While there is a commonly cited statistic about contact data decaying at a 2.1% rate per month, the nature of this decay has been less reported. Predictive Analytics company Radius conducted a study of 10,000 businesses and assessed the rate of decay over three months. Data quality was assessed by external vendors in May and August 2016. The Move or Unreachable value of 27% is similar to the often cited annual decay rate of 25% for contacts.
Radius published only three month decay rates, but I annualized the data using a four-period compounding formula.
Radius three-month data decay rates with imputed annual rates calculated by GZ Consulting.
One statistic that I did not annualize is the “Emails become Invalid” rate. If 7.6% of contacts are not reachable after three months, then why are only 2.5% of emails becoming invalid? There are several reasons: First, approximately 8% of companies set their mail servers to not send bounce messages (or 0.6% of the three-month spread). Secondly, most companies do not immediately turn off email messages when a person leaves the firm. They generally forward the emails for a period of time to an administrative assistant or the individual who has assumed the departed person’s role. This tends to be a temporary situation, but it explains the 5% gap between the two rates. As one would expect companies to eventually decommission old emails, the annual rate of emails becoming valid should be closer to 25% than the non-displayed CAGR rate of 9.7%.
Radius is looking to address the decay problem in its database via leveraging their clients’ second-party data to obtain network effects for augmenting and updating their file. Customers opt into the network with their data immediately anonymized and aggregated, “providing additional points of validation and verification.” Customer contributions now cover 70% of the businesses in Radius’ Business Graph spanning one billion interactions.
Zoominfo has employed a similar model over the past few years for building out their contact file. Their Community network has lifted their coverage of active US B2B contacts to 80 million.
Radius claims that the network improves the accuracy, comprehensiveness, and freshness of their data. For example, phone connect rates improve from 84% to 93% when there are at least five data validation points. Likewise, physical address accuracy improves from 85% to 96% when there are at least five validation points.
The comprehensiveness of firmographic data also improves with additional members. Without the customer network, only 64% of records had full firmographic or contact attributes. The population of comprehensive records rises to 81% with fifty network members.
Finally, Radius claims it’s network is “up to 20 times faster” at updating the Business Graph “than with traditional, manual methods of data collection and validation.”
“Network effects have long been a driver of business value and innovation across many industries, particularly for B2C companies,” said Radius CEO Darian Shirazi. “At Radius we are pushing the envelope on what B2B companies can come to expect from data. Now, leveraging customer network effects opens the door to further transform B2B data and develop new marketing innovations. By tapping into our predictive expertise and already robust data set, customer network effects can help marketers make smarter, faster decisions that drive revenue and growth.”
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.
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 alerts widget.
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 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.”
Crunchbase, which has long offered a free database of PE/VC funded companies, is launching a subscription service called Crunchbase Pro. The new service helps users “discover new companies, people, and deals based on highly customizable search.” The new service was unveiled at TechCrunch Disrupt a few weeks ago.
Crunchbase was spun off of TechCrunch last year and now has a staff of sixty following a Series A round. The firm has trebled its revenue over the past year led by database extract revenue. They have also seen a ten-fold increase in customers.
Crunchbase CEO Jager McConnell set a high objective for his firm. “If we can become the LinkedIn for companies or the Facebook for companies and help companies connect with one another, I think that is a really interesting challenge that can take us into the long term.”
Crunchbase Pro supports an active homepage with Crunchbase editorial content, saved lists and searches, and trend data.
The service lists core five capabilities:
Identify prospective partners, customers, investors or investments
Quickly see what matters most with Crunchbase Rank and Trending Score
Conduct faster, deeper due diligence on new business deals
Receive email alerts when there is activity that users care about
Drill into search results to see the interconnections between entities as well as get quick analysis of market trends
Features include “multi-join dynamic searches” (a techie way of saying “list building with immediate results”), custom lists, shareable searches and lists, and CSV export. Alerts are provided for lists, saved searches, and user defined topics.
Multi-join dynamic searches support firmographics, biographics, and funding selects.
According to TechCrunch, the Trend Score for companies and VC firms “uses metrics like size of round, date of last financing, and profile page views to produce a ranking of these entities that changes with time.”
Crunchbase now covers a half million companies and 2,700 VC firms. Data is maintained by a team of editors with updates provided to Crunchbase by their member community. The database also benefits from VC firm updates and machine learning tools which search for anomalous information.
The new service is free of advertising and available at an introductory price of $29 per month (billed annually). Next month the price rises to $49 per month. The pricing is aggressive with respect to sales intelligence vendors which generally run in the $100 to $150 per month range prior to volume discounts and well below the pricing of their PE/VC database competitors such as CB Insights, DataFox, and Mattermark.
John Mannes lauded the Pro service’s speed and usability in TechCrunch, “Side by side with comparable platforms like PitchBook, CB Insights, and Mattermark the new CrunchBase Pro is fast and simple. Nearly every task can be done from the main page and there is little to no lag, even on complex search queries. The new colorful design, taking a page from Google’s material design, is a huge improvement on its dimly lit predecessor.”
Pro is a standalone service, but Crunchbase plans on CRM connectors and integrating external data sources such as SimilarWeb, Glassdoor, Apptopia, Enigma, and Product Hunt. The firm gives the example of a job hunter searching for Glassdoor jobs using a combination of Crunchbase and Glassdoor data. Crunchbase also has search intent data for marketers on their roadmap. Intent data will provide “visibility into who has searched for their company or competitors.”
Clients include Bain & Company, Citibank, Deloitte and Microsoft.
DiscoverOrg provides Org Charts which uncover the reporting structure of organizations. Information includes emails, direct dials, titles, and headshots.
DiscoverOrg is rolling out a trio of new datasets for Sales, HR, and Executive Management. Sales and HR join DiscoverOrg’s line of functional datasets which also cover Technology, Marketing, Finance, and Product Management (the awkwardly named TEDD dataset of product managers which was launched in Q1). The Executive Management dataset provides coverage of C-level contact and biographic information at Fortune 1000 companies.
A 2015 DiscoverOrg study found that 25% of C-level executives cannot be found in LinkedIn. Furthermore, those executives do not publish their contact information in LinkedIn, making it difficult to reach them absent a direct referral. The CxO dataset helps end-run gatekeepers with direct dials and emails while DiscoverOrg triggers and OppAlerts provide a reason to call.
“As technology and service budgets increase in departments like Sales, HR, and Executive Management, the number of vendors serving and selling into those departments has also increased. We recognized a gap in the market to provide highly accurate, verified data for companies seeking to reach the decision-makers in those departments and deep intelligence for effective engagement. These datasets fill that gap.”
– Henry Schuck, CEO, DiscoverOrg
Back in 2011, Gartner forecast that the CMO’s budget would exceed the CIOs by 2017. At the time, the statement made for good copy as it represented a significant shift in organizational power with budgets migrating away from IT to other departments (and from capital to operating budgets). However, the growth of MarTech and cloud computing have demonstrated the prescience of this forecast. While DiscoverOrg continues to be sold mostly to technology vendors, the addition of non-technical buyers in their various datasets is further evidence of this trend towards departmental technology licensing.
With 150 dedicated researchers, DiscoverOrg has increased its database size by 150% this year. All of the new datasets are guaranteed to meet DiscoverOrg’s 95% accuracy SLA and are subject to a ninety-day refresh cycle. Along with direct contact information (98% email and 96% direct dial fill rates), DiscoverOrg maintains deep biographies which include responsibilities, education and work histories, social media links, and reporting structures (org charts).
