Demandbase Keynote: Three New Products

At Demandbase’s virtual Keynote on St. Patrick’s Day, Demandbase discussed the evolution of ABM platforms and three new products: Site Analytics, Data Stream, and Self-Service Targeting.

The first product announcement was around Site Analytics and improved web engagement metrics.  The functionality is not a replacement for Google or Adobe Analytics but enables an understanding of account-level interactions across the company website.  It also provides page-level intelligence concerning which pages matter the most, allowing marketers to promote and optimize high performing pages.

Site Analytics also helps uncover new audiences for sales and marketing outreach, such as verticals outside your current ICP.  It can also be used for understanding which accounts are demonstrating interest in a new product launch for SDR outreach, optimizing content based upon key account viewing activity, and monitoring trends to determine campaign performance and the impact of various marketing activities.

Marketers may filter by page, URL keyword, account filters (industry, revenue, and employment), page performance, and audience.  Marketers may also save filters and create new audiences based upon site traffic.

The second launch was Data Stream, which lets analysts push data from Demandbase into BI platforms for expanded account-level reporting.  Data Stream is designed for firms that have already invested in data modeling and reporting and that have a data team or data analyst working with a BI or reporting platform.  Demandbase data includes audience and account intelligence, campaign metrics, site analytics, and intent.

Daily, data is pushed into a data warehouse (e.g. Google Big Query, Amazon Redshift, Azure Synapse Analytics) or Cloud Storage (e.g. Google Cloud, Amazon S3, Azure Blog Storage).  From there, customers can load the data into reporting tools such as Tableau, Domo, or Google Data Studio and combine account-level data with other data sources.  This process provides an account-based lens to digital marketing alongside intent data and other corporate datasets.

Data Stream “helps you form a complete picture across your prospects and customers,” said VP of Marketing Phil Hollrah.  “Being able to deliver this data in an automated fashion with no manual intervention needed is a huge benefit to our customers.  You can set up your reports, you can auto-refresh this data daily, and then those reports are going to be up-to-date with the latest information.

Demandbase Self-Service Targeting Campaign Builder

The third release was Self-Serve Targeting for account-based advertising.  Previously, this was only available as a managed service, but now marketers can set up campaigns and creative, then modify and optimize the campaigns.  Self-Serve Targeting is supported by a five-step wizard that allows marketers to upload and change creative.  Marketers set up campaigns with budget, geolocation, duration, and audience.  And because it is self-serve, marketers can quickly adjust campaign budgets, scheduling, frequency, or creative, allowing them to make real-time changes.

The wizard provides a campaign forecast that estimates the max spend versus projected budget, estimated impressions against qualified accounts, and the likely reach across the targeted accounts.

Self-Serve Targeting supports multiple campaigns for different segments, whether performing 1-to-1 or 1-to-many advertising.

Site Analytics and Data Stream are generally available.  Self-Serve Targeting is available as part of an early adopter program.

Demandbase Keynote: Three Phases of ABM Evolution

Like other vendors that have canceled public events, Demandbase gave its ABM Innovation Summit keynote as a virtual event on St. Patrick’s Day.  This year’s theme was “ABM Next,” though CMO Peter Isaacson admitted that their annual conferences are always forward-thinking.  Demandbase also announced three new product offerings: Site Analytics, Data Stream, and Self-Service Targeting (covered in tomorrow’s blog).

Demandbase is a long-time champion of Account Based Marketing (ABM), having been a lone voice in the woods for many years.  Back in 2007, they began offering a visitor intelligence service that mapped IP addresses to firmographics.  Since then, they released a B2B DSP, account-based retargeting, website personalization, account-based chat, and an AI-based ABM platform.  In 2020, they are launching buyer committee targeting, though they did not provide any details on this roadmap item.

Demandbase contends that we are now entering the third phase of ABM. The “Evangelical” phase was aligned with the development of initial ABM technologies and “an awareness of the importance of the account,” said CEO Gabe Rogol.  The Evangelical phase shifted the focus of B2B marketing efforts from leads and individuals to accounts.  In late 2015, the “Early Adopters and Buzz Phase” began with crystallization around the term ABM.  Phase II included point solutions, the beginning of AI tools, and the first full-scale implementations.  While Phase II included significant topical buzz, there was not a great deal of consistency and best practices for ABM success.  Phase III is a definitional phase where “ABM is table stakes,” but “there is not a clear definition, yet, as to what are the core technologies that make ABM successful and what are the best practices that make ABM successful.”

Rogol offered three core requirements for ABM success:

Core ABM Platform
  • Core ABM Platform: A comprehensive ABM platform consists of
    1. A data layer containing first and third-party data that “provides a unified view of your accounts”
    2. A decisioning layer that manages planning, segmenting, orchestration, and measurement
    3. An actioning layer that supports advertising, site personalization and engagement, sales enablement, and third-party marketing activity integration
    4. An AI and machine learning layer which helps “understand which accounts are most likely to buy and what are the next best actions to take both as a marketing organization and a sales organization”
    5. An intuitive user experience
  • Account Based Audiences: Rogol called Account Based Audiences “the fundamental unit of B2B Marketing.  Much like a people-based audience that’s united by common behaviors and demographics, an Account Based Audience is united by the way it is behaving across your CRM, your website, [and] marketing automation.” It should be “marketed to in a similar way to drive through the customer journey.”  Account Based Audiences should be accessible to all customer-facing teams, including marketing, sales, customer success, and data and engineering “so that your organization can act in a unified way that amplifies the strategy and impacts the ABM.”  Finally, Account Based Audiences should be available through all marketing, advertising, and sales channels.
  • Control and Access: Although “AI drives the decision making,” B2B marketers still want access and control over their data.  “ABM is one of the most important categories in B2B marketing,” Rogol added, “but you need to be able to control and access the data.”

“B2B marketers are overwhelmed by the sheer volume of data available to us every day.  Being the control freaks we are, marketers are constantly frustrated trying to extract the right insights to tailor our campaigns and reach our target audiences,” said Rogol.  “We are launching new solutions that will empower all of us to take control of data to create tailored campaigns that will drive growth for their organizations.  These new solutions are a reflection of what’s coming next in the world of ABM.”

Tomorrow, I will be covering Demandbase’s product announcements on the virtual keynote.

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 first, second, 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.

“The case for intent data is clear. If only 3 percent of the potential buyers for any given product or service are in the market at any given time (while 40 percent are poised to begin and 56 percent aren’t interested), identifying and focusing on those buyers, and those close behind them, is the key to efficiency and effectiveness in revenue growth. That’s been the Holy Grail of marketing and sales for years. After all, how many times have you heard a sales rep say, ‘If I’m sitting at the table, I win more than my fair share of deals. Just get me to the table!’

That’s the promise of intent data. And practice shows it’s more than just a theory. Fifty-percent increase in close rates and an 82 percent reduction in sell-cycle have been attained.”

Buying Guide: From the Black Box to Revenue Metrics – Translating Buzz into Results,” IntentData.io.

Unfortunately, intent data is often anonymous.  Unless the individual submits a web form, 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, KickFire, Clearbit, IntentData.io, Zoominfo, 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. Some vendors include technographics as well.

Real-time visitor intelligence can assist with the user experience. By providing immediate firmographics, websites can be immediately customized based upon size, location, or industry.

As visitor intelligence is beginning to feed chatbots, it is possible to prioritize customer support and sales queries. As bots become more intelligent, they will digest the firmographics and customize the conversation. Likewise, ABM customers and prospects can be given priority over non-targeted prospects. If these teams are verticalized, chats can be routed to specialized teams.

External third-party intent data is provided by vendors such as Bombora, The Big Willow, and True Influence.  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. One limitation of third-party data is you don’t know which individuals are researching specific topics.

TechTarget Priority Engine provides technology specific second-party 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.  TechTarget is considered second-party intelligence because it owns the content directly and contacts have opted in.  It also offers first-party intent data through KickFire

G2.com (FKA G2Crowd) is another well-known source of second-party intent data. G2.com is a technology review site, so site traffic is highly associated with company and product research.


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.”

Demandbase Acquires Spiderbook

Programmatic advertising vendor Demandbase, a leading advocate of account based marketing, acquired web crawled company intelligence vendor Spiderbook.  This is Demandbase’s second acquisition in the past six months as they acquired WhoToo late last year to augment their company file.

Spiderbook combines predictive analytics with web crawling and sales intelligence to help identify and research additional prospects.  As Demandbase had no ABM offering below the top of the funnel, Spiderbook allows them to extend into ABM prospecting and sales intelligence.  Spiderbook positions itself as a “a system that replicates the intuition and knowledge of a successful strategic account executive who knows the account intimately through years of working with them.”  To accomplish this task, Spiderbook has “automated some of the best account executive practices, such as knowing the right account to pursue, identifying the buying team at the account, having high quality sales conversations as the deal progresses, and leveraging existing relationships to get the deal signed.”

The combined solution allows Demandbase to expand its ABM solution down the funnel to provide what Spiderbook CTO Aman Naimit calls “the world’s first end-to-end Account-Based Marketing Platform that spans from account identification all the way to deal close, all while providing a consistent brand experience.”

According to Spiderbook, their first large customer, Host Analytics, was using Spiderbook to identify ABM targets for programmatic marketing.  The target list was then fed into Demandbase as an advertising targeting list.  Positive feedback from Host Analytics resulted in rolling out the Spiderbook solution to the Demandbase sales and marketing teams.  “Spiderbook has quickly become a household name within our marketing and sales teams,” said Demandbase CEO Chris Golec.  “We were so impressed with the results generated from our Spiderbook campaigns, the scalability of the technology and the team, that it quickly became clear that trheir technology was a critical element of a complete ABM funnel.”

“Over the last several years, we have evaluated multiple solutions to help our marketing and sales teams more efficiently identify the accounts most likely to buy our own products,” said Golec. “Spiderbook’s technology was simply head and shoulders above anything we tried, and we heard similar feedback from our mutual customers.  We were so impressed with the results generated, scalability of the technology and their team, that we decided to join forces to bring the most robust and comprehensive ABM solution to the marketplace.”

Company intelligence includes business descriptions, company news, company ecosystems (partners, suppliers, customers, competitors), phone numbers, emails, and social links.  Company news is tailored to the topics specific to the client.  Thus, a sales rep could target company news around social media and digital marketing.

The ecosystem allows the firm to identify mutual corporate connections and competitive threats:

Spiderbook is one of the few vendors which looks to identify the broader ecosystem around companies.
Spiderbook is one of the few vendors which looks to identify the broader ecosystem around companies.

Not only does Spiderbook gather company ecosystems of business relationships, but they allow users to filter the graph.  Relationships include supplier, partner, competitor, purchaser, investor, and litigant.  Thus, sales reps can identify companies that do business with Boeing from the software industry:

Researching business relationships with Spiderbook
Researching business relationships with Spiderbook

This business relationship graph is a concept I’ve long waited for a vendor to build out.  Revere Data gathers an ecosystem graph tied to a deep product/service taxonomy, but it is focused on public companies.  Likewise, DCA has a partial graph, but it is focused on corporate advisors and banks.  I haven’t seen the Spiderbook business relationships graph in action so cannot speak to whether they have a true business relationship graph solution or simply have it for highly visible companies.  Nevertheless, the idea is compelling.

While mining the web, Spiderbook identifies the “skills, people, and deals” relevant to the target company and suggests talking points and contacts.  These are represented as a set of topics (e.g. Director of Social Media, NLP, Brand Strategy).  Executives are tailored by function, level, and have indicated an interest in the product category being sold (i.e. Marketers that are interested in social media).

Contact profiles include social links, direct email and phone, executive specific talking points, extracts from “recent and relevant documents” linked to the source, deals involving the executive, and the executive’s team.  Contact data is licensed from Zoominfo.

There is no mention of broad company and contact list building or peer searching beyond the relationship filter.  However, sales reps can search for execs at a company by name, title, or keyword.

“B2B marketers are evolving their Account-Based Marketing strategy to what we call Account-Based Everything—the coordination of personalized marketing, sales development and sales efforts to drive engagement and conversion at a targeted set of accounts,” said Craig Rosenberg, chief analyst at TOPO. “The Demandbase acquisition of Spiderbook extends their account-based platform into sales development and sales and allows organizations to move closer to realizing this vision and ultimately see significant lift in pipeline and revenue.”

Spiderbook has 10,000 sales users.  Spiderbook clients include IBM, Appirio, and Host Analytics.

Terms of the deal were not disclosed.