TechTarget, which offers both IT media sites and technology sales and marketing intelligence, posted $31.5 million in Q2 earnings, up 18% year-over-year. Growth was driven by their Priority Engine Technology Sales Intelligence service which grew revenues 60% year-over-year. The firm noted that “revenue growth continues to be driven by our leadership position in purchase intent data and our customers’ transition to becoming data driven sales and marketing organizations.”
IT Deal Alerts revenue was up 21% to $14 million as the customer base grew from 500 to 600 clients. The firm also signed more than 40 new Priority Engine clients in the quarter (to approximately 250).
The new Priority Engine enhancements, which were released in early May, have been “well received in the market place.” New features included improvements to the user experience, a new Salesforce widget, persistent URLs, list assignments, user roles, and improved topic filtering.
Furthermore, Priority Engine is shifting the firm to a recurring revenue model with 34% of revenue now attributable to longer-term contracts. Approximately 80% of subscription revenue comes from medium and large firms and 20% from smaller firms, “typically VC-backed start-ups.” The revenue renewal rate for medium and large customers is “well over 100%,” said the firm in its earnings press release. “Those customers are finding great value in our purchase intent data and are renewing and buying more from us at high rates.”
However, TechTarget has a higher churn rate on smaller customers due to common issues inherent to smaller firms such as “changing go-to-market priorities, budget or funding reductions, personnel turnover, etc.” The issue of small customer churn exists for both their core services and longer-term contracts. To reduce churn, the firm is taking three steps: improving the ease of use of products; expanding their Customer Success team which owns customer on-boarding, training and monitoring; and building a dedicated sales team responsible for renewing and upselling. Splitting sales into hunters and farmers will allow the “existing sales team to hunt for new opportunities.”
Bombora partnered with Marketo to natively integrate its intent file into the Marketo Engagement Platform. Bombora collects anonymous company-level activity from B2B media sites and determines intent as a company surge score across 4,100 B2B topics and 2.8 million companies. The surge score indicates an uptick in baseline interest in customer-specific B2B topics.
The intent scores help identify and prioritize sales and marketing activities around prospects with active purchasing research programs. Firms with high surge scores can then be targeted for programmatic marketing, digital messaging, and sales outreach. Surge data also flag dormant accounts which are re-entering the purchasing cycle, assist with messaging around key topics, and inform lead scoring and routing decisions.
According to Bombora, only fifteen to twenty percent of companies in a target list are engaged in an active purchase cycle. Thus, understanding which companies are in market is critical to marketing effectiveness. “It arms you with insight into which of your target companies are more actively researching your products or services compared to historic baselines – indicating intent to take action,” said Bombora.
“With the Marketo Engagement Platform acting as the hub to make this understanding more actionable, there is exponential value to how Bombora’s intent data can be leveraged across an organization,” stated Bombora CEO Erik Matlick.
“As a marketer, the best buyers to engage with are the ones that are already actively researching,” said Marketo SVP of Strategy and Alliances TK Kader. “Providing intent data from Bombora to our customers creates an opportunity for them to engage with buyers who have a high propensity to buy, ultimately delivering better pipeline to sales and increasing the velocity of the sales process.”
Marketo is also offering best practices and campaign setup advice.
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 Enginesannounced 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.
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
Website visitor id
Look-a-like company and contact prospecting
Segmentation, TAM, and pipeline analysis
CRM, MAP, and sales engagement connectors
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.
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)
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.
Sales Engagement vendor Outreach is teasing a new predictive analytics capability called Amplify which leverages the history of a firm’s sequences and workflows. The firm will not be employing a black-box AI strategy but providing recommendations with explanations.
CEO Manny Medina faults deep learning strategies which lack “the ability to make inferences, such as the ability to figure out why things work” and require users to trust the recommendations without providing a basis for the suggestions.
“We believe we need to tackle this problem following general scientific principles. Hypotheses need to be testable, data should be very carefully examined to verify the quality of the data.”
Yifei Huang, Machine Learning Lead, Outreach
“When we built Amplify, we built it with the core belief in mind that, the human needs to understand why things work so that machine can understand why things work so that the machine can get better at helping the human,” said Medina.
For example, Amplify deploys natural language processing (NLP) around email responses to help identify whether responses are unsubscribes, objections, or positive. Outreach claims that their NLP classification is 92% accurate, only three points behind manual classification.
NLP will also be used to assess objection handling to identify reps who handle objections well and which ones need improvement. This feedback is then available to managers to assist with coaching.
Amplify addresses two key managerial questions: “Is my team adopting the new technology? Is the new technology delivering a measurable lift?”
Amplify will be unveiled at their May Unleash conference.