Getting Started with Machine Learning: 3 Things Marketers Need to Know
April 25, 2017
This article was originally published on CMSWire by Sean Zinsmeister, Vice President of Product Marketing at Infer.
The buzz surrounding machine learning and artificial intelligence (AI) in the consumer world has rapidly bled over into the enterprise.
Much of this hype stems from the new consumer trends that hint at the possibilities of AI, such as self-driving cars and intelligent voice-first products like Amazon’s Alexa and Apple’s Siri.
At the same time, mainstream cloud adoption and ever-increasing computing power in the form of new solutions like Google Spanner are accelerating the development, accuracy and speed of AI’s underlying foundations, from data availability and spam detection, to machine learning, predictive analytics and natural language processing.
So it should come as no surprise that sales and marketing leaders are questioning what all this means for their departments and companies.
At a basic level, AI is about replacing human function with computers. Without using machine learning, people simply couldn’t sort through the plethora of data that’s out there without making costly errors.
Today’s machines have proven they can successfully process, understand, translate and interpret that data — parsing its meaning into visual and actionable outputs. That said, in AI’s current state, what we’re talking about is computers taking over very simple, mundane tasks as opposed to complicated, nuanced actions.
Machine Learning Already Exists in the Enterprise
Several machine learning and AI related breakthroughs are already having an impact on business outcomes, most notably predictive analytics, chatbots and natural language processing (NLP).
For example, predictive analytics is helping enterprises process their CRM and marketing automation system data, combine it with external signals from across the web, and determine where their highest revenue potential lies so they can double-down on the best bets.
Chatbots are more efficiently routing people to services and quickly answering customer questions 24/7, as well as creating new ways for business people to interact with their company’s data.
B2B enterprises and consumer companies alike are experimenting with these conversational user interfaces, which use NLP to recognize speech patterns, essentially mirror a human, and (hopefully) create better customer experiences. But using these technologies carries risks, and businesses in the US can learn plenty of lessons from mobile-first companies like WeChat out of China and India.
But in spite of the success stories, most companies are still wondering if they’re really ready for machine learning and AI. The truth is nearly any enterprise can benefit from infusing more data into its operations, especially if they watch out for common pitfalls.
3 Requirements for Successful AI
1. Start with a business problem
To start with, ask yourself questions like: How well is my business utilizing data to make decisions? Do we have a problem that better data and accurate insights can solve?
Rather than just inventing a problem to have, look towards the business practitioners who are succeeding with machine learning and AI today. Find out what solutions they’re using, and you’ll uncover communities of innovators that have similar market problems.
The real differentiation among today’s emerging vendors is in how well they address customers’ most important business needs, so choose a platform that’s built for your specific use case and will be able to scale and adapt as your business grows.
2. Optimize your technology stack
Few companies embarking on the AI journey recognize the importance of setting up their systems for success — both from a cost and performance perspective. Rather than thinking strategically about which tools they need in their technology stack, teams are too often distracted by shiny new objects and go on a spending spree only to find themselves with a bunch of fragmented tools (many of which end up as shelfware).
The truth is less can sometimes be more, especially when it comes to a scalable stack that’s architected to leverage AI for greater efficiency and effectiveness.
Another consideration is data storage. It can get very expensive, especially when you’re trying to pull in data to, say, a CRM system from a variety of different sources across and beyond your business. Some vendor’s data fees are in line with hardware storage costs from the ’90s, and can even cost more than your annual license.
Of course, you can’t succeed at AI without data, and even small companies have customer data spread across applications. Unfortunately, most of today’s automation systems weren’t architected to scale to the volumes of data that are now typical for midmarket, high growth companies. Get creative from the get-go and find strategic ways to more affordably connect all of the data you’ll need for your AI initiatives.
3. Make systems smarter through integrations
One of the best ways to address the stack efficiency challenge is to find an AI platform with an open architecture that can serve as your company’s system of intelligence.
When it comes to marketing technology in particular, recent studies suggest only 21 percent of companies use a single-vendor suite, while nearly half go with a best-of-breed approach. The integration of these multi-vendor stacks is key to their ability to deliver value.
Your system of intelligence should seamlessly connect with things like your system of record (CRM) and system of engagement (marketing automation), as well as any other business applications the company uses — and then leverage that data to make accurate and actionable predictions.
The result is you’ll be able to quickly and easily infuse predictive analytics into your business’ decision making without disrupting current workflows or adding more complexity to daily activities. Once you’ve gathered all of your data in one place, you can increase the intelligence of all your systems, thanks to the smart outputs you’ll get from your machine learning and predictive initiatives.
The Road Towards Self-Driving Business
Once enterprises address these considerations, they are well on their way to reaping the rewards of AI.
Yet these technologies will never be a silver bullet for every business woe. The industry is still at the early stages of its journey towards disruptive machine and data-guided business models.
We still has a long way to go when it comes to shifting routine enterprise work away from humans and towards machines. And we will always need the expertise and intuition of people to make AI successful, but I anticipate a bright future ahead.
In the next five to 10 years, we’ll see the business world’s version of self-driving cars (‘self-driving’ enterprise software) make headway in functions like sales, marketing, finance and human resources.
As computers successfully transform unstructured data from disparate systems into actionable intelligence, AI will upend the manual, rules-based workflows of traditional systems of record — causing vendors and early adopters alike to re-imagine what true automation should look like.
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