The Predictive Marketing Analytics Buying Checklist


To help B2B marketers make the right choice, reference the Predictive Marketing Analytics Buying Checklistto formulate your needs and objectively evaluate Predictive Marketing Analytics (PMA) vendors.

The many ROI benefits of predictive marketing analytics are fueling adoption of the technology.  That said, frequently the messaging from vendors is confusing, overlapping and sometimes forward thinking versus what capabilities are available today.

Set your organization up for success by focusing on your unique B2B marketing needs or specific use cases to ensure that the predictive marketing analytics solution you choose will solve your specific business problems. Ask the tough internal questions about your organization’s specific needs and the specific use cases that are driving the purchase.  And, ask the PMA vendors about challenges that B2B Marketers face when implementing predictive marketing analytics solutions.

Predictive Marketing Analytics Buying Checklist Needs / Use Cases

Predictive Marketing Analytics Buying Checklist – Market Sizing & Opportunity Analysis

  • Do you need to quantify the total available market, served market or target market?
  • Is your target market primarily SMBs, midmarket or large-enterprise companies?
  • Do you sell direct, online, hybrid (online/inside sales) or through channel partners?
  • Do you need to facilitate territory planning for sales regions or sales reps?

Predictive Marketing Analytics Buying Checklist – Drive Sales Pipeline, Conversions and Revenue

  • Is there an objective to drive sustainable business growth through predictable marketing spend?
  • Is there a requirement to improve win rates, deal velocity and deal size?
  • Is there a demand to increase share of wallet from existing customers?
  • Is there a net new customer target?
  • Are you goaled with finding new opportunities in existing markets?
  • Is there a specific goal to find new opportunities in new markets?
  • How much growth is expected from existing accounts?
  • Do you need to support cross-sell and upsell efforts?
  • Do you need to support account based marketing?

Predictive Marketing Analytics Buying Checklist - Objectives

Predictive Marketing Analytics Buying Checklist –  Correlate Marketing Spend to Revenue

  • Are there clear, consistent reports that quantify marketing spend to demand gen programs and demand programs to sales pipeline and revenue?
  • Is there a need to improve segmentation, account selection, demand generation and lead scoring to increase conversion rates and improve marketing’s contribution to pipeline and revenue?
  • Does management demand a steady, consistent pipeline of high quality of leads that close?
  • Does management require you to demonstrate marketing spend cause and effect to avoid budget cuts and gain budget increases?
  • Is there a requirement to predict segments that represent the best opportunities for sales?
  • Is there a goal to increase conversion rates throughout the sales and marketing funnel?
  • Does marketing need to increase its contribution to the sales pipeline?
  • Do you need to determine where to best focus marketing dollars and resources?
  • Are leads being scored and ranked to a high level of satisfaction by the sales team?
  • Is there a need to find look-alike prospects (those in the sales pipeline or customers) for sales to better use their time and energy?

Predictive Marketing Analytics Buying Checklist – Demand Generation Marketing

  • Do you need to find the specific companies that comprise the total available, served or target market?
  • Is it important to intelligently generate inbound demand gen and enable ABM planning and execution to drive pipeline and correlate marketing activity to revenue?
  • Is it important to optimize integrated demand gen campaigns based on hyper-segments and enable ABM to provide a steady, consistent flow of opportunities with a high propensity to close?
  • Do you need to find the specific contacts that are part of the buying process and comprise the total available, served or target market?
  • Do you need to support high touch campaign execution?
  • Do you need to help tailor dialogue and outreach with prospects and customers throughout the buying process?
  • Do you need to use a high touch approach to land and maintain targeted customers?
  • Do you need to build deeper and wider engagements with targeted customers?
  • Do you need to stretch digital advertising dollars further to generate more MQLs?
  • Do you need to support A/B testing to quantify the lift in conversions, pipeline value and customer acquisition?

Predictive Marketing Analytics Buying Checklist – Marketing Environment

  • Do you have an internal team of data scientists?
  • What SFA system does your organization use?
  • Is there a sales operations team?
  • What Marketing Automation system does your organization use?
  • Is there a marketing operations team?
  • What third party data does your organization leverage?
  • How clean, accurate and complete is your internal and external data?
  • Are there systems, processes and people in place to ensure healthy data?
  • Can marketers access the data they need to design, develop, execute and monitor integrated demand generation programs in a self-service environment?

Predictive Marketing Analytics Buying Checklist – Challenges for B2B Marketers

Predictive Marketing Analytics Buying Checklist –  Data & Integration

  • Is there native integration with SFA and marketing automation systems?
  • How do you help to identify the right data from a vast set of transactional systems?
  • How do you help identify relevant third party data and integrating that data?
  • How do you enrich customer and prospect data with demographic, firmographic, technographic, transactional, social and website information?
  • Do you access, updating and enriching data for the US, Western Europe, Eastern Europe, Japan, China, and Asia Pacific?
  • How do you reconcile multiple sources of data at the company and contact level?
  • How does an end-user import data for modeling (for example, prediction, classification, clustering, affinity analysis, simulation and optimization) and model deployment?
  • How do you provide automated and accurate access to integrate data from various sources (from both on-premises and in the cloud) and different types (text, transactional data, streams, linked data), in order to transform and prepare that data for modeling?

Predictive Marketing Analytics Buying Checklist – Intent

  • Beware of solutions that require a manual process to extend data to social and digital media platforms.
  • Look out for behavioral signals that are limited to nurture scores from the customer’s marketing systems or intent scores from a third party.
  • Seek out high-quality demographic and behavioral attributes at the contact level.
  • Make sure the ability exists to leverage third-party intent signals in modeling.
  • Determine intent not only at the account level but also at the contact level.
  • Remember that insightful sales intelligence and contextual relevance needs to be layered on top of intent to facilitate meaningful and relevant sales conversations.
  • It’s key to not only identify prospects with a high propensity to buy (fit) but to also evaluate intent (a high probability that a company is actively looking).

Predictive Marketing Analytics Buying Checklist – Modeling

  • It’s important to have a library of pre-built models that are best in class that can be modified.
  • How do you empower end-users to create accurate models at the end-user level?
  • Beware of purchasing a black box solution and not understanding the signals behind its’ scores and predictions.
  • How are insights provided to B2B marketers in an intuitive manner to help then interpret the significance of indicators so they can recommend actions?
  • Dependency on MRP for model building, inspection, and management can affect prediction quality.
  • Machine-learning algorithms and processes that are sophisticated and technically intricate need to be examined and understood.
  • How does your solution model to find look-alike accounts with similar attributes should be out of the box?
  • How does your solution define the right segments on which to model – hyper-segment customers or prospects?
  • Automatic and continuously updating algorithms that are based in data science and machine learning should be a checkbox item.
  • The ability to visually interact with and explore the data, and perform basic descriptive statistics and pattern detection should be standard.

Predictive Marketing Analytics Buying Checklist – Self-Service UX

  • Does the solution offer an intuitive UI suited for demand gen and ABM marketers to facilitate flawless operational execution is a basic requirement?
  • It’s important to have automation that eliminates the need to outsource core sales and marketing functions.
  • Self-service means that training, the need for an administrator or customizations performed by the vendor are not required.

Predictive Marketing Analytics Buying Checklist – Prerequisites

  • Have a go to market strategy in place that is mutually agreed upon by B2B sales and marketers.
  • Have a mature marketing team (one that is beyond scoring) that can take advantage of the more advanced business-level outcomes.
  • Ensure strong analytics and data science practices are in place, understood, are updated with intelligence.
  • Your organization is able to incorporate fully automated marketing operations that are outside of less-tech-savvy marketers’ comfort zones.

In sum, there are many factors to consider when making a predictive marketing analytics purchase.  Start today with the Predictive Marketing Analytics Buying Checklist. And freely add, delete or modify it to ensure that your organization makes an informed, objective and fact-based purchase decision.

This article first appeared in

Seeking to build and grow your brand using the force of consumer insight, strategic foresight, creative disruption and technology prowess? Talk to us at +9714 3867728 or mail: or visit

About Author

Comments are closed.