explain why they chose the answer. That helps you see how they think through problems. BTW, if you are curious and want to see the answer, click here!
What are specific skill sets you look for when hiring?
When it comes to hiring in the digital analytics field, I would say that creativity and curiosity are the skills I value most. It is relatively easy to find great statisticians, but I have found that the people who do the best in the analytics field are those who come up with creative ways to use data to solve business problems.
Digital analytics is a hard business. You have to collect data, make sure it is right, interpret the data, come up with a hypothesis as to why something isn’t performing better, come up with ideas on how to change it, and then test your theories to see if you generate ROI! You have to be a curious person who likes to figure out why a website or mobile app isn’t performing as it should, and be creative enough to identify potential solutions. Many people don’t realize that crunching the numbers is a relatively small part of being an analyst.
I have also found that being creative is important when identifying different ways to use the analytics tools at your disposal to answer questions. For example, I once had a crazy situation in which my boss asked me to use our corporate website to sell golf tickets to a charity outing (long story!). The way I solved this was by finding data in our CRM system around our users’ golf handicaps, importing that into our web analytics tool and then finding pages that good golfers were viewing more than others. That helped us sell the golf tickets in a few days, and it was creativity, not math skills, that was the key to accomplishing the task!
Make a prediction. Any prediction.
If I had to predict something related to the field of digital analytics, I would say that the future will be much more technical in terms of data extraction and analysis. For the past decade, it has been somewhat easy to be a digital (web) analyst. You simply collected website data via JavaScript and then ran reports in Adobe Analytics/Google Analytics.
But with so much data being collected these days, I believe analysts of the future will have to know how to extract data from multiple “big data” sources using programming languages like R or Python, and that these data sets will grow exponentially. So it will get harder to obtain all of the disparate data you need to fully understand customers.
On the analysis side, interpreting these massive amounts of data will become more difficult. At some point, analysts will have to rely on algorithms to interpret the data, so managing/tweaking AI/machine learning tools will become a new skill that has be learned.
For someone who wants to be a more data-driven marketer, where can they start? Any tools, groups, or books you can recommend?
I would encourage any who wants to be a more data-driven marketer to really understand how marketing works and, initially, focus less on the data part.
I see many people in the field who are great with Excel, statistics and data, but they lack the fundamental understanding of marketing. For example, while it is technically possible to use cookies to obsessively hound people to buy your product via aggressive display advertising remarketing campaigns, is that the best marketing decision? Will that help your brand or get people to despise you? Many people fall into the analytics field accidentally and have not studied marketing, so if they have not taken classes or worked in a non-digital marketing position, I recommend starting there. A great podcast to listen to related to this is: “Everyone Hates Marketers” by Louis Grenier.
In terms of books, I suggest reading as many Seth Godin books as you can and all blog posts from the folks at Basecamp (formerly 37Signals). I also highly recommend reading the book Rework by Basecamp co-founder Jason Fried.
Lastly, there is a great, free public Slack group with thousands of digital marketers that people can join called #Measure Slack. In this forum, you can post questions related to digital analytics and any tools used in the industry. You can join the community by filling out a form at http://join.measure.chat.
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