Jim Sterne is the founder of the Marketing Analytics Summit (former eMetrics Summit) and co-founder and Board Chair of the Digital Analytics Association. An internationally known speaker and consultant, he is the author of numerous books, including Artificial Intelligence for Marketing, 101 Things You Should Know About Marketing Optimization Analysis, Social Media Metrics, and The Devil's Data Dictionary. He has spent more than 35 years selling and marketing technical products and has devoted all his attention to the Internet as a marketing medium since 1994.
J.S.: People, process, and technology are the top challenges for leveraging marketing data effectively. You have to collect the data, transform it, and integrate it, and store it into something that will let you do analysis. That’s hard work. And if you’re a small company, first of all, you don’t have a lot of data, so statistical significance becomes a problem. Some companies ask themselves: "I had twice as many people come to my website today compared to yesterday! What does it mean?". Well, it means that you had 6 people instead of 3, and that marketing data is not informative.
J.S.: Scale of marketing data is also a challenge. On the one hand, as a small company, I don’t have enough data. On the other hand, as a large enterprise, I have too much data. There is no end-solution to that. There is an opportunity in identifying the data you think is most useful, and focusing on that, for enterprises. And for small businesses, intuition is very valuable. Because if I don’t have enough data, it is not terribly useful.
And here’s where we blend the two together. There’s an eCommerce platform called Shopify. And Shopify is for small businesses. If I sell 100 dollars of product per day, it is enough for me to do analysis. But Shopify has a million stores, so they are watching a million behaviors and they can see that the 5 people that come to my store today are exhibiting behavior across the 999.999, and therefore they can make a recommendation on how I should advertise on Facebook. That’s amazingly valuable.
So, even as a small business, I can take advantage of data by going to a third-party and seeing how do I compare to others. I can look into my first-party data and say my best customers have these attributes, let me go to LinkedIn or Facebook or Google, and say bring me the other people who have these attributes". So now I start leveraging third-party data.
D.D.: How insights are communicated can also become a challenge for marketing data. Something that I’ve noticed by talking to analysts is that the traditional role of the analyst is changing. There seems to be more focus put on communication skills and being able to argue why we need to leverage and optimize marketing data, more than providing the actual statistics. This is an interesting shift.
J.S.: Yes, and there’s an interesting reason for that, which is that data has become so much more complex.
It used to be that, as a business decision-maker, if I know my income and my expenses, I can adjust my budget and decide how to move money around in order to make the process more efficient and look for new ways of investing my money in order to get better outcomes. But as soon as we got to the internet… things changed. I’m ok with income, expense, shop floor control, and supply chain. And you know what? Market surveys, market research, I can manage that information too. But as soon as we get to the internet with behavior and I get something as useless as 'page views' and 'click-through rates' that add up to behavior, I cannot, as a business manager, understand that, so I need to bring in an analyst.
But my training says “Just show me the numbers. How many people clicked on the website? How many people responded to the ad? I’ve spent this much, just give me the number and I can figure this out myself.” Well, actually, I am sorry, but you can’t. Because it’s not accounting, it’s statistics.
Here’s an example: if you go to a restaurant this evening without wearing a face mask, you will or will not get COVID-19. It’s the likelihood, the percentage chance. It’s the difference between counting and predicting and statistical analysis. So, let’s bring in statisticians, let’s bring in the tools, to help munch through all of this data. And then let’s bring in the people who understand what the data mean.
The reason why I love doing analysis and being an analyst is that it means that I can understand the business and the data, and I can blend the two. And this is extremely creative. This is new, so getting companies to understand the value of analytics is a mind shift in how they approach information. The 'just give me the numbers' mentality doesn’t work anymore. Tell me what the data implies, tell me if there’s a surprise in there, tell me the correlations. Then I can think about if there’s causation in there or maybe not, but essentially tell me something I didn’t know instead of a dashboard that’s just green light and I can ignore it.
D.D.: And tell it in a way that I understand it, as well. I guess that’s one of the challenging things that take some work, right?
J.S.: Precisely, because you were talking about communication skills. This is enormous. It’s a classic stereotype that people who are interested in math, are bad at communicating. No, they are just not trained. It is a skill. If you are an introverted, shy, statistically-capable professional who has trouble communicating your findings to business people, I recommend taking a class in improvisation and theater and practice being fast on your feet.