Moving Beyond Predictive Lead Scoring

November 30, 2015 J.J. Kardwell

This post was originally part of a TrustRadius blog, “Predictive for ABM: Interview with J.J. Kardwell, President and Co-Founder of EverString”.

Predictive analytics can be applied throughout the sales and marketing funnel, and much more broadly than for just lead scoring. The main uses of predictive analytics for sales and marketing fall into three primary categories:

1. Prioritization

Predictive scoring helps companies prioritize both the prospects which are already inside their pipeline, as well as new prospects as they are added to the pipeline (from inbound, imports, etc.). The best scoring products should be providing three-dimensional scoring, evaluating prospects on each of fit, engagement, and intent.  Additionally, the best scoring products are not just for “predictive lead scoring”, but rather “predictive scoring” broadly, which can be applied against leads or accounts, opportunities, etc. depending on the goals of the customer. Great predictive scoring products can also be used to identify and optimize cross-sell, up-sell, and churn-reduction – all of these are variants of the prioritization use case.

If you are (or want to become) an account-based marketer, you should start by scoring accounts (i.e. companies) before scoring leads (i.e. people). Account scoring is most relevant for evaluating the “fit” of a company as a potential buyer. After doing account scoring, you can also do person-level scoring (i.e. lead scoring) based on engagement and intent (i.e. behavioral signals) and job title.

EverString’s platform has been built with an account-based architecture, which provides unique advantages in optimizing performance. Most predictive platforms were built with a lead-based structure, essentially as extensions of marketing automation systems, and are therefore trapped in the lead object. A narrow focus on the individual person (i.e. lead) can often be a dead end. For example, what if you have a lead that is a person with the right job title, but the company 100% isn’t a good fit to become a customer? That is a dead end, no matter how “good” the person’s job title or engagement is.

Conversely, what if you are talking to the right company, but the wrong person? That isn’t a dead end, as you can always append additional contact information for the right people if you are starting with the right account. If you aren’t focused on the right accounts, nurturing and retargeting are a waste of time, and no amount of selling matters. This is why starting with account-based scoring is so important for B2B marketers, and why it is so alarming that most predictive companies are pushing “lead scoring” for every single customer. Given the choice, the vast majority of EverString’s customers have chosen to focus on account scoring.

While account based marketing (ABM) has been around for years, the broader market wasn’t focused on it until the past 12 months. Marketers started realizing that ABM is a very real thing when they saw B2B marketing expert Jon Miller leave Marketo to start Engagio, and when people began hearing marketing thought leaders like Maria Pergolino at Apttus and Craig Rosenberg at TOPO publicly explaining how ABM can be used. Three years ago when we starting building the EverString platform, we weren’t using the term “ABM”, but we did very intentionally set out to provide customers with an account-based platform for predictive marketing.

2. Pipeline Building 

While many people only think about using predictive for scoring their existing prospects, predictive technology can also be used to build new pipeline. “Predictive demand generation” involves using predictive analytics and data gathered from the web to find completely new prospects that are not yet in your pipeline. While predictive scoring is internally focused on your current pipeline, predictive demand generation is externally focused on those prospects which are not part of your current pipeline. Within predictive demand generation there are two primary subcategories: intent-based predictive demand generation, and fit-based predictive demand generation.  Some companies only offer one or the other approach, but the best predictive platforms offer both, as the optimal way to evaluate prospects is using all three dimensions of fit, engagement and intent signals at the same time.

Intent-based predictive demand generation looks at person-level behavioral data that can’t be seen by your marketing automation. Marketing automation captures engagement information, which is the set of interactions prospects have directly with you (e.g. website visits, looking at your pricing page, webinar attendance, downloading whitepapers, etc.).  At its essence, intent is engagement which is not with you.  In other words, it involves people visiting your competitors’ websites, reading third-party blogs and consuming other content related to your market, and searching for relevant keywords. EverString has access to an unrivaled amount of intent data, as we needed to build this depth to support our new Predictive Ad Targeting product, and the value of that data flow benefits all of our customers. Engagement can be long-tailed, whereas intent (companies in market, ready to buy now or soon) has a much shorter fuse. Intent signals also have very limited reach, since there is obviously a very small percentage of all prospects who are truly in market trying to buy at any given time, but those signals can be very powerful when they are present.

Fit-based data provides much broader coverage than intent and engagement, so it is very impactful from a reach standpoint.  Fit data includes traditional firmographic information (e.g. company size, industry and geography), tech stack data (i.e. what products and technologies are they using), and person-level data about title, background skills, etc.  Fit data coverage is much higher than for engagement or intent data, as fit signals can be captured on almost all potential prospects in your addressable market, regardless of whether they are engaged or in an active buying cycle.  Your sales team probably shouldn’t be on the phone with anyone unless the predictive platform has automatically qualified the prospect account for key fit signals.

With both intent-based and fit-based predictive demand generation, B2B marketing and sales teams can now have the benefit of a predictive model trained on their existing pipeline, but applied exclusively outside of the existing pipeline on the entire addressable market of potential prospects.  For the typical company with only tens of thousands or hundreds of thousands of prospects in their existing CRM and marketing automation, predictive demand generation can now provide a point-of-view on all of the millions of potential prospects in their market.

3. Generating Engagement

The newest frontier of applied data science for B2B marketing is using predictive analytics to identify high-potential people and companies, and then proactively deliver ads directly to them in order to create engagement. EverString’s newest product, Predictive Ad Targeting, does exactly that, and it is the first B2B product of its kind. Historically, B2B marketers have been limited to only non-predictive ad targeting solutions, which require already knowing the identities of the companies you wish to target. Predictive Ad Targeting gives B2B marketers unprecedented control over the top of their funnel, as they can now predictively build target audiences that look and act like their best customers, and then proactively nurture them with targeted advertising.

Summary

The use of applied data science and predictive analytics in B2B marketing has opened a new frontier of opportunity for enterprise marketing and sales professionals. Approaches that once relied on near-random audience selection are now being replaced by hyper-targeted predictive solutions. As the notion of predictive marketing is becoming more mainstream, marketing and sales professionals are also beginning to benefit from the full array of predictive capabilities: Predictive Scoring for prioritizing existing prospects, Predictive Demand Generation for identifying net-new prospects, and Predictive Ad Targeting for generating engagement from ideal audiences. Not every B2B marketing and sales professional will need all three predictive capabilities, but those who make the investment of time and money to deploy predictive marketing should be aware of the full range of potential applications, so that they can maximize the short-term and long-term benefits for their company.

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