As macroeconomic and technological changes have made ABM more essential and achievable for B2B marketers, they have begun to experiment with implementing predictive analytics solutions to enhance their efforts. But target account selection is merely one use case where predictive analytics can have a major impact on account-based marketing.

B2B marketers can also use predictive analytics to identify and prioritize the right contacts within their target accounts, the right products to sell to those prospects, and the right messages that will resonate and enhance the buyer’s journey.

1. Prioritizing known prospects, leads, and accounts based on their likelihood to take action. Predictive scoring adds a scientific, mathematical dimension to conventional prioritization methods that rely on experimentation and iteration. This use case help sales and marketers identify productive accounts faster, spend less time on accounts less likely to convert, and initiate targeted cross-sell or upsell messaging to accounts that are likely to be responsive.
2. Identifying and acquiring prospects with attributes similar to existing customers. In this use case, accounts that exhibit desired behavior (make a purchase, renew a contract, or purchase additional products and services) serve as the basis of an identification model. This approach helps sales and marketers find valuable prospects earlier in the sales cycle, uncover new markets, prioritize existing accounts for expansion, and power account-based marketing (ABM) initiatives by bringing to the surface accounts that resemble existing clients.
3. Using uncovered attributes to personalize messaging. Traditionally, B2B marketers have been limited to personalizing by generic attributes and did so with such manual effort that it applied only to the most highly prioritized campaigns. Now, attributes used to feed predictive algorithms appended to account records support more intricate and automated segmentation. This use case help sales and marketers drive outbound communications with relevant messages, enable substantial conversations between sales and prospects, and inform content strategy more intelligently.

Data Informed: Please discuss the similarities and differences between predictive and prescriptive analytics.

Guy Yehiav: Prescriptive analytics is simply the next level of predictive analytics. More and more companies are now seeing the value of providing prescriptive analytics and are excited to adopt this approach.

The key difference between the two is that prescriptive looks at current pattern sets and provides actionable outcomes. Prescriptive allows a tailored approach for understanding both user behaviors and unlikely patterns that can cause organizations to lose money and waste resources. While both prescriptive and predictive analytics rely on big data for gathering and understanding customer information, what sets the two apart is who is assigned an actionable plan to fix the task.

Prescriptive solutions take predictive to the next level by providing a desired outcome and the right person to fix the problem at hand. Instead of relying solely on predictions based on educated guesses and past results, prescriptive analytics provide pattern seeking machine algorithms that promote positive customer experience and provide resolution.

Marketing Land’s all new “B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide” examines the market for B2B marketing analytics software platforms and the considerations involved in implementing this software into your business.

This 35-page report is your source for the latest trends, opportunities and challenges facing the market for predictive marketing analytics software tools as seen by industry leaders, vendors and their customers.

Included in the report are profiles of nine leading predictive marketing analytics vendors, (plus four enterprise marketing cloud solutions), pricing charts, capabilities comparisons and recommended steps for evaluating and purchasing.

With demand generation so clearly a part of the critical path to revenue, B2B marketers are eager to discover and explore approaches, technologies, and data sources to drive more effective demand generation.

Radius and Demand Metric recently completed a study in an effort to understand current pain points for B2B demand generation marketers and how predictive analytics address those challenges now and in the future. This study measured the current challenges for B2B demand generation marketers with the objective of understanding specific, current pain points as well as how and where predictive analytics can address them.

The San Francisco-based company announced today the launch of SearchSense, which it says is the “only solution able to reveal” which companies are conducting specific searches. That is, not only the volume of a specific set of search terms, but which firms are looking for the things that interest 6sense’s clients.

This allows client companies to have “immediate competitive advantage of being first to know when prospects are actively researching their company, product or even competitors,” 6sense said in its announcement.

The founders of Infer Inc. have long been in the business of figuring out what consumers will buy next. With Infer, they've been applying those same techniques to businesses that sell to other businesses.
To that end, the company last week announced a partnership with Terminus to extend predictive analytics to B2B advertising by combining the Infer Profile Management platform with Terminus' Account-Based Marketing platform. Moving into the B2B represents a “greenfield” opportunity for Infer, according to Vik Singh, CEO. Businesses already keep their data in the cloud. With the right APIs, that data can be accessed easily for Infer's predictive software to crunch.

eMarketer: Where is B2B adoption at this point?

Heinz: We are still definitely in the early days. There is an increasing number of companies that are adding predictive technology to their stack and are reaping the benefits. We are seeing greater adoption among tech companies and younger companies that don’t have an established software stack. Older companies tend to be slower to adopt predictive.

B2B companies are leveraging advanced analytics. Analytics can help companies to customize buying experiences through ecommerce channels and to provide sales reps with guidance about what customers are likely to purchase and what prices make sense to quote in the context of the deal. Customers want their buying experience to be finely tuned to their unique needs from a product perspective as well as from a pricing perspective. Across channels, whether on the web or in sales rep interactions, B2B customers expect companies to be able to anticipate their needs.

For B2B companies, transaction data holds a wealth of information about where and how to sell more for more, but much of this information goes undetected. Algorithms and predictive models are needed to do the heavy lifting to turn this data into actionable information about each customer for sales reps. This information, or prescriptive guidance, enables sales reps to have more strategic conversations with customers and, in turn, builds the sales team’s confidence in advanced analytics. This same actionable information also can easily be fed into ecommerce platforms to provide a personalized web buying experience for customers.

Learn how leading companies like Demandbase, Cisco and others are applying predictive analytics to scale their Account-Based Marketing beyond their top 50 accounts to their top 500 to top 5000 accounts.  


Account-Based Marketing (ABM) flips the traditional funnel on its head. In contrast to traditional lead generation, ABM forces a focus on quality, not quantity 


Most companies have been doing Account-Based Marketing for years - yet have been constrained to focus primarily on their top 50 or top 100 accounts. Leading marketers have found that the only way to scale their ABM efforts, is to add predictive analytics capabilities to their martech stack.

Let’s take a look at 3 practical ways to make advanced machine learning personalization work for everyday marketing:

1. Simplify your system
Take comfort in outsourcing this process to the data science professionals. There are tons of companies out there to meet your unique needs — from e-commerce product optimization, to predictive content for media and content companies, to B2B business intelligence systems. 

2. Simplify your integration
Look to leverage APIs and tools that help you forget all the noise behind the scenes, and allow you to implement predictive recommendations across channels seamlessly.

3. Simplify your data analytics
One thing we as society haven’t gotten quite used to is putting our full trust into the machines. That needs to change. In order to receive the best insights and best predictive content for users, we need to give over as much control as possible in this one area.