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.”
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.
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.
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
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.
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.”
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.
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.
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.”
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.”
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.”
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:
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:
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.
For several years, Account Based Marketing (ABM) was discussed by Demandbase with little interest from other firms. In the past twelve months or so, it has taken off and become a full blown marketing fad well into its hype cycle (time will tell whether it becomes a movement). Now that other firms are discussing ABM, Demandbase has formed an ABM Leadership Alliance with Oracle and several other firms.
Account Based Marketing identifies a firm’s best prospects and then looks to deeply market and sell to those organizations. As B2B purchasing decisions span multiple individuals and departments, targeting single decision makers is no longer sufficient.
“If you are only looking at an individual, you may only get a part of the picture; seeing the collective acting together helps you to truly understand how interested an account is in your solution,” said Demandbase CMO Peter Isaacson.
Last month, SiriusDecisions released a “2016 State of Account-Based Marketing,” report which found that over seventy percent of B2B companies are looking at implementing ABM and have dedicated staff to ABM-specific programs. The process is still relatively new with 58% of surveyed firms only in the pilot or test phase. The percent of B2B marketing departments with full ABM programs in place grew from twenty percent in 2015 to 41% last year.
This year’s data shows ABM continues to gain rapid acceptance for B2B marketers. What is most exciting is that marketers are rapidly building ABM skills so they can deliver on its promise. Marketing teams understand there are many ways ABM can deliver business impact and are reporting on a range of metrics focused on both demand creation and relationship improvement objectives. In the coming year we’ll see marketers continue to invest in ABM technologies to deliver on their goals.
Megan Heuer, VP of research at SiriusDecisions
The alliance is expanding ABM beyond Demandbase’s original programmatic marketing slogan to a broader concept spanning Engagement, Account Selection, Infrastructure, Measurement, and Sales Enablement (Sales Intelligence).
Sales Enablement is broken into three categories all supported by traditional sales intelligence platforms: Sales Intelligence, Account Insights, and Contact Development. ABMLA has defined these terms a bit differently than I have. By Sales Intelligence, they mean improved coordination between sales and marketing including target account intelligence gathered by marketing. Account Insights focuses on buying signals to assist with outreach prioritization. Contact Development focuses on deeper intelligence around decision-makers.
Missing from their model is ABSD (Account Based Sales Development) services that provide sales reps with pre-defined messaging and campaign cadences which can be adjusted by sales reps. ABSD firms provide what SalesLoft refers to as “sincerity at scale.” Other firms in the ABSD category include QuotaFactory and KiteDesk.
“For ABM practitioners, being able to go beyond the account to the contact level is critically important to drive deeper relevance and specificity,” said Matt Senatore, research director at SiriusDecisions. “For those companies focused on lead generation within target accounts, technology that can help automate this is an important component that enables their ABM programs to scale more efficiently.”
The alliance noted that “B2B companies face a set of unique marketing challenges, which require specific technology to address. As you navigate the space, you’ll need to identify the technologies and vendors that have solutions built for the unique needs of B2B.”
At this point, the alliance does not have any Sales Enablement members which is understandable because Demandbase operates at the very top of the funnel targeting anonymous individuals at named companies. However, several sales intelligence firms have already adopted ABM positioning including Avention, Zoominfo, DiscoverOrg, and DataFox.
The alliance was announced on the opening day of Demandbase’s annual user conference.