Hybrid Engagement Platforms

Cognism Intelligence within Salesforce. Hybrid Engagement Platforms Continuously update CRMs and MAPs.

The market is beginning to evolve a set of hybrid engagement vendors that deliver a broad set of sales and marketing services.  The boundary between sales and marketing is quickly crumbling.  Hybrid engagement services manage both data and workflows.  Features include

Future functionality will include Next Best Actions, Embedded 1:1 Video, SNAP (Sales Navigator) Integrations, and Programmatic Advertising.

No vendor provides all of these services and some provide them as separate offerings, but firms such as Dun & Bradstreet, Zoominfo, Infogroup (Salesgenie), Lead411, LinkedIn Sales Navigator, and Cognism have all taken steps over the past two years to meet the emerging requirements of the CRO.

For the moment, I’m calling these emerging offerings Hybrid Engage Platforms, but that is a placeholder name as the market evolves.

Sparklane Predictive Account Scoring

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.  

Predict 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.

Sales Reps are shown list segmentation while reviewing individual leads.  Along with business descriptions and firmographics, reps see fit and need scores.  When reps flag a lead as interesting or not interesting, the decision is fed back into the ICP model.

Sparklane 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

Sparklane also supports multi-product line upsell and cross-sell models.

“Unfortunately, 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%.

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.

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.

What Is Intent Data?

Bombora Intent Data Collection Model
Bombora Intent Data Collection Model

I am beginning a monthly series entitled What Is where I provide an overview of one of the underlying sales and marketing intelligence technologies or processes being deployed at B2B firms.  I will begin with Intent Data.

Intent Data is one of the three informational elements of B2B Lead scoring (the other two are Fit and Opportunity).  Intent data consists of first, second, and third-party elements and identifies when companies are actively researching specific product categories.  First-party data is captured in your marketing automation systems and web logs.  Typical first-party intent data includes

  • Web Logs
  • Webform Submissions
  • Email Clicks
  • Downloads
  • Page Views
  • Webinar Attendance
  • Trade Show Booth Visits

In short, if somebody is viewing your website, reading your collateral, meeting with you at a tradeshow booth, or attending your webinars, then he or she is displaying purchase intent.  Of course, not everybody doing so is a potential purchaser, but a high percentage of individuals digitally interacting with your firm are somewhere in the buyer’s journey for your products and services.

“The case for intent data is clear. If only 3 percent of the potential buyers for any given product or service are in the market at any given time (while 40 percent are poised to begin and 56 percent aren’t interested), identifying and focusing on those buyers, and those close behind them, is the key to efficiency and effectiveness in revenue growth. That’s been the Holy Grail of marketing and sales for years. After all, how many times have you heard a sales rep say, ‘If I’m sitting at the table, I win more than my fair share of deals. Just get me to the table!’


That’s the promise of intent data. And practice shows it’s more than just a theory. Fifty-percent increase in close rates and an 82 percent reduction in sell-cycle have been attained.”

Buying Guide: From the Black Box to Revenue Metrics – Translating Buzz into Results,” IntentData.io.

Unfortunately, intent data is often anonymous.  Unless the individual submits a web form, you are most likely limited to an IP address.  As B2B visitors are usually accessing your platform from a corporate IP address, it is possible to tie the IP address to the company and at least associate the activity with a company.  Companies such as DemandBase, Bombora, KickFire, Clearbit, IntentData.io, and Dun & Bradstreet offer Visitor Intelligence services to map IP addresses to companies.  Along with the company name, they enrich the visitor intelligence with firmographics such as location, size, and industry. Some vendors include technographics as well.

Real-time visitor intelligence can assist with the user experience. By providing immediate firmographics, websites can be immediately customized based upon size, location, or industry.

As visitor intelligence is beginning to feed chatbots, it is possible to prioritize customer support and sales queries. As bots become more intelligent, they will digest the firmographics and customize the conversation. Likewise, ABM customers and prospects can be given priority over non-targeted prospects. If these teams are verticalized, chats can be routed to specialized teams.

External third-party intent data is provided by vendors such as Bombora, The Big Willow, and True Influence.  External intent data is gathered from B2B Media websites that evaluate topics of interest across their network and determine which topics are of interest to companies.  Interest is gauged by articles viewed, white papers downloaded, searches performed, case studies read, etc.  Generally, each company is baselined by topic with interest determined with respect to the baseline.  A surge of interest takes place when short-term interest in a topic is well above the baseline for the company.  Intent data is generally delivered as a numeric score by topic with companies licensing the topics of interest.  As intent is determined at the corporate level, it works best in lead scoring. One limitation of third-party data is you don’t know which individuals are researching specific topics.

TechTarget Priority Engine provides technology specific second-party intent at the individual level along with contact information, buying stage (early or late based upon content viewed and downloaded), and key influencers (companies of interest).  TechTarget is focused on Technology topics across its 140 media sites.  TechTarget is considered second-party intelligence because it owns the content directly and contacts have opted in.  It also offers first-party intent data through KickFire

G2.com (FKA G2Crowd) is another well-known source of second-party intent data. G2.com is a technology review site, so site traffic is highly associated with company and product research.


Additional Resources: