The buzz around artificial intelligence (AI) is likely to be with us for many years to come. As with any new technological shift, in part it is feared for potential job destruction, and in part is misunderstood due to the paucity of specific examples with measurable benefits. So it is gratifying that analysts are starting to give actionable guidance on how B2B marketers can approach the use of AI.
The new SiriusDecisions matrix unveiled
At this year’s SiriusDecisions Summit, Demand Creation analysts Monica Behncke and Kerry Cunningham introduced a new framework – a matrix laying out the four types of AI for B2B Marketers. Using the X axis of ‘simple’ to ‘complex’ (i.e. the end business goal achieved by the AI) and a Y axis ‘evolutionary’ to ‘real-time’ (i.e. the learning timescale for the AI), the duo detailed how this looks in the B2B enterprise:
Simple-Evolutionary: This involves simple goals with evolutionary timescales and focuses on improving and enriching data repositories, thus allowing AI to better detect things like accounts most likely to enter a sales pipeline.
Simple-Real Time: This pertains to simple goals where the AI must learn in real time. This occurs in dynamic, changing environments and requires continuous feedback loops that help inform the AI about when to change tactics.
Complex-Evolutionary: Here a master repository of buyer data doesn’t just look for potential accounts, but tries to find larger accounts where deals can close faster. At this point, investments in people, process and technology become imperative: AI applications need to be properly aligned to change conflicting goals into complex – yet achievable – goals.
Complex-Real Time: This final quadrant in the matrix denotes AI that tackles complex goals and evolves its learning in real time and is still very much an abstract frontier.
The duo described these four applications as akin to crawling, walking, running and flying. In B2B terms, they compared this to building a master intent data repository (crawl), building a personalization and tactic performance engine (walk), merging disparate AI goals (run), and putting your feet up and letting the AI run off on its own and even create new applications that can do this as well (fly).
Idio’s work with Pure Storage: A SiriusDecisions case study
It was lovely to see that Idio’s work with our client Pure Storage was used to illustrate a best-in-class example of ‘simple-real time’ AI at work. As with many enterprise B2B companies, Pure Storage has a complex portfolio of products endpoints, buyer personas and content journeys. This necessarily leads to problems such as low engagement, prolonged nurture cycles and fewer net new leads because of imprecisely marketing to segments (instead of segments-of-one) and the inability to put buyers onto the right nurture path.
Idio’s client Pure Storage was used to illustrate a best-in-class example of simple-real time AI at work
Pure Storage turned to Idio to automate content tagging, enable personalization and web visitor profiling at scale to drive content engagement. Idio’s AI ‘reads’ web assets (content) and maps the content to the appropriate product endpoint, visitors to the website are tracked and a unique buyer profile created from their content consumption is used by the AI for content decisioning on the website. The results included click-through rate up 158% and onsite form-fills via gated content assets up 289%. This is the power of using Idio to power omni-channel demand generation.
Bad data is no barrier to adopting AI
One of biggest objections to adopting AI is the lack of clean and available data, but Cunningham took that obstacle head on: “Having bad data of your own is not a reason to put off adopting artificial intelligence. Instead, AI is the best tool for repairing and continually improving your data.” And Behncke pointed out that “organizations already using AI to perfect their knowledge of their audience have a competitive advantage over less advanced organizations.”
Pegasystems uses Idio to gain a deeper knowledge of buyer intent on over 3 million customers
There are a range of 1st and 3rd party data sources from which B2B marketers can build a picture of the customer. AI can assist this process greatly, leapfrogging multi-year data clean-up initiatives to provide a high definition view of the buyer from their behavior and interactions. Cunningham pointed out “In most cases, the best way to do that is going to be to use AI to evaluate thousands of data points to develop that really great high-definition view of your buyers.” This was a view echoed by Tom Libretto, CMO of Pegasystems, in another SiriusDecisions session when he discussed how the organization uses Idio to gain a deeper knowledge of buyer intent on over 3 million customers.
The Artificially Intelligent Revenue Engine: Everyone’s responsibility
Amidst the unveiling of the new SiriusDecisions matrix, Behncke and Cunningham left the audience with a practical roadmap for deploying AI in the B2B environment:
Step 1: Begin by using AI to build data repositories that can be activated to solve critical
business problems across the revenue engine
Step 2: Build a “performance feedback repository”; use AI to automate the algorithms with all the feedback from every action taken.
Step 3: Examine goals wholistically; get an executive perspective and oversight to avoid consequences of misaligned AI efforts.
Emulating Pure Storage and Pega’s successes requires cross-departmental cooperation. Marketers were charged with using AI to develop a deep understanding of prospects and customers, know what’s possible with AI and engage third parties to execute common AI applications. Sales leaders must build a culture of openness into the integration of AI into the revenue engine, and use AI to boost the science of selling to leave more room for the “art”. Product owners were encouraged to collaborate with marketing to develop a high-definition view of the buyer’s journey and help to identify applications of AI within the business. Building an AI-powered revenue engine should not just be a corporate priority, but a team effort.