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No Lead Left Behind: Why Marketers Must Invest in Artificial Intelligence

No Lead Left Behind: Why Marketers Must Invest in Artificial Intelligence


It’s the golden age of artificial intelligence (AI). No longer are machine learning algorithms implemented in covert server rooms with advanced code scripted exclusively by research scientists. Instead, AI-driven insights are readily available with cloud-based software solutions (like Pardot Einstein!) which are flexible, simple to integrate, and tremendously powerful.

Still, some marketers remain skeptical of AI’s vast promises. But organizations today have every reason to embrace artificial intelligence in the Fourth Industrial Revolution. Here’s why. 



AI fundamentally addresses two types of questions.

  • Is X similar to Y?
  • What influence does A have on B?

The first question is foundational to marketing segmentation. After all, a segment is useful only if there are some similarities among its records. (Otherwise, it’s a random population, and not particularly actionable for content-specific marketing campaigns).

AI can help marketers with segmentation because machine learning algorithms can identify similarities between records. Marketers can specify an audience of interest – like historically high-converting leads – and then use similarities in this population to create new segments to target. An application of this would be the generation of Lookalike Audiences in Facebook – something especially valuable in B2C marketing where markets are routinely wide and varied.

The second question, that of identifying causal influences in datasets, speaks to the heart of all B2B analytics. At its core, a prediction is a hypothesis about variables. An example is that the mailing state within a contact record influences whether it also has associated opportunity. With large enough datasets, machine learning can not only forecast whether certain variables have a causal influence on a target result, but also how much each value within that variable influences a target. So not only can it determine whether mailing state is an influencing variable, but it can also identify whether a value of “CA” is more likely to have an associated opportunity than a value of “NY.” What’s more, that increased likelihood of conversion can be codified into a quantitative score, which rates a contact from California with a higher score than one from New York.

Use cases like these should pique the interest of all B2B marketers. With large datasets, and potentially thousands of data points, there’s a tremendous appeal to technology that can identify which variables are most likely to influence a conversion rate, and, likewise, which kinds of campaigns are more effective in attracting business than others.


Despite the attractions of AI, many companies aren’t positioned to hire a data science team. After all, data scientists are expensive and often leverage a tech stack that doesn’t integrate into existing business systems.

Moreover, even with an all-star data science team, there’s the risk that AI-driven predictions won’t be relevant. If their prediction engine isn’t integrated with the dynamic data sources in a CRM or marketing platform, they won’t be able to leverage these insights to make a business impact. How useful are predictions leveraging click behavior if they only get updated once per month?


B2B marketers have extraordinarily rich datasets at their disposal. Thus, if they’re interested in the maximizing the return on an investment in AI, the need for an integrated solution is clear. In other words, predictions ought to leverage all available contact and campaign data, and that’s only possible with an AI solution that with the CRM and marketing systems that house them.

Cue Pardot Einstein, the plug-and-play AI tool for B2B marketers. In contrast with proprietary data science solutions, Pardot Einstein natively integrates with both Salesforce and Pardot, so there’s no lengthy configuration and testing process. Once Pardot Einstein is activated, data from both systems flow into the Einstein prediction engine. At that point, predictions get generated in hours, not years, and they get updated dynamically as prospects engage with marketing assets in Pardot.

Because Pardot captures an incredible diversity of prospect data, these predictions are sensitive to a myriad of marketing behaviors. What’s the conversion coefficient for a new landing page? While a data scientist might struggle to build a system responsive to new variables, a CRM-integrated AI engine, like Pardot Einstein, adapts naturally. Moreover, the predictions are better because they’re leveraging more variables and, thus, more complete datafrom both Pardot and Sales Cloud.


It’s an exciting time to be a B2B marketer. Marketing and CRM platforms are in a mature state, and cloud-based storage and computation seems virtually unlimited. But there’s one thing missing: how to leverage the data in a system to build better audiences and make better predictions.

That’s where AI fits in. In our estimation, nothing else has the possibility of driving business results than this area of digital transformation. In 2018, 30% of B2B marketers were using AI, a 23% increase from the year before. Do you want to be at the vanguard of marketing technology? Today marks an opportunity to be a leader. What are you waiting for?

Digitally transform your marketing with Salesforce. Built on the world’s #1 CRM, know your customers with a single view of their activity across your business. Personalize their experience with AI. Engage them across every step in their journey with your brand. And use powerful analytics to optimize campaigns as you go. Learn more at