Machine Learning Has Huge Potential for B2B MarketersApril 18, 2019 - 7 minutes read
For a while now, business-to-consumer (B2C) businesses have been racing to incorporate machine learning (ML) into their sales and marketing campaigns. In contrast, business-to-business (B2B) organizations have been slower to adopt this subset of artificial intelligence (AI) into their own customer-facing functions.
But this will soon change. B2B companies have seen the benefits and possibilities that machine learning unlocks—and they’re eager to seize these opportunities.
Adjusting to the New Age of Data-Driven Marketing
For B2B companies, the process of selling to organizations can be a long, complex journey. But the return on investment from a single conversion is usually much greater than in the consumer space. Unfortunately, this only raises the stakes higher; a misguided marketing strategy can lead to a financial loss of millions of dollars.
So it’s no surprise that translating data into actionable marketing strategies is easier said than done for most B2B organizations. But the arrival of machine learning in the B2C space has changed the game of marketing, leaving B2B companies with little choice but to adapt.
“The availability of data and the importance of having the focus on the full customer journey is coming a little later to the B2B world,” explains Laura Beaudin, a Bain & Company partner. “A lot of expectations in terms of customers manifested themselves in the consumer world before they brought those expectations to their business-purchasing world.”
As a result, marketers in the professional services sector are intent on bringing machine learning into their fold. The professional services sector includes high-tech advisors, systems integrators, third-party consultants, and a plethora of other niches focused on bettering business.
MIT Technology Review recently conducted a survey of 1,419 marketers and found that professional service firms ranked among the top categories actively working on applying machine learning and data analytics to their operations. The main reason? The professional service survey participants are 16% more likely to believe that predicting customer intent could result in better marketing results than their B2C counterparts.
It’s All About How You Apply Your Data
When it comes to machine learning development and implementation, the cards can seem stacked against B2B companies. Generally speaking, B2B data is not as accessible or plentiful compared to B2C data. But with the benefits readily apparent, this does little to deter B2B marketers.
58% of the professional service marketers surveyed believe that how companies apply their data will play an integral role in their future success. With that being said, this brand of marketers is ready to retool their capabilities and reconstruct their marketing strategies to come out on top.
This means that embracing machine learning is a necessity. ML algorithms can allow B2B businesses to leverage the information they’ve gathered to produce unprecedented insights into customer behavior. In turn, these insights can lead to much more effective marketing strategies and material.
But where should marketers begin? With organizing, standardizing, and unifying all of the data they have at hand. A strong data backbone is required to access machine learning’s true capabilities. Jay Bowden, Google’s Tech B2B Industry Director, hints that this could be a challenge for some organizations: “If marketers knew 10 years ago that they were going to use all this data they were accidentally collecting, they would have kept it in one place.”
This task can become even more difficult for companies that have acquired other businesses and utilized multiple customer relationship management systems (CRMs). “It requires finding a common place to store the data from the silos before finding a way to make some decisions from it,” Bowden explains. “Then you can let machine learning look at it and find some commonalities.”
Cloud technology has removed much of the legwork required to accomplish this by providing a scalable data repository and platform where ML algorithms can work without compromising security. But the true challenge remains in convincing all humans in an organization to do their part. While marketers may be onboard from the get-go, other departments and teams may not understand why they must hand over their data. But in reality, the insights derived from data can also benefit them.
Gaining a Competitive Advantage Through Insights
Almost two-thirds (64%) of the professional service marketers surveyed by Boston-based MIT Technology Review believe machine learning could give them a competitive advantage through unique insights. Predicting customer intent and lifetime value would not only align sales and marketing better than ever before but also allow companies to tailor pricing and promotional offers accordingly.
But with all of this knowledge in tow, the question begs to be asked: Why have B2B marketers been waiting to jump on the ML train? It’s likely that many of the sector’s marketers have concerns about where the technology stands in terms of maturity and complexity. With such a steep learning curve, ML makes many marketers question whether they have the resources available to put it to good use
But Sarah Travis, Google’s Industry Director for Business and Industrial Markets, thinks these assumptions shouldn’t stop marketers from trying: “The B2B world has a lot more to gain from using machine learning technology than any other industry. Machine learning opens up a space where they can think about their marketing function and about reaching their customers in ways they never thought about before.”
Are you a B2B marketer or company who has integrated machine learning into your processes? What has your experience been like? And what are your results so far? Let us know in the comments!Tags: advertising, AI, AI and ML, app development Boston, artificial intelligence, artificial intelligence app development, artificial intelligence app development boston, Boston artificial intelligence app development, Boston machine learning apps, machine learning, machine learning apps, machine learning apps boston, marketing, ML, ML in advertising, ML in marketing