Subscribe

Dreamforce 2016: How Disruptive Will AI Be In B2B Technology?

Featured Dreamforce 2016: How Disruptive Will AI Be In B2B Technology?

Image Source: Instagram

Artificial intelligence (AI) and machine learning are by far the biggest innovations — and topics of discussion — for marketers at Salesforce’s Dreamforce 2016 Conference in San Francisco this week.

Salesforce, which just debuted its Einstein AI solution for CRM, indicated that AI  “will be more disruptive and powerful than any previous shift in technology.” Other major cloud providers, including Oracle and Microsoft, have separately made announcements about their own AI initiatives in the past few weeks.

At the conference, Shannon Duffy, SVP of Marketing for Salesforce Pardot, told Demand Gen Report that more announcements will surface later this week around AI and the Salesforce Marketing Cloud. She was unable to reveal further details at press time.

Several companies at the conference have launched solutions, or are about to debut new capabilities within their solutions, that incorporate additional AI and machine learning features to help marketing and sales teams garner intelligence on prospects and customers. Predictive analysis is built on machine learning and artificial intelligence, but many of these companies and marketers agree the big benefit of this next wave of intelligence is that it simplifies the power of AI by providing the average salesperson or marketer easier access to it.

“Salesforce’s additional AI announcements coming during the show this week are about ‘AI for everyone’ and taking things out that are complicated about it,” Salesforce’s Duffy said. “It’s about ease of use and insights. Einstein will have access to my data. If I’m a marketer, what I want is the insights and tips surfaced for me.” Duffy said it has been difficult to get that type of information in the past without a business intelligence tool.

The New Wave Of AI Rollouts

Lattice Engines said it has enhanced Buyer Insights, its Salesforce app, by adding in AI-driven trackers and dashboards that, for example, help sales reps understand nuances such as how much time that prospect or customer is spending with brands. “It will make data-driven marketers smarter about what the ideal customer looks like,” said Caitlin Ridge, Director of Corporate Marketing at Lattice.

ADVERTISEMENT
Ridge said Lattice customer Hootsuite has used its enhanced Buyer Insights to zero in on business customers who are a good upsell opportunity. She said the social platform was able to use AI to prioritize its database and find the companies that were the best fit for its paid, enterprise solution. As a result, deal cycles were 30% quicker and pipeline increased 10%.

Demandbase unveiled DemandGraph this week, a business graph driven by AI that now powers the company’s solutions for customers. Peter Isaacson, CMO of Demandbase, said the tool uses Spiderbook technology — a company it acquired in May — to deliver insights to marketing and sales.

One Demandbase customer, Host Analytics, is using that AI technology to prioritize accounts, tap into account activity and power web advertising. Ultimately, Isaacson said, the technology helps to “feed information to the SDRs so they can have better conversations. It replaces cold calling and emails.”

Dayna Rothman, VP of marketing at Everstring, said predictive technology is built on machine learning algorithms and has always had AI capabilities. However, she suggested the terminology in the marketing world could move away from “predictive.”

“A lot of the vendors will move away from predictive,” she said, noting the whole discipline of “predictive” has been stuck in a feature box that it will break out of soon. “Calling it predictive sets them up in a Catch 22,” she said. “You cannot predict an outcome. There are so many variables. The value lies within surfacing the critical data.”

Rothman agreed self-service and the ability to simplify data sets for people who are not data scientists is a major trend. With new enhancements to Everstring’s platform debuting next month, “a salesperson can do models within minutes,” she said. “They’ll have access to modeling capabilities and out-of-the-box templates [that provide] results more easily.”

AI’s Achilles’ Heel

Many marketers are still trying to figure out how to apply AI to their current strategy. Angus Lindsay, Director of Enterprise Technology at athenahealth, a company that provides network-enabled services to the healthcare industry, said AI is “the aspiration.”

His company is not using AI currently, and he said he thinks the B2B marketers that are incorporating it are likely the exception. The company uses an account-based marketing strategy for its enterprise and hospital segment, but for its small-business customers, such as group doctor practices, its marketing approach is more akin to consumer marketing, he said. “That means we have two completely different predictive models,” he said, explaining that it is “something that needs to be figured out in order to have those models co-exist” before layering something like AI on top of that.

Some industry experts cautioned that data accuracy, an ongoing challenge for marketers, will remain a stumbling block in this arena.

“Data is AI’s Achilles’ heel,” said Doug Bewsher, CEO of Leadspace. “No matter how smart the algorithms, their effectiveness is crippled if you use them with the same old basic, static contact data in your CRM or purchased from traditional data vendors,” he said.

Scott Brinker, Publisher of chiefmartech.com, agreed there is an ongoing data quality issue for marketers that could hamper their success. “That’s something marketers will need to solve,” he said. Brinker explained that “predictive gets its power from habits in the past,” but for machine learning to work well for marketers, the data needs to be accurate to be “relevant to the future.”

Bewsher agreed. “For AI to succeed in B2B, go beyond the hype stage and win the confidence of salespeople, AI needs up-to-date, accurate data to work with,” he said. “The contact information in the average corporate database is more than half outdated, and the only way to solve that problem is to combine AI with on-demand data, aggregated in real time. Otherwise, marketers will see the same increasingly poor results they’re getting now from traditional marketing automation.”

Another predictive vendor, Radius, just debuted its customer network effects enhancement, which could be one answer to the data quality issue. Company executives say it “will be a game-changer for the state of B2B data.” Since it started applying this approach, Radius has seen data accuracy for certain key business attributes within its Business Graph increase to more than 95% and audience reach on digital networks increase by 3x to 19x, depending on company size.