This “In the News” roundup highlights Infer’s insights and coverage from around the web.
One topic that’s been trending lately is account-based marketing (ABM). The folks at Demand Gen Report featured our customer Booker’s best practices in this area in the premier issue of their new “ABM in Action” e-zine:
“At the onset of using predictive, Booker’s A- and B-Leads made up 17% of the business’ total raw lead volume, yet drove 74% of the company’s sales pipeline. With this knowledge, and coupled with two new attribution tools, Bizible and InsightSquared, Booker increased its A/B lead scoring by more than 25% in less than 60 days. D’Arcangelo noted that this resulted in “far more efficient ad spend, ad targeting by channel, segmentation, sales operations tactics and lead flow strategy.”
In a recent byline article, Infer’s senior director of product marketing, Sean Zinsmeister, explains the pitfalls and opportunities of DIY predictive modeling, and which three key questions teams should ask themselves before putting the task of model building into the hands of the everyday marketer or other business function.
“While self-service modeling solutions represent an exciting new frontier for these markets, businesses should be cautious to understand the tradeoffs before jumping in feet-first. Companies might initially save some time and money by shortcutting the heavy lifting of data science, but they will be remiss if they ignore the risks.”
“The Risks of Blending Customer Signals from Disparate Sources” by Infer engineer Joel Dodge details important considerations that can greatly impact the accuracy of your predictive model.
“One of the more perilous steps in building a data model is determining the right signals to include….Timeliness is especially critical with behavior signals, which offer insight into how much a customer or prospect is engaged with your company in a given day, week, or month… Fit signals, on the other hand, focus on how much an incoming prospect resembles a likely buyer, and don’t change much over time…. With two distinct models, you can look at each lead through multiple dimensions so that you leave no stone unturned.”
Accessibility to all types of data has increased over the years, triggering savvy businesses to demand technologies that provide them with more transparent and actionable intelligence from their data. Infer is embracing this shift, releasing updates to our solutions that make predictive models more open, accessible and easy for sales and marketing teams to understand. To this end, we recently announced new self-service capabilities to our Profile Management platform as well as Infer Glance for Salesforce. In this destinationCRM article, Sean Zinsmeister explains why these updates help the average marketer get up and running quickly with predictive:
“While predictive analytics has certainly crossed the chasm for sales and marketing, businesses today still lack the foundational technology required to execute core go-to-market principles of segmentation, targeting, and positioning. That’s because marketers are inundated with data from ever-more-complicated sales and marketing stacks. In today’s environment, companies need better tools in order to succeed with the basics. Predictive scoring has gotten us partway there, but with Infer’s new release we are giving our customers more control and transparency beyond the score.”
“Infer Glance summarizes predictive account, contact and lead scores, as well as top signals behind each score to provide sales reps with account intelligence on a prospect’s business model, the technologies they use and website engagement.”
We aim to extend even more value to our customers by expanding Infer’s partner ecosystem and deepening integrations. In April, we announced our partnership with Terminus and introduced the Infer Predictive Ad Targeting platform to helps businesses target their highest-potential accounts and fuel deeper engagement. DestinationCRM, Demand Gen Report, and FierceCMO covered the news:
“The product incorporates Infer’s Profile Management Platform, which combines Infer’s predictive scoring capabilities with internal and external signals to create tailored audiences. These signals are pulled from a company’s internal systems and third-party sources, including marketing automation platforms and other data solutions, to determine which new accounts are interested enough to warrant further focus. Infer’s system then filters through these attributes to interpret and match them, creating richer customer profile.”
Additionally, we released an updated behavior scoring solution for Oracle Eloqua that enables the marketing automation platform’s users to increase the impact of their sales and marketing programs through better lead prioritization and campaign optimization. Articles from DestinationCRM, Demand Gen Report, and MarTech Advisor tout the benefits of the deep integration:
“Infer’s latest offering has deep inroads into platforms like Eloqua and Marketo to develop a model and leverage every record of a prospect’s behavior. This allows for the automatic prediction of not only which prospects should be targeted by the marketing and sales teams, but also what is the right time for doing so.”