Sales Acceleration Platform Cognism closed on a $10 million Series B from PeakSpan, bringing total funding to $16.5 million. Previous investors included Oliver Wyman, South Central Ventures, LCIF, and Newable Ventures Limited. The firm, founded in 2016, grew ARR over 4x last year.
will be deployed for opening local offices in Singapore and New York City as
well as growing its machine learning team. According to CEO James Isilay,
the firm has already “won significant business remotely” in both regions.
Isilay called Asian expansion “particularly interesting” due to SaaS being relatively young in the market. Cognism has already leveraged its partnership with investor Oliver Wyman to acquire two “blue-chip financial clients” in Asia. “Investment here looks to be a promising tonic to the barriers to trade growth that are disrupting business in Europe and America.”
granted our first machine learning patent in December 2018, Cognism is now
building a portfolio of IP which will drive the next evolution of sales and
marketing technology,” said Isilay.
“Sales and marketing technology has been a core focus of ours for many years, arming us with a long-term perspective on the segment and a nuanced understanding of market trends and buyer dynamics that drive strategic value. With such a proliferation of tools and technologies emerging across the marketing and sales landscape over the last decade, it’s no secret that go-to-market leaders today aren’t asking for more software tools – they’re demanding better outcomes. Cognism’s pragmatic application of AI, which underpins the whole solution suite, is paving the way for a new category focused on turning disparate data points into coordinated insights to drive predictive and prescriptive lead generation and improve conversion across all engagements.”
▪ Matt Melymuka, Co-Founder and Partner at PeakSpan
database spans 400 million contacts and 10 million businesses. Cognism’s
products support Prospecting, Sales Triggers, and CRM Enrichments.
CRM Connectors are available for Salesforce, Microsoft Dynamics,
HubSpot, and PipeDrive. A cadence tool sends templated emails through
Gmail and Outlook.
Marketing Features include A/B testing, Personas, ICP and TAM Analyses, and webform enrichment.
TechTarget Priority Engine has begun integrating first-party intent data with its proprietary third-party intent intelligence. First-party intent data enhances account rankings and insights. According to TechTarget, “Marketing and sales teams will now be able to leverage this new intelligence to get to the right accounts and prospects faster, increasing conversions and accelerating pipeline.”
Priority Engine launched three years ago with TechTarget’s firmographics and third-party intent derived from its 140 enterprise technology media sites. The new data includes Ideal Customer Profile (ICP) matching, vendor website engagement, and customer content and advertising across the TechTarget network.
First-party website intent is available through a partnership with KickFire.
account intelligence and tools include
Personalized account rankings based on first and third-party intent data
ICP definition and filtering
Enhanced qualification intelligence including buying stage, ICP match, and confirmed projects
Improved engagement signals such as account website visits, content downloads, and banner clicks
Recent activity indicators.
has always focused on delivering ROI,” said Michael Cotoia, CEO, TechTarget.
“These new updates now make it even easier for our customers to close
deals faster by helping them find the prospects that are directly in their
recent product enhancements include opted-in “Project Insiders” and “Confirmed
Project Details” that have been validated by project insiders.
Priority Engine has been deployed at over 400 customers including Oracle, Citrix, IBM, HPE, and AWS.
Your ideal customer profile (ICP) defines who are your best customers and prospects. It is defined by firmographics, intent data, technographics, business signals, etc. ICPs are focused on Accounts.
Your question implies that the firm has a single decision maker. But that is generally only the case at small firms. Generally, B2B mid-sized and larger procurement decisions are made by a buying team which can consist of multiple individuals at different levels and functions / departments. For these, you should define a set of personas that cover economic decision makers, users, influencers, reviewers (e.g. technology gatekeepers).
Many of the ICP vendors support contact searching for ABM accounts. Once the ABM list is defined, they allow users to prospect for contacts by persona (job function/level/title) at ABM accounts.
These vendors include emails and direct dials for contacts along with company profiles, sales triggers, financials, technographics, family trees, filings, etc.
While LinkedIn Sales Navigator does not offer an ICP tool, it includes a Buyer’s Circle which allow sales reps to quickly identify potential contacts at accounts and drag and drop them into their role. They can then review all open opportunities, including buying committees, via a single-pane Deal report which combines LinkedIn intelligence with Salesforce or MS Dynamics.
French Sales and Marketing Intelligence vendor Sparklane released its Predictive Account Scoring Solution for B2B sales. Sparklane Predict now supports dynamic account scoring based upon Ideal Customer Profiles (ICP), sales feedback, and CRM win/loss data. The service is currently available in the UK and France with additional European markets in development.
According to the firm, Predict
supports a “human-in-the-loop” lead review process which “feeds lead decisions
back into the ICP model, providing additional intelligence towards distinguishing between good
and bad prospects.” Predict also collects CRM intelligence on opportunity
outcomes, providing an additional basis for model refinement.
supports bi-directional syncing with Salesforce, Microsoft Dynamics, Marketo,
and Eloqua. Sparklane uploads suggested accounts and leads to CRMs and
gathers historical outcomes for ICP modeling and dynamic scoring.
claims that it shortens sales cycles by 28%, increases contract volume by 25%,
and improves the business conversion rate by 70%.
Sparklane Predict leverages Artificial Intelligence (AI) tools such as machine learning and natural language processing to dramatically improve sales productivity and customer insights. Sales rep attention is directed towards accounts and leads most likely to close based on both fit (company attributes) and need (sales triggers such as international expansion, employee growth, or product launches). Furthermore, automated data enrichment ensures that reps are working with accurate, complete, and current data.
Sparklane Press Release
When building Sparklane models, both win and loss scenarios are employed, providing a more robust model than current customer lists. Along with win/loss scenarios, Sparklane supports other binary outcome scenarios:
Account Renew vs. Account Drop
Account Upgrade vs. Account Downgrade
High Margin Profitable Accounts vs. Low Margin Unprofitable Accounts
also supports multi-product line upsell and cross-sell models.
many of the vendors now marketing ideal customer profile solutions (ICP) are
offering little more than basic prospecting or look-a-like lists under the ICP
banner,” said Sparklane CEO Frédéric Pichard. “A true ICP service begins
with both positive and negative accounts so the platform can distinguish
between accounts that closed and those that failed to close. A true model
also contains feedback loops from sales reps and the CRM. It is the
addition of feedback that refines the model over time, improving the predictive
precision of account scores.”
Sparklane supports nearly 250 customers out of offices in Paris, Nantes, and London. Last year, Sparklane grew its recurring revenue by 60%.
Human-verified contact vendor DealSignal added Bombora intent to its B2B marketing data service. The combined solution offers intent-based leads with verified emails and direct dials “so that marketing and sales teams can reach out to ideal buyers directly and drive more conversions.” DealSignal applies Bombora intent data to an Ideal Buyers Profile. Users will be able to identify net-new, surging accounts with accompanying contacts and buying teams.
excited to partner with Bombora to help marketing and sales teams finally
answer the most elusive question: Who is out there actively looking for what we
sell and how can we reach them before our competitors,” said DealSignal CEO,
Rob Weedn. ”The integration of Bombora intent data and DealSignal’s
verified contact and account data means that revenue-driving teams can now see
which companies are actively in-market, plus get complete, accurate contact
data for ideal buyers at those companies, so they can reach out and convert
that intent into a purchase.”
begin by defining their target buyer personas on the DealSignal platform and
then select up to 50 Bombora intent topics. DealSignal identifies
accounts that match buyer profiles along with surging intent and delivers a set
of accounts with contacts and firmographics. By tying together intent,
firmographics, and human-verified contacts, DealSignal delivers a set of leads
that are more likely to close than with traditional firmographic prospecting.
“Intent-based leads help B2B marketers uncover accounts that are actively in-market — even if they’re not already on their target account/ABM lists. We then deliver complete, enriched and verified contact and account data that helps marketing & sales teams reach out to prospective target buyers with highly personalized messages, to help them convert more intent into a purchase,” said Weedn.
Third-party Intent data from Bombora and The Big Willow has suffered from poor actionability as intent scores lack context and clear next steps. Several vendors have begun to address this issue by combining intent with company and contact intelligence, turning an intent number into an ABM lead. DealSignal ties together Ideal Buyers, Personas, Bombora Intent Data, and Human-verified contacts to indicate which ABM targets are in market and who should be contacted.
DiscoverOrg redesigned its OppAlerts service to identify companies with surging interest in key topics, rank companies by purchase intent, route high-intent prospects to sales reps, and synch intent data with Salesforce for key topics.
By converting intent signals into leads or opportunities, firms are beginning to translate billions of weekly datapoints (thousands of intent topics across millions of companies) into actionable intelligence for sales and marketing teams.
In December, Aberdeen acquired The Big Willow to deliver Intent Qualified Opportunities which combined third-party intent with technographics, firmographics, content, and research.
“Intent data has been trapped in marketing tools as just another score,” said Aberdeen CEO Marc Osofsky, Aberdeen’s CEO. “Aberdeen Intent for Salesforce delivers what sales wants – accounts looking to buy that are fed directly into Salesforce for sales to engage and increase pipeline.”
I am pleased to announce that the first in a series of sales and marketing intelligence profiles is available through this website and my partners at Tenbound. These reports are written to assist with the purchasing decision. InsideView is the first purchasing profile to be completed, but additional reports for D&B Hoovers, LinkedIn Sales Navigator, and DiscoverOrg are planned for release.
InsideView Buyer’s Guide
Buyer's Profile of InsideView Sales and Marketing Intelligence (Single License)
InsideView, based in San Francisco, provides a set of sales and marketing tools for browsers, CRMs, Marketing Automation Platforms (MAPs), and mobile devices. Key tools support sales research and account monitoring, list building, sales connections (“six degrees”), CRM viewing and hygiene, company and contact enrichment, web form enrichment, Ideal Customer Profiling (ICP), Total Addressable Market (TAM) sizing, and marketing automation hygiene.
InsideView targets technology, finance, corporate/consulting services, manufacturing, commercial real estate, etc.
Firms of all sizes license InsideView solutions.
This 22-page report covers the following topics:
Content Coverage Numbers
InsideView for Sales
InsideView for CRM
InsideView Append (Lightning Data)
InsideView Open API
Expert and Data Services
Competitors by Category
GZ Consulting / Tenbound reports are independently written and not sponsored by any of the profiled vendors.
InsideView added Bombora intent intelligence to its Apex “go-to-market” ICP/TAM service. InsideView highlighted two use cases: Refining a list of ideal prospects to focus on those showing intent and expanding a list of buyers to find additional prospects with similar intent and other characteristics.
Refining an ICP list helps marketers target their best prospects based upon open web research currently being performed at their ICP accounts. Thus, custom campaigns can be targeted to accounts likely in market for specific solutions. Instead of blanketing ICP accounts with general messages, a more refined approach can be taken with a higher likelihood of the message resonating.
Conversely, Intent data can be used to expand an ABM list.
“InsideView’s Targeting Intelligence platform provides a single point of access for all B2B data and targeting signals, from firmographics, contact details, news events, personal connections, technographics, and now intent data. Adding Bombora intent data makes go-to-market planning in Apex that much more powerful. Now you can discover even more ideal prospects, and home in on the specific accounts in your target market that are not only an ideal fit for what you sell but are also currently in-market.”
InsideView VP of Products Marc Perramond
I find the first use case, refining a list for targeted messaging, to be more compelling. Intent data is ephemeral. A firm that is researching a topic this month will have moved onto other topics a month or two later. Using intent data to message to these companies on intent-based topics today is powerful. It allows vendors to reach out to prospects before they have begun talking to prospective vendors. It is a powerful method to answer the questions whom to call (account-wise), when to call, and what to say.
However, vendors should be careful about using intent to expand their targets. Identifying the key intent topics is crucial, but building an ABM list based upon ephemeral intent signals risks adding firms that had surging interest over the past few weeks, but have already made purchasing decisions, chosen not to pursue a technology, or were simply performing due diligence. Surge scores simply mean that there was a recent peak in topical interest above the mean.
Defining who are your best candidates amongst your pool of ABM accounts for specific messages is a clear winner. Adding accounts to an ABM list based upon a short-term surge in interest is likely to result in wasted marketing dollars.
Until recently, Bombora has had more success selling their intent file to predictive analytics companies than sales intelligence firms. However, sales intelligence companies are now figuring out how to present intent data to sales reps and marketing professionals without having to provide sales training sessions on the nuances of intent data. For example, DiscoverOrg recently redesigned its OppAlerts to focus on the few key topics relevant to the client and then limited the intent signals to the top few percent of surging accounts. This level of refinement gives sales reps confidence that when they see a surging topic at an account, it is truly surging and not simply an anomaly. And because numeric surge scores have been removed, the rep merely needs to know that there is a high certainty that individuals at the account are actively researching the topic in question.
B2B tech media company TechTarget delivers intent data to sales reps from its Priority Engine service. While other intent services are at the company level, TechTarget has opted-in readers performing current research on a topic. Thus, TechTarget can identify company, executive, topic, and buying stage for its intent file.
Back before the development of ICP / TAM tools and predictive analytics platforms, B2B marketers would simply describe their target market as the Global 2000 or Global 5000. The description was overly broad, but it generally meant global enterprises with revenue in excess of $1 billion.
Of course, you could easily refine the list with broad segmentation. For the sales intelligence vendors of 2005, it was the intersection of G5000 and (Professional Services, Financial Services, Tech firms).
So while there are now tools to refine your target universe, there remain companies that continue to focus on the G5000 concept. This includes startups and companies with expensive B2B solutions. It also includes Enterprise Sales groups.
Harry Henry has built a business around the G5000 concept. For a long time, the Global5000 database consisted of a hand-researched list of billion dollar revenue companies; but, a few weeks ago he released a companion dataset of top US execs for G5000 with plans to sell international contacts in the future. Henry has partnered with Salutary Data to build his new offering.
Marketers can license the G5000 company set for $2,300. The accompanying US dataset of 25,000 executives spanning 2,100 firms is available for $3900. Fields include
First & Last name
Address (street, city, state & zip)
Email address — 100% fill rates
Phone number – Two possible phone numbers with a 35-50% direct dial fill rate.
The contact dataset focuses on Executive Management, Finance, HR/Personnel, Technology/IT, and Marketing.
“To provide you a sense of our vetting process, the contact records are aggregated from some 8 supplier sources and then tested using separate vendors who verify and score the accuracy of emails, phones, and name/title/company. The results of these tests are used to identify the most accurate data, which enables us to create a data stack. In addition, external and internal corroboration sources and techniques are also applied to further help identify the most current and accurate records.”
The contacts file is available with quarterly refreshes. Segmented versions by industry or job function are not available.
The G5000 database consists of over 5,000 active companies generating $60 trillion in annual revenue and employing 130 million employees. Revenue per employee of the G5000 firms is $397,000. The file includes five-year employee and revenue data along with recent events, business descriptions, year founded, industry, segment, market and ticker, and business contact details (e.g. address, phone, URL).
Last month, I discussed intent data, one of a trio of datasets that assist with lead scoring. This month I’m touching upon Fit data and next month I’ll be discussing Opportunity data.
Fitness data consists of firmographics, technographics, and verticalized datasets that help define whether a company is a good prospect. Biographic values such as Job Function, Level, Skills, and Responsibilities should also be employed when evaluating contacts or leads.
Firmographics are the basic variables that have long been used to define a good prospect. Firmographics include location, size (e.g. revenue, employees, assets, PE/VC funding, and market cap), industry, and year founded. Other commonly used dimensions include Ownership Flags (Minority Owned, Woman Owned, Veterans Owned, SOHO, Franchise), Ownership Type (Public, Private, Nonprofit, Government), and Parent/Sub/Branch.
Ownership flags are used for both inclusion and exclusion with SOHO and Franchise flags generally used to exclude small businesses and those with limited purchasing authority. Subsidiaries and Branches are often excluded as they also have more limited purchasing authority, but are included when looking for locations to sell into after an MSA is signed or when evaluating entry into overseas markets. In these cases, knowing all of the locations of current accounts and top prospects is quite valuable. Likewise, logistics companies look for companies with many locations.
Several vendors support radius searching around a ZIP code. This select is valuable for both event planning (e.g. 50 miles from a tradeshow) or for sales reps when traveling and looking to include additional accounts and prospects on a trip.
A recent study by Dun & Bradstreet found that three of the top five dimensions used when targeting B2B accounts are firmographic (Location, Industry, and Company Size).
Furthermore, Account specific lists for ABM generally employ firmographic criteria when building or extending ABM lists. (Online activity is an intent variable which was discussed in my last What Is.)
Technographics are an example of a verticalized dataset. Generally they consist of vendors, products, and product categories. Originally, such data was only available from technology sales intelligence vendors such as DiscoverOrg and HHMI (now Aberdeen Services), but HG Data built and licensed a technographics dataset which is now widely available in data marketplaces, predictive analytics, and sales intelligence platforms. Aberdeen followed suite in licensing their dataset as well.
LinkedIn Sales Navigator offers a set of unique selects for targeting departments, department headcount growth, and employment growth. Unfortunately, this data is not downloadable or available for lead scoring.
Biographic variables are also important when determining fit. Job function and level help determine whether a lead is likely to be a decision maker, influencer, or noise. Most vendors map job titles to taxonomies of between 8 and 60 job functions and 4 to 8 levels. Other biographic variables include education, years at company, former companies, and interests.
Data availability and currency may also play into Fit both directly and indirectly. If a select is weakly populated (e.g. Education, Skills), then many potential targets will be omitted from lists or given low scores. In some cases, lowering the lead score due to a missing field makes sense. Lead scores should incorporate the availability of emails, direct dials, and LinkedIn handles because this information increases the likelihood of successfully communicating with a prospect.
TIP: When evaluating vendors, ask about the fill rates on key fields you anticipate using in your lead scoring or prospecting.
In a similar vein, last update dates should also be used as a filter. Data from SHRM indicates a 2016 average contact decay rate of 27% when accounting for job departures, lateral moves, and title changes. And this is only at the contact level. The rate is even higher when including company name changes, relocations, and bankruptcies / facility closures. Thus, the last update field is a relevant fitness variable for prospecting but not inbound lead scoring.
In short, lead fitness can be defined by a broad set of who, what, and where variables related to companies and contacts.
DealSignal, which offers an on-demand platform for Total Audience and Contact Data Management for B2B marketing and sales, recently rolled out its Total Audience Metrics (TAM) module. The new platform helps sales and marketing professionals improve Go-to-Market and Demand Planning processes by allowing them to measure and visualize their total audience and determine coverage gaps in their CRM and MAP. The new platform analyzes TAM by persona, account segment, and buying committees (what SiriusDecisions calls Demand Units).
“We’ve run hundreds of TAM analyses for B2B marketing teams in various industries and customers are consistently surprised to find that they’re missing more than 80 percent of their target audience—the contacts that fit their target personas and ideal customer profile. TAM coverage is currently averaging 18 percent in existing CRM and MAP systems. It’s a big ‘aha moment’ to learn that you’re missing out on marketing or selling to a large majority of your potential buyers. Often, the best potential buyers – those most likely to convert – are among the missing contacts found in the gap analysis,”
DealSignal CEO Rob Weedn
The firm is seeing rapid uptake on its TAM service which is available as either a freemium (TAM Estimates) or paid option (TAM Actuals). “Early feedback is that this is a great way to verify the counts and size up the Outbound and/or ABM marketing programs over the upcoming year,” said Weedn.
According to DealSignal, TAM Estimates are accurate to ± 20% of Accounts and Contacts. “We’ve been offering this for a few months and it is very popular” with customers and prospects “leveraging this analysis for initial demand planning and budgeting,” said Weedn. “TAM Actuals is a Paid Offering, charged based on credits on our platform, which provides perfectly accurate Total Audience metrics based on Accounts and Contacts.”
The DealSignal platform dynamically discovers, refreshes, and verifies records based on the TAM criteria.
DealSignal has adopted the term TAM, but calls it Total Audience Metrics instead of Total Addressable Market. Weedn explained the difference between the DealSignal and Classic TAM approach:
Total Addressable market is classic and static top down analysis, based on sample/partial market data, typically performed by market research and analyst firms like IDC, Gartner, etc. “Classic TAM” is not necessarily an accurate sizing of the market, it is not frequently updated, and, most importantly, there is no real way for marketing and sales teams to plan marketing and sales programs with a classic and static top-down TAM, and definitely no way to execute against the Accounts and Contacts in that TAM.
DealSignal, is here to help marketers market and sellers sell, so we perform an accurate, bottoms-up, dynamic analysis, based on complete market data, of the actual counts of the Total Audience – which we define as the Accounts that meet Target Market criteria (Industry, Employee, Revenue, Technologies Used, etc.) and Contacts that meet Ideal Buyer Persona criteria. Further, our Total Audience Metrics/Measurements include a process to dynamically discover and verify the underlying Accounts and Contacts, so TAM Analysis is dynamic, based on actuals, and can be updated on demand. The Accounts and Contacts can then be converted, with one click, to fully enriched and verified with full Account/Contact Profiles and Contact Information to be used in marketing and selling initiatives.
Using the DealSignal platform, users can define target personas and Ideal Customer Profiles (ICPs) to build out their TAMs, using micro-targeting criteria such as Titles, Profile Keywords, and Locations that yield results as ranked lists of relevant accounts and contacts. The module compares the TAM against the CRM and identifies gaps by account, industry, geography, etc. DealSignal provides the TAM based not only on CRM data and large third-party sources, but through dynamic sourcing and verification, so the TAM results are “comprehensive and accurate” with net-new accounts and contacts.
DealSignal combines APIs, algorithms, and human intelligence to achieve a much higher level of contact accuracy (95 – 100% according to the firm) than most vendors. The company provides a 100% guarantee on all Account and Contact data. The system enriches and verifies existing leads, contacts and accounts. As it conducts dynamic data sourcing, DealSignal claims account enrichment match rates between 95 and 100% and lead enrichment match rates between 85 and 100%.
DealSignal TAM Analysis Module
DealSignal dynamically discovers, enriches and verifies account and contact lists through a combination of AI robots and researchers combined with CRM and MAP feedback loops. The firm claims a deliverability rate between 94 and 97% and reverifies data on demand for every customer request, with a two week window for contact aging. Records that fall outside of the two-week window are reverified overnight.
“Since static data-at-rest quickly becomes dated, we do not trust it, you should not trust it, and you should certainly not rely on it to define or optimize your vital marketing or sales programs. It must be renewed and refined at runtime,” said Weedn. “We believe in dynamically refreshing and re-verifying data on-demand, when it needs to become active and put into a marketing or sales process—and we’ve uniquely designed the DealSignal platform to do just that.”
DealSignal has automated and editorial processes that place its data quality at a level claimed only by DiscoverOrg. Both firms utilize editorial teams for staying ahead of the 25 to 30% contact decay rate suffered by static databases. DiscoverOrg performs a full data verification every 90 days while DealSignal performs a just-in-time data quality review overnight.
“Marketers and sales teams currently rely on solutions that provide 50 to 80% quality. That is a B- or F on a test, and we need to change the expectation to impeccable quality, at 95-100% (A or A+) to greatly improve marketing and sales performance,” said Weedn.
Last month, DealSignal released a GDPR risk assessment module which enriches CRM data with contact locations and flags EU-based leads. Users can also choose to exclude EU-based leads.
“B2B marketers are faced with many challenges today: identify and engage their total audience, try to keep their audience data fresh and accurate, and comply with new regulations like GDPR. Given the negative consequences associated with GDPR, most marketers are scrambling to review and re-verify the location and status of their contacts,” said Weedn.
Leads are pre-purchased on a volume basis with 1,000 credits running $895. Volume discounts kick in at 5, 10, 25, 50 and 100 thousand credits.