ZoomInfo hinted at an even broader vision of automated lead qualification and workflows in a recent blog that listed four categories of qualifying data:
ZoomInfo does not support programmatic advertising, chatbots, or Slack notifications, so there is significant running room for product development, particularly around expanded intent. For example, a recent study by XANT found that inbound lead response rates decay quickly, but reps fail to respond promptly, and many fall between the cracks. The study analyzed three years of inbound leads at over 400 companies. XANT looked at 5.7 million inbound leads and found that 57.1% of first call attempts took place after a week or more, and only 0.1% of inbound leads were responded to within five minutes. However, firms that responded within those first five minutes had an 8X conversion rate versus later return calls.
“Maybe we simply didn’t realize what we were leaving on the table,” wrote XANT. “Maybe we over-rotated on targeted ABM strategies at the expense of speed-to-lead. Marketing automation shouldn’t replace meaningful and quick sales engagement.”
XANT proposes a second problem that slows lead response times: the manual assignment of leads to individuals, resulting in two sets of delays – the lead routing process and the sales reps’ ability to respond quickly when a batch of leads is handed to them.
Tying inbound leads (emails, webforms, chatbots) to workflows is the next step beyond enrichment. It allows for immediate lead scoring, assignment, and routing decisions, speeding up the response rate while determining each lead’s best course of action. The Trigger / Filter / Action methodology for intent and event-based leads fits perfectly with these other inputs. Furthermore, Chatbots and FormComplete often gather a few extra qualifying details that would be filter inputs.
“There is perhaps no greater need than for sellers to be calling on the right people at the right time,” said SalesTech analyst Nancy Nardin. “Fortunately, the level of accuracy and timeliness of data has improved by leaps and bounds with the emergence of AI, and improved data collection, cleansing, and enrichment.”
B2B IT Marketing Agency BNZSA (pronounced BEN-zah) entered the intent data space with the BNZSA Intent Activation Engine product launch. The new service identifies, tracks, and activates buyer intent. BNZSA combines technographic, firmographic, intent, NLP, and B2B telemarketing data to deliver a set of intent-activated leads.
The new service “connects all the disparate tools available to deliver the most accurate buyer Intent data, with the highest possible lead qualification and industry-standard GDPR compliance.”
Madrid-based BNZSA supports buying committee identification; intent, firmographic and technographic insight; and prospect engagement.
“At the heart of the BNZSA Intent Activation Engine is a combination of data and digital capabilities with inter-personal engagement,” stated CEO Brahim Samhoud. “No one else offers this. There are intent vendors, technographic vendors, firmographic vendors, contact vendors, digital agencies, and tele-agencies. Some provide pieces of the puzzle, but none does everything – until now. No other offering provides B2B sales and marketing leaders with so many different execution options.”
BNZSA describes itself as a customizable, full lifecycle intent data solution for B2B sales and marketing teams.
“The BNZSA Intent Activation Engine realises an end-to-end value journey through information enrichment via broad-based Intent, firmographics, and technographics, to digital warming through social media, content syndication, email, display, and PR, to local language phone-based BANT qualification,” wrote the firm.
The BNZSA Intent Activation Engine supports the following processes:
BNZSA Intent Data Pool: An aggregated database of billions of global, weekly intent records.
BNZSA Tech-Lab: An AI, NLP platform that analyzes intent potential and selects relevant records. The NLP supports twelve European languages and combines it with machine learning and knowledge graphs. High-intent records are matched with “client TALs [tele-prospecting accepted leads] for advanced re-targeting and adapted nurture tracks” while “the remaining selected data is further filtered by criteria specific to clients’ needs.”
BNZSA OmniDatabase: A reference database holding millions of company records for firmographic and technographic enrichment. The OmniDatabase gathers data from half a dozen third-party data sources and is enriched by BNZSA’s data research team.
BNZSA Pipeline: Local-language demand-generation teams engage with prospects to generate a “predetermined number of highly qualified, information-rich leads” that are delivered to client sales and marketing teams. The demand-generation teams support fifteen languages and places 15,000 calls per day.
BNZSA does not publicly disclose its data partners, but they are all respected firmographic, technographic, and intent data sources.
Leads are fed to Marketo, Salesforce, PipeDrive, and Microsoft Dynamics. They also support warm handovers to clients where the demand generation rep schedules the call and joins the first meeting. Because leads are BANT qualified, 70% of leads convert to opportunities with a 35% faster lead-to-close window.
UK Country Manager Paul Stacey argues that personalizing messaging through digital campaigns alone is difficult and that ABM campaigns should never be purely digital. The need for a human touch is even more important during the pandemic when face-to-face meetings are no longer possible.
“If you read the ABM technology vendor’s marketing claims, you would be led to think that automation can overcome marketing and sales teams’ current challenge for intimacy with clients and do it all instead – identify, reach, and engage with your highest-value prospects – at the touch of a button. But can these off-the-shelf solutions truly automate at scale while retaining key customer insights and preserving intimacy? I think not.
There is a place for automation of course, but it’s worthless without high-quality data, and essentially, the intervention of people.
I would argue that the human touch is necessary in at least one, if not multiple, touchpoints in any company’s ABM campaigns. Demand generation must ultimately be powered by people.”
BNZSA UK Country Manager Paul Stacey
BNZSA is based in Europe, so it is well-positioned to conform to GDPR and country-specific data privacy regulations. It was founded seven years ago as a marketing agency focused on tele-based demand generation. It has steadily grown at 30% per annum since launch and employs 200 in Spain, the UK, France, and Morocco. Last year, it grew revenue by 38%.
BNZSA has over 100 clients and a 95% client retention rate.
Technology marketing services vendor Aberdeen acquired intent vendor The Big Willow, creating anew marketing category of intent qualified leads for sales reps. No financial details were provided.
The Big Willow describes itself as the “the leader in buyer intent data science and intent-targeted digital advertising.” The firm monitors billions of daily web interactions to determine the interest intensity level across product categories. The goal of intent data is to identify prospects early in the buying cycle so that vendors can begin marketing to them before they reach out to competitors, “thereby providing sellers a first-mover advantage and resulting in vastly more effective marketing and sales investment.”
Aberdeen CEO Marc Osofsky explained why a market research firm bought a source of intent data, “B2B marketing is undergoing a fundamental change as buyer journeys are now primarily online, and massive new data streams become available to improvethe performance of marketing and sales. Our role is to capture and analyze this new buyer behavior data to help our clients improve marketing and sales performance.”
The Big Willow captures keywords and IP addresses and links them to D-U-N-S locations. The firm also performs natural-language indexing of web sites for keyword assignment.
“We are focused on helping clients convert intent data into new wins,” said Osofsky. “The addition of The Big Willow makes us the only company with all ofthe necessary capabilities to deliver results from the power of intent data.”
Buyer intent data captures the online research of actual buyer journeys and determines a purchase intent signal from the noise of normal activity. Doing this at internet scale with keyword precision creates the most accurate way to predict who’s in market for your products or services. Companies use these predictions to improve the performance of account-based marketing, targeted advertising, demand generation programs, content marketing and more.
By combining The Big Willow’s online interactions (topic, keywords, PageURL, andOpt-in), Aberdeen’s targeting data (company, location, contacts), and first-party visitor intelligence and win/loss history, Aberdeen builds models to identify sales ready leads. Aberdeen further helps identify opted-in, qualified contacts via its research library and call center. Models are based on 18 months of buyers’ journeys indexed down to the device id.
“Marketing often struggles to deliver sales ready leads – content syndication leads can stall out in nurture, ABM activity does not lead to sales meetings,” says Aberdeen. “Our Intent Qualified Demand programs deliver because we do what the other approaches lack. We reach out as Aberdeen to target titles at in-market companies with research-based self-assessments to qualify
Aberdeen’s approach differs from predictive analytics in that they identify specific contacts showing current interest whereas predictive analytics models focus more on identifying companies which are similar to current customers. Based on their buyer journey data and client closed/loss history, Aberdeen claims that their models achieve 91% accuracy in predicting purchase intent based on blind tests run by clients.
The Big Willow tracks buyer journeys across 3.7 billion device ids and 12 billion webpages. The firm captures 480,000 keywords. Aberdeen claims to offer “the largest, most accurate and highly targeted [intent data] in the market today.”
To further its goal of identifying Intent Qualified Opportunities, Aberdeen has grown its contacts file to 60 million names tied to geolocations and companies.
“Combined, Aberdeen and the Big Willow now deliver intent-qualified opportunities that include the specific company location of the intent and the target titles’ contact info,” said the firm. “Clients have the option of a full-service, cost per lead program, a data lake delivery, or the opportunities and contacts sent directly into their CRM.”
The Big Willow CEO Charlie Tarzian has been named President and Chief Innovation Officer of Aberdeen while Keith Blackwell has assumed the position of Aberdeen Chief Operating Officer.
The Aberdeen Group was spun off of Harte-Hanks several years ago and contains Aberdeen market research and the old AccessCI (aka Harte-Hanks Market Intelligence) technographics database.
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.
At last month’s Growth Acceleration Summit, ZoomInfo previewed a lead scoring feature which will be available later this year. Users will build models for ideal customers and the associated scores will be displayed across the product including in lists, profiles, and enriched web leads. The goal is to “customize ZoomInfo to each and every one of you,” said CMO Hila Nir at her Product Roadmap presentation. Customization also includes routing and territory management. ZoomInfo will continue to offer tools which foster sales and marketing alignment and look to “take noise out of sales and marketing organizations.”
The company hinted at email templating and territory dashboards, but did not provide details on these future product concepts beyond conference screenshots. Email templating is most commonly found in Sales Engagement services such as Outreach, SalesLoft, and ConnectLeader.
While Zoominfo has not released financials, Garlick indicated that the firm had a strong 2017 marked by “really fast revenue growth.” The firm also added over 100 staff and 2,000 customers in the past year. He attributed the firm’s success to hard work, teamwork, sweat, and tears.
ZoomInfo pricing is a hybrid between number of seats and number of records licensed. While the firm used to be transparent about its pricing, they stopped posting such details a few years ago.
Poor data quality is a disease which slowly destroys the value of your marketing database. Quality is damaged through incomplete information, poor data entry, and data decay. A traditional response is to purchase new records, but this only provides a temporary (and expensive) respite from your data quality issues.
The data I’ve seen indicates that contacts decay at a 25 to 30 percent annual rate. This means that a prospect list that is 90 percent accurate today will be little more than 50% accurate two years later. Thus, a prospect list purchase strategy is like steroids, it makes your marketing database look healthier on the day the list is purchased, but it simply masks the growing disease within your database. Treating one or two symptoms does not address the underlying problem — a lack of a broad, continuous data strategy.
However, if you take a holistic view around data quality which includes continuous DaaS validation, ABM look-a-likes, web form enrichment, lead-to-account mapping, duplicate management, data standardization, and reference database appends, you will have a healthy database that ensures your MAP and CRM platforms contain the richest, most accurate data.
Vendors that support holistic data quality include ReachForce, D&B Optimizer (FKA Workbench), Zoominfo, InsideView, Oceanos, and Openprise. So if you are concerned about your ability to target, segment, pass quality leads to sales, score leads, or build predictive models, then begin with a holistic data strategy. Symptoms of poor data quality include high email bounce rates, declining email sender scores, returned direct mail, duplicate records, incomplete records, accelerating unsubscribe rates, and sales reps that ignore your marketing qualified leads.
Any firm that is adopting ABM, advanced lead scoring, a single view of the customer, or predictive analytics, should begin with a holistic data quality strategy. Otherwise, these advanced marketing strategies are bound to fail.
Sales Intelligence vendor RampedUp added account scoring to their platform. Other new features include saved searches for leads and trigger events, lead and trigger event downloading to CSV files, importing corporate URLs into searches, and the auto-population of decision makers and preferred technologies.
The new scoring doesn’t employ predictive analytics, but rates accounts on a zero to five basis, with a star awarded for each of five conditions:
One of top 5 industries based on the client roster
One of top 5 market segments based on client employee count
Installed Technology based on products important to the client’s sales process
Contacts present with preferred title based on selected buying committee
Recent trigger event article showing activity over the last 90 days
“Two things that have always set RampedUp apart from other sales intelligence platforms have been the tailored nature of the data we provide,” said CEO Scott Miller. “Our customers are exposed to contacts that are unique to their buying committee. We also share look-alike customer data based on a Salesforce.com sync that pulls customer data into our platform in near real-time. RampedUp also tracks triggering events and installed technology used by companies to help sellers understand their prospects better. All this information is used to create our unique scoring methodology.”
In a 2016 survey of predictive analytics companies, Gartner sized the global market at between $100 and $150 million. Although Gartner remains bullish on the sector, the size must be disappointing to both the firms in the space and their investors. One of the early companies in the space, Lattice Engines, continues as a market leader with over 200 global deployments.
Lattice Engines supports both enterprise clients and high-growth companies with deployments beginning around $75,000. Pricing is based upon the number of managed leads or contacts in the instance along with the number of users. With revenue between $25 and $50 million (GZ Consulting estimate), the firm has a strong position in the nascent market.
Lattice Engines combines first and third-party data to build predictive models. External content includes firmographics, intent data, technographics, social data, and web crawled business signals. Content is licensed from leading vendors such as Dun & Bradstreet (WorldBase global company file), Bombora (intent captured from over 3,000 B2B media sites), and HG Data (technographics). The Lattice Data Cloud covers over 200 million global companies, 21,000 buying signals, 100 million tracked domains, and over one billion daily interactions. Internal content spans transactions, CRM, marketing behavioral data, usage data, and support services.
“Predictive analytics is one of the few types of marketing technology that has the ability to solve issues at every step of the funnel, because it aligns sales and marketing against the right targets, and provides them with the right data to create targeted campaigns. By infusing fit and intent data into our models we enable teams to have a complete understanding of their ideal customer profile, which enhances the programs teams orchestrate against their targets.”
Director of Corporate Marketing Caitlin Ridge.
Firms can build multiple models to support various geographies, product lines, and scenarios (e.g. win/loss, upsell/cross-sell, renew/churn). Lattice scores and modeled data are integrated with many of the key SalesTech and MarTech platforms:
Ads/Web: DemandBase, Oracle Data Cloud, doubleclick (Google), AdRoll, Facebook
CRM: Salesforce, MS Dynamics, Oracle Sales Cloud, SAP
This platform coverage enables Omni-channel ABM campaigns across programmatic platforms, email, direct mail, and field marketing. Scores, insights, and recommendations are provided to sales reps within CRM i-frames.
“Lattice remains the most visible “face” of the market,” said Gartner analyst Todd Berkowitz in September 2016. “With its focus on security, level of integrations and ETL tools, the company is a fit for enterprise clients (both in high-tech and other industries) and/or companies planning to deploy in multiple regions. Gartner clients report that the company’s go-to-market approach is unique in the way it addresses complex problems and help customers operationalize the insights from the models. Lattice is one of the few vendors that can recommend key plays at both the lead and account level across the entire funnel.”
According to Lattice, customers enjoy a broad set of improved metrics:
2X Higher Conversion
3X Greater Pipeline
35% Higher Deal Sizes
6% Increase in Quota Attainment
85% Rise in Revenue per Customer
20% Reduction in Customer Churn
The firm sells broadly across B2B sectors. Customers include Amazon, Dell, PayPal, Staples, and SunTrust Bank.
Back when I was a product manager, I used to conduct sales training classes. I often opened up the session by asking the question, “Who is your biggest competitor?” The reps invariably listed a company or two they had heard over the prior day and a half of training. Even seasoned reps would answer the question incorrectly.
Unless you are in a duopoly or there is a competitor that controls half the market, your biggest competitor is probably NO DECISION. Either the purchasing decision is kicked down the road or no funding is found. It may also be that the opportunity was poorly qualified to begin with.
Sales reps no longer control the conversation due to the informed buyer who leverages the Internet and social media in order to research vendors prior to contacting them. This is one of the reasons that marketing is looking at digitally influencing anonymous individual on the web via Visitor ID, SEO, SEM, and Programmatic. Sales reps are also confounded in their sales efforts by a second change in purchasing patterns. B2B budgetary decision making processes have become more complex.
Budgetary centralization and committee-based buying decisions have increased the number of decision makers in the purchasing process, resulting in a greater likelihood of no decision. According to a Forrester survey of IT sales reps, 43% of lost deals weren’t to competitors but to a category titled “lost funding or lost to no decision: customer stopped the procurement process.”
Furthermore, the rise of cloud computing has shifted budgetary decision making authority away from the CIO to the heads of various functional departments. Purchasing decisions are being compared to a broader set of non-related purchases from across the organization. It is therefore critical that sales reps “understand and navigate complex agreement networks and processes within the buying organization that span different altitudes and functional roles,” blogged Forrester Sales Enablement Analyst Mark Lindwall. “Because decisions are more cross-functional, every dollar is compared against how it could add value in potentially completely non-related areas of investment.”
Thus, sales reps need better tools for identifying who to engage and when best to engage. They also need to be better informed about companies, individuals, and the industries into which they sell. In short, they need to know who to call, when to call, and what to say. They need to quickly navigate what Forrester calls agreement networks to establish relationships across multiple levels and job functions at the organization.
Fortunately, Sales 2.1 tools provide rich biographies and full family trees for navigating these networks. Users can target specific job functions and levels across the corporate hierarchy, research the appropriate individuals, and reach out to them via social media, email, or phone.
Newer ABM tools help identify the Ideal Customer Profile (ICP), score leads based on the ICP, and call out similar accounts and contacts that are not on the company’s radar. Thus, it’s not just about selling more intelligently based on insights, but targeting and prioritizing one’s sales efforts more effectively.
Sales triggers assist with identifying executive changes, M&A events, product launches, and other reasons for reaching out to individuals. Triggers can also indicate an expanding opportunity or that a proposal is potentially at risk due to company or market dynamics.
And yes, sales reps should research both the company and the executive. They need to understand the key trends in the prospect’s industry, why their last quarter was soft, and what does the executive muse about on social media. While such facts may not be immediate hooks, they provide context and potential talking points down the road. It also shows that the rep is willing to invest time in understanding the exec, her company, and the environment in which she is making decisions.
There is an opportunity cost to poor targeting, prioritization, and account planning. It shows up as No Decision in your CRM, slow deal velocity in your pipeline metrics, and disappointing sales growth.
Intelligence vendor DiscoverOrg announced a new Account Based Marketing (ABM) tool called AccountView which helps marketers identify the attributes of their Ideal Customer Profile (ICP). The new feature analyzes an account file which it calls a portfolio, enriches it with firmographics and technographics, and then provides a portfolio visualization dashboard of the accounts. The service also identifies similar companies to the top accounts, prioritizes them, and identifies best fit decision-makers at the net-new accounts.
The portfolio segmentation dashboard tiles include
Size: Revenue and Employee Bar Charts
Industry: Primary Industry Pie Chart; SIC and NAICS top frequency lists
Technology: Technology lists
Ownership: Ownership Structure Pie Chart
Companies: Portfolio companies with employee and revenue data. Company names are hyperlinked to their DiscoverOrg profiles.
Although geographic segmentation is not yet available, it is on the product roadmap.
Within the list tiles, users can search for specific elements (i.e. SIC, NAICS, technology, or company name).
Proposed contacts are shown within org charts with direct dial phones and emails to assist with organizational context and reach out. DiscoverOrg also provides detailed platform information and a set of sales triggers.
Marketing and sales teams can drill into specific bars or wedges to further research segments. To quickly refine models, customers can remove outliers to focus the ICP around high frequency variables.
Portfolios may be uploaded as CSV files, bulk matched within DiscoverOrg, or generated via DiscoverOrg prospecting. Result lists may be saved as lists, viewed as searches, or exported to CSV files. Models may also be loaded into DealPredict where company lists are displayed with Deal Predict scores of zero to five stars. Next to DealPredict scores, DiscoverOrg displays a lightning bolt icon if the company has a Sales Trigger or OppAlerts in the past sixty days. OppAlerts are intent based triggers which have been researched by DiscoverOrg editors or gathered through B2B publishers’ online content consumption data. By clicking on the lightning bolt, reps are shown the related events.
Within DealPredict, company lists are dynamically maintained to reflect the current firmographic and technographic lists of companies. If there is a change in company size or implemented technology, the DealPredict scores are automatically updated every time a search is conducted. Likewise, companies which are added to the DiscoverOrg database are automatically scored.
The very foundation of successful sales and marketing is figuring out who your best customers are, understanding why they are the best, and finding more prospects just like them. What could be a painful analytical exercise is made simple and straightforward with DiscoverOrg’s account-based marketing features, and the result is faster growth for customers who can more effectively identify, understand, and engage with their ideal buyer.
DiscoverOrg CEO Henry Schuck
DiscoverOrg suggests a number of account list categories that can be analyzed including the full customer list, high or low spend customers, renewing or non-renewing customers, high or low profitability customers, competitor customer lists, and prospect accounts. For example, running a competitor’s customer profile through AccountView helps you “determine ways to improve your product, messaging, or positioning. Likewise, running the non-renewed customer list through AccountView will help identify high-churn candidates for special programs.
Although DiscoverOrg recommends sets of strong and weak account lists, AccountView does not have the ability to discriminate between the two categories. Thus, marketers would need to separately run the paired lists, compare the portfolio results, and adjust the models for overlapping variables. For example, knowing that Microsoft Office is heavily used by both strong and weak accounts would indicate that MS Office is a frequently occurring, but non-predictive variable.
Future features include support for multiple models, grouping tech functions by category, sharing models across all users, geographic segmentation reports, and uploading contact information to assist with defining job functions and levels.
AccountView is the latest capability within DiscoverOrg’s ABM Toolkit. Other features include DiscoverOrg’s DealPredict predictive rankings for companies and contacts, OppAlerts intent-based opportunities, and sales triggers.
DealPredict provides predictive scores similar to those provided by predictive analytics companies. DiscoverOrg CMO Katie Bullard noted that unlike some black-box predictive platforms, AccountView analysis and DealPredict models are fully visible to sales and marketing users.
The AccountView analytics and net-new account service is included as part of the DiscoverOrg service. Firms license access to specific DiscoverOrg datasets and a set number of seats. Licensed users then have unlimited access to the licensed content for viewing, uploading, or downloading.
Other sales intelligence companies that have developed AccountView-like functionality include Dun & Bradstreet (Workbench), Avention (DataVision), and Zoominfo (Growth Acceleration Platform).
DiscoverOrg, which hit $71 million in Annual Recurring Revenue (ARR) at the end of 2016, has expanded its customer base beyond technology companies. Over 15% of revenues now come from marketing agencies, staffing firms, and consultancies.