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Why Machines Won't Replace B2B Marketers

  • Written by Josh Mueller, Former CMO, Dun & Bradstreet
  • Published in Demanding Views

0Artificial intelligence (AI) and machine learning (ML) have long been on marketers’ radars, but it’s only recently that they’ve begun to show up in customer relationship management tools. AI and ML are often viewed with skepticism, but many B2B marketers will discover that these features can enhance their effectiveness in contacting the right leads, closing deals and nurturing client relationships. The underlying fear has been that machines will replace marketers, but the reality is much more promising.

One common critique of business data and analytics initiatives is that they can easily result in information overload. When combined with a strong master data strategy, AI and ML are capable of quickly highlighting interesting behaviors and patterns that would require days, weeks or months of analysis for a human to spot. Give a trusted employee a 100-row spreadsheet and she can pull out trends and insights. Send that same person 100,000 rows of data and panic sets in. Luckily, the algorithms at the root of AI are very good at processing and analyzing copious amounts of data. Once the machine discovers something interesting, a marketing manager or analyst can evaluate the output rather than wade through terabytes of raw data.

B2B marketers are already seeing benefits from integrating AI and ML into their daily workflows. For example, automated chatbots can be trained to act as the first point of contact on a company’s website, answering frequently asked questions and routing qualified leads to the proper salesperson. Such an integration can give employees more time to focus on closing deals rather than answering basic questions and chasing prospects. There are also opportunities to use this processing power to deliver the right message at the right time to customers who might not even be on your leads list.

Marketers are well acquainted with tracking cookies, IP targeting and how unique identifiers can reveal a wealth of information about website visitors. AI and ML are able to take that collection of data and tie individual visitors to the companies they represent. Forms can be autofilled, saving prospective customers time and removing an obstacle to conversion.

Perhaps most exciting, sophisticated AI and ML implementations allow for content personalization, where what a visitor sees on a website is tailored to their position, company or industry. In the B2B context, it’s even feasible to serve up account-based pages that speak directly to the challenges that a particular account is most likely looking to solve rather than taking a one-size-fits-all approach. This personalized messaging can be a powerful differentiator for your brand and help get real ROI out of sales and marketing collateral.

Like all technologies, AI and ML won’t change the way you sell and market your goods or services right out of the box. Marketing leaders will need to take ownership of projects while defining business needs and the proper success metrics. Data quality is also key; inaccurate data breeds false conclusions. Along these lines, any new technology will need to be integrated into the existing marketing stack. Finally, there’s the issue of internal knowledge and support. CRM solutions are getting better at baking AI and ML into dashboards and reports, but there’s no substitute for having a data scientist or analyst on staff to model use cases and sanity check results.

B2B marketing stands to become more efficient and effective with the improvement of AI and ML capabilities. Old fears of being outsmarted by an algorithm should be replaced by the knowledge that many tedious tasks can be offloaded to a machine. A broad understanding of the potential behind these advances ought to spur even more creative solutions to persistent challenges faced by B2B marketers.

Josh Mueller is the former CMO of Dun & Bradstreet. Prior to that, he worked at Dell in various marketing leadership roles with a specialization in digital, content and demand generation.