Machine learning permeates every industry, and if you aren’t using it, you may feel as though you aren’t keeping up. How do you ensure you are leveraging machine learning as more than a should-have but rather as a must-have to drive quantifiable growth?
At its essence, machine learning is a set of algorithms that generate rules based on inferences from data. With an estimated 1.7MB of data created every second for every person on earth, machine learning is taking off.
Amazon’s Top Picks for You is a great example of how these rules play out in online retail. Using machine learning to improve your digital marketing requires a diagnostic approach to flesh out the business challenges you want to solve and design specific solutions to meet those challenges.
To implement machine learning as part of your digital marketing strategy requires you to:
Articulate organizational vision, business goals, and a strategy for meeting those goals.
Determine the digital strategy that rolls up to the overall business strategy.
Detail tactics needed to execute the digital strategy and the measurement metrics.
Map metrics back to higher-level business goals.
Next, examine the tactical challenges that are: a) not performing the way you need them to move you forward; b) performing well but need to be iterated upon to adapt to the changing customer, market, and competitive landscape. Identifying these challenges fuels machine learning, and learning is key — you want to build, measure, and learn, then use that learning to make decisions on what comes next.
Tools such as Google Analytics and Optimizely can gather customer data on, for example, an e-commerce experience with data iteratively used to personalize various touchpoints in the customer journey. Machine learning can accelerate this process and immediately identify real-time buying patterns, helping to quickly get to the endgame of meeting business goals and closer to the vision..
There are several technologies using machine learning from experimentation to recommendations to marketing automation and beyond. The data required by these technologies comes from your customer data — spanning personal information, engagement, behavior and attitude.
How data is stored for machine learning is critical as well. Having data in a supply and format that is useful is the challenge. Retailers must have a strategy for long-term customer data storage and retention such as in the public cloud; and that strategy must address high performance computing platforms for processing the tsunami of data that can easily overwhelm typical computing resources.
The longer you wait to harness the power of machine learning, the harder it will become to organize and structure the data. And, the longer and harder it is, the greater the challenge of using machine learning to determine its value for your brand.
Machine learning is a science but the art of applying an iterative learning process drives successful digital marketing strategies.
Shamir Duverseau is co-founder and managing director of Smart Panda Labs, a digital consultancy that uses data and creative intelligence to drive customer lifetime value.
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