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SaaS Predictive Analytics Market for B2B Sales and Marketing

B2B SaaS Predictive Analytics Market - Sales and Marketing

The SaaS predictive analytics market encompasses an emerging category of data, process and productivity applications for B2B sales and marketing professionals.  SaaS predictive analytics are designed to be used at all stages of the sales cycle – for net new customers and expansion within existing accounts.

SaaS predictive analytics help:

  • B2B salespeople improve win rates, deal velocity and size
  • B2B marketers hyper segment prospects and customers, serve up persona-based content and power account-based marketing

SaaS Predictive Analytics Market Highlights

Gartner reports (subscription required) that the market for SaaS-based predictive analytics applications is in its infancy and estimates the range for the market between $100M to $150M at the end of 2016.  However, the ROI potential for predictive analytics is so huge for B2B sales and marketers that the market is expected to experience significant double digit growth over the next few years.

Traditional CRM, SFA, SPM & CPQ Markets

Most B2B sales organizations use SaaS-based sales force automation (SFA), sales performance management (SPM) or incentive compensation management (ICM).

  • SFA applications support the automation of sales activities, processes and administrative responsibilities for B2B salespeople—basically account, contact and opportunity management
  • Sales performance management (SPM) is the collection of operational and analytical functions that automate and unite operational sales processes–sales incentive compensation management (ICM), objective/quota management and territory management and appraisal/evaluation
  • The configure, price and quote (CPQ) market has moved beyond supporting traditional sales configuration to supporting self-service, e-commerce, contact center and partner channels

SaaS Predictive Analytics Market for B2B Sales and MarketingWhile SFA vendors have incorporated basic functionality to perform opportunity scoring capabilities, that functionality is not what the application was built to do.  Some of the SFA vendors offer predictive analytics for B2B marketing or sales functionality as part of a service as opposed to a stand-alone product — but very few vendors offer truly integrated predictive analytics capabilities within their platforms.

While traditional customer relationship management (CRM) and sales force automation (SFA) solutions are starting to incorporate artificial intelligence (AI) and machine learning (ML), most of these analytics are based on predefined rules, diagnostics and descriptive analytics.  Gartner estimates that adoption rates for CRM are approximately 20% and 50% for SaaS SFA applications as of 2016.

What B2B Marketers Plan to Achieve Through SaaS Predictive Analytics

In general, predictive analytics adds science to the art of marketing.  Management teams and boards are focused on business outcomes and gravitate towards anything that can demystify marketing – i.e. the old “tell me what you will provide to the business for each dollar of marketing spend.”

When it comes to B2B marketer’s demand generation and management needs there is quite a spectrum.  On one end, some marketers focus on generating responses or leads and passing them to sales development reps, inside sales people or field sales people to sort out whether there is an opportunity or not.  On the other end of the spectrum, the demand generation or account-based marketer is focused on serving up a prospect with a high probability of converting to a qualified sales opportunity.  Depending upon the B2B marketer’s placement on the spectrum, the desired outcome may vary.  View three marketing use cases for predictive analytics.

SaaS Predictive Analytics Market Outcomes for B2B MQL Marketers

  • Increase conversion rates throughout the sales and marketing funnel
  • Increase marketing’s contribution to the sales pipeline
  • Accelerated pipeline creation – build a list of the best companies and contacts in real-time
  • Access to accurate, actionable data on all companies all the time (single source of truth)

SaaS Predictive Analytics Market Outcomes for Revenue Centric B2B Marketers

  • All of the above plus
  • Document the total available market, served market and target market
  • Increase the ROI on marketing spend
  • Optimize the allocation of sales and marketing resources by focusing resources on only the most relevant prospects
  • Decrease lead volume by reducing or eliminating bad leads to increase revenue and sales productivity
  • Increase the time spent calling – SDRs & ISRs do not have to spend time searching for companies and researching contacts to call as a steady flow will exist that can be accessed in a self-service manner
  • Decrease marketing expenses by targeting demand gen and ABM programs to the right companies and contacts

What B2B Sales Organizations Plan to Achieve Through SaaS Predictive Analytics

When it comes to sales there is only one metric that matters and that is percent of quota attained.  Utopia is to bottle the magic formula of those sales reps that meet or exceed quota and to raise the quota performance of all sales reps.  In developing the formula, the optimal sales process needs to be decomposed, analyzed and optimized.  To that end, sales leaders (and their sales operations teams) seek to create a managed, repeatable process to add new logos faster and sell more to existing customers.  Specifically, here are some of the expectations sales leaders have for predictive analytics:

SaaS Predictive Analytics Market Outcomes for Sales

  • Improve win rates, deal velocity and deal size by determining the characteristics of deals
  • Increases revenue in existing markets by finding new customers that were previously unknown
  • Enables upsells & cross-sells (deeper penetration into existing customers) by capitalizing on hidden relationships residing in historical customer data
  • Generates revenue in new markets by creating an ideal customer profile or by finding companies and contacts based on user-based criteria
  • Optimizes the allocation of sales resources (sales development, inside sales and field sales) by focusing each resource on the most relevant and value based task
  • Improves territory planning and account selection to provide each sales rep with the appropriate potential revenue opportunity and quota assignment
  • Utilizes sales intelligence which provides insight into intent and stage of the customer buying process
  • Enables accurate forecasts and systematic pipeline management
  • Provides a supplement and sometimes replacement for stand-alone opportunity scoring

Net – SaaS Predictive Analytics Market for B2B Sales and Marketing

B2B marketers and sales leaders know the potential value of having an optimized prospect-to-customer process to achieve, predictable, steady and sustained growth.  B2B marketers are enamored with the idea of an automated, managed, repeatable process for identifying prospects high in the funnel who have either a propensity to buy or are actively looking.  B2B sales teams seek solutions targeted at the middle-and-lower sections of the sales funnel that seamlessly integrate internal data from SFA, CRM, ERP, email, calendars, data warehouses and external data for all prospects and customers.  SaaS predictive analytics leverage AI and machine learning so that sales teams benefit from continuously learning to deliver timely, personalized cross-channel targeting and engagement that supports account based sales and marketing.


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