Last week on our Marketing Garage podcast, we interviewed a Fortune 500 company, Reckitt Benkiser, about the success they’ve had using machine learning to guide their marketing strategy across a number of well-known consumer brands.
As we went through the interview, I couldn’t help but wonder why more brands aren’t using machine learning in that way. Then at FUSE Digital Marketing, I got to hear Christopher Penn, co-founder of Trust Insights and host of the Marketing Over Coffee podcast, talk about where marketing data is, and where it needs to be for machine learning to add value.
The truth is, there’s a gap there. Companies have spent years developing data capabilities to support the various flavors of direct, automated and digital marketing. Now that effort needs to shift toward corralling even larger data sets for even more business benefit.
That’s why Reckitt Benkiser has both their own internal head of e-commerce analytics and still brings in help from Fractal Analytics to tap into AI insights. And why more marketers need to lay the data ground work to make machine learning work for them in the future.
Artificial Intelligence Is Math, Not Magic
“All machine learning begins with stats and probability,” said Penn. “Machine learning is math, it’s not magic.”
And when it comes to applying that math to today’s marketing data, he identified five core problems:
- Volume: There’s so much data being crated today that any attempt to quantify it just sounds like you’re making up words. For example, Penn said “this year it is estimated, as a civilization, we’re going to create about 30 zettabytes of data.” Your brain cannot count to a “zettabyte.” (Go ahead and try, I’ll wait.)
- Variety: There are more kinds of data than ever.
- Velocity: It’s coming at us faster than ever.
- Veracity: Data has to be verified. If it’s not accurate, it’s harmful to your business.
- Value: Penn compared data to oil. Raw, it’s sticky and not very useful. It has to be refined and used to create value.
Machine learning can help marketers overcome all of those problems, but you need to get your data and organization into position to process it and put it to work.
One note: There is a difference between artificial intelligence and machine learning. According to Penn, AI is when you develop an algorithm that allows a computer to some extent to “think” for you towards achieving a goal. Machine learning is when you let the computer create an algorithm to solve for the goals you give it based on large pools of data. (And “deep learning” is when you have many machine learning algorithms with deep pools of data working in conjunction.) They’re sort of different levels of the same idea.
The Enterprise AI Journey
Penn says there are five stages of enterprise data usage.
- Identification: You can use data to identify what happened.
- Diagnostic: You can use data to diagnose problems and why it happened.
- Predictive: You can predict what will happen.
- Prescriptive: You can use data to determine what should happen.
- Proactive: A machine can use data to make it happen for you.
Most companies are somewhere around stages 2 and 3. Getting to that fifth stage is an enterprise AI journey of establishing the capabilities to move form identifying data to proactively using it. Penn sees that as a seven-step process:
- Data Foundation: You have the base tools to house and process data.
- Measurement and Analytics: You are able to accurately measure and analyze what you’re doing.
- Insights & Research: You have the tools to turn the measurement and analytics into business intelligence.
- Process Automation: You have the tools to automate these processes, so turning your data foundation into business intelligence happens automatically with minimal personnel intervention.
- Data Science: You install the capability to recognize next-level data science insights.
- Machine Learning: You have the data science and tools in place to integrate AI insights into your business.
- AI Powered Enterprise: You solve for AI first, breaking down business challenges into process-oriented steps that the AI can solve with minimal human intervention. “How can we use AI to do this for us on an iterative, continuously optimizing process o create business value?”
Again, almost no one has gotten to step 7, or even 6. Th most advanced brands today around around step 5, and most are still in the first half of the journey.
Should You Buy or Build AI?
If you feel like your data and processes are ready for AI, Penn says whether to buy or build yourself comes down to time, money and strategy.
If you have money but no time, a vendor can help. Look to buy or hire a service.
If you have time but not money, look to build in-house. A lot of AI in use today is using open source software. There’s no fee, just a knowledge cost. Budget about two years per person to get up to speed. (For many marketers in 2018, this could be prohibitive.)
Strategy: Before doing either, make sure you’re going to be able to use what you get to add value to the company. Bot of those options are significant investments. Do you have the strategy to support AI whichever you choose? One good benchmark is to look at the state of your digital transformation. A complete digital transformation is essential to do this. Without being digital, you can’t be AI.
How to Prepare Your Company for AI
Once you’re ready and committed, Penn said you need three kinds of people you need to have to enable your AI strategy.
To go back to the oil analogy, data is messy stuff that’s useless on its own without refinement. So you need:
- Developers to extract the data
- Data scientists to refine the data
- Marketing technologies to figure out how to use that data
Beyond those roles, certain skills become more important in a digital, AI-driven business. So look to train or hire overall talent with skills like these:
It also takes some different ways of thinking, according to Penn.
To be successful in an AI enterprise, marketers must learn to think algorithmically. Learn to think like a machine, process-focused on how to solve that problem.
Also, it’s important to understand that machines need oversight. Algorithms can spit out a bias if you don’t actively watch for it. It happened to Amazon, it’s happened in police use, and it can happen to you to if you don’t watch.
And finally, AI-focused organization must be built around outcome-focused people. It’s easy to get lost in AI if your goals aren’t clear, measurable and accountable.
Who Does the Machine Serve?
In the future, Penn said there will be two kinds of jobs: “Either you will manage the machines, or the machines will manage you. We want to be the people who manage the machines, not the kind of people who are managed by them.”