B2B Data Solutions

Get the most out of your B2B data using Infer’s AI-powered predictive analytics to instantly score and identify your highest-quality B2B leads – the ones most likely to convert.

Make Your B2B Data Work For You

Big data fuels business growth in the digital era, giving companies insights into their customers like never before. Naturally, businesses are investing heavily in third-party B2B data to improve demand generation and make more informed sales and marketing decisions. But more data doesn’t necessarily mean more actionable intelligence. Quantity doesn’t always mean quality. 

It typically takes a lot of refining, time and labor to extract good opportunities from the raw lists provided by B2B data vendors. It’s a highly inefficient, hit-or-miss process, with significant time wasted on dead-end leads. 

Infer’s predictive lead scoring software eliminates the errors and inefficiencies, and helps you get much more value from B2B data solutions. Infer uses advanced AI and machine learning to enrich and refine your B2B data, identify the leads most likely to convert and provide the detailed intelligence your marketing and sales teams need to drive high-impact campaigns, target the best opportunities and close deals fast. Rather than spending months trying to find the needles in the haystack, Infer gives you precise guidance up front, so your teams can start working the high-quality leads from day one.

Want to see how this plays out with your own data?

What Is B2B Intent Data?

Behavioral data, sometimes referred to as intent data, is activity information that can signal potential buyers’ propensity to purchase products. Behavioral data comes in two forms. 

First-party data is prospect information captured on your own website, application logs and marketing automation platform (MAP). It contains detailed information on prospects’ website activity, responses to campaigns, participation in events, requests for product information and other marketing interactions. Some of these (or some combination of them) may be highly predictive signals of a prospect’s readiness to buy.

Third-party, or external intent data, is available through B2B data providers, such as content syndicators and other publishers. Third-party data lacks the rich behavioral signals of your own first-party data. In many cases, it’s nothing more than contact information for companies who’ve performed a single action, like clicking a link in an email. That’s precious little behavioral data – and not nearly enough to signal buying intent. And B2B data usually provides little to no background on the prospects themselves, beyond an email address or phone number.

+ 12 x
conversion rate
+ 3 x
average deal size
+ 4 x
return on investment
+ 19 %
sales effort saved

Getting From Data To Deals 

Only a small percentage of the contacts in B2B data lists typically turn out to be worthwhile leads, but in the past there’s been no way to know in advance which ones to target. Instead, companies have to embark on a long, costly journey: scrubbing the list (to remove spam, duplicates and other invalid information), appending and enriching the data (to create a fuller profile of the prospects beyond mere contact info), creating and launching campaigns, and making sales calls – until at last, a few genuine opportunities materialize, some of which hopefully result in sales. That winnowing process can take months or more, and significant expense – most of it spent chasing leads that go nowhere.

Cut To The Chase With Predictive Lead Scoring

Infer short-circuits this entire process and gives you exactly the information you need up front. It automatically scrubs and enriches your B2B data, combining it with the wealth of behavioral information captured in your marketing automation platform, along with company information from your CRM and Infer’s massive database of more than 19 million companies and 42 million prospects. This is used to build fully fleshed-out company profiles.

After enriching your data, Infer analyzes the collected behavioral information and conversion data from your systems, looking for intent signals that indicate a propensity to buy. AI and machine learning enable Infer to explore connections among thousands of signals and construct a highly predictive model of buying behavior. It then assigns lead scores that tell you just how likely various prospects are to buy your products, and how soon. It also assigns fit scores that tell you how good a match the companies are for your products and sales priorities.

Infer’s lead scoring models are statistically proven to be accurate, providing a precise roadmap for your sales teams. You know exactly which leads to go after, which to nurture and which to ignore. Rather than wasting resources, time and money separating the wheat from the chaff, you can target the most qualified leads from day one. And the results get even better over time: models and scores are proactively updated as new win/loss data comes in.

Instead of purchasing tens of thousands of leads and manually slugging through them, Infer helps us programmatically evaluate list quality upfront and only pay for the best leads. Not only will this save us time and energy, it will also assure that our sales team can focus on the right prospects.

Scott Broomfield

CMO of Xactly

Data Enrichment Through Integrations

With pre-built connectors to Salesforce, Marketo, Pardot and Eloqua, and robust, flexible APIs for connection to other systems, Infer makes MAP and CRM integration simple and fast. Integrating your systems enables Infer to enrich your data with information from our database of firmographic, demographic and technographic intelligence to generate detailed customer profiles, predictive models and lead scores on each of your prospects. 

Once we’ve derived your lead scores, they’re automatically fed back into your CRM and MAP, so your sales and marketing teams receive immediate guidance on which prospects to target and when. Infer also displays detailed intelligence on each account, right in Salesforce.

Supercharge your sales and marketing productivity in days, not months.