Creating Insight from Data that is Actionable – Are Machine Learning and AI the Answer?
by Nick Sleeth
From everything we hear and read today, it sounds like machine learning and artificial intelligence (AI) are going to help us make data more insightful and actionable. The reality is that AI and machine learning are very complex and expensive technologies to leverage, if you include all the costs for the technology, processing power and labour. In a recent Boston Consulting Group survey of over 3,000 companies, 85% believed they could gain a competitive advantage from AI but only 5% of organizations were using AI extensively.
Today, AI and machine learning are mostly used in a “Black Box” approach, meaning AI and machine learning technologies are imbedded inside other solutions to enhance their effectiveness rather than using AI and machine learning independently. This shields us from the complexity but this does not help us as marketers to build better insights and make data actionable.
Marketers have always tried to leverage data and insights to learn for our next campaign. We think of how Google Analytics helps us get a summarized view of our web traffic. The issue we face now is that there are more data sources available so using only once source is not enough to give us the insight into our customers or the business. We know we can access more sources of data like 1st party, 2nd party and 3rd party data but the challenge is bringing it together. So why do we need to integrate these multiple data sources to make better decisions?
Three reasons come to mind as to why we need to integrate more available data to create insights. First, more detailed data is now available and easier to access than in the past so ignoring these new sources would be foolish since only looking at a very high level view is no longer enough. Second, investment in marketing budgets is at an all-time high, so we must make better decisions, therefore data and better insights will provide that answer to justify this spend. And last, the single point marketing data used previously is not well understood by other departments.
For example, the concept of unique visitors does not mean much to a Chief Financial Officer (CFO) who wants to understand future revenue and how much budget to allocate. Instead, we need to link web traffic to leads, the value of these leads to sales and how many of those leads do we need to reach revenue objectives.
We agree for the need to integrate more data sources to build better insight, but where do you start? Start small with identifying data and insight objectives driven by what the business needs.
Don’t build a complete data dictionary with access to all your data sources as your starting point as this will be a wasted effort. And definitely do not start with machine learning and AI solutions as both of those approaches require a massive amount of information to be useful and are very complex, so leave this to the experts.
Instead look at sources of data you have or could easily acquire to be used to help your organization make decisions. For example, adding local demographic information to a customer lists give insights into why and how those people purchase your product. You may want to acquire a simple tool to bring multiple sources of data so you can run simple reporting and analytics to learn and grow. Is there a chance that you throw away this tool in a year? Absolutely but that is the benefit of Software-as-a-Service (Saas) based tools. Easy in and easy out so when you are done with it, you can graduate to a more complex tool.
You may wonder, should we use consultants to help? Yes, of course, as this is a great way to learn and reach your goals faster. Make sure you have an objective clearly defined before you start with them and keep steps small for quick returns. Stay away from solutions and consultants that promise AI and machine learning solutions as your initial projects. Those are likely to fail.
One last point, all departments in your organization are trying to figure out how to use all this data from multiple sources to make better decisions. Marketing has to continue to innovate as well.
More organizations are using data to make smarter decisions and those who embrace this will be more successful.
