Predictive Analytics
for B2B
Executive Summary
Predictive analytics is way ahead in helping companies close new business, and new SaaS vendors make it easier for companies to adopt it. B2B marketers can make use of predictive analytics to provide double digit increases in leads, opportunities and sales. Large B2B companies have been using predictive analytics for years, too, to better prioritize sales leads, determine which products a prospect would be most likely to buy, nurture contacts who aren’t yet ready to buy, and develop more reliable sales forecasting. It used to be that only the largest companies had the data and data scientists to do it, but that’s now changing.
Predictive analytics is being democratized. Companies are providing cloud-based B2B predictive
analytics services that eliminate the need to hire increasingly-pricey data scientists internally. Their SaaS services start with the company’s internal CRM and marketing automation data, and then they add in data from thousands of public sources such as company revenue and income, number of employees, number and location of offices, executive management changes, credit history, social media activity, press releases, news articles, job openings, patents, etc.
Abstract
Data science is being used to identify common characteristics of the accounts that were won by sales, and predict the likelihood of closing each prospect. Sometimes the signals are far more obscure than that, though. Sales has prioritized leads and sales people have important new information about the accounts, which cuts down on their research time. Marketing has segments of lower-priority prospects to nurture. And predictive analytics can be equally useful in growing existing accounts and closing new ones.
This goes way beyond the lead scoring of a marketing automation system, as valuable as that is. Marketing automation typically just uses the information from the CRM and the 'digital body language' of a prospect’s online interactions with the company’s website, emails, and other digital communications. Predictive analytics companies are adding a huge amount of data to that, and then sifting through all of it to find the most useful buying signals.
With new sales and marketing technologies like marketing automation and predictive analytics there’s a huge advantage to early adopters who get it right.
Data Transformation
Predictive analytics is something of a white whale for business-to-business (B2B) marketers. They look at their counterparts on the business-to-consumer (B2C) side of things with envy: Every life event, every feeling, every thought is a trigger. Customer-facing marketers have it all at their fingertips. Predictive analytics gave B2C marketers the insights they needed because they simply had access to more information.
Thankfully, things are beginning to change. Big data has shifted the B2B marketing paradigm by enabling marketers selling to other businesses to learn more about their prospects and improving their ability to analyze the information. All of that data about target markets has given B2B marketers a chance to develop strategies based on deeper corporate information, real-time triggers, and behavioral information.
Good campaigns rely on data and use it effectively. Great campaigns transform that data into predictive analytics that offer even deeper insights for strategy.
Early Adoption is the key
Predictive analytics is one of today’s hottest B2B marketing technologies. Fueled by drivers such as big data, SaaS delivery models, and data-driven marketing and sales, predictive analytics garners a
tremendous amount of attention, particularly given how few customers are in actual production. While the hype can sometimes be excessive, early adopters are realizing demonstrable ROI as they use statistical modeling, machine learning, and scoring technologies to identify and prioritize accounts, leads, contacts, and customers at specific points in the marketing and sales funnel. It’s this
demonstrable ROI that’s causing more and more marketing organizations to look at predictive analytics as a new, non-negotiable element of their marketing technology stacks.
B2B predictive analytics is providing double digit increases in leads, opportunities and sales - sometimes high double digits. Early adopters of sales and marketing technologies can reap huge sales increases while their competitors are wondering what hit them. But several years from now predictive analytics is likely to be table stakes – everyone will be doing it, or being left behind. To the early adopters go the spoils!
Holistic Approach
To justify marketing’s elevated role in the business, marketing leaders must be able to directly link specific outreach activities to revenue. But a transactional view of return on investment (ROI) simply does not fit the complex, often lengthy B2B technology purchase process. You need to develop a holistic approach to quantifying marketing’s impact at each stage of the revenue cycle through the adoption of Revenue Performance Management practices. This methodology involves explicitly defining the stages of the buying process in close coordination with your sales team and then selecting the metrics that will yield the clearest insight into Return on Marketing Investment (ROMI).
The Basics
The most basic firmographic – the demographic-style information for businesses – data is perhaps the most significant development for B2B marketers to come from access to massive stores of
information. Predictive analytics are based heavily on the detailed foundational information of certain companies. Now, B2B marketers have those details. They know companies’ sizes, locations, recent purchases, and the ways they use different products and services.
With this foundational information, marketers can perform broad segmentation, create basic buyer profiles, and run marketing campaigns against target segments. For example, an information service provider might segment its market by vertical and size to target enterprise companies in the financial services and high-tech verticals. The company is able to estimate the addressable market size, analyze its penetration rate in each segment, and develop marketing strategies against the target segments
Metrics That Matter
With improved lead management capabilities and analytics tools, you’ll be better equipped to quantify marketing’s success, including the number of people at each buying stage, conversion rates and trends, lead-to-revenue velocity, and total pipeline value. According to one of the marketing automation leader, to accurately communicate ROMI, marketing leaders must be able to track and report these metrics:
Aggregate number of leads generated
Number of leads at each stage of the buying cycle Lead-to-revenue velocity
The Future of Marketing Measurement Is Predictive
But it doesn’t end there. Predicting revenue is integral to the expanded role of marketing in today’s organizations. You must move beyond tracking which campaigns and channels have performed well in the past to forecasting future sales. The most important question that marketers will need to be able to answer is: “what if?” And the only way you can answer this question in a reliable way is to apply a framework for measuring performance at every stage of the revenue cycle. The challenge is clear. So is the payoff. By detailing the depth of marketing’s impact on present and future pipeline and revenue, you’ll secure credibility that lasts.
A Step Further
Predictive models become even stronger when companies take the data they use for prospective customers further. Beyond firmographics, there’s more behavioral information to integrate and psychographics that suggest companies which may be ideal for targeting at a specific point. Big data enables companies to create richer buyer profiles, perform hyper segmentation, and run highly targeted marketing campaigns with relevant content.
Imagine the information service provider from the example above is launching an innovative cloud-based solution. With the help of big data, it can hypersegment its market and target early technology adopters. Along with the basic information that made B2B marketing good, integrating additional data sources enables B2B marketers to target precisely the audience they are after – for example, analytics-driven organizations or companies with strong diversity policies. These insights help marketers adjust different aspects of their campaigns, such as content and timing, in line with prospects’ behavior. The data inform predictive models by pointing to specific actions prospects are likely to take based on how they run their companies. Frequently, purchase decisions at these companies involve a few different people. This, also, is the kind of data that B2B marketers now have at their disposal, so they can develop and launch strategies aimed at convincing the right people. It all comes down to understanding the fine nuances of prospects to deliver marketing content when it’s most likely to compel a conversion.
Going from Good to Great
Here, it becomes about timing. This is where all of that data leads B2B marketers who are trying to develop predictive models to make their campaigns as actionable as possible. The firmographic and psychographic data that B2B marketers have are even more useful when paired with behavioral triggers. For example, imagine that a company recently hired a new chief information officer (CIO). If the
information service provider from the previous example has this information, it can distribute targeted content to this company after the new CIO is on the job for a couple months and get the right
understanding each aspect of a company and its current position to maximize the value of different strategies. Predictive analytics helps B2B marketers do what B2C has done for years: Pair high-level data with more specific, actionable information so that strategies are more likely to drive conversions. Effective marketing to businesses calls for a thorough understanding of who the prospects are, where they are, and what they need. Predictive analytics helps B2B marketers take their marketing efforts to another level, making good campaigns great by using big data to lead the way. There are some challenges to face, of course. Data management and analysis are integral to launching any successful campaign. Executive buy-in is important, as well. But B2B marketers simply cannot let barriers like these interfere with big data’s ability to deliver predictive analytics that result in sales. For years, marketers believed this information was the key to improving on good campaigns. Now the opportunity exists, and a great campaign is the reward for doing it right.
B2B Predictive Analytics Trends
The report analyzes the key trends and dynamics that are shaping the predictive analytics market as it evolves from a nascent market to mainstream, must-have technology. While there are numerous trends that buyers and vendors alike should track, a few of the more notable ones include:
B2B predictive analytics is an emerging market - The predictive analytics market for B2B is still in the early market stage of the technology adoption life cycle. Vendors are small, and most have been in business for only a few years. Most importantly, few customers are actually running in production.
Forecast of rapid growth, high growth companies investing in predictive analytics over the next 12 months. While it’s still a nascent market, the promise of using predictive analytics to increase revenue growth is real. Early adopters have seen compelling, demonstrable ROI from their predictive analytics programs. These early adopter wins are spurring investment from a larger swath of the market, particularly among companies with demonstrated marketing automation success and high volume funnels.
As the market accelerates, buyers need a framework to reduce adoption risk and demonstrate ROI. Given the number of vendors, conflicting messages, and use cases in this market, it’s critical that buyers use a framework to:
1. Identify priority use cases (start with lead scoring and ICP) and requirements; 2. Shortlist and select a vendor; and
Evaluating Predictive Analytics Vendors
For prospective buyers, the B2B predictive analytics vendor landscape can be confusing. A number of dynamics make understanding the overall landscape and specific vendor capabilities a challenge. First, a large number of vendors currently have a predictive offering. Second, marketing and sales can apply predictive to many different use cases. Third, very few vendors are able to articulate how they are different from the competition. Finally, the vendor landscape will undergo rapid change over the next five years. To overcome these challenges, buyers should use a number of evaluation best practices and tactics, a handful of which are described below:
Demonstrate success at a specific point in the funnel, such as lead scoring or ICPs - For most organizations, the best predictive analytics starting point is lead scoring and building ideal customer profiles (ICPs) for target account selection. We recommend demonstrating success in one of these two areas before moving to other areas in the revenue chain.
Compare vendors against your starting use case requirements and talk to customers that are in production in that specific area - Marketing organizations should develop requirements against their starting point use case and evaluate vendors against those requirements. Buyers should also work with vendors to speak with customers that are in production on a use case that closely mirrors yours.
Marketing teams must be prepared for rapid iteration during the pilot and production phases -
Despite SaaS delivery models, predictive analytics does require implementation and adoption resources. The most successful early adopters rapidly iterate on data inputs and scoring models during the pilot. Marketing should be familiar with data modeling and scoring methodologies. Most importantly, they should be prepared to work iteratively with vendors on building and maintaining models.
Anticipated vendor consolidation; so buyers must consider the long term viability of vendors. Over the next 3-5 years we expect to see market consolidation as products and datasets commoditize, larger marketing technology vendors get acquisitive and many predictive analytics vendors either pivot or go out of business. Its anticipated that 2-3 market leaders will emerge. When selecting a vendor, buyers must be aware of long term issues such as contract length and model/data ownership.
Why Hindsight Analytics Fail
B2B companies often choose the path of least resistance when embarking on a pricing project. That path usually begins with analytics — report-centric, hindsight analytics, to be more specific. While there may be some value in knowing where you’ve been, backward-looking analytics can’t provide value when it comes to making better pricing decisions in the future.
When it comes to pricing, reports and hindsight analytics often show where pricing mistakes were made, such as when a company discounted significantly from the list or matrix price. However, these reports don’t tell you how to set and dynamically update prices, negotiate pricing agreements going
forward, and more importantly, how customers will respond to those prices. This lack of insight often causes companies to either leave money on the table or lose the sale as a result of being too aggressive. Despite their appeal to B2B companies, the reality is that the hindsight-analytics approach to pricing crumbles for companies that face the massive complexity of modern B2B pricing. These companies inadvertently relegate millions of price decisions to their sales reps, forcing them to guess the best prices to hit company objectives. The predictive analytics approach based on the measurement of price elasticity can quantify the true factors that affect price outcomes, predict customer buying response to different prices and enable companies to predict and control how pricing strategies will impact their profit and loss statement.
Price Elasticity Measurement is Central to Optimization
Optimization is not just about using automation tools to make faster decisions or using data to make better decisions, although both will happen when optimization is applied. The deeper purpose of optimization is to find the decisions which lead to the maximum output. In other words, find the prices that result in the best revenue or margin outcomes for each part of your business. The goal is not just to have different prices tomorrow than you had yesterday, it’s to hit specific revenue and margin targets using price as the lever.
In order to predict the revenue and margin outcome of any price change, you must know how different customers will react to price changes across various circumstances, which requires knowledge of price elasticity. Price elasticity is the single most-important factor, ahead of average selling prices, cost-plus margin targets or firm limits on price discounting authority, in setting profitable prices while keeping revenue risk to a minimum. These other well-meaning factors are blind to their own impacts, but elasticity sees what the outcomes will be before you make any price moves. If you don’t understand price elasticity for a given customer segment, you risk leaving money on the table or losing profitable sales.
Most B2B companies do not use price elasticity to set prices because they assume they can’t. Instead, these companies rely on backward-looking analytics or statistical distributions of prices. It’s been a long-held belief that price elasticity is impossible to calculate in a B2B selling environment. That’s simply not true. Advanced analytical techniques make it possible to measure how individual market segments respond to price changes in B2B markets and thus optimize outcomes.
The data needed to take this scientific approach to price optimization already exists. It’s readily available transaction data — the customer, product and order data that every company captures in the course of doing business. From that data, you can segment customers into small groups that have similar price responses and measure the price elasticity on an ongoing basis for each segment. Taking a surgical
Conclusion
Understanding customers, a fundamental marketing activity, is now being augmented by the ability to predict how buyers or markets are likely to behave in the future. Predictive analytics, enabled by huge volumes of data and analytical models, is the next big thing in business-to-business marketing
Using data to look forward at customers and markets will move marketing from “reporting on
performance to driving business. While marketers want to head in this direction, many are stymied by organizational and cultural obstacles.
To align with the increasing amounts of data being provided by marketing departments, companies have worked to fine-tune the sales funnel by improving their measuring and monitoring of sales performance. Tighter scrutiny of sales teams, regions, processes and messaging using new technologies allows
management to tweak efforts to drive optimum results.
Organizations that have learned how to make technology work in their favor are reaping the benefits of sales and marketing departments that are functioning in harmony.
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