Marketing vs. Sales: 3 Ways Predictive Intelligence Evens the Score

The relationship between sales and marketing is often contentious, and here’s why. Marketing and sales are accountable for different metrics, have different best practices, speak different languages and have very different day-to-day lives. This disconnect might manifest  itself in measurably low standards like these:

This endless battle rages with sales maligning the quality and quantity of the leads it receives and with marketing criticizing the way sales follows up with them. How do you bridge a gap that is as wide as the Grand Canyon?

How Predictive Intelligence Aligns the Teams

Predictive intelligence operates on the premise that reliable data will help marketing and sales teams become more focused, efficient and effective by providing insights into the needs, motivations and constraints faced by their prospects and clients. Both through its processes and its insights, predictive intelligence brings into alignment three major areas of discord: communication, expectations and metrics.

  1. Clear Communication Rooted in Data

Today, the reigning currency of B2B marketing is the marketing-qualified lead, or MQL. The definition of a MQL and the agreement between marketing and sales on this definition is paramount to a smooth hand-off process between the two teams.

Predictive intelligence will help you develop a data-driven definition of a MQL by tracking the buying signals exhibited by prospects over time — behaviors and actions indicating their interests and likelihood to become a customer. For many organizations this would be a huge improvement, as MQL scoring and logic are often based on the preferences and biases of the marketers developing them. When MQL definitions reflect data and real-world scenarios, sales executives can better understand and internalize the definition and value every lead they receive from the marketing team.

  1. Expectations: Prioritization and Focus Driven by Intent Data

A common complaint from enterprise sales teams is that marketing-driven leads, while having a strong profile fit, are often not in buying mode. For example, marketing might send sales an executive from a target industry that perfectly fits marketing’s target persona but that lead turns out to not be interested in buying despite perfectly fitting the profile.

Predictive intelligence reduces these false positives by modeling off more than just demographic fit and limited engagement on a company’s website. Predictive intelligence uncovers the prospects showing buying intent by tying together the buying signals of prospects across the web and on your digital properties. The data will tell you who is likely to buy, what they’ll buy and when. This precise prioritization based on demonstrated interest (not guessing and hoping) will set the right expectation for results. 

  1. Metrics and Improvement

B2B marketing and sales is a game of “what have you done for me lately.” A batch of hot prospects and a seven-figure deal will only get you through a quarter, or more likely a month, and in some organizations, barely a week. The pressure to constantly and consistently improve prospect quality and volume from the marketing side and opportunity pipeline and closed business from sales is unrelenting.

Predictive intelligence is built on model refinement. Results improve as predictive models evolve and machine-learning algorithms learn from activities generated from ongoing campaigns. The more you use predictive, the more accurately the models will perform to find  your next customer.

Predictive intelligence removes biases from every part of your lead-to- revenue process and aligns marketing and sales around the same set of verifiable data and results.

Interested in learning more? Check out SiriusDecisions, Cisco and 6sense as they discuss how to roll out and implement predictive intelligence across the enterprise.

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