B2B marketers are in a constant struggle to refine and tweak the performance of their campaigns and activities.
Whether you’re in marketing operations or sweating at the coalface of demand gen marketing, knowing how best to prioritize your limited time and energy on a near-infinite universe of potential customers is one of your greatest concerns.
That’s why most marketers understand the importance of setting strategic benchmarks, like creating the right buyer profiles, setting and monitoring your KPIs, and so on.
The frustrating thing is, no matter how well you plan, and how meticulously you consider the above, there are always a hundred other factors at play — usually factors you aren’t even aware of — which can turn everything on its head and still hinder your best efforts.
It’s this volatile dynamic that makes the emergence of Artificial Intelligence such an important — and exciting — development for B2B demand gen.
AI: The Key to Unlocking Your Marketing Data
AI, broadly speaking, refers to technology “able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” (Source)
The one crucial difference — and the reason why AI has so much exciting potential — is that it can carry out these tasks at a scale and speed that no human could ever match. An effective AI algorithm can process volumes of data the average human being couldn’t get through in a lifetime, and accurately translate it into actionable intelligence in mere minutes.
This powerful capacity also means AI models can consider, compare and contrast multiple data sets at once, to discover hidden patterns which would evade the human eye.
At Leadspace, we’ve seen how using AI modeling in this way often reveals important trends our customers would otherwise have missed — and, perhaps even more importantly, flag up things they were getting wrong without even realizing it.
Take the following illustration:
The table above shows a sample from a fictional customer database (“Customer X”). While the actual data and figures are fictional, the scenario is one we often encounter.
We’ve spoken before about the power of predictive scoring. Combining AI with comprehensive data coverage empowers marketers to strategically target the ripest prospects from any angle (leads, accounts, industries, company size, installed technologies, etc.)
Watch — Predictive Scoring: How it Works
What’s revealing about this table in particular though is the discrepancy between Column A and Column B.
Column A represents the Customer X’s past performance selling to accounts in the industries listed, based on the historic first-party data in their Marketing Automation Platform. Industries with positive scores (above zero) are those to whom Customer X has been successful selling to — and are therefore seen as ideal industries to target. Negatively-scored industries are those in which Customer X experienced relatively little success; these are subsequently seen as less qualified industries to target.
Column B, on the other hand, is the Leadspace predictive score, which combines this narrow, subjective first party data with more than 40 third-party data sources, together with a layer of Artificial Intelligence, to gain a far wider, more holistic picture. This in turn provides a more objective score vis-a-vis Customer X’s Total Available Market, as opposed to the specific set of accounts they have in their database.
In the two examples highlighted (Business Services and Healthcare), the scores conflict significantly.
According to Company X’s historic data, the Business Services industry is poor target, with a notably negative score in Column A. Historically, their sales reps have not had much luck selling into that vertical. However, it turns out there’s more to this than meets the eye.
Yes, it’s true that historically they have performed poorly in the Business Services industry, but this “trend” is purely incidental. After considering dozens of other common factors, our AI model reveals that this past failure was due to reasons unrelated to the industry those accounts happened to share. Perhaps the companies they targeted had incompatible installed technologies, a relatively small budget, or weren’t in an active buying cycle at the time. Or maybe, that industry is scored low simply because their sales team hadn’t been putting much effort into that vertical to begin with.
Sometimes, Machines Really Are Smarter Than We Are
This underscores the power of Artificial Intelligence: whereas a human analyst would have noted this superficial trend and written off the Business Services industry (as Customer X would have been doing), the AI model was able to see beyond that incidental pattern to reveal the true, hidden factors at play.
As it turns out, the Business Services industry as a whole is a pretty good fit for Customer X’s product/service; when third-party data and AI was added into the mix, the Leadspace predictive score came out as positive. This means Customer X had been setting their priorities based on faulty intelligence — ignoring a potentially lucrative industry on the basis of a past performance which was in fact anomalous.
What’s more, since Leadspace isn’t a “black box” (i.e. we let customers see the data behind the score), their sales and marketing teams would be able to understand why this industry scored so much higher than their own database seemed to indicate. They could then learn from their past mistakes and come up with a strategy to successfully target the Business Services industry effectively in subsequent campaigns.
By contrast, Customer X had scored companies in the Healthcare industry as better-than-average performers — but our AI predictive modeling reveals that their historical success selling into that industry was the result of “good luck” more than anything else. Once again, unrelated factors came into play which skewed their performance positively; e.g., an existing personal relationship, or other unique factors pertinent to those specific account they sold to. Or, again, the positive score may be due to the fact that their sales team were focused disproportionately on the Healthcare industry, based on a false notion that it is a good target.
In fact, when all the relevant data was considered, the Healthcare industry received a much lower than average score. This means their sales teams were spending a lot of time and energy on prospects that weren’t actually in line with their true Ideal Customer Profile. By diverting their resources to other, more positively-scored prospects, Customer X could significantly shorten their sales cycles, and score more wins in less time.
And once again, using our transparent model Customer X can understand not only why, despite their past successes, the Healthcare industry is in fact a poor fit — but also how they managed, in practice, to do so well. Perhaps there exists a narrow group of accounts in the Healthcare industry who are worth targeting. Or maybe “industry” just isn’t the angle they should be focusing on (at least not solely).
After implementing Leadspace predictive scoring, these kinds of revelations would help Customer X sell more deals of a higher value, by focusing their resources more effectively.
This is just one of many ways Artificial Intelligence can be used to boost demand generation, by finally giving B2B marketers the ability to use their marketing data to its full potential.
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