At last month’s Growth Acceleration Summit, ZoomInfo previewed a lead scoring feature which will be available later this year. Users will build models for ideal customers and the associated scores will be displayed across the product including in lists, profiles, and enriched web leads. The goal is to “customize ZoomInfo to each and every one of you,” said CMO Hila Nir at her Product Roadmap presentation. Customization also includes routing and territory management. ZoomInfo will continue to offer tools which foster sales and marketing alignment and look to “take noise out of sales and marketing organizations.”
The company hinted at email templating and territory dashboards, but did not provide details on these future product concepts beyond conference screenshots. Email templating is most commonly found in Sales Engagement services such as Outreach, SalesLoft, and ConnectLeader.
While Zoominfo has not released financials, Garlick indicated that the firm had a strong 2017 marked by “really fast revenue growth.” The firm also added over 100 staff and 2,000 customers in the past year. He attributed the firm’s success to hard work, teamwork, sweat, and tears.
ZoomInfo pricing is a hybrid between number of seats and number of records licensed. While the firm used to be transparent about its pricing, they stopped posting such details a few years ago.
Poor data quality is a disease which slowly destroys the value of your marketing database. Quality is damaged through incomplete information, poor data entry, and data decay. A traditional response is to purchase new records, but this only provides a temporary (and expensive) respite from your data quality issues.
The data I’ve seen indicates that contacts decay at a 25 to 30 percent annual rate. This means that a prospect list that is 90 percent accurate today will be little more than 50% accurate two years later. Thus, a prospect list purchase strategy is like steroids, it makes your marketing database look healthier on the day the list is purchased, but it simply masks the growing disease within your database. Treating one or two symptoms does not address the underlying problem — a lack of a broad, continuous data strategy.
However, if you take a holistic view around data quality which includes continuous DaaS validation, ABM look-a-likes, web form enrichment, lead-to-account mapping, duplicate management, data standardization, and reference database appends, you will have a healthy database that ensures your MAP and CRM platforms contain the richest, most accurate data.
Vendors that support holistic data quality include ReachForce, D&B Optimizer (FKA Workbench), Zoominfo, InsideView, Oceanos, and Openprise. So if you are concerned about your ability to target, segment, pass quality leads to sales, score leads, or build predictive models, then begin with a holistic data strategy. Symptoms of poor data quality include high email bounce rates, declining email sender scores, returned direct mail, duplicate records, incomplete records, accelerating unsubscribe rates, and sales reps that ignore your marketing qualified leads.
Any firm that is adopting ABM, advanced lead scoring, a single view of the customer, or predictive analytics, should begin with a holistic data quality strategy. Otherwise, these advanced marketing strategies are bound to fail.
Sales Intelligence vendor RampedUp added account scoring to their platform. Other new features include saved searches for leads and trigger events, lead and trigger event downloading to CSV files, importing corporate URLs into searches, and the auto-population of decision makers and preferred technologies.
The new scoring doesn’t employ predictive analytics, but rates accounts on a zero to five basis, with a star awarded for each of five conditions:
One of top 5 industries based on the client roster
One of top 5 market segments based on client employee count
Installed Technology based on products important to the client’s sales process
Contacts present with preferred title based on selected buying committee
Recent trigger event article showing activity over the last 90 days
“Two things that have always set RampedUp apart from other sales intelligence platforms have been the tailored nature of the data we provide,” said CEO Scott Miller. “Our customers are exposed to contacts that are unique to their buying committee. We also share look-alike customer data based on a Salesforce.com sync that pulls customer data into our platform in near real-time. RampedUp also tracks triggering events and installed technology used by companies to help sellers understand their prospects better. All this information is used to create our unique scoring methodology.”
In a 2016 survey of predictive analytics companies, Gartner sized the global market at between $100 and $150 million. Although Gartner remains bullish on the sector, the size must be disappointing to both the firms in the space and their investors. One of the early companies in the space, Lattice Engines, continues as a market leader with over 200 global deployments.
Lattice Engines supports both enterprise clients and high-growth companies with deployments beginning around $75,000. Pricing is based upon the number of managed leads or contacts in the instance along with the number of users. With revenue between $25 and $50 million (GZ Consulting estimate), the firm has a strong position in the nascent market.
Lattice Engines combines first and third-party data to build predictive models. External content includes firmographics, intent data, technographics, social data, and web crawled business signals. Content is licensed from leading vendors such as Dun & Bradstreet (WorldBase global company file), Bombora (intent captured from over 3,000 B2B media sites), and HG Data (technographics). The Lattice Data Cloud covers over 200 million global companies, 21,000 buying signals, 100 million tracked domains, and over one billion daily interactions. Internal content spans transactions, CRM, marketing behavioral data, usage data, and support services.
“Predictive analytics is one of the few types of marketing technology that has the ability to solve issues at every step of the funnel, because it aligns sales and marketing against the right targets, and provides them with the right data to create targeted campaigns. By infusing fit and intent data into our models we enable teams to have a complete understanding of their ideal customer profile, which enhances the programs teams orchestrate against their targets.”
Director of Corporate Marketing Caitlin Ridge.
Firms can build multiple models to support various geographies, product lines, and scenarios (e.g. win/loss, upsell/cross-sell, renew/churn). Lattice scores and modeled data are integrated with many of the key SalesTech and MarTech platforms:
Ads/Web: DemandBase, Oracle Data Cloud, doubleclick (Google), AdRoll, Facebook
CRM: Salesforce, MS Dynamics, Oracle Sales Cloud, SAP
This platform coverage enables Omni-channel ABM campaigns across programmatic platforms, email, direct mail, and field marketing. Scores, insights, and recommendations are provided to sales reps within CRM i-frames.
“Lattice remains the most visible “face” of the market,” said Gartner analyst Todd Berkowitz in September 2016. “With its focus on security, level of integrations and ETL tools, the company is a fit for enterprise clients (both in high-tech and other industries) and/or companies planning to deploy in multiple regions. Gartner clients report that the company’s go-to-market approach is unique in the way it addresses complex problems and help customers operationalize the insights from the models. Lattice is one of the few vendors that can recommend key plays at both the lead and account level across the entire funnel.”
According to Lattice, customers enjoy a broad set of improved metrics:
2X Higher Conversion
3X Greater Pipeline
35% Higher Deal Sizes
6% Increase in Quota Attainment
85% Rise in Revenue per Customer
20% Reduction in Customer Churn
The firm sells broadly across B2B sectors. Customers include Amazon, Dell, PayPal, Staples, and SunTrust Bank.
Back when I was a product manager, I used to conduct sales training classes. I often opened up the session by asking the question, “Who is your biggest competitor?” The reps invariably listed a company or two they had heard over the prior day and a half of training. Even seasoned reps would answer the question incorrectly.
Unless you are in a duopoly or there is a competitor that controls half the market, your biggest competitor is probably NO DECISION. Either the purchasing decision is kicked down the road or no funding is found. It may also be that the opportunity was poorly qualified to begin with.
Sales reps no longer control the conversation due to the informed buyer who leverages the Internet and social media in order to research vendors prior to contacting them. This is one of the reasons that marketing is looking at digitally influencing anonymous individual on the web via Visitor ID, SEO, SEM, and Programmatic. Sales reps are also confounded in their sales efforts by a second change in purchasing patterns. B2B budgetary decision making processes have become more complex.
Budgetary centralization and committee-based buying decisions have increased the number of decision makers in the purchasing process, resulting in a greater likelihood of no decision. According to a Forrester survey of IT sales reps, 43% of lost deals weren’t to competitors but to a category titled “lost funding or lost to no decision: customer stopped the procurement process.”
Furthermore, the rise of cloud computing has shifted budgetary decision making authority away from the CIO to the heads of various functional departments. Purchasing decisions are being compared to a broader set of non-related purchases from across the organization. It is therefore critical that sales reps “understand and navigate complex agreement networks and processes within the buying organization that span different altitudes and functional roles,” blogged Forrester Sales Enablement Analyst Mark Lindwall. “Because decisions are more cross-functional, every dollar is compared against how it could add value in potentially completely non-related areas of investment.”
Thus, sales reps need better tools for identifying who to engage and when best to engage. They also need to be better informed about companies, individuals, and the industries into which they sell. In short, they need to know who to call, when to call, and what to say. They need to quickly navigate what Forrester calls agreement networks to establish relationships across multiple levels and job functions at the organization.
Fortunately, Sales 2.1 tools provide rich biographies and full family trees for navigating these networks. Users can target specific job functions and levels across the corporate hierarchy, research the appropriate individuals, and reach out to them via social media, email, or phone.
Newer ABM tools help identify the Ideal Customer Profile (ICP), score leads based on the ICP, and call out similar accounts and contacts that are not on the company’s radar. Thus, it’s not just about selling more intelligently based on insights, but targeting and prioritizing one’s sales efforts more effectively.
Sales triggers assist with identifying executive changes, M&A events, product launches, and other reasons for reaching out to individuals. Triggers can also indicate an expanding opportunity or that a proposal is potentially at risk due to company or market dynamics.
And yes, sales reps should research both the company and the executive. They need to understand the key trends in the prospect’s industry, why their last quarter was soft, and what does the executive muse about on social media. While such facts may not be immediate hooks, they provide context and potential talking points down the road. It also shows that the rep is willing to invest time in understanding the exec, her company, and the environment in which she is making decisions.
There is an opportunity cost to poor targeting, prioritization, and account planning. It shows up as No Decision in your CRM, slow deal velocity in your pipeline metrics, and disappointing sales growth.
Intelligence vendor DiscoverOrg announced a new Account Based Marketing (ABM) tool called AccountView which helps marketers identify the attributes of their Ideal Customer Profile (ICP). The new feature analyzes an account file which it calls a portfolio, enriches it with firmographics and technographics, and then provides a portfolio visualization dashboard of the accounts. The service also identifies similar companies to the top accounts, prioritizes them, and identifies best fit decision-makers at the net-new accounts.
The portfolio segmentation dashboard tiles include
Size: Revenue and Employee Bar Charts
Industry: Primary Industry Pie Chart; SIC and NAICS top frequency lists
Technology: Technology lists
Ownership: Ownership Structure Pie Chart
Companies: Portfolio companies with employee and revenue data. Company names are hyperlinked to their DiscoverOrg profiles.
Although geographic segmentation is not yet available, it is on the product roadmap.
Within the list tiles, users can search for specific elements (i.e. SIC, NAICS, technology, or company name).
Proposed contacts are shown within org charts with direct dial phones and emails to assist with organizational context and reach out. DiscoverOrg also provides detailed platform information and a set of sales triggers.
Marketing and sales teams can drill into specific bars or wedges to further research segments. To quickly refine models, customers can remove outliers to focus the ICP around high frequency variables.
Portfolios may be uploaded as CSV files, bulk matched within DiscoverOrg, or generated via DiscoverOrg prospecting. Result lists may be saved as lists, viewed as searches, or exported to CSV files. Models may also be loaded into DealPredict where company lists are displayed with Deal Predict scores of zero to five stars. Next to DealPredict scores, DiscoverOrg displays a lightning bolt icon if the company has a Sales Trigger or OppAlerts in the past sixty days. OppAlerts are intent based triggers which have been researched by DiscoverOrg editors or gathered through B2B publishers’ online content consumption data. By clicking on the lightning bolt, reps are shown the related events.
Within DealPredict, company lists are dynamically maintained to reflect the current firmographic and technographic lists of companies. If there is a change in company size or implemented technology, the DealPredict scores are automatically updated every time a search is conducted. Likewise, companies which are added to the DiscoverOrg database are automatically scored.
The very foundation of successful sales and marketing is figuring out who your best customers are, understanding why they are the best, and finding more prospects just like them. What could be a painful analytical exercise is made simple and straightforward with DiscoverOrg’s account-based marketing features, and the result is faster growth for customers who can more effectively identify, understand, and engage with their ideal buyer.
DiscoverOrg CEO Henry Schuck
DiscoverOrg suggests a number of account list categories that can be analyzed including the full customer list, high or low spend customers, renewing or non-renewing customers, high or low profitability customers, competitor customer lists, and prospect accounts. For example, running a competitor’s customer profile through AccountView helps you “determine ways to improve your product, messaging, or positioning. Likewise, running the non-renewed customer list through AccountView will help identify high-churn candidates for special programs.
Although DiscoverOrg recommends sets of strong and weak account lists, AccountView does not have the ability to discriminate between the two categories. Thus, marketers would need to separately run the paired lists, compare the portfolio results, and adjust the models for overlapping variables. For example, knowing that Microsoft Office is heavily used by both strong and weak accounts would indicate that MS Office is a frequently occurring, but non-predictive variable.
Future features include support for multiple models, grouping tech functions by category, sharing models across all users, geographic segmentation reports, and uploading contact information to assist with defining job functions and levels.
AccountView is the latest capability within DiscoverOrg’s ABM Toolkit. Other features include DiscoverOrg’s DealPredict predictive rankings for companies and contacts, OppAlerts intent-based opportunities, and sales triggers.
DealPredict provides predictive scores similar to those provided by predictive analytics companies. DiscoverOrg CMO Katie Bullard noted that unlike some black-box predictive platforms, AccountView analysis and DealPredict models are fully visible to sales and marketing users.
The AccountView analytics and net-new account service is included as part of the DiscoverOrg service. Firms license access to specific DiscoverOrg datasets and a set number of seats. Licensed users then have unlimited access to the licensed content for viewing, uploading, or downloading.
Other sales intelligence companies that have developed AccountView-like functionality include Dun & Bradstreet (Workbench), Avention (DataVision), and Zoominfo (Growth Acceleration Platform).
DiscoverOrg, which hit $71 million in Annual Recurring Revenue (ARR) at the end of 2016, has expanded its customer base beyond technology companies. Over 15% of revenues now come from marketing agencies, staffing firms, and consultancies.
In a blog on Sparklane’s website, I had the opportunity to discuss how sales and marketing can digitally transform their departments by focusing on data enrichment and sales intelligence.
Firms have traditionally taken a haphazard approach to data quality, failing to recognize that data quality is a function of both initial data (keyed data, web forms, trade show scans, purchased lists, etc.) and time. Data is dynamic. It can be accurate today and inaccurate tomorrow. That’s why data quality is often broken down into three dimensions: Accuracy, Completeness, and Timeliness.
So not only are firms failing to enrich data in real-time as data is acquired (or batch if purchased), they are ignoring the simple fact that
Offices are shuttered
Execs change companies or positions within companies
Corporate URLs and email domains are changed when companies are acquired or renamed
Companies grow and shrink
The result has been saw-tooth data quality charts with quality spiking at data refresh and then quickly declining. Both company and contact data are subject to data decay with contact data declining at a rate of 25% per annum (A recent Radius study has it at 27%).
To address this problem, firms should evaluate third-party solutions which provide a reference database matched against their sales and marketing datasets. By standardizing on a reference dataset, sales and marketing operations can deploy a single source of truth across data acquisition (e.g. list loads, prospecting, web forms) and maintenance (ongoing updates to their CRM and Marketing Automation platforms).
There are many benefits to this approach:
Web Form and other keyed data is immediately verified and graded.
Lead Scoring is based upon richer and more accurate data.
Duplicates are detected before being created, allowing leads to be matched to current customers and prospects.
Leads from subsidiaries and branches are tied to ABM accounts, ensuring they are properly scored and routed.
Addresses, Phones, and other key firmographic and biographic fields are standardized ensuring they are properly segmented, targeted, and routed.
Sales and Marketing no longer waste resources targeting individuals who have left an organization.
Sales has more complete data for lead qualification, prioritization, and messaging.
Higher quality data is propagated to downstream systems, reducing the long-term cost of maintaining those platforms and helping prevent downstream errors and duplicates created by low quality upstream data.
And those benefits are simply those from cleaner data. That is before we begin to consider the value of sales intelligence platforms in account planning, messaging, current awareness, identifying additional contacts at current accounts and prospects, and opportunity prioritization.
So if you want to begin to improve enterprise decision making and efficiency, an excellent place to start is in improving the data which is the lifeblood of your digital platforms.