Sales Engagement vendor SalesLoft announced Deal Engagement scores, a “machine-learning capability [that] gives frontline managers an unbiased way to prioritize deals based on the calculation of over 30 data elements captured across Cadence, Conversations, and Deals.”
Instead of a black-boxed score, SalesLoft provides recommendations and an explanation of the score, helping sales managers identify opportunity issues and risks and take actions to improve close rates. Thus, Deal Engagement Scores serve as early warning signs that deals may be going south, allowing them to take proactive actions that improve close rates.
Deal Engagement Scores are shown over time and include a set of stage progression indicators such as days since the last meeting, days until the next meeting, and close date pushes. A seven-day summary details recent engagement activity and deal progression.
“It’s not enough to have just a Cadence product,” said Frank Dale, SalesLoft’s SVP of Product Development. “With Cadence, Conversations, and Deals on one platform, we collect data across the full buying cycle, from the first email, every call, meeting, and communication, through to deal closure and renewal. Only SalesLoft can analyze all of this data to predict revenue outcomes. No other Sales Engagement provider can offer this.”
35 data elements are fed into their machine-learning model to prioritize and identify opportunity issues and risks. Engagement is measured across emails, phone calls, and meetings, with interactions measured by level. Over 120 million customer interactions were fed into the machine-learning model. As a machine-learning capability, the model continues to improve and adapt.
“Having this capability allows front-line sales managers an instant gut check on specific health for deals in flight,” posted CEO Kyle Porter on LinkedIn.
Deal Engagement Scores are available to early access customers with Deals functionality in the Enterprise and Sell plans. It will GA by June.
Sales Engagement Platform vendor SalesLoft became the latest SalesTech unicorn, following a $100 million equity investment led by Owl Rock Capital. Insight Partners, HarbourVest, and Emergence also joined the round. The Series E funding raised SalesLoft’s valuation to $1.1 billion, nearly doubling its April 2019 Series D valuation of $600 million.
The funds will be dedicated towards “transforming the sales industry and helping the world’s companies sell more successfully.” SalesLoft will invest in “new vertical markets, AI / ML-driven insights and product innovation, and further international expansion.”
SalesLoft had a successful 2020, setting up the firm for the valuation raise. While they were doing well before the pandemic, it provided a “tailwind” that accelerated the need for Sales Engagement solutions.
“The effects of Covid have been a tailwind due to the effects of digital selling,” Porter told TechCrunch. “All sellers immediately became remote. But now the genie is out of the bottle and not going back in. It’s meant that inside sales are now all sales. Whether the opportunities are mid-funnel or upgrades or renewals, we are establishing ourselves as the engagement platform of record because it’s all becoming digital and all sellers are finding more success.”
SalesLoft, which had focused on the mid-market, is enjoying significant success selling to enterprise clients, including Google, LinkedIn (also a strategic partner), Cisco, Dell and IBM. Other clients include Cargill, 3M, and Standard & Poor’s.
Last year, SalesLoft doubled recurring revenue and expanded the breadth of its offering. When SalesLoft went fully work from home last year, it forced them to rely more fully on their platform. “It was an opportunity to immerse ourselves in our own best practices,” blogged Porter. “And since then, our sales cycles have shortened by 40% and we’ve exceeded our growth plans. Many of our customers are experiencing similar results.”
SalesLoft was also named a leader in Sales Engagement in “The Forrester Wave™: Sales Engagement, Q3 2020.”
“Our goal is and always will be to help our customers win. This year has accelerated the need for revenue teams across all industries to transform through a digital selling strategy. SalesLoft is a crucial technology for sales teams to perform at their highest potential.”
SalesLoft CEO Kyle Porter
SalesLoft claims to be the only SEP supporting “the three most critical products in digital selling – Cadence for managing customer communications, Conversations for recording calls and meetings, and Deals for managing opportunities.” SalesLoft helps customers build pipeline, manage active deals, and engage customers across the buyers’ journey.
SalesLoft gave a sneak peek at their 2021 roadmap in December, unveiling two new features: Deal Engagement Scores and Pre-Built Cadence Frameworks.
Deal Engagements Scores employ machine learning to calculate “deal health based on 30+ factors including activity and deal progression data.” They will assist with prioritizing deals in need of attention and improve forecast accuracy “by identifying mismatches between forecast category and deal score.
Pre-built Cadence Frameworks will improve SalesLoft’s time to value by providing a set of templates and cadences across the full lifecycle and various roles (e.g., SDR, AE, CSM). Inbound frameworks are also supported. Cadences include a preview with a visual display of the cadence, description, objective, function, and implementation complexity level. Pre-built cadences offer best practices from SalesLoft and SalesLoft’s partners.
SalesLoft’s product vision is focused on performance across both efficiency and effectiveness and looks to answer three questions:
What is our performance versus plan? Forecasting for revenue execs
Why are we above or below plan? Outcome-driven reporting for frontline managers
How can we improve and take action? Coaching, Workflow, and an AI/ML Recommendation Engine for sellers and frontline managers.
“We know which sales activities lead to the best revenue outcomes,” stated Porter. “Our data science team is bringing insights and best practices into the platform to tee up next best actions and benefit our customers.”
Forecasting and outcome-driven reporting are part of the SalesLoft vision. Coaching and the Recommendation Engine are areas of continuing development. SalesLoft is already delivering an “integrated, efficient workflow.”
SalesLoft is moving to quarterly releases. The next release pack is scheduled for March 15, 2021.
Sales and Marketing Intelligence vendor BuzzBoard has built a graph of global SMB organizations spanning 100 million companies, 20 million in the US. The seven-year-old firm supports advanced segmentation and prospecting based upon a deep set of technographic and digital marketing variables. During the pandemic, they added COVID prospecting and recommendation tools to help customers target segments and personas that are doing well during the recession.
It is their focus on SMBs and their digital signals that they describe as their competitive advantage. “Anyone who sells to SMBs should have deep knowledge of the prospect’s digital footprint, their needs, attitudes, triggers, and ability to pay. These are reflected in the SMB’s operations stack and in their external presence.”
BuzzBoard assigns a Buzz Score to each of the companies in its database. The Buzz Score is a digital marketing score that grades a company’s digital presence based on a 0 to 100 scale. Users may drill into the score to see the underlying components. Thus, a web design company can determine whether a prospect has multi-screen compatibility. Initially, an estimated score is provided, which is +/- 10 points of the probable score. When adding a profile, BuzzBoard automatically regenerates the Buzz Score and digital profile, though the process takes 60 to 90 seconds.
Prospecting variables include firmographics, technographics, website, SEO, advertising, social, estimated spend, etc. A new set of COVID-19 risk filters let users select companies based on location, industry, operational status, ongoing Google Ad spend, and recent technology investments. For example, restaurants may be filtered operationally by Temporarily Closed, Dine-in, Delivery, and Take-out.
On the user home page, they added two recommendation cards. The “COVID-19 – Categories to work on now!” card highlights verticals that are growing and “need marketing support during the COVID-19 pandemic.”
The “COVID-19 – Recommendations” card contains personas and segments defined by BuzzBoard’s data scientists “based on target locations that need marketing support during the COVID-19 pandemic.”
The homepage consists of a customizable set of cards for training videos, COVID recommendations, Knowledge Base, Favorite Signals, etc.
The fixed search bar at the top of each screen lets users search by location combined with business name, URL, category, or keywords. A left-handed filter bar lets users revise the prospecting list. For example, a user can quickly search for roofers within 40 miles of Los Angeles with websites and Google ad spend, but a poor mobile experience (e.g. slow loading, not responsive). Lists are displayed via a card-view or plotted map. List parameters may be saved for future re-use.
Prospects may be added as profiles. When added, BuzzBoard goes out and re-evaluates the Buzz Score based upon its current digital footprint. Profiles contain a summary view along with tabs for Detailed view, Competition, Category Insights, and Recommendations.
Beyond prospecting lists, users may also quickly research a company from a BuzzBoard Connect Chrome extension or via a file upload. When visiting a web page in Chrome, users click on the BuzzBoard icon in the right corner of the Chrome bar, and BuzzBoard opens a window with company details including firmographics, Buzz Score, Website and SEO details, technographics, social media, competition, communications, and recommendations.
Up to 300 records may be uploaded at a time. The file must contain either name and URL or name and address for matching.
Part II continues tomorrow with a discussion of their Competition, Category, and Recommendation tabs.
ABM Platform vendor Terminus announced the immediate availability of the Terminus Engagement Hub, which applies data and attribution for advertising, email, web, and chat. The hub includes integration of RambleChat, its newly acquired account-based chat service. Other new capabilities include Trended ABM Scorecards and new target account list building rules.
Terminus’ Chat from Anywhere functionality allows sales and marketing to embed chat links in email, LinkedIn, Twitter, digital ads, QR codes, proposals, and other outbound communications and media. Chat from Anywhere moves beyond the company website and enables it contextually through a broad set of communications channels. Thus, chats are attached to the proper account, routed to the account owner, and attributed to the campaigns and actions which drove the engagement.
Chat from Anywhere is also integrated into SalesLoft and Salesforce, with employees responding via web browsers or mobile apps. Chat-based Leads are mapped to Salesforce. If the lead is not in Salesforce, then a new contact or lead is created by Terminus Chat.
The enhanced ABM Scorecards “help identify success across market segments over time, prove value, track win rates, and compare performance to prior periods.”
Terminus expanded its ABM Dashboards and custom views, including views of Engaged Accounts, ABM Win Rate, Opportunities Created, Revenue by Industry, and Pipeline by Program.
New Data Studio features include account filters ”based on advertising performance and engagement metrics for next best actions.”
New list building functionality “enables users to import and connect Salesforce Account IDs and push lists across engagement channels while also connecting those audiences to powerful measurement and attribution analytics.”
“We’ve taken our powerful data and attribution capabilities in Terminus and separated it into two parts – Data Studio and Measurement Studio. We’ve also integrated new marketing channels to help marketers create experiences for their prospects and customers. Advertising Experiences, Email Experiences, Web Experiences, and Chat Experiences are all now available in one place; all in the Terminus Engagement Hub. It’s never been easier to run coordinated, multi-channel marketing campaigns.”
Terminus Chief Product Officer Bryan Wade
“ABM is more than a marketing strategy, it’s a business strategy. And it’s more important than ever,” continued Wade. “Marketers are challenged to nurture an existing customer base while still driving quality top-of-funnel activity, all in a digital world. Now with the Terminus Engagement Hub, our customers can own every point of engagement with target audiences and track all activity at the account-level in a single platform. Full-funnel ABM is now easier than ever.”
B2B data hygiene vendor Openprise announced the availability of Openprise Agile CDP, the “first and only B2B Customer Data Platform (CDP) built on a data orchestration platform.” As a data orchestration platform, Openprise offers a single customer view combined with no-code business rules, third-party data, and business process automation.
Openprise emphasizes the advantage of being a data orchestration platform with a B2B CDP. “Because it’s built on the Openprise Data Orchestration Platform, Openprise Agile CDP includes all the capabilities Openprise has developed over the years to improve data quality in tools like Salesforce and Marketo—including lead routing, account scoring, and attribution—advanced features not typically found in traditional CDPs.”
Openprise supports data unification, data enrichment, normalization, deduplication, lead-to-account matching, and lead-to-contact conversion. Analytical tools include advanced segmentation, lead and account scoring, ABM activity analysis, campaign attribution, lead routing, and account assignment.
“One of the biggest challenges marketers face is making sure their systems of record deliver accurate, high-quality data to drive marketing initiatives. A CDP solution that automates all the critical business processes required to make the data work gives marketers high confidence in the accuracy and quality of the data they manage.”
Julian Archer, VP, Principal Analyst in the Marketing Operations Research Service at SiriusDecisions
Openprise claims it can be up and running within ninety-days, much faster than its competitors. Firms can build custom apps with automated business processes, package them as an API, and create web-based UIs and Chrome extensions for end-users. Openprise no-code app use cases include advanced segmentation, attribution, upsell, and cross-sell.
The Openprise data marketplace supports data enrichment from leading B2B and B2C vendors, including Zoominfo, Dun & Bradstreet, InsideView, Sales Genie (Infogroup), Cognism, Bombora, KickFire, Synthio, Oceanos (TechTarget), Acxiom, Bing, and Google Places. Once enriched, data is normalized based upon customer-defined and Openprise taxonomic rules. Normalization ensures that key values such as addresses, industry codes, job functions, and job levels follow a standard set of rules and taxonomic codes.
Sales and Marketing Intelligence vendor Zoominfo acquired Redmond, WA startup Komiko. The deal extends Zoominfo’s sales AI capabilities with CRM automation, playbooks, lead scores, and predictive analytics.
analytical and recommendation tools support sales, account executives, and
customer success teams.
“Organizations are realizing that how they manage and leverage data is a strategic function that can accelerate or inhibit lead, pipeline, and revenue generation. While our offering is a SaaS platform for GTM, we feel ZoomInfo is in the business of helping marketing and salespeople hit their numbers. So, when we see an opportunity to build or buy additional capabilities essential to strengthen that edge — as we did with Komiko — it’s an easy decision.”
Zoominfo CEO Henry Schuck
Komiko employs machine learning and data science “to
better automate CRM processes.” InboxAI gathers contact and activity data
from email inboxes and calendars and populates the CRM. The mined
intelligence also triggers alerts and generates “analytics essential to
supporting renewals, managing new business pipelines, and more.”
Komiko offers a “data-driven platform” which helps
reps understand the likelihood of each opportunity closing. The platform
also captures all customer-facing interactions and contacts. Komiko
claims to “make it easy to see who is interacting with the customer and what
activities are taking place.”
Komiko data includes the strength of connection with
each account (k-score), the relationship of contacts at accounts, the last
communication with the account (outbound or inbound), and key contacts at
Komiko integrates sales playbooks into the CRM and
recommends when to deploy them.
customers will continue to receive the Komiko service with no changes in
support or service.
InboxAI is already deployed at Zoominfo. The firm discovered 60,000 records that had not been logged into Salesforce. “We found a number of accounts where we were only talking to one buyer – when we know that we need four buyers engaged to get across the finish line. InboxAI not only completes our CRM, [but] it gives us the visibility we need to push the right opportunities at the right time,” said Zoominfo CRO Chris Hays.
Komiko functionality will be integrated into the
recently launched Zoominfo powered by DiscoverOrg platform.
Komiko is GDPR compliant and qualifies as a data
processor. It supports the right to be forgotten through a blacklist of
blocked emails. The system also deletes any historical emails related to
Komiko does not monitor internal emails and includes
an external blacklist for blocked processing. Thus, HR, Payroll, Board,
and Legal department communications will not be ingested. Komiko does not
add Salesforce accounts but employs Salesforce accounts as a whitelist.
Komiko also positions itself as a “dynamic coaching”
service which goes beyond informal or “formal, random” processes:
Dynamic coaching is not just a buzz word. It has been proven that taking this approach makes a big impact on win rates. Since taking the dynamic path means defining a formal process combined with your CRM to monitor, evaluate and support your coaching processes…Komiko builds playbooks based on your definition of success, the accounts segments you identify and the input from email capture and CRM. Your playbooks will outline actions that drove success in the past. Each recommended action will include recommended target and its weight (significance) to the overall success. Komiko will enhance your team’s efficiency by triggering call-to-actions based on the customer profile and playbook in real-time.
Komiko claims that clients can “get up and running”
within 24 hours after only 30 minutes of work. They support “customers of
all sizes” across software, healthcare, distribution, professional services,
and insurance. Clients include Adecco, Tata Communications, Pemco
Insurance, and Chorus.ai.
Terms of the
deal were not released.
founded in 2015 by former Microsoft engineers Hal Howard and Ami Heitner. Owler
lists Komiko’s revenue at $3 million. However, marketing activity (blogs,
LinkedIn) seems to have slowed around three months ago, indicating a firm that
was reserving cash for a managed exit.
60 customers and expects to double the count by the end of the year.
Komiko CEO Howard, “We want our product to be seen by millions of people. Our
choices were we could take an additional round of venture funding and build our
market, or partner with ZoomInfo and use an already-existing go-to-market. This
was the fastest path to that market and to millions of customers.”
Komiko’s machine learning chops with ZoomInfo’s data pipeline creates a much
stronger value proposition than either company could have offered
independently, so the combination makes a ton of sense for both,” said Chris
DeVore, managing partner at Founders’ Co-op, Komiko’s Seed Round lead investor.
“Everybody dreams of the unicorn exit. And
those are all well and good, but the goal of every technology innovator is to
get your technology in the hands of as many people as possible,” Howard told GeekWire.
over 1,100 employees and more than $300 million in revenues.
is beginning to evolve a set of hybrid engagement vendors that deliver a broad
set of sales and marketing services. The boundary between sales and
marketing is quickly crumbling. Hybrid engagement services manage both
data and workflows. Features include
French Sales and Marketing Intelligence vendor Sparklane released its Predictive Account Scoring Solution for B2B sales. Sparklane Predict now supports dynamic account scoring based upon Ideal Customer Profiles (ICP), sales feedback, and CRM win/loss data. The service is currently available in the UK and France with additional European markets in development.
According to the firm, Predict
supports a “human-in-the-loop” lead review process which “feeds lead decisions
back into the ICP model, providing additional intelligence towards distinguishing between good
and bad prospects.” Predict also collects CRM intelligence on opportunity
outcomes, providing an additional basis for model refinement.
supports bi-directional syncing with Salesforce, Microsoft Dynamics, Marketo,
and Eloqua. Sparklane uploads suggested accounts and leads to CRMs and
gathers historical outcomes for ICP modeling and dynamic scoring.
claims that it shortens sales cycles by 28%, increases contract volume by 25%,
and improves the business conversion rate by 70%.
Sparklane Predict leverages Artificial Intelligence (AI) tools such as machine learning and natural language processing to dramatically improve sales productivity and customer insights. Sales rep attention is directed towards accounts and leads most likely to close based on both fit (company attributes) and need (sales triggers such as international expansion, employee growth, or product launches). Furthermore, automated data enrichment ensures that reps are working with accurate, complete, and current data.
Sparklane Press Release
When building Sparklane models, both win and loss scenarios are employed, providing a more robust model than current customer lists. Along with win/loss scenarios, Sparklane supports other binary outcome scenarios:
Account Renew vs. Account Drop
Account Upgrade vs. Account Downgrade
High Margin Profitable Accounts vs. Low Margin Unprofitable Accounts
also supports multi-product line upsell and cross-sell models.
many of the vendors now marketing ideal customer profile solutions (ICP) are
offering little more than basic prospecting or look-a-like lists under the ICP
banner,” said Sparklane CEO Frédéric Pichard. “A true ICP service begins
with both positive and negative accounts so the platform can distinguish
between accounts that closed and those that failed to close. A true model
also contains feedback loops from sales reps and the CRM. It is the
addition of feedback that refines the model over time, improving the predictive
precision of account scores.”
Sparklane supports nearly 250 customers out of offices in Paris, Nantes, and London. Last year, Sparklane grew its recurring revenue by 60%.
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).
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.
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 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.”
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.
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 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.