Data and marketing expert Christopher Penn discusses the data trends impacting marketing, the skills required of marketers today and more
“You have to be able to dig in and measure your performance goals. Be very careful with what you deem a technology problem because, in a lot of cases, it’s not a technology problem. Ninety per cent of it’s going to be a people or a process problem. If you fix that, the tech you have can be coerced into doing what you want,” said Christopher Penn, co-founder and Chief Data Scientist, Trustinsights.ai.
Penn is also a four-time IBM Champion, bestselling author, and the Marketing Over Coffee podcast co-host. He talks to us about the importance of privacy-safe data collection, challenges that marketers face today, and recommended skills. He believes most marketing strategies boil down to people and their understanding of technological tools.
Excerpts from the interview
What’s the next data trend to impact marketing teams?
There is a lot that we know is coming down the pipe in terms of data privacy. Data privacy, privacy-safe Machine Learning (ML), privacy-safe data collection will are all going to impact marketers. Now, whether marketers know this or not, that’s the big question. The laws have been passed. Some have taken effect already, and many are on the way. The big data trend that we as marketers need to be paying attention to for the next two to five years is privacy safe data collection. What data you’re collecting and, more importantly, what you are doing with the data. Unused data is a financial waste and a massive security risk. The less data you have, the less liability you have. One of the things that is important for marketers to think about is, of the data that you do have, how much of it is predictive? If you run a sophisticated statistical analysis and realise that there is no predictive power in that type of data, stop collecting it.
Additionally, people are harnessing AI for marketing purposes. Many software vendors are doing it, but marketers themselves are not because the output may not make much a whole lot of sense. In the next two to five years, marketers from particularly large companies will have to start using AI if they want to be able to unlock the value of all the data they’ve collected.
What are the technology challenges that marketers face today?
The biggest challenge is the cleanliness, the correctness, and the completeness of your data. If it is not for clean, correct and complete, all the fancy technology tools will not work with it. If we think about the different ways to address technology, it should be the 5Ps. The first thing you have to be clear on is the brand purpose. The second is people. This is where most of the problem that plague marketers actually exists. It’s not in the technology because it is agnostic. Different products have different features, but it always comes down to the people. Do they have the right aptitude and attitude? It might not be a tech challenge, but it impacts technology because those people will be the decision-makers. Next, figure out what processes you have in place to leverage the tech. Fourth, the platform. Is the tech you are using the right fit for the problem you are trying to solve? And finally, performance. It comprises the technology, people, and the process you have in place, together are you achieving your purpose? This, again, is something marketers don’t measure well.
You have to be able to dig in and measure your performance goals. Be very careful with what you deem a technology problem because, in a lot of the cases, it’s not a technology problem. Ninety per cent, it’s going to be a people or a process problem. If you fix that, the tech you have can be coerced into doing what you want. Or worse, you have a good solid business case for why you need to change technologies once you fully understand the scope of the problem you are trying to address.
What are the new skills that marketers entering the field need to learn?
There are the obvious ones like Maths and statistics. They are deeply overlooked and the ability to use and understand data and be able to draw useful conclusions and make decisions based on data. Those are skills that are essential in today’s data-driven world and today’s AI-powered world.
Another set of skills that are lacking in the population in general, especially in marketers, are skills around empathy. Marketing has had a track record of being very self-centred. This has resulted in crappy marketing. So many make as much noise as possible in the hopes of attracting attention in the hopes of getting business, but attention is the most scarce commodity right now. Compassion and true customer centricity is what will drive your business.
When marketing is imbued with compassion, you have the customer’s attention. That is the skill going to set apart marketers now and going forward. This is not a new skill. It goes back to virtually thousands of years ago. But it’s a skill current marketers still need to learn.
What new-age business intelligence tools would you recommend for marketers?
I wouldn’t, and here’s why. Business intelligence tools are like appliances, like a blender. It is only as good as the person using it, ingredients that go into it and the recipe you are trying to make. Recommending a shinier, faster, and a more powerful blender or a BI tool doesn’t help if you don’t know how to use the tool properly.
There are several BI tools out there; classics like SPSS, Tableau, Alteryx, Python, and Google data. There are so many to choose from, and here’s the secret that vendors don’t want you to know — for the most part, they all do pretty much the same thing.
There are certainly little differences. User interface and customer support are valid differentiators, but in terms of core functionalities, BI tools are about as different as blenders are.
So the question then becomes if the tool isn’t the differentiator, what is? The answer is you. What I recommend for marketers is not more tools; it’s to understand how to use the ones you have and to know if you have the recipe and the book of right ingredients. Let’s presume you’ve got good data, and it’s clean. Do you personally know the different statistical techniques that your BI tool is performing? Do you know what questions to ask of it? Do you know when you should use regression over classification? Do you know the different types of regression? That knowledge is what’s going to make a BI tool useful. BI tools need to be driven by business and data requirements and by the skills of the team.
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