InsideView rolled out a pair of enhancements to its CRM Refresh product. Along with Accounts, Refresh now cleanses and enriches Contact records, matching both record types against its reference database of 13 million companies and 33 million contacts.
Refresh also supports email validation, an important function for maintaining data quality. Both matched and unmatched records are processed on a semi-annual basis. Email validation is performed by StrikeIron, an Informatica company.
The Refresh dashboard (on right) provides match and field-level fill rates for both account and contact records.
“Valid email addresses lower bounce rates and increase deliverability of marketing campaigns, resulting in greater response rates and higher ABM program success,” wrote the Customer Success Team to its customers. “Successful sales outreach also depends on accurate contact information, including whether anexisting contact has moved on to another job.”
“Employees just don’t stay in one job at one company any more, and keeping up with all that job shifting is a nightmare of CRM management. Today we’re giving sales and marketing ops one more weapon in the battle against stale CRM data. Other cleansing solutions only clean the contacts that exist in their systems, but email validation allows InsideView Refresh to add value to any contact with an email address.”
Adam Perry, InsideView director of product management
Pricing is available both on an à la carte volume basis and a seat-basedmodel similar to Data.com. The idea is to provide an “easy switch” fromData.com Clean and Prospector to InsideView. “We match allcapabilities and price to make it very easy for customers to switch,” said VPof Product and Solution Marketing Joe Andrews. “We’ve seen a significant growth in demand for this since it’s become more generally known that Data.comis being sunset.”
Few firms have integrated email validation into their cloud or CRM hygiene offerings, leaving firms with bad contact records which cannot be matched against reference datasets. Products such as D&B Optimizer, InsideView Refresh, and ReachForce SmartSuite are the exception, helping improve delivery rates and email sender scores by verifying emails, even for unmatched records.
Refresh is available for both Salesforce and Microsoft Dynamics.
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.
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.
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.
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.
This is part III of my Mmojo coverage. Part 1 covered data enrichment and part II covered prospecting. My final blog covers Mmojo’s data partners and pricing model.
Both prospecting and uploaded lists may be appended via the Mmojo data marketplace. While basic company firmographics are included with the subscription, additional data sets may be appended, some for a fee:
Contacts: Contact Function, Level, Title, Email, Direct Dial, Social Links. Licensed from multiple partners. Only Stirista has been disclosed so far.
Premium data set descriptions are provided which include the list coverage rate, update frequency, refresh period (how long licensed without paying for the record again), price per record, fields, and column definitions.
Unlike other firms which treat their company identifiers as proprietary, Mmojo will be open sourcing their ExC company identifiers. Currently, Dun & Bradstreet D-U-N-S Numbers serve as the de facto global company identifier, but Mmojo will be challenging that status next year when they roll out international company profiles with open sourced ids.
The ExC numbers also support list appends and merging.
“Once appended, users can view their contacts and associated contact analytics. The analytics enable Mmojo users to detect gaps by showing total number of contacts, percentage of companies with contacts, and the distribution of contacts by function and title, providing key data intelligence to B2B and SMB sales and marketing organizations.”
CEO Hank Weghorst
Members of the Austin-based Mmojo team include several former members of the Avention product team including CEO Hank Weghorst, Chief Data Officer Brad Palmer, and CTO Ray Renteria. While there are some broad stroke similarities between the platforms, Avention never offered a data mart service.
Mmojo does not yet provide marketing automation or CRM connectors, so marketers must upload and download CSV files between Mmojo and these other platforms.
Mmojo is priced at $95 per month with additional charges for premium datasets. The base service includes support for up to 250,000 unique companies under management, basic company and contact enrichment, and list prospecting. Credit card and ACH billing are supported. Premium data may be purchased in blocks of credits as follows:
Pricing varies by record type. For example, Mmojo contacts are priced at 10 credits per record, but Aberdeen technographics are priced at 16 credits per record. Thus, Aberdeen appends are priced between $0.112 and $0.16 per record.
Contracts may be canceled at any point.
Mmojo tracks which records have been previously downloaded and does not charge again for a record if it is being downloaded within a refresh window (six months for most vendors). Users are only charged for premium data downloads.
An Enterprise service option is available for firms requiring multiple seats, more than 250,000 managed records, or custom configurations.
Mmojo is offering free ten-day trials. Trialers have view-only access to the tool and do not need to provide payment details during the trial. When lists are shared with non-users, they are also eligible for ten day trials.
The service includes a set of context sensitive help tools and videos. A customer forum is also available for asking questions.
This is one of the most mature product launches I have seen. The service includes a broad set of functionality, clean user interface, deep content partnerships, complete help and training tools, and a full press page. When discussing the product pre-launch with Weghorst, there was a clear product positioning and defined target market segment. The service also offers unique product pricing (hybrid subscription with premium data sets) and business models.
A new data marketplace, called Mmojo, was launched late last month. The service provides cloud based prospecting, hygiene, segmentation analysis, look-a-likes, and data enrichment for B2B marketers. CEO Hank Weghorst sees the SMB market as Mmojo’s sweet spot. Mmojo describes itself as “the B2B data marketing data everything application.”
“Excellence in B2B Marketing depends on data, particularly in today’s world where data is everywhere,” explains the firm on its website. “The problem is you have to do all the work: find the data, license the data, cross-reference the data, analyze the data, and maintain the data. Your Marketing Automation and CRM systems are not designed to do this. In fact, they REQUIRE data to feed them and make them effective. What if you had an easy-to-use system that did all that work for you? Mmojo does all of this for your existing data and for new data to drive your marketing programs.”
The Mmojo database spans 20 million US companies and 80 million contacts with plans to add international data in 2019. Contacts include titles, job functions, emails, direct dial phones, and social handles. Company intelligence includes firmographics, Aberdeen technographics (premium dataset), M&A heat scores, and public company financials and ratios.
“It’s been proven that the use of high-quality data will drive better marketing results. We built Mmojo with the sole purpose of providing simple and affordable access to this valuable data commodity,” commented Weghorst. “This approach, executed using our partner relationships and most importantly our state of the art technology, has enabled us to change the game.”
Data matching is performed against company, domain, phone, IP address, email, and additional variables as selected by the Mmojo AI match logic. Matching is done automatically so users do not need to create a field mapping table between Mmojo and their source file; however, users can remap fields if Mmojo selected the wrong field or there are multiple similar columns (e.g. shipping and billing addresses). The match results list includes the match score and Mmojo ExC company identifier. Users can click on a match score to see additional details about the match. They can also quickly modify the match threshold and see how many records meet the adjusted threshold level.
Once a list is matched, the user can click on the List Analytics tab and view segmentation (state, revenue, employees, top industries) along with match analytics. The “Sky Profile,” a trademarked feature, represents the “absolute centroid” of the list by revenue, employees, and industry. The Sky Profile should be viewed as a typical record.
A List Details tab shows the field fill rates of the original file and the matched Mmojo field.
Tucked 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.
Data Orchestration vendor Openprise expanded its Data Marketplace with the addition of seven new vendors: Dun & Bradstreet, Oceanos, DiscoverOrg, KickFire, Acxiom, Cognism and People Data Labs. The Openprise Data Marketplace is a third-party data mart which assists with “onboarding, ingesting and normalizing data” into major platforms such as Salesforce, Marketo, Eloqua, Microsoft Dynamics 365.
“Our customers benefit from having access to accurate and complete B2B marketing data – from verified account and contact data to organization charts to intent signals and buying scoops,” said Katie Bullard, DiscoverOrg Chief Growth Officer. “The depth of our data gives sales and marketers a 360-degree view of target accounts and contacts, and our integrations ensure that data is always fresh, complete and up-to-date.”
“Openprise users can now incorporate Oceanos‘ contact hygiene and provisioning solutions directly within their automated processes to improve their demand generation and Account-Based Marketing initiatives,” said Oceanos’ CEO Brian P. Hession. “Our API wraps five leading hygiene vendors into a single solution, further amplifying the benefits marketers realize.”
Openprise assists with cleaning and normalizing customer data, assesses match rates, recommends new data providers, coordinates data processing, and unifies data across systems.
John Donlon, Senior Director of Marketing Operations Strategies at SiriusDecisions, called acquiring and standardizing high quality prospect data as “one of the biggest challenges marketers face” and “critical” to implementing the SiriusDecisions Demand Unit Waterfall. “Any technology that can facilitate that will give organizations a huge leg up not just in understanding their target audience, but in driving meaningful interactions throughout the buyer’s journey.”
Openprise claims that no single data vendor can provide superior data than their platform. They also warned that a multiple vendor strategy is often ineffective due to industry content white labeling, resulting in little incremental value. “With our Multi-Vendor Enrichment Strategy Service, our customers know quantitatively how each incremental vendor’s data will improve their database and they have the processes in place to easily integrate new data in a way that conforms with their existing data policies.”
The Openprise platform supports data onboarding, data cleansing and enrichment, data unification across systems, and data delivery.