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.

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.

Radius Intelligence Merges with Leadspace

Radius graphic highlights the complementary assets of the two firms (Source: Radius Intelligence)
Radius graphic highlights the complementary assets of the two firms

On Monday, Leadspace and Radius Intelligence announced their merger.  The two firms were early entrants into the predictive analytics space, but the market for standalone predictive intelligence services has not developed as predicted.  Thus, VCs and private equity companies are sitting on large bets that have yet to pay out.

The merged company will continue under the Radius brand as the “leader in B2B data intelligence.”  Leadspace CEO Doug Bewsher will take over as the CEO of the merged firm while Radius founder Darian Shirazi will assume the role of Chairman.  The two positioned the merger as a coming together of firms with complementary assets across company (Radius) and people (Leadspace) intelligence.

“Radius and Leadspace as one company will deliver a standout go-to-market platform with the best data, artificial intelligence and integrations at its core… What’s truly exciting is that our mission remains the same. Radius will be the nucleus that powers data and intelligence across all B2B applications, channels, and users — now built on The Global Network of Record.”

  • Radius Intelligence Statement to Customers

Both Leadspace and Radius have edged closer to prospecting and data enrichment than other predictive vendors.  Leadspace has long offered contact enrichment and prospecting, even appearing in a 2015 SiriusDecisions report on Contact Data Management.  Meanwhile, Radius has built its own database of US company and contact data which it named “The Network of Record” and positioned as the “single source of truth for account data.”  The Radius database spans 18 million US companies and 25 million contacts with verified emails and direct dials.  Radius also offers digital ad targeting.

Radius is positioning itself as being at the center of B2B predictive analytics, B2B Audience Management, and B2B data management. However, several of the data solution vendors also offer advertising solutions including Infogroup and Dun & Bradstreet. (Source: Radius Intelligence)
Radius is positioning itself as being at the center of B2B predictive analytics, B2B Audience Management, and B2B data management. However, several of the data solution vendors also offer advertising solutions including Infogroup and Dun & Bradstreet. (Source: Radius Intelligence)

The new Radius will help sales and marketing teams “find the right data on the right buyers, and reach those buyers across any channel.”  Revenue teams will have access to the “industry’s most comprehensive data intelligence solution.”  Features include account and people targeting, data management, ABM execution, and integrations with Salesforce, Microsoft Dynamics, Marketo, Eloqua (Oracle Marketing), and Pardot (Salesforce).

Shirazi is positioning the company as an Einstein competitor with expanded assets to compete against Salesforce.  “We’re excited about this because it will create the largest number of customers, largest revenue base and really provide a company that is at scale in B2B data and intelligence,” said Shirazi.  “The only other major player in this space we believe is Salesforce Einstein, and we’re excited to really give them a run for their money.”

Shirazi provided the following list of planned enhancements to be rolled out over the next year:

Master Data. Master Growth

  • Extend reach and accuracy as The Network of Record unites with Leadspace’s proprietary, real-time virtual database sourcing
  • Take complete control of data governance with added data dashboard functionality
  • Enhance data matching and append for contacts, as well as lead-to-account matching features

Real Intelligence. Real Buyers

  • Strengthen targeting on individual decision makers in both the U.S. and international markets
  • Access enhanced segmentation, scoring, and insights on contacts
  • Leverage features from two effective sales intelligence tools

Scale Channels. Scale Revenue

  • Expand audience reach with the largest deterministic reach of any platform
  • Source more contacts with even higher accuracy and contactability rates
  • Connect more channels with more seamless integrations and partners 

Shirazi describes Radius as the next “backbone go-to-market platform.”  The combined assets “will enable marketing, sales, revenue ops, and customer insights teams to finally address their data gaps and conquer their targeting challenges. We will create a standout solution in a crowded, fragmented space of point-solutions where customers are forced to stitch together multiple products or change vendors every year.”

Back in 2015, Radius Intelligence had a market value of $500 million and funding of $107.6 million.  Leadspace never disclosed a valuation, but it received $59 million in funding with a $21 million Series C in December.

LinkedIn lists 100 Leadspace employees and 160 Radius employees.

The merged company has over 200 customers including Sam’s Club, Hewlett Packard, Microsoft, Comcast, MetLife, and American Express.  Both firms maintain “innovation centers” in San Francisco and Israel.



Part 2: Predictive Analytics Is Losing Steam as AI Becomes Prevalent across SalesTech & MarTech

Leadspace: Series C, AI, and ABM

LeadSpace Use Cases
LeadSpace Use Cases

Predictive Analytics and Audience Management vendor Leadspace completed its Series C.  The funding round was led by Arrowroot Capital and joined by JVP.  The $21 million round will be used “to grow our customer team in San Francisco and Denver, and our AI and data management product teams in Israel.”

The firm is assessing additional locations, including possible offices on the East Coast and Europe, “perhaps” London.

Arrowroot has taken a seat on Leadspace’s Board.  The firm wanted growth equity advisors instead of traditional VCs for Round C.  “At this point the investment is not just in the idea and the team, but also the underlying metrics and performance of the business,” said CEO Doug Bewsher.  “Once you have “Product/Market fit”, the kinds of questions investors ask are whether you are ready to scale; what are the opportunities for further growth; and apart from additional investment can we be an investment partner that can help you address these opportunities?”

Bewsher noted that marketing has been transformed over the past seven years since Leadspace was founded.  Firms are switching from tactical demand generation programs to targeted Account Based Marketing (ABM) communications.  “No longer is it OK to just send out blanket “nurture” emails to everyone and hope that will generate positive customer engagements. No longer can you rely on a single data source as the basis to know your customer. No longer is it enough for marketers to just think of leads — they need to market to accounts, and teams of people. Neither can marketers afford to ignore intelligence and information from external parties, and simply rely on the limited info they gather internally.”

Not only has the nature of B2B marketing been transformed, but “world class B2B sales and marketing organizations” need to become more like consumer companies with a deep understanding of the account at multiple levels.  Echoing Sirius Decisions, Bewsher said that B2B marketers need to “really know your customer at the account, demand unit and individual level, and then target and personalize your messaging to cut through the noise. And think customer-first.”

As an analytics company, Bewsher talks up the value of AI for sales and marketing as it begins to address specific problems and workflows:

AI is everywhere. While there is no doubt that it is going to change every corner of our life, both as private users and business people, I think we will start to move from the promise to the reality in 2018. In business-to-business sales and marketing in 2017, it was enough to say: “We have a ton of great data scientists who are working on new ways to better engage your customers.”

But in 2018 customers will look to see actual results — like the 90 percent increase in email connection rates we have seen from the deployment of AI to recommend the right way to engage a specific user. This will require a maniacal focus on specific use cases from the emerging area of AI.

One area where AI will improve revenue generation effectiveness is in ABM programs which has been limited by the human ability to consume information and the historical lack of data availability.  However, “AI is changing all this, with the ability to consume and understand unprecedented amounts of information and turn this into action at scale and in real time. So sales and marketing teams now have the opportunity to drive much more relevant and effective engagement programs for their entire potential target audience.”

According to Leadspace, they are trusted by over 130 B2B brands and seven of the top ten enterprise software companies.  Clients include Microsoft, Marketo, Oracle, and RingCentral.

 

SFDC Einstein: Once Again We’re Discussing AI

einstein-artificial-intelligence-in-business

In the late 1980’s, when my career was first beginning, I worked on a technology helpdesk for an insurance agency automation system (Aetna’s Gemini platform).  Many of the calls were routine with an easily road mapped set of resolution steps.  So the firm decided to invest in artificial intelligence (AI) and began interviewing its most seasoned experts to identify the problem resolution path.

After several months of development, an AI module would be unveiled that walked the user through problem resolution.  It was basically a set of if-then-else and case statements providing pre-coded branching logic.  Support reps started with a category and were walked through a set of questions to ask and resolution steps to convey over the phone.

The solution was expensive and lacked the ability to learn.  Thus, if new problems arose or the problem resolution changed due to new hardware or software being introduced, the rules no longer applied.

It was far from intelligent.  Heck, I’d coded a twenty-questions game in a first semester programming class that was more intelligent than the service.  At least my Q&A game had the ability to learn new questions to ask without requiring an expensive consultant.

Finally, it was only used by new hires as much of the routine steps were just that — routine.

Solutions like this quickly proved that Artificial Intelligence wasn’t intelligent and after a few years, the term AI fell from favor and returned to the realm of sci-fi killer robots.

Nearly three decades later, the term AI is once again being rolled out.  But now it does convey an impressive level of intelligence which makes our devices feel smart.  It’s why we call them smartphones.  They are able to leverage vast amounts of data and make decisions in the blink of an eye.  Whether it is asking Siri a question or having Google map the best route to a location subject to current traffic patterns and transportation mode, we expect our devices to be intelligent.

AI represents a massive change in technology. You might call it a “paradigm shift” or “disruption” or we could just stick with “massive change.” What we’re trying to say is, AI is kind of a big deal. And just like the arrival of the personal computer, cloud computing, and the mobile smartphone, AI is going to fundamentally change the way things work, forever.

AI is not killer robots. It’s killer technology.

So it was with a smile that I saw the term AI being used by Salesforce in positioning their new Einstein service.  Each year at Dreamforce, CEO Marc Benioff discusses a new underlying technology or cloud.  Most recently it has been Lighting (UI and workflows), Wave (analytics), and the Internet of Things Cloud.  At Dreamforce 2016, it is Einstein, their artificial intelligence platform to assist with sales, marketing, and service.

Salesforce presents AI simply as

Lots of data + cloud computing + good data models = smarter machines

So while much of this technology has been provided as consumer applications for over a decade, businesses have been lagging behind when the scope goes beyond a mobile app or e-commerce portal.

Shouldn’t the full transactional and service history be available to help understand past purchases, preferences, and potential cross-sell and upsell opportunities?

Wouldn’t we want it delivered no matter the touch point?

That is the type of intelligence that Einstein is looking to bring to Salesforce customers.  Einstein is “the world’s first comprehensive artificial intelligence platform for CRM. I’ve never been more excited about the innovation happening at Salesforce,”  said Benioff.

Einstein is available both programmatically (for developers) and “declaratively for non-coders,” said Benioff.  It is integrated directly into the SFDC platform and available across all of the clouds.  For example, an Einstein widget displays a set of insights identifying competitor news, recommended actions, and account intelligence.

Einstein Insights Widgets provide intelligence both programmatically for developers and data scientists and declaratively for end users.
Einstein Insights alerts widget.

 

Einstein can surface competitor mentions even if the end user hasn't trained it to do so.
Einstein Insights surfaces insights both programmatically for developers and data scientists and declaratively for end users.  It can even infer competitors from emails and deliver alerts within SFDC widgets.

 

 

 

 

 

 

Einstein builds models with no coding or initial training by users.  For example, the system is able to determine which trigger events are important to sales reps and surface news about competitors without asking “who are your competitors?”  The system also can make recommendations concerning high-scoring leads based upon both fit (firmographics, biographics) and behavior (e.g. recent viewing of a demo).

Einstein recommends actions to sales reps. In this case, it is suggesting an email requesting a meeting be setup with the VP of Sales at a high-scoring account.
Einstein recommends actions to sales reps. In this case, it is suggesting an email requesting a meeting with the VP of Sales at a high scoring lead who recently viewed a product demo on the website.

Not only does the system recommend activity, but it then offers recommended email copy including a proposed call time.

The platform is built on a series of recent acquisitions including RelateIQ (rebranded SaleforceIQ), MetaMind, Implisit, PreductionIO, and TempoAI.  The firm now has a team of 175 data scientists “stitching together this amazing platform,” said Benioff.

“The new platform will “democratize artificial intelligence” and “make every company and every employee smarter, faster and more productive,” continued Benioff.  “This is going to be a huge differentiator and growth driver going forward as it puts us well ahead of our CRM competition once again.”

The new platform infuses their sales, cloud, and marketing platforms with AI capabilities for “anyone” regardless of their role or industry.  According to Salesforce, Einstein lets employees “use clicks or code to build AI-powered apps that get smarter with every interaction.”

Einstein is positioned as having your own data scientist focused on applying AI to customer relationships.  Einstein has access to a broad set of intelligence including CRM data, email, calendar, social, ERP, and IoT to “deliver predictions and recommendations in context of what you’re trying to do. In some cases, it even automates tasks for you. So you can make smarter decisions with confidence and focus more attention on your customers at every touch point.”

Several predictive analytics companies used the launch to shout, “hey wait, we’ve already mastered AI for sales and marketing.”  LeadSpace CEO and former Salesforce CMO Doug Bewsher stated, “B2B marketers need a complete solution that works across multiple channels, in their existing marketing stack.”

“Bad data is the Achilles heel of AI,” continued Bewsher. “AI is only as good as the data available to it. Marketers who want to get the full benefit of AI need to address their data problems first, or they’ll see the same diminishing returns as with traditional marketing automation.”

Shashi Upadhyay, CEO at Lattice Engines was a bit more diplomatic in welcoming Einstein.  “After having led the market for several years, we are really excited to see the mainstream attention shifting towards AI-based solutions for marketing and sales.  The Einstein announcement from Salesforce is a great step forward, as it will serve to educate the market and signal that predictive solutions are here to stay.”

Image Credit: Salesforce.com

Gartner Cool: Radius, Everstring, SalesLoft…

SalesLoft, DemandBase, Datanyze, and Leadspace made Gartner’s “Cool Vendors in Tech Go-to-Market, 2016” list.  According to the report, “Marketing and sales enablement leaders should consider these software-as-a-service applications to complement existing CRM tool investments.”  Gartner also recognized predictive analytics firms Everstring and Radius in the data-driven marketing category.

Providers are doing a better job in responding to the changing B2B technology buying cycle and the higher expectation that buyers (both prospects and customers alike) have when they look to make a purchase. Some of this involves process and training improvement, improved messaging and positioning. But there is also a technology element, particularly as it relates to things like data, analytics, content, targeting, personalization and engagement.  And clients are increasingly leveraging the latest tools that allow them to make better, smarter decisions.

  • Gartner Research Director Todd Berkowitz

Here is what Berkowitz said was cool about the firms:

  • Datanyze – The technology tracking firm helps identify “when a particular piece of SaaS or mobile software (say from a competitor) is added and fire off alerts.” This feature helps SDRs and sales reps see “which companies are in market and engaging with them.”  Datanyze also offers a “cool” Chrome browser extension called Insider which displays firmographics and technographics from a company website, performs on demand email detection, and uploads this information to Salesforce.
  • Demandbase – The firm supports Account Based Marketing (ABM) marketing with IP-based advertising and personalization tools.  The firm delivers “a unique ‘one-two punch’ of real-time IP identification and technology that makes it possible to deliver company advertising (targeting and retargeting) and website personalization to help marketers increase awareness, drive up conversions, generate net-new and upsell/cross-sell leads from named accounts, and measure program effectiveness across the funnel.”
  • LeadSpace – The predictive analytics vendor helps customers “generate demand, enrich and prioritize accounts/leads from companies with a higher propensity to buy.”  Berkowitz also commended their “virtual data management platform that drives their models and recommendations.”
  • SalesLoft – The Account Based Sales Development (ABSD) firm assists sales development reps (SDRs) with lead qualification and prospecting.  “Their suite of templates, an integrated dialer and real-time analytics are a lot cooler for SDRs than the old way of working. And they work much better.”  Their Cadence tool helps streamline prospect communications such that “some of their customers reported more than doubling the number of successful connections, appointments, demos and sales-qualified leads (SQLs), while reducing follow-up time from leads by more than 75%.” Their new Sales Development Cloud provides prospect intelligence to SDRs from DiscoverOrg, Crystal, Owler, InsideView, Datanyze, RingLead, Sigstr, and ExecVision.
  • Everstring – A more recent entrant to the predictive analytics space (founded in July 2014), Everstring offers predictive demand generation and scoring models at both the lead and account level.  Everstring covers eleven million B2B companies and 20,000 different attributes with “rapid deployment of models across many points in the funnel.”  The firm is also a strong proponent of ABM and helps marketers identify accounts.  “EverString’s predictive account models enable marketers to identify high-potential ABM candidates and then push them to third-party ABM platforms for use,” said Berkman.
  • Radius – The predictive company was lauded for its segmentation tools which help SMBs “determine total available market, create attractive segments and identify accounts to target.”  Radius was also praised for its  “clean interface,” “data and analytics tools,” and the ability to train and validate models within one business day.  Berkman warned that Radius has faced little competition in the SMB market to date but is likely to face stiffer competition as both Radius and the market for predictive analytics solutions grow.

Berkowitz noted that these tools are all focused on making it easier for marketers and down-the line sales to make better decisions” noting that they all rely on the “heavy use” of data and analytics.

Radius provides easy to interpret segmentation, success analytics, and net-new lead prospect acquisition tools from within SFDC.
Radius provides easy to interpret segmentation, success analytics, and net-new prospect acquisition tools from within SFDC.

While many of these firms provide predictive analytics, Berkman contends that vendors will soon be offering “prescriptive analytics” that help firms decide what should be done.  Thus, analytics will shift from predictions based upon historical analysis to recommendations concerning which actions to take.  Prescriptive analytics utilizes graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.

“We have seen it [prescriptive] used a little bit on the sales analytics side already,” said Berkman. “It is likely that we will get to that stage with marketing so the marketer will know not only who is most likely to buy and what they will buy.”

For marketing, prescriptive analytics will assist with prospect identification, optimizing customer communications, and improving prospect offers.

Another trend you will find amongst the cool vendors is the heavy citation of ABM and ABSD tools amongst these vendors.  Everstring, SalesLoft, and DemandBase are all strong proponents of ABM while Leadspace recently partnered with ABM vendor Engagio.