What Is Intent Data?

Bombora Intent Data Collection Model
Bombora Intent Data Collection Model

I am beginning a monthly series entitled What Is where I provide an overview of one of the underlying sales and marketing intelligence technologies or processes being deployed at B2B firms.  I will begin with Intent Data.

Intent Data is one of the three informational elements of B2B Lead scoring (the other two are Fit and Opportunity).  Intent data consists of both first and third-party elements and identifies when companies are actively researching specific product categories.  First-party data is captured in your marketing automation systems and web logs.  Typical first-party intent data includes

  • Web Logs
  • Webform Submissions
  • Email Clicks
  • Downloads
  • Page Views
  • Webinar Attendance
  • Trade Show Booth Visits

In short, if somebody is viewing your website, reading your collateral, meeting with you at a tradeshow booth, or attending your webinars, then he or she is displaying purchase intent.  Of course, not everybody doing so is a potential purchaser, but a high percentage of individuals digitally interacting with your firm are somewhere in the buyer’s journey for your products and services.

Unfortunately, intent data is often anonymous.  Unless the individual submits a webform, you are most likely limited to an IP address.  As B2B visitors are usually accessing your platform from a corporate IP address, it is possible to tie the IP address to the company and at least associate the activity with a company.  Companies such as DemandBase, Bombora, and Dun & Bradstreet offer Visitor Intelligence services to map IP addresses to companies.  Along with the company name, they enrich the visitor intelligence with firmographics such as location, size, and industry.

External intent data is provided by vendors such as Bombora, The Big Willow, True Influence, and TechTarget.  External intent data is gathered from B2B Media websites that evaluate topics of interest across their network and determine which topics are of interest to companies.  Interest is gauged by articles viewed, white papers downloaded, searches performed, case studies read, etc.  Generally, each company is baselined by topic with interest determined with respect to the baseline.  A surge of interest takes place when short-term interest in a topic is well above the baseline for the company.  Intent data is generally delivered as a numeric score by topic with companies licensing the topics of interest.  As intent is determined at the corporate level, it works best in lead scoring. With the exception of TechTarget, you don’t know which individuals are researching specific topics.

TechTarget Priority Engine provides technology specific intent at the individual level along with contact information, buying stage (early or late based upon content viewed and downloaded), and key influencers (companies of interest).  TechTarget is focused on Technology topics across its 140 media sites.


Additional Resources:

DemandBase ABM Analytics Launched

ABM Analytics provides auto-generated look-a-like control groups for pipeline stage analysis.
ABM Analytics provides auto-generated look-a-like control groups for pipeline stage analysis.

At its annual conference, ABM vendor Demandbase rolled out a new ABM Analytics module for campaign assessment.  ABM Analytics supports full pipeline analysis “to understand the progression of their most valuable accounts across the buying cycle” for account lists and audiences.  The platform also displays vendor comparisons, performs segmentation analysis, and provides recommendations for “next best actions to drive higher conversion rates through the funnel.”

“The ability to measure and articulate the effectiveness and impact of ABM programs is important to long-term ABM success,” said Alisa Groocock, Research Director, SiriusDecisions. “ABM solutions that bring extended measurement visibility can better support critical decision-making, and as a result, enable ABM programs to grow more quickly.”

Demandbase lists the new capabilities:

Marketers can monitor the health of their ABM strategies by examining the progress of their most valued accounts through the buying cycle; create side-by-side comparisons of audiences with different revenue ranges, employee sizes or verticals to understand how their segments perform at every stage of the funnel; understand the performance of individual marketing tactics such as advertising or direct mail; diagnose problems and opportunities along the customer journey and take targeted actions to improve performance; and build credibility throughout the organization by sharing transparent ABM progress reports.

ABM Analytics also matches and assesses data across advertising platforms, MAPs, content management, web analytics, and CRM to provide a unified view into “which accounts are responding to ads, engaging with content, moving into sales cycles and contributing to revenue.”

“For years, B2B marketers have struggled to connect disparate data sitting at agencies, in their web log files and CRM to measure the true impact of their marketing programs,” said CEO Chris Golec. “Our new analytics functionality leverages the best practices from some of the world’s most sophisticated B2B marketers and brings them to life for every company, no matter where they are on their ABM journey.”

Demandbase also announced a Salesforce Pardot connector which will be available in June. The connector will complement Einstein ABM and deliver “a deeper understanding of every customer” to sales and marketing teams.  According to Michael Kostow, SVP and GM of Pardot, “Einstein ABM arms teams with the insights necessary to deliver personalized campaigns and build relationships with their most valuable accounts.”

The Pardot solution evaluates activity at both the contact and company level.  The new solution will flag target account visits to corporate websites, relevant keywords and topics for online search, contact page viewing details on the website, and news and blog mentions.  These insights will be provided within SFDC, email alerts, and Slack.

“ABM has transformed how marketing teams drive new business and retain customers,” said Golec.  “But many B2B companies still struggle to deliver the comprehensive account view that can help sales teams drive pipeline and close business.”  The Salesforce partnership “gives marketers the ability to empower sales teams with a complete picture of their target accounts so they can increase their productivity and win rates.”

DemandBase Revenue Growth

One of Demandbase's core technologies is real-time visitor intelligence for ABM.
One of Demandbase’s core technologies is real-time account-level visitor intelligence for ABM.

Nathan Latka interviewed Demandbase CEO Chris Golec back in Q4. Demandbase is growing rapidly and now employs 300. In November, Golec said the firm was likely to achieve 50% or greater growth in 2017. 2016 revenue was around $75 million and the firm was above a $100 million run rate in November. Average revenue per customer is around $20,000 per month. Small customers may select a single module for $2K to $3K per month but then add multiple solutions as they grow. Net revenue retention is around 110%.

The firm has between 50 and 60 quota carrying reps, 20 to 25 marketers, and 10 to 15 administrative staff, with two thirds of the company focused on data, R&D, engineering, and other functions

The firm has 400 to 600 customers with top customers spending a couple million dollars per annum.

Golec expects the firm to be cash flow break-even during the first half of this year.

Demandbase, founded in 2007, was an early and forceful proponent of Account Based Marketing. For several years, they had a monopoly on the positioning, but ABM caught fire as a B2B sales and marketing process with several enterprise software firms including Marketo and Salesforce now offering ABM solutions.

“ABM as a category – the interest level has reached the investment community and so as investors do their research they discovered that Demandbase is the largest and pioneered the category itself.  So we had a lot of inbound interest.  At the same time, we started developing some new innovations using AI and massive data that we’re sitting on. So it really unfolded into a whole new level of innovation.”

  • DemandBase CEO Chris Golec

DemandBase has already received $156 million in funding, including a $65 million round last May. Both Salesforce and Adobe have taken investment stakes in Demandbase.

While some MarTech firms are struggling with revenue growth and churn, that has not been an issue at Demandbase. “ABM is more of a business process and our position is much more of a platform where we’re helping customers throughout the whole lifecycle of attracting, updating, engaging, converting, and upselling them.”

The firm has ten staff in London helping grow European sales. “ABM adoption in the UK and Western Europe is really starting to pick up.”

Source: Nathan Latka SoundCloud Interview of Chris Golec

Intent Data — Why and When?

One of the important recent B2B MarTech innovations is the development of intent data from vendors like Bombora.  As prospects are now using the Internet to self-educate, they are reaching out to a smaller set of pre-screened vendors later in the sales cycle.  But if firms are being stealthy to avoid detection during this initial phase, B2B firms have been looking to uncloak this veil of secrecy and reach out to firms during the initial phase.

One response to anonymity was content marketing which looks to deliver information (and perhaps uncover prospects) during this early phase.  But it is difficult to customize messaging to anonymous individuals.  Thus sprung up visitor id services such as Demandbase that map IP addresses to company firmographics in real-time.  For example, a visitor from a P&C insurance IP address would be shown a website and content that speaks to their industry specific needs.

Firms also engaged in SEO and SEM to drive traffic to vertical content.  While these activities were an improvement, they provided no indication concerning whether the prospect was in the market for a firm’s solutions.

Intent Data Publisher Network and Tracked Activities (Source: Bombora)
Intent Data Publisher Network and Tracked Activities (Source: Bombora)

Firms like Bombora and The Big Willow work with B2B media sites to map site traffic and actions (e.g. downloading white papers, webinar attendance, site searches), to specific companies.  Thus, each IP address has a baseline activity trail which indicates topics of interest.  Intent firms then match B2B media site visitor actions to an intent taxonomy covering thousands of topics.  Of course, larger firms will leave more distinct trails and firms will display heavy footprints around their own industry and target segments.  These patterns are company-specific background noise.  To find the intent signals, intent vendor analytics determine which topics are surging at each company.  For example, If GE has X searches per week on cloud computing, then this activity rate is general background noise.  But if activity spikes to 2X, then there is likely to be some initiative underway at the firm concerning cloud computing.  It is these surges that identify firms to be targeted.  Intent data provides a mechanism for placing calculated bets on which accounts and prospects deserve additional resources.

Keep in mind, this activity remains anonymous.  A cloud computing vendor does not know who at GE is involved in cloud computing initiatives, but they know it is the appropriate time to target GE with stepped up marketing (SEM, email, sales calls, etc.).

Thus, intent data is integrated into predictive marketing platforms such as Lattice Engines, LeadSpace, Mintigo, Everstring, and Radius.

Just this month, Everstring added Bombora’s intent data to their Audience platform.  Surge data is also available for programmatic targeting on platforms such as BlueKai (Oracle), Krux, and Lotame.  Thus, it is possible to target advertising for firms that have shown a surge of interest in a topic.

Like any technology, intent data has its limits.  While it helps identify when to call into an account and topics of interest, it doesn’t identify whom to call and whether there is an actual initiative related to the topic.  Furthermore, intent data does not indicate whether a firm is a good fit (e.g. size, industry, technographics) or how far along they are in the discovery process.

In a blog earlier this month titled “Intent Data is Great. Except When it Isn’t,” Gartner Research Vice President Todd Berkowitz listed the following limitations concerning intent data:

There are a large number of scenarios where intent data and models don’t add nearly as much value (if any).  It’s not because the intent data is inaccurate. It’s because there is simply not enough data available to use directly or to put in models. They include:

  • New and emerging technology categories

  • Certain geographies, industries or other niches

  • Non-technology products

  • Solutions (especially services) that can’t be easily categorized

Thus, intent data works best for well-established technology segments (versus emerging ones).  Just make sure to also look at fitness indicators when building surge-based campaigns.

Addendum

Within 15 minutes of posting this blog, I saw that Bombora was named a 2017 Cool Vendor by Gartner.

“We believe it’s a true milestone to be recognized by Gartner as a Cool Vendor in SaaS for 2017,” said Erik Matlick, founder and CEO of Bombora. “Our customers choose Bombora so that they may access the largest source of B2B intent data for use in their account-based marketing strategies. For us, being a ‘Cool Vendor’ serves as a validation of our ‘everybody wins’ approach to the ecosystem and the impact that our dynamic, quality intent data is having across B2B sales and marketing.”

 

 

 

Sales Intelligence Vendors Move Upstream

ZoomInfo Personas provide a multi-dimensional cluster analysis for identifying persona categories and prospecting against them.
ZoomInfo Personas provide a multi-dimensional cluster analysis for identifying persona categories and prospecting against them.

Five years ago, Sales Intelligence vendors avoided selling into the marketing  department.  While there were a few enrichment projects for CRMs, these were driven by Sales Ops, not marketing departments.  Furthermore, SalesTech products are sold on a per seat basis for sales reps while marketing revenue is generally volume based (e.g. number of prospecting records sold or records enriched).  This made pricing of services difficult.

But MarTech was receiving heavy investments and several firms shifted their focus from sales to marketing.  Zoominfo began discussing Sales and Marketing Alignment and developed a set of marketing tools.  The firm, which had been struggling to grow revenue for several years, is again on a growth trajectory and made the two most recent Inc. 5000 lists.

InsideView also began developing marketing functionality and now treats the two departments equally.  Most of InsideView’s recent investment has been in building out marketing solutions or expanding their company and contact coverage (which benefits sales and marketing equally).

At the beginning of 2015, Dun & Bradstreet acquired NetProspex for its contact database and Workbench hygiene platform.  The firm also used NetProspex as the basis for their Audience Solutions programmatic marketing service which was launched in 2015.

In 2016, the Sales Intelligence vendors continued to move upstream into marketing intelligence and hygiene.  InsideView continues to enhance its Target, Enrich, and Refresh marketing tools while Avention launched OneSource DataVision for web form enrichment, continuous enrichment, segmentation, look-a-like prospecting, and TAM analysis.  Avention also launched Marketo and Eloqua connectors for their OneSource service.

“OneSource DataVision naturally extends the sales and marketing benefits our customers can gain from OneSource Solutions by being even more targeted with campaigns and programmes – including account-based,” said Avention SVP of Product Lauren Bakewell. “Better qualified leads and more targeted account-based approaches should bring better sales results, which should in turn strengthen sales and marketing alignment; we feel alignment happens best when sales forecasts are being met and exceeded!”

Zoominfo has repositioned itself as a MarTech company with a rebranding of their platform as the Zoominfo Growth Acceleration Platform.  While sales reps are still supported, the emphasis is on data enrichment, segmentation analysis, cluster analysis, and look-a-like prospecting against clusters.

DiscoverOrg and RainKing also placed greater emphasis upon marketing and ABM capabilities.  Both services support predictive rankings of accounts and contacts, MAP and CRM enrichment, and new opportunities (Inside Scoops from RainKing and OppAlerts and sales triggers from DiscoverOrg).

In 2017 and 2018, expect the walls between SalesTech and MarTech to crumble.  The opportunity to offer a solution for both departments via a shared reference database will continue to drive strategy at these firms.  As MarTech begins to consolidate, expect M&A activity within the sector and vertically with SalesTech vendors.

Sales Intelligence vendors have key assets that benefit marketing departments including large company and contact datasets for prospecting and enrichment; firmographic data for lead scoring, targeting, segmentation, and routing; and the growing ability to tie leads to accounts in real-time.  They are also well positioned to support ABM functionality with profiling, analytics (segmentation, Total Addressable Market analysis), and look-a-like prospecting.

Of course, MarTech is also beginning to eye SalesTech.  Last spring, Demandbase acquired Spiderbook and leveraged its capabilities to launch their DemandGraph relationship dataset.  The expanded content set employs semantic mining and machine learning to assemble the “entire business network of a company” which helps  “identify which companies and buying committees are in-market for particular solutions.”  The DemandGraph helps users target in-market accounts, identify key buyers, uncover meaningful insights, and deliver personalized content.  While they have not announced specific predictive tools or capabilities, they are hinting at such tools.

Demandbase DemandGraph
Demandbase DemandGraph

Meanwhile, the predictive analytics companies, which originally focused on lead scoring, are now building sales functionality including net-new contacts at accounts, account prioritization, flagging churn candidates,  and providing recommendations for sales reps.

Things are just beginning to get interesting.

Marketers Expectations for AI

A November study by Demandbase and Wakefield Research of 500 B2B marketers (250+ employees) found that while marketers are confident that Artificial Intelligence (AI) will reshape marketing by 2020, they lack confidence in how to implement the new technology.  According to Demandbase, “80 percent of all marketing executives believe AI will revolutionize marketing over the next 5 years, but only 26 percent are very confident they understand how AI is used in marketing and only 10 percent of marketers are currently using AI today.”

Marketers had numerous concerns about implementing AI, including

  • Integrating AI into their existing technology (60%)
  • Training employees (54%)
  • Difficulty interpreting the results (46%)
  • Implementation costs (42%)

On the benefits side, marketers listed

  • Better insights into accounts (60%)
  • More detailed analysis of campaigns (56%)
  • Identifying prospective customers (53%)
  • Expediting daily tasks (53%)

“As someone who has been studying AI for many years, I’ve recognized the promise of AI and B2B marketing for some time, which makes it really rewarding to see this vision is now shared by marketing executives,” said Aman Naimat, SVP of Technology at Demandbase. “This data reveals that in order to be successful, marketing leaders need to lead the charge and present opportunities for AI instruction and experience for their teams, to ensure implementing it into their B2B technology stacks is effective.”

 

marketersonai

In a November Harvard Business Review article titled “How Artificial Intelligence Will Redefine Management,” (Vegard Kolbjørnsrud, Richard Amico, and Robert J. Thomas), the authors offered a set of best practices for managers.  Noting that managers spend 54% of their time on administrative tasks such as scheduling, monitoring, and reporting, they suggest that managers transition administrative tasks to AI.  Instead managers should focus more on judgment work which combines rules with “their knowledge of organizational history and culture, as well as empathy and ethical reflection.”  Thus, there will be a greater emphasis upon “judgment-oriented skills” such as “creative thinking and experimentation, data analysis and interpretation, and strategy development.”

The authors also suggested viewing AI as a trusted colleague instead of a “race against the machine.”  Thus, managers can merge judgment with AI-based decision support, simulations, and search and discovery activities.  A full 78% of managers believe they will trust the advice of intelligent systems.  Furthermore, because AI will be approachable through voice and other intuitive interfaces, AI will be their “always-available assistant and adviser.”

Another recommendation was harnessing the creativity and ideas of co-workers and team members.  With time freed from administrative tasks, there is more time for synthesizing multiple ideas and formulating new products and processes.  “Manager-designers bring together diverse ideas into integrated, workable, and appealing solutions. They embed design thinking into the practices of their teams and organizations.”

Finally, managers will need to hone their social skills with an emphasis on networking, coaching, and collaborating.

The authors concluded that “writing earnings reports is one thing, but developing messages that can engage a workforce and provide a sense of purpose is human through and through. Tracking schedules and resources may soon fall within the jurisdiction of machines, but drafting strategy remains unmistakably human. Simply put, our recommendation is to adopt AI in order to automate administration and to augment but not replace human judgment.”

DemandBase: DemandGraph Company Ecosystem

Demandbase DemandGraph
Demandbase DemandGraph

ABM vendor DemandBase announced a new dataset it calls the DemandGraph which combines its WhoToo dataset of crawled business information with Spiderbook relationship data.  The expanded content set employs semantic mining and machine learning to assemble the “entire business network of a company” which helps  “identify which companies and buying committees are in-market for particular solutions.”  The DemandGraph helps users target in-market accounts, identify key buyers, uncover meaningful insights, and deliver personalized content.  While they have not announced specific predictive tools or capabilities, they are hinting at such tools.

This expanded information set of customers, partners, suppliers, competitors, and investments is built from:

  • Unstructured business knowledge such as SEC filings and annual reports
  • Demandbase’s proprietary identification technology that maps billions of network IP addresses to businesses worldwide
  • Complex corporate hierarchies extending beyond subsidiaries and remote offices to include vendor, customer and partner relationships
  • The digital footprint of web activity by businesses including ad impressions and web traffic from more than 3 billion B2B interactions every month

“DemandGraph isn’t exactly a product but rather a resource that Demandbase will use to power other products,” said analyst David Raab of Raab Associates.  “It lets Demandbase more easily build detailed profiles of people and companies, including history, interests, and relationships. It can then use the information to predict future purchases and guide marketing and sales messages. There’s also a liberal sprinkling of artificial intelligence throughout DemandGraph, used mostly in Spiderbook’s processing of unstructured Web data but also in some of the predictive functions. If I’m sounding vague here it’s because, frankly, so was Demandbase. But it’s still clear that DemandGraph represents a major improvement in the power and scope of data available to business marketers.”

The DemandGraph captures what I’ve long called the “company ecosystem” that goes beyond lists of competitors to include partners, advisors, investors, customers, etc.  An understanding of corporate relationships creates an opportunity to extend beyond traditional six degrees solutions when looking for introductions and relationships.  A few companies have attempted to gather this data, but none have figured out how to market this broader relationship intelligence outside of industry niches such as technology (e.g. DiscoverOrg, RainKing, HG Data), advertising (e.g. TheList/WinMo), and PE/VC datasets (e.g. CB Insights, Mattermark, DataFox, Crunchbase).

Likewise, when LinkedIn describes their Economic Graph, they are focused on people and their relationships to other people and organizations, not the relationships between organizations.

Demandbase claims that company relationships captured within their business graphs offer twenty times the predictive power of social network relationships.  Demandbase SVP of Technology Aman Naimat asserted that “DemandGraph has proven that it can be 7-8 times more accurate than an account executive trying to predict a potential customer, which provides better targeting and conversion.”

Chief Product Officer Alan Fletcher dubbed DemandGraph a “personal concierge” which supports personalization across all sales and marketing touchpoints. “That consistency in messaging throughout the whole sales funnel is what we’re trying to do, and you can only do that if you have the underlying data.  It’s what the best account managers already do today, but it obviously doesn’t scale. Large companies can only do it for their top 200 targets.”

Fletcher suggested that this relationship ecosystem is also predictive of investments, acquisitions, and potential partnerships but that the company is “focused on predicting the next customer.”  The DemandGraph provides insights into the culture of an organization.  “Do they do businesses with startups?  Do they only like to do business with established companies?  Do they typically sell t0 people that are only involved with McKinsey?” asked Fletcher.  “There are a bunch of signals that may not be directly related to you and your products.”