Radius-Leadspace Merger Aborted

Radius and Leadspace quietly called off their merger back in August, agreeing not to point fingers at each other and continue supporting joint customers.

“At the end of the day, private to private mergers are incredibly hard to pull off. In this case, despite all of the best intentions in the world, we could not get to something that would work for all sides,” Leadspace CEO Doug Bewsher told Demand Gen Report. “We are excited to see the evolution and clarity around the whole customer data platform really starting to define itself in B2B.”

Bewsher remains bullish about Leadspace and the Audience Management space:

Leadspace pioneered this space when we launched our Audience Management Platform two years ago. We continue to see great success with customers as they both simplify their data management processes and bring additional data driven insights and recommendations into their activities. Whether driving an ABM strategy, a content marketing / inbound lead driven strategy, or outbound prospecting, the right data and insights deployed into systems of engagement is typically the first step in any company’s success…

We look forward to working with you to develop, build and lead this category as we continue our mission to help B2B sales and marketing teams drive a new level of engagement, targeting and resulting revenue for their organizations.

Leadspace had a strong Q2 with its “best ever revenue growth.” New customers include SAP and Splunk. Growth was driven by the increasing recognition that B2B firms require a data-agnostic Customer Data Platform “which brings together many different data sources at the company- and individual-level, drives recommendations, insights and a single source of truth through AI, and then has a single point of integration into multiple executions systems (CRM, MAP, Ads etc),” said Bewsher.

“Radius and Leadspace agreed to continue operating independently and are now partnering to support joint customers,” said Radius. “Radius’ customer data platform is the first for B2B, and we will focus on offering enterprise companies integrated, unified and trusted data across all go-to-market systems, while Leadspace’s audience management platform will continue to equip companies with audience enrichment and analytics.”

Both firms no longer talk about predictive analytics and emphasize Customer Data Platforms. The Predictive Analytics space has been squeezed by both DaaS vendors with light scoring tools and integrated AI solutions such as Einstein.

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: “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.

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.

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 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.

MintigoAI Launched

Demand Center is a Predictive Audience product that helps marketers generate the right audience for each prospect. — Demand Center is a Predictive Audience product that helps marketers generate the right audience for each campaign.

Predictive Analytics company Mintigo unveiled its new MintigoAI service. Mintigo describes their solution suite as “a comprehensive intelligent customer engagement platform powered by AI” for mid-size and enterprise companies to “drive greater pipeline and revenue.” The “customer lifecycle solution” includes CRM and MAP connectors, ABM targeting tools, inbound lead enrichment and prioritization, upsell / cross-sell recommendations, and a new Buying Stages capability.

Buying Stages determines where accounts and leads are in their buyers’ journey. The solution assesses lead enrichment, fit, intent, and behavior data to determine customer intent and stage. Data is sourced from 1.7 billion user interactions per day spanning 13 million global companies. Buying Stages tags accounts into three categories: Target (based on fit), Awareness (based on intent), and Consideration (based on behavior). Buying Stages evaluates the “aggregate actions of leads” and weighs both anonymous web traffic and site visit activity. Mintigo also factors in firmographic, technographic, and social intelligence.

“To plan, strategize and execute B2B marketing effectively in today’s world, marketers need a high-definition view of their customers,” blogged CEO Jacob Sharma. “Using AI and predictive analytics, we built MintigoAI to mine billions of data points and identify the set of insights that make a company’s actual customers unique. These insights range from hiring patterns to technology installs to firmographic data, and much more. The result of this process is the ICP, which MintigoAI uses to identify right targets within existing marketing databases and proactively discover new high-propensity targets that display the ICP characteristics.”

Lattice Atlas Customer Data Platform

Lattice Atlas provides a unified customer view for omnichannel audience activation and personalization.

Lattice Engines announced commencement of a private beta for its Atlas Customer Data Platform (CDP). Lattice Atlas matches internal and Lattice Engines data sources, provides a single view of the customer, and supports a centralized audience platform for cross-channel creation and measurement. The formal launch is planned for the end of the year.

According to Lattice Engines, “Marketing organizations struggle to scale their Account-Based Marketing (ABM) programs because each application they deploy has its own data, segmentation, activation and measurement modules. This has led to a fractured buyer journey because banner ads, social ads, emails and sales calls communicate different messages, which creates confusion. Lattice Atlas solves this problem directly by integrating all the application data into a single place and providing the ability to manage this data, segment on it, and activate it through open APIs.”

“A CDP connects existing systems to create a unified customer view that makes ABM possible. In a world that never stops changing, the power and flexibility of a CDP will help marketers deliver on the promise of ABM. The features you need in a Customer Data Platform (CDP) will depend on your business, existing systems, and intended use. There are a few key considerations when evaluating CDP solutions for executing ABM programs, including a unification of all data sources, segment creation, campaign execution and predictions.”

David Raab, founder of the CDP Institute

Lattice contends that ABM at scale requires a CDP supporting four key attributes:

Unified Customer Data: After aggregating and consolidating customer data, a CDP must link identity, behavior, purchase history, and firmographics.
AI-driven Audiences: The CDP must not only score accounts and contacts, but identify buying committees, assess buying stage, and recommend the next-best offer.
Omnichannel Activation and Personalization: The CDP suggests highly personalized campaigns across relevant channels. The messaging must remain consistent across all of the channels.
Enterprise Grade Governance: The CDP maintains data security and privacy while complying with relevant laws such as GDPR.

Lattice Atlas aggregates client data across platforms and appends it with data from the Lattice Data Cloud. First-party content is gathered from CRM, marketing automation, web visitor logs, transaction histories, product usage details, etc. The Lattice Data Cloud enriches the customer view with firmographics, intent data, and technographics. Lattice also maintains an ABM Identity Graph which organizes customer data by account, buying center, and contact.

“Lattice Atlas was a natural evolution of our platform,” blogged VP of Products Chitrang Shah. “Since day 1, our approach has focused on being deeply integrated with each execution application and managing all data under one platform. Because of this we not only capture the largest amount of data, but also all that relevant metadata that describes it. Lattice Atlas is built on our understanding of these applications and their data to create the first CDP for enabling ABM at scale.”

Audience creation tools predict conversion likelihood, purchase window, and likely spend. Atlas also supports next-best targets and next-best actions.

Lattice Atlas connectors support Marketo, Eloqua, Salesforce, and a set of REST APIs.

Other features include GDPR opt out for campaigns and all marketing communications, engagement thresholds to prevent marketing fatigue, and lead-to-account mapping.

The initial Atlas application will be Playmaker which offers prescriptive recommendations to sales teams. “Playmaker lets them quickly identify top products to sell across all audiences and programmatically deliver those recommendations to the sales teams,” said Shah. “It also has built-in interactive dashboard to track the engagements (or lack of it) and its impact on the pipeline, enabling out-of-the-box visibility into play ROI measurements and the ways to improve it.”

“The holy grail of B2B marketing is creating 1-to-1 experiences across the entire buyer’s journey. This is why the B2B world is so interested in ABM these days. In order to craft personalized experiences at scale, our customers need a data foundation to better understand their target audiences, and an execution platform to engage those audiences in meaningful ways. With Lattice Atlas, we now enable companies to engage their buyers with 1-to-1 omnichannel experiences, making B2B marketing as personalized as B2C marketing,” said Lattice Engines CEO Shashi Upadhyay.

Lattice has over 200 customers including PayPal, Adobe, Dell, and SunTrust Bank.

Predictive Analytics Is Losing Steam as AI Becomes Prevalent across SalesTech & MarTech

On Monday, Radius Intelligence and Leadspace announced their merger and plans to become the “leader in B2B data intelligence.” The firm, which will continue under the Radius brand, is no longer emphasizing predictive analytics.

The predictive analytics market has failed to develop as a standalone segment. According to Radius Chairman Darian Shirazi, the total investment in the space was over $600 million. However, Gartner sized the market at $100 million to $150 million in 2016 revenue, suggesting that the promise of predictive analytics was developing slowly.

In his just released 2018 MarTech Landscape, Scott Brinker removed Predictive Analytics as a segment as machine learning is being integrated broadly across marketing products.

For B2B predictive tools to work, they require high quality reference data sets for initial and ongoing enrichment, but the predictive analytics companies black-boxed their data sourcing. Radius was one of the few exception to this opacity as they were transparent about their data acquisition model (web crawling combined with a customer contributed data model), but most of the other firms have been vague about their data models.

The predictive analytics companies were also slow to offer ABM tools and similar company and contact recommendations. These features are now commonly offered by both predictive analytics companies and sales and marketing intelligence firms such as D&B Hoovers, InsideView, DiscoverOrg, and Zoominfo. What’s more, the sales and marketing intelligence firms have all developed light predictive scoring or ranking tools. While none of these firms approaches Radius or Leadspace in predictive capabilities, they all provide company and contact insights for sales reps, ABM tools for sales and marketing, and integrated data enrichment processes.

The predictive analytics firms also initially black boxed their models, preferring to hide complexity. They have since become more transparent and begun displaying the top reasons for recommendations. However, Salesforce Einstein has provided similar functionality with predictive scores and insights.

Todd Berkowitz of Gartner summed up the situation well.

I’ve been covering the market for B2B predictive marketing analytics for almost four years. A few years ago, predictive lead scoring was all the rage. Then it became about fit and intent models for demand generation and prospecting. Then these tools were used for selecting accounts for large-scale ABM programs. But in the end, the standalone market for these applications never fully reached its potential. Many of the original vendors got acquired for their technology (Fliptop, SalesPredict, Infer and others) and predictive scoring became a standard feature of marketing automation and SFA systems.

Just because the standalone market went away, doesn’t mean there isn’t a lot of value here. In fact, the solutions have essentially moved into two other markets (and you’ll see this reflected in our upcoming Hype Cycle reports). On one end, you have the Data Intelligence for Sales market where predictive and AI-driven solutions are competing with traditional data vendors for demand gen, prospecting, and segmentation use cases. On the other end, you have the broader ABM solutions market where these applications not only help with account selection and planning, but are moving towards engagement and orchestration.

Berkowitz predicted that one or two of the remaining predictive analytics vendors will be acquired in the next six months.

With over 6,000 MarTech companies, the market is quite fragmented. Although the MarTech sector continues to expand, there is already momentum towards consolidation as clients look for broad, integrated functionality instead of many point solutions. For example, marketing and sales departments adopting ABM need a broad set of functionality which includes

AI scoring and recommendations
Real-time, batch, and continuous company and contact enrichment
Data hygiene (e.g. de-duplication, data standardization, and verification services)
Third-party verticalized data enrichment
Programmatic marketing
Website visitor id
Lead-to-account mapping
Look-a-like company and contact prospecting
Segmentation, TAM, and pipeline analysis
CRM, MAP, and sales engagement connectors
Sales triggers
Account social media monitoring
Company and contact intelligence

At this point, nobody offers a full suite of these ABM capabilities for sales and marketing departments.

GZ Consulting

Advice for Vendors & Purchasers of Sales Intelligence Products

Tag: Predictive Analytics