Oracle Acquires DataFox

Oracle recently acquired DataFox, providing them with access to 2.8 million company profiles, including funding and M&A data.  DataFox “gives customers real-time insight to know when a business exhibits noteworthy behaviors.”

“The combination of Oracle and DataFox will enhance Oracle Cloud Applications with an extensive set of trusted company-level data and signals, enabling customers to reach even better decisions and business outcomes,” wrote Oracle’s EVP of Applications Development Steve Miranda to customers and partners.

Oracle provides the following deal shorthand:

Oracle Cloud Applications + DataFox = Even Smarter Decisions

DataFox is growing its database at 1.2 million companies annually.  The database will deliver real-time insights into its cloud-based ERP, CX, HCM and SCM platforms.

DataFox Data Engine Overview (Oracle Presentation, October 23, 2018)
DataFox Data Engine Overview (Oracle Presentation, October 23, 2018)

In a bit of extreme puffery, Oracle described DataFox as the “the most current, precise and expansive set of company-level information and insightful data.”  Bureau van Dijk and Dun & Bradstreet have 50X the active company coverage including detailed global linkage, risk models, and multi-year financial data.  Bureau van Dijk also offers the Zephyr database, an M&A and funding dataset with over twenty years of closed, pending, and rumored deals.  Where DataFox may have an advantage is in their focus on mid-size and emerging companies which have been recently funded, but this is a small subset of the company universe.

DataFox will continue to sell and support its products.  However, the DataFox roadmap and product line are fluid:

“Oracle is currently reviewing the existing DataFox product roadmap and will be providing guidance to customers in accordance with Oracle’s standard product communication policies. Any resulting features and timing of release of such features as determined by Oracle’s review of DataFox’s product roadmap are at the sole discretion of Oracle. All product roadmap information, whether communicated by DataFox or by Oracle, does not represent a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. It is intended for information purposes only, and may not be incorporated into any contract.”

Along with AI insights, Oracle called out the needs for quality data to back data maintenance, artificial intelligence, and business signals.

Customer Data Challenges (Oracle Presentation, October 23, 2018)
Customer Data Challenges (Oracle Presentation, October 23, 2018)

DataFox has over 275 customers including Goldman Sachs, Bain & Company, Outreach, Live Ramp, and Twilio.

DataFox raised $19 million in funding.  Terms of the deal were not disclosed.  In January 2017, DataFox was valued at $33 million by Pitchbook.

Oracle should study Salesforce’s acquisition of Jigsaw (later renamed Data.com) as a cautionary tale.  Software companies struggle in selling data files as company and contact data decays rapidly and it is difficult to push data quality above 90% absent large editorial investments.  Furthermore, Jigsaw never represented more than 1% of Salesforce revenue so quickly fell off of the company’s internal radar.  The firm is now looking to decommission Data.com and asking its AppExchange partners to fill the sales intelligence and data hygiene gap left in its absence.  Coincidentally, DataFox is one of Salesforce’s Lightning Data partners.

On the positive side, LinkedIn hit $1.3 billion last quarter and has thrived under Microsoft’s ownership.  However, LinkedIn was a much more mature company at acquisition than DataFox with multiple revenue streams and a unique user generated content model.  Microsoft has provided LinkedIn with development capital and allowed it to maintain its independence.  It has also looked to leverage LinkedIn and Microsoft strengths when building sales and marketing products, instead of simply copying other vendors.  For example, Sales Navigator continues to respect the privacy of its members while using aggregated data to provide hiring and employment insights that other companies cannot deliver.  Navigator has also added strong messaging tools (chat, InMail, and PointDrive) which work around its lack of company emails.  Other innovations include SNAP workflow connectors, its new Pipeline CRM updating tool, and Buyer’s Circle for identifying the buying committee at large firms.

Global5000 Database Adds Contacts

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
  • Job Title
  • 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.”

  • Global5000 Website

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).

 

What is Fit Data?

A Subset of the D&B Hoovers location selects with regional filters for the US and UK.
A subset of the D&B Hoovers location selects with regional filters for the US and UK.

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).

Firmographic variables such as geography, industry, and company size are commonly used for specifying target accounts (Source:
Firmographic variables such as geography, industry, and company size are commonly used for specifying target accounts (Source: “The 6th Annual B2B Marketing Data Report,” Dun & Bradstreet, Sept 2018).

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.

LinkedIn Sales Navigator offers a set of unique variables for building lists. Unfortunately, the variables are not exportable.
LinkedIn Sales Navigator offers a set of unique variables for building lists.

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.

Mmojo Data Marketplace Launched (Part II)

 

 

Mmojo offers segmentation analysis and look-a-like prospecting.
Mmojo offers segmentation analysis and look-a-like prospecting.

(Part II of my coverage of the Mmojo launch.  Yesterday I covered Mmojo’s enrichment capabilities and tomorrow I will cover pricing and data vendors)

Mmojo’s prospecting module supports both traditional prospecting and ABM list cloning.  The Build a List User Interface is straightforward with filtering by

  • Location: State, City, ZIP
  • Company Name
  • Company Attribute: Public/Private/Government, HQ/Sub/Branch
  • Size: Revenue, Employees
  • Technology
  • Industry: Industry Keyword, SIC
  • Contacts: Function, Level, Keyword
  • Indicators: Home Office, Woman Led, Minority Led, Franchise

Type-ahead suggestions help with quickly entering cities. technologies, industry, and job functions/levels.

Missing geographic selects included counties, MSAs, ZIP Ranges, Email and Direct Dial Availability.  The inability to easily refine by location may be a hindrance to SMB sales.

The Industry selects are by keyword and SIC code, but the keyword search which supports typeahead suggest is sluggish. NAICS codes are coming next month.

Mmojo offers a matrix for quickly selecting job functions and levels.
Mmojo offers a matrix for quickly selecting job functions and levels.

A nice feature is the ability to quickly select contacts by function (Sales, Marketing, Engineering, IT/IS, HR, Finance, Operations, Planning) and Level by clicking or dragging the clicked mouse across a grid (see image on right).  Users can also enter keywords, but the list was mostly high-level titles and general functions.  Missing were key roles such as purchasing, sales operations, accounts receivable, accounts payable, security (except CISO), and compliance (except CCO).

Another welcome feature is the ability to save multi-variable filters allowing marketers to store territories or industry segments for quick recall.

Previously uploaded or built lists may be used for list suppression (e.g. exclude current customers and prospects) or as a constraint list (e.g. subsetting of a current list for targeting).  The system also maintains a Master List for this purpose.

When prospecting, marketers can grab a random subset of the list for a campaign or for forwarding directly to sales reps.

Lists can also be used to find mMore-like-these cloned companies.  The peer feature allows users to define the relevant variables and weights to be assigned to them.  Thus, a regional tradeshow list can be used as a seed file for additional prospects, but with the location variables relaxed; additional variables (e.g. deployed technology, growth indicators, corporate attributes) can then be assigned corresponding weights.  This feature is easily managed via a drag-and-drop tool and visual indicators.  As a segmentation analysis is also displayed, marketers can analyze the seed file as they adjust the selection criteria and weights.

I had one significant concern: the workflow from building a list to viewing it is not clear and is likely to frustrate trialers and new users.  Nevertheless, the user interface is otherwise straightforward and the dynamic segmentation (see left side of top image) is beautifully rendered and informative.


Part III discusses Mmojo content and pricing.

Sigstr Pulse Released

Sigstr recently announced the launch of its new relationship marketing platform and Sigstr Pulse application.  The new cloud offering analyzes email and calendar patterns to determine the strength of relationships between employees and prospects.  Instead of determining engagement as clickthroughs and web visits, Sigstr Pulse determines relationship strength based upon employee interactions with prospects.  Data is collected passively with sales reps not required to take any action.

According to Sigstr, “Revenue lags relationships. When you understand the quality of relationships, marketers can provide better air coverage and sales can forecast better.”

Sigstr calls out relationships between employees, accounts, contacts, and location; scores the strength of those relationships; assesses relationship strength over time; and helps identify warm introductions.  As a relationship marketing platform, Sigstr visualizes the relationships with key accounts and determines “which contacts you know best and which you need to know better.”

Sigstr argues that corporate inboxes and calendars are the best source for measuring relationships.  Relationships “live and grow in the inbox,” said Sigstr CEO Bryan Wade.

“Relationships are the lifeblood of every business, and no other system tracks who has relationships with whom better than a corporate email system. Sigstr Pulse allows marketers to effortlessly solve a problem everyone knows they have, making it easy to understand your organization’s complex web of relationships and take action on them. One practical example is in event marketing, as brands can send invitations to potential attendees based on the hierarchy of relationships within an organization,” said Wade. “Our platform is already in the email flow of hundreds of thousands of employees at some of the world’s largest brands, which means they can flip a switch to turn on relationship marketing via Sigstr Pulse. As we’re marketing in the era of GDPR, tapping into coworkers’ existing business relationships means less cold calling and more productive marketing.”

.Sigstr Location Intelligence analyzes the strength of connections at the metro level.
Sigstr Location Intelligence analyzes the strength of connections at the metro level.

Sigstr provides location-based intelligence to help identify where contacts are located.  This intelligence assists with on-site meeting planning, territory assignment, and assessing relationship strength at the location level.  Location-based intelligence can also be employed for event planning and marketing.

Sigstr evaluates relationship strength based upon the frequency, recency, and directionality of communications along with the acceptance of calendar invites. Users are able to build targeted lists, identify strong relationships with the company for referrals, and evaluate how relationships are strengthening or atrophying at ABM accounts.

“Sigstr has expanded the opportunity for marketing and sales teams by allowing them to make the person-to-person connections they need through existing relationships within the organization.  Email is at the center of nearly every professional’s daily workflow, and now they can use those interactions to build their business beyond just the conversations they’re having.”

  • Matt Heinz, President of Heinz Marketing

Sigstr does not yet have the functionality to exclude specific individuals or departments from your relationship data, but there are controls that manage which inboxes are integrated with Sigstr Pulse. Users cannot yet block access to relationships for teams involved in confidential communications such as litigation, M&A, and partnerships. Likewise, individuals cannot opt out if they wish to retain control over their relationships. As this is a V1 release, it is likely that their customers will demand such controls to be added.

Sigstr does have GDPR controls in place to modify or delete specific users, if users wish to remove their personal information.

Sigstr Pulse supports a Chrome Connector which provides on demand company and contact relationship insights while browsing the web.

Sigstr Chrome Connector.
Sigstr Chrome Connector.

Sigstr Pulse pricing is based on number of users (logging into the application and downloading the Chrome extension) and email volume.

Sigstr also offers an email signature marketing application which provides custom messaging and banners within employee email signature blocks.

DiscoverOrg: Next Generation OppAlerts

DiscoverOrg announced the next generation of its OppAlerts intent-driven technology intelligence service.  The premium service now delivers ten-times as many OppAlerts as before and integrates the alerts into its Build-a List-prospecting.  Only surging companies with Bombora Surge scores of at least 75 are flagged.

Surge scores are early indicators of intent to purchase based upon B2B media site activity.  A 75 signifies companies in the top five to ten percent of interest in a topic as compared to their baseline level of interest in that topic.  As much of the buyers’ journey takes place before purchasers contact a firm, reaching out to prospects during the early stages of the journey provides sales reps with an early movers’ advantage.

“The holy grail of the B2B marketing and sales world is to know when customers are actively researching your product or service,” said DiscoverOrg CEO Henry Schuck. “The DiscoverOrg – Bombora partnership allows our customers to know specifically what their prospects are researching and then which decision-makers to connect with, all in one place.”

The OppAlerts Surge score view allows users to see other topics currently surging at an account.
The OppAlerts Surge score view allows users to see other topics currently surging at an account.

DiscoverOrg switched from the Bombora firehose API, which delivered bulk raw data, to Bombora’s processed surge feed.  The upgraded service allows DiscoverOrg users 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.

Marketers can load a ListMatch file and have it immediately enriched with OppAlerts Surge scores by selected topics.  They can then filter by topic, review trends, and assess week-over-week changes in scores.  As the list is loaded into their prospecting engine, marketers can further refine the list by firmographics, technographics, biographics, and recent Scoops (sales triggers). DiscoverOrg has mapped all 4,100 topics to related job functions, allowing sales and marketing reps to quickly build targeted contact lists most likely to be interested in surging topics at key accounts.

OppAlerts identifies the contacts most likely to be buyers of products related to the intent topic.
OppAlerts identifies the contacts most likely to be buyers of products related to the intent topic.

The OppAlerts Build a List view displays current and historical intent data by company.  Users see the week-by-week score changes along with other surging topics at companies.  Lists may be saved for ongoing monitoring within the platform or via a weekly alert.  Thus, sales reps can monitor their ABM accounts and place calls when intent spikes at them.

The email alert highlights New OppAlerts, Biggest Gains, and OppAlerts by Topic.

Bombora OppAlerts are delivered to sales reps for stored alert lists.
Bombora OppAlerts are delivered to sales reps for stored alert lists.

“Bombora is the only provider of Company Surge data. Combining our insights about which businesses are more actively researching specific products and services with DiscoverOrg’s best-in-class firmographic and contact data brings the most actionable form of Intent data to B2B sales teams,” said Erik Matlick, Bombora Founder and CEO.

Pricing was not released, but the service is sold in both light and unlimited tiers.  Light tiers provide up to 100 surging companies per topic per month for 12, 25, or 50 topics.  Joint subscribers only pay a small fee for delivery of Bombora data from within DiscoverOrg.

DiscoverOrg has been working to build out its datasets.  They now cover 3.7 million contacts across 150,000 companies.

TechTarget Scoops up Oceanos Marketing

TechTarget LogoTucked into the tail end of TechTarget’s earnings release last week was notice that they had acquired Oceanos Marketing, a contact data management company.  Both firms are based in the Boston suburbs.  Oceanos brings “data quality and data management expertise that will help us improve our offerings and deliver better results to our customers.”

Oceanos began as a list broker in 2002, but has since evolved into a B2B contact aggregator and data refinery.  The firm aggregates 97 million active US contact records (as of August 2017) and retains millions of inactive names and emails to assist with hygiene.  Data is aggregated from over a dozen vendors and includes social data from FullContact and Pipl.  Oceanos provides data enrichment, TAM analysis, net-new contacts, and a set of data specialists to assist with projects.

TechTarget manages a smaller set of 18 million subscriber profiles, 16 million of which are technology professionals.  The Oceanos acquisition should allow TechTarget to improve both the quality of their subscriber dataset and expand coverage into non-technology positions.  As technology purchase decision making has expanded beyond tech titles, Oceanos provides significant lift into other job functions.  Oceanos contacts are mapped to 12 Job Functions, 109 Sub-functions, and 7 Job Levels.

Oceanos President Brian P. Hession identified their differentiators as their unique blend of technology, professional services, and data quality. With data quality being critical to ABM sales and marketing initiatives, the inclusion of real world project fulfillment through their program specialists provides Oceanos with data quality insights that are used to continuously inform and enhance the data quality processes. “We apply both technology and real-world insights to ensure the highest quality of data before we are releasing it. We are incorporating a continuous stream of data quality insights into our code to address the many nuances that a program specialist encounters manually on a dataset,” said Hession last summer.  “The way that Oceanos is going to be successful in the future is if we can assemble an internal contact database that is of the highest quality in the industry.  So there’s been a lot of focus on putting models on top of our contact data.”

“Social data plays a role in our data hygiene process and serves as a ‘signal’ within both our Data Quality Score (DQS) and ABM Usability Score. The social information is sourced from reputable partners,” said Hession.  “Oceanos does not scrape contacts across LinkedIn or, in that vein, any social media site. All of our contact records originate from carefully selected third party data providers.”

The acquisition cost was not announced but was deemed “non-material.”  Oceanos 2017 revenue was around $5 million.