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As a software engineer whose clients are marketing professionals, I’ve gained a great deal of empathy for marketers over the years. Not because marketing technology is evolving so rapidly (which it is), but because the proliferation and misuse of buzzwords is so rampant, with the term “AI” leading the way. This is unfortunate because AI — or more accurately, machine learning (ML), a subset of AI — has huge potential for marketers. Making matters worse, when vendors combine buzzwords like AI with other buzzy, ill-defined technologies like “marketing automation,” the result is a buzzword soup of confusion. How can marketers separate reality from the fake marketing news?
It’s important for marketers to gain a high-level understanding of how ML works. If you don’t understand the basic concepts, it’s much easier to be taken for a ride. For ML newbies, I recommend Microsoft’s Data Science for Beginners. This video series provides an excellent non-technical overview of ML.
Once you understand the basic concepts of ML, you’ll quickly realize that its superpowers are far less grand than the hype purports. Companies will not use ML to create marketing manager robots. Rather, ML is an algorithmic tool companies can use to optimize existing processes.
For example, instead of sending a marketing email to everyone on your list at the same time, you can use ML algorithms to send emails to each recipient at the time of day they are most likely to open it, based on past behavior. ML algorithms typically use historical data in this way to make predictive estimates that lead to higher engagement or conversion. But ML is rarely the main event. Rather, it is a glue technology used in conjunction with other functionality. Therefore, be wary of vendors who make grand claims about their AI solutions — they’re probably exaggerating.
The difference between traditional marketing automation and ML-powered marketing automation often comes down to who defines the business logic. For example, if you are manually creating rules based on data to score leads feeding into your CRM, then this is traditional marketing automation. Yes, you are using data, and yes, you are doing automation, but a human is still writing the rules. If instead, the lead-scoring rules are generated automatically from the data based on historical conversion information, then you are using ML. If instead of a lead score, you are predicting the amount of revenue that each lead will generate based on past data, then you are using ML.
Most martech vendors are making investments in ML, but some are doing it more aggressively than others. Salesforce has been on an ML-focused acquisition spree for years and has merged its technology into a branded AI capability called Einstein. Einstein performs many tasks, but it is perhaps best known for its strength in predictive lead scoring. Adobe has also developed a branded AI — Sensei — that companies use for optimizing content, predicting marketing ROI, and identifying anomalies in site traffic. Upstart CRM provider HubSpot is also investing in AI, including the recent acquisition of Motion.ai, a startup focused on chatbots and natural language processing.
The field of AI and ML is evolving rapidly, so it’s important for marketers to keep up with the latest developments. Two great sources of information about AI for marketers are the Marketing Artificial Intelligence Institute (MAII) blog and MarTechExec.com. MAII is a great source for practical information about AI tools and techniques for marketers, and MarTechExec is a good place to see marketing experts sound off about AI and marketing.
Now is the time to get intelligent about the opportunities AI presents — no faking.
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