There are three B2B predictive marketing use cases that are gaining traction with marketers today.
B2B organizations (CEOs, CFOs & CROs) are increasingly evaluating marketers on revenue impact. As such, B2B marketers identify their top three execution challenges as:
- Generating inbound demand that converts to pipeline
- Correlating marketing resources to the sales process
- Attributing marketing spend and activity to revenue
Because B2B marketers are being held accountable for lead conversion, pipeline health deal acceleration (Revenue Centric Marketing) and impact on revenue, tremendous enthusiasm exists about predictive marketing. Simply put, predictive marketing uncovers the best fit, look-a-likes and most likely-to-purchase accounts – in other words, the accounts with the greatest impact on marketing ROI.
Three B2B Predictive Marketing Use Cases
With all the hype and attention on predictive marketing, B2B marketers should approach these new technologies with a clear strategy that prioritizes the specific business goals which are aligned with specific outcomes. Today, three use cases dominate the predictive marketing landscape: lead scoring, prospecting, and hyper-segmentation.
B2B Predictive Marketing Use Cases – Prioritization (Lead Scoring)
Previously referred to as lead scoring, prioritization is simply prioritizing known prospects, leads and accounts based on their likelihood to take positive action.
Lead scoring was originally based on demographic information (now referred to as firmographics). Prioritization moves beyond traditional scoring to capture a combination of known and external attributes and signals from third-party sources that inform fit, intent and behavior.
B2B sales and marketing teams that use prioritization typically have too many leads or have leads with a high percentage of non-conversion.
The primary business benefit is to minimize time, energy and money spent on leads that will never materialize and to raise the high scoring leads (those most likely to convert) to the top of the list for immediate attention. This requires mutual agreement (and continuous) review between sales and marketing to optimize the prioritization criteria.
Typically, B2B marketing organizations start with lead scoring and then progress to prioritization. Prioritization can leverage machine learning and data science to progress to predictive scoring. This adds a scientific, mathematical dimension to conventional scoring that typically relies on speculation, experimentation, and iteration to derive criteria and weightings.
B2B organizations that successfully deploy lead scoring / prioritization / predictive scoring typically end up with a better alignment between their marketing and sales teams.
It’s important to note that when lead scoring / prioritization / predictive scoring is ineffective, there is typically:
- Tension between sales and marketing as there may be too small a percentage of marketing qualified leads (MQLs) that convert to sales qualified lead (SQLs)
- Huge decreases in productivity and morale when marketers spend time and energy creating leads that sales reps feel are a waste time – i.e. following up with accounts that have no interest
B2B Predictive Marketing Use Cases – Prospecting (Demand Generation / ABM)
Predictive marketing for prospecting is used to identify and acquire prospects with attributes similar to existing customers.
The theory is basically “birds-of-a-feather flock together.” Once one is found, it is relatively easy to find others. A similar account, whether a prospect or customer, is thought to have a higher propensity to convert, and as such, represents the most streamlined path to revenue. This technique is particularly useful for account based marketing.
Derived from a combination of existing accounts (those in the sales pipeline or that have closed) along with positive and negative outcomes and external data, signals, and indicators, algorithms can then determine the degree of a match. Note that sales and marketers can adjust this threshold or degree based on actual experience.
The best application for prospecting based on similar or look-a-like companies are organizations not concerned about prioritizing but with identifying net-new accounts that were not previously on the radar.
Typical sales and marketing applications include:
- Building a quality pipeline
- Expanding into new markets
- Calculating the Total Addressable Market (TAM)
- Prioritizing accounts for an Account Based Marketing (ABM) strategy
- Planning sales territories
Prospecting based on similar (or look-a-like companies) requires historical data (positive and negative outcomes) to establish attributes likely present or not present in future positive outcomes. Note that care must be taken to ensure that historical data does not include positives and negatives for the same company as they will cancel each other out.
B2B Predictive Marketing Use Cases – Message Personalization (Hyper-Segmentation)
B2B sales and marketers use hyper-segmentation to improve message relevance by personalizing messages, content and conversation.
Personalization is a powerful tool when sales and marketers want to increase audience engagement through relevance. The challenge for B2B sales and marketers has been limited characteristic data – usually basic demographic data and inconsistent and/or vague industry classifications. In addition, many segmentation projects never start or are “one and dones” as they are usually manual processes which are both time consuming and subject to human error.
B2B sales and marketers that successfully employ hyper-segmentation can:
- Drive outbound prospecting communications with meaningful, relevant, targeted messages as the data used to build models reveals key insights into the issues prospects really care about.
- Enable more substantial conversations between salespeople and prospects as useful insights and relevant content is supplied which supports more meaningful and customer-centric dialogue.
- Empower sales to engage person-to-person, thereby responding to inquiries with insight and intelligence at lightning speed.
- Use predictive approaches to drive a content development strategy that appeals to prospects conducting ultra-specific searches – especially when part of an account based marketing (ABM) strategy
A precursor to hyper-segmentation is clean, accurate and comprehensive data. Unfortunately, the data is either “assumed” or “looked over”, and few organizations have good data discipline. To be most effective, data should be gathered from CRM, SFA and accounting (at a minimum) and also include firmographic, technographic, social and intent data. With this base, machine learning can go to work to support data science and the creation of algorithms to provide actionable insights that will move the needle.
Net: Three B2B Predictive Marketing Use Cases
Done right, predictive marketing facilitates more substantial conversations between sales and marketing with customers and prospects.
If one’s marketing campaigns produce pounds of leads that sit idle when sales believes them to be useless (or they lack the insight and commitment to pursue them,) then predictive marketing may be the answer. This means creating and maintaining the data that can build predictive marketing models to support the most meaningful customer-centric dialogue with marketers and the sales team.
New technologies make predictive marketing more accessible and more valuable. These technologies include advances in AI, data analytics, modeling, machine learning and algorithmic matching. As marketing automation and customer relationship management (CRM) solutions continue through the technology curve (late majority now buying), revenue-centric marketers are in the best position ever to predict and correlate marketing to revenue.