B2B Predictive Marketing Analytics

Go to Market Strategies – B2B Predictive Marketing Analytics


B2B Defining Predictive Analytics

Predictive analytics has been defined in many ways. The more technical definition reads “Predictive analytics combines vast amounts of historical and contextual data to create a probabilistic model that predicts which actions or audiences have a high likelihood to succeed and which have a high probability to fail.”

However, the description of this emerging technology category can be drastically simplified via a system that uses past data to predict outcomes. Or, as the Chief Analytics Officer at Swift Capital says, “Predictive analytics is simply the 80/20 rule.”

We know 80% of our revenue will come from 20% of our prospects. And 80% of the pipeline will come from 20% of our campaigns. Predictive identifies the 20%.

But you don’t have to just take our word for it – we’ve also compiled industry experts’ answers to the question. While their responses vary, the underlying concept is the same – predictive involves predicting where you’ll have success.


B2B Predictive Marketing Analytics – The Perceived Value


Increase Revenue

Usually a mandate from management or a cascading company objective requires marketing to be accountable for revenue generation.  In the ecommerce business, marketing may be 100% responsible for revenue generation but in a direct, indirect, channel or hybrid model, marketing may only be responsible for a fraction of total revenue.  At any rate, revenue is a quantifiable objective that is expected to be definitively achieved.

Correlate Marketing’s Contribution to Revenue and the Sales Pipeline

In this situation, marketing may be reactive or proactive.  In a reactive environment there is usually concern about the marketing budget. This is because marketing program dollars are always top of mind for the CFO when expenses need to be reduced to reach profitability targets or to reduce the burn rate.

In a proactive situation, a CMO knows that if they can clearly document a positive correlation between marketing spend and sales pipeline (and better yet, revenue), that he or she has a good shot at preserving or increasing the marketing budget.

In either scenario, marketing must create a predictable model that documents each stage of the marketing and sales funnel as well as the financial contribution of marketing (marketing sourced and/or marketing-generated) at each stage of the sales cycle.

Increased Marketing Efficiency

In any organization there’s always too much to do and not enough resources to do it.  Often a good solution to this problem is automating time-consuming, manual tasks that are riddled with human error.  These are tasks such as content creation, management and personalization, campaign scheduling and execution, data hygiene, lead scoring, routing and nurturing.

Increase Sales Productivity    

One of the largest drains on a sales organization’s productivity is following up on bad leads – i.e. wrong companies and or wrong title or function.  Reducing the volume of leads to sales is a good thing if the leads will not be fruitful.  And, if a sales organization is struggling to manage the capacity of leads passed to them by marketing, more is not better. In fact, less is definitely better if less means that only good leads are passed to sales.

Intelligently Generate the Most Qualified Leads    

Qualified leads consist of two dimensions: quality and quantity.  While some organizations require both, some benefit by reducing leads (bad leads) so more resources can be applied to generate and follow-up on qualified leads with the highest propensity to purchase (P2P).

Multichannel View of Prospect, Pipeline and Customer Behavior    

The phone book, bingo cards and a completed web form are NOT leads.  A name, phone number and email is not sales intelligence.  The target market should be well-defined so that specific companies and specific individuals within each company can be identified.  After that, integrating multiple channels will create the most comprehensive prospect profiles and more holistic views of prospect, pipeline and customer behavior and intent.  Marketing automation provides the ability to combine multiple criteria including demographic, technographic, transactional and behavioral data – 24/7 – without mistakes and never tires. In fact, it only gets smarter through machine learning.

Sales & Marketing Synchronization    

Marketing automation software helps align sales and marketing organizations by getting both functions on the same page with systems, processes and terminology.  By working collaboratively to establish the target markets (right companies and titles), setting scoring parameters, tightly defining qualified leads, agreeing on content and co-developing messaging, sales and marketing become one team.


B2B Predictive Marketing Analytics – Data, Content & Personalization


True 1:1 Personalization Requites a Multi-Faceted Marketing Strategy

Prospects and customers want simplicity and demand a custom, tailored experience including customized content.

All data (sources, types, descriptive, behavioral, transactional, social, etc.) are required for B2B marketers to truly understand their targeted prospects and customers. This is so that meaningful and relevant conversations (online and offline) can occur to attract, engage, convert and onboard prospects and customers.

While dynamically personalized experiences and content are a major pillar of personalization, marketers should step back and develop a holistic, multichannel go-to-market plan focused on specific use cases, the buying process and differentiation.


B2B Predictive Marketing Analytics Statistics


Here are some interesting and relevant statistics on predictive marketing analytics:

  • Approximately 70% of B2B organizations report that when researching vendors they want relevant content that speaks directly to their company – Dem Gen Report 2016
  • 45% of B2B buyers want a relevant website that speaks directly to their industry, market and company – Dem Gen Report 2016
  • 70% of B2B marketers use big data, historical information and predictive analytics to improve marketing’s effectiveness – Progress
  • 38% of B2B marketers believe a key benefit of marketing analytics is the ability to precisely identify customer needs – Regalia
  • 41% of B2B marketers use descriptive data to develop marketing strategy and guide marketing selection.  Basic information includes name, company, title, location, email address and phone number – Demand Metric
  • 39% of B2B marketers use behavioral data in developing and executing their go-to-market plan.  This includes information about the prospect or customer’s interactions with marketing materials (content that was downloaded, web pages visited, emails opened, etc.) – Demand Metric
  • Over 50% of US B2B marketers say content personalization is the most effective use of marketing data – eMarketer
  • 49% of US B2B marketers state delivering personalized or customized content for each account is part of their ABM plan – Dem Gen Report 2016
  • 47% of US B2B marketers say their organization’s website serves up personalized/custom content for each account that is part of their ABM plan – Dem Gen Report 2016
  • 40% of B2B marketers state they personalize their lead nurturing program with a series of emails determined by the prospect or customer’s actions and interest – Pardot
  • 43% of B2B marketers worldwide communicate that improving email personalization is a significant goal – eMarketer
  • 39% of B2B marketers worldwide say improving email personalization is a barrier to success for their email marketing efforts – eMarketer
  • 69% of B2B marketers use online targeting to alter digital experiences based on behavioral attributes – Monetate
  • 57% of B2B marketers use demographic, technographics or other statistical attributes to alter digital experiences based on behavioral attributes – Monetate
  • 52% of US B2B marketers currently use “look-a-like” targeting as a data-driven advertising and marketing tactic – and 23% more plan to do so in 2017 – Dun & Bradstreet
  • 37% of sales people worldwide want to provide the customer with a more personalized experience – eMarketer
  • Montage published research that shows that only 6% of B2B marketers felt that their implementation of personalization strategy was advanced; 56% say they are in the process while 28% state that they are just beginning – eMarketer
  • 45% of marketing executives worldwide believe personalization technologies are a trend that will have the biggest impact on marketing companies by 2020 – Economics Intelligence

B2B Predictive Marketing Analytics Statistics – Gartner’s Definition


There’s a lot of confusion in the marketplace around predictive marketing analytics as the market begins to take shape and vendors jostle for position, adoption and market share.  Vendors, analysts, thought leaders and many customers and prospects have an opinion about Artificial Intelligence (AI), Big Data, Machine Learning (ML) and a host of other technologies on the horizon.  Gartner has published some information that attempts to define Predictive Analytics by comparing it to descriptive, diagnostic, predictive and prescriptive analytics:

Descriptive – what has happened in the past (rear view window)?

Diagnostic – why did it happen (try to establish a cause and effect based on historical data and information?

Predictive – what will happen (based on historical, current and intent)?

Prescriptive – what should I do (automatic or recommended courses of action for systems, processes and people and strategy)?


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