Category: What Is?

A set of primers on industry topics.

Engagement Data Is Becoming Integral to SalesTech

Chorus Momentum identifies deal risks.

One of the most important SalesTech trends, besides the emergence of ChatGPT, is the rapid incorporation of engagement datasets alongside intent datasets for prioritization and messaging.

A few years ago, we saw the emergence of intent data sets such as first-party web visitor tracking, second-party product review site research, and third-party B2B media research.  Initially, this content was integrated into MAPs, ABX platforms, and CDPs, but it was not well integrated into SalesTech.  We are now seeing intent data being integrated into SalesTech platforms in a simplified fashion (e.g., High Intent Topics in CRM profiles and Slack alerts) that is digestible for sales reps. 

However, intent data only indicates whether a company is in-market, not whether the buying committee is considering your offering or seriously engaged with your sales team.  This intelligence comes from a new category of engagement data captured from digital interactions between the revenue team (sales, marketing, and customer success) and the buying committee.  Engagement intelligence consists of both traditional digital interactions (e.g., clickthroughs, downloads) and Natural Language Processing (NLP) analytics derived from sales and buying team activities.

NLP helps RevTech platforms determine who is interacting with your firm.  It also analyzes buyer sentiment, buyer concerns, deal health, and risk flags.  The primary sources of engagement data are emails, recorded phone calls, and recorded meetings.  However, any digital interaction between buyers and sellers can be captured such as activity in digital sales rooms, webinar attendance, chat messaging, and scheduled meetings.  I anticipate that customer support platforms will also be tapped for engagement data to help gauge churn risk and friction during product trials.

Engagement data indicates whether a deal is on track and what issues could result in lost deals or pushed out pipeline.  For example, engagement data assesses whether:

  • Discussions are single or multi-threaded
  • Key decisionmakers are involved (e.g., has a security review been performed or has legal been included?)
  • Competitors have been mentioned
  • Pricing concerns were raised
  • Follow on meetings have been scheduled
  • Meetings had a positive flow or were dominated by the sales rep

In short, engagement data provides sales reps and managers deal health and risk analytics that improve forecasting and ensure that deal risks are quickly mitigated.  And as interactions are digital, managers can discuss these issues during one-on-ones or offer quick tips on next steps.  They can even review the discussion associated with the risk and identify skills and knowledge gaps for coaching.

Nektar’s Insights Hub details buyer-seller interactions, leading indicators, buying committee engagement, MEDDIC adherence, etc.

The interesting thing about intent and engagement data is they are highly complementary with each other.  Operations teams should be looking at integrating intent data alongside engagement data.  Intent data is valuable for identifying who and when to reach out to ideal customers.  However, once a relationship is established, the focus shifts to engagement data for monitoring deal health.  After a deal is signed, both engagement and intent data are in play.  Intent data identifies cross-sell opportunities and churn risk through second and third-party intent topic monitoring while Engagement and Product Usage data evaluate adoption rates and potential implementation issues.

Engagement data and deal health analytics can be found in Revenue Intelligence services (e.g., Clari, Revenue Grid), Sales Engagement (e.g., Salesloft, Outreach, Groove), Conversational Sales (e.g., Gong, Chorus), Revenue Operations (Nektar), and Sales Enablement (e.g., Seismic, Bigtincan) platforms.

Revenue Grid Deal Guidance

What is Sales Engagement?

Over the past year, Sales Engagement has become the third pillar of my coverage (alongside Sales Intelligence and B2B DaaS). So it is only fair that I write an overview of the space.

Sales Engagement began about four or five years ago as Account Based Sales Development (ABSD) with a focus on automating the SDR function. Its initial functionality consisted of a cadence tool which automated emails and outbound dialing for appointment setting. Cadences, also called sequences, are a set of scheduled steps that usually begin with an email but also include outbound calls, social steps (many vendors have integrated LinkedIn Sales Navigator via SNAP connectors), and direct mail.

Cadences can be paused if the prospect is out of office or halted if the prospect unsubscribes. Outreach just announced out of office functionality which pauses the call and checks whether an alternate contact is listed.
Functionality is similar to that of LeadGnome. Outreach noted that 18% of email responses are out of office emails. 25% of out of office emails include an alternate contact name with over half the names being manager titles or above.  The Out of Office reply detection extracts the return date and alternate names then pauses sequences until the prospect returns.  The sales rep is notified of automated actions.

Email is supported by targeted templates which can be personalized. Thus, reps can call cadences by function, level, industry, etc. and the associated templates are customized by target audience. Reps can view the emails prior to sending and personalize them. This helps bring authenticity to the email. SalesLoft estimates that the peak personalization level is 20%.

Most sales engagement solutions include a digital dialer for outbound calling. The system suggests the best time of day and adjusts for time zones. Other features include local dialing, call recording, and voice mail drops. Upon completion, the rep enters call disposition and sentiment information which is synched with the CRM.

Once calls are recorded, they are transcribed and indexed, allowing sales reps or managers to quickly review calls and quickly locate pain points, objections, pricing, and next steps. Vendors such as Outreach and SalesLoft are going a step further and analyzing the calls, providing a set of team reports. More broadly, machine learning tools are being applied against the calls to determine best sales practices.

Meeting Management is emerging as a key feature set. Some vendors offer simple Calendly-like scheduling while others provide full meeting transcription and analytics.

Video is becoming increasingly important. Vendors support both video meetings (e.g. Webex, Zoom, BlueJeans, JoinMe) and video attachments (e.g. Vidyard, Videolicious).

The Outreach Partner Directory offers ten categories of partners. Sales Intelligence and Data supports lead generation, intelligence, and management.

Other information and decisioning tools include leaderboards, dashboards, AI recommendations (e.g. who to call or email next), CRM synchronization, and A/B testing. Some of these tools are directly integrated into the service while others are available through app directories. Similar to the Salesforce AppExchange or Marketo LaunchPoint, functionality may be free, freemium, or premium. Likewise, you may need to separately license the partner solution prior to enabling the integration. Outreach and SalesLoft offer a broad set of app partners.

There are a broad set of Sales Intelligence partner services which feed leads or display lead intelligence within Sales Engagement platforms. Vendors include DiscoverOrg, Zoominfo, Datanyze, Owler, Crunchbase, and SalesIntel.io.

Sales Engagement platforms are evolving into a system of engagement that sits alongside CRMs (systems of record). There is already a shift taking place from CRMs to Sales Engagement platforms. While Sales Engagement platforms are not looking to displace CRMs, sales reps are increasingly shifting screen time from CRMs to Sales Engagement platforms with the Sales Engagement platforms syncing with the CRMs. This is one of the reasons that Salesforce.com recently launched its High Velocity Sales service which combines cadences, Salesforce Inbox, the Lightning Dialer, work queues, and Einstein. High Velocity Sales starts at $75 per user per month ($90 with outbound calling).

The Salesforce Lighting Dialer supports click-to-call, power dialing, inbound calling, voicemail drops, and call logging.

At the Salesforce World Tour in Boston this week, a sales rep told me that Salesforce is heavily investing in Sales Engagement and hopes to catch up to the market leaders in the next year (I think this is overly optimistic as the leaders are quickly building out functionality and partnerships). While Salesforce could catch up in a few years, I believe it is more likely that SFDC will acquire one of the leaders in the space followed quickly by Microsoft and Oracle acquisitions in the sector (of course, Microsoft or Oracle could be the first movers). Adobe, which recently acquired Marketo, may also be interested in expanding its presence in B2B sales and marketing applications.

The top vendors in the space are SalesLoft, Outreach, Salesforce High Velocity Sales, Groove, and XANT (FKA) InsideSales. Other vendors include ConnectLeader, Yesware, Toutapp (Marketo), Mixmax, and VanillaSoft.

What is Meeting Management?

Chorus.ai Meetings are recorded, transcribed, indexed, and analyzed.
Chorus.ai Meetings are recorded, transcribed, indexed, and analyzed.

One of the recent additions to Sales Engagement feature sets is meeting management. Two years ago, meeting functionality was little more than a Calendly link, but now meeting setup and analytics are being automated, providing a much better experience for both sales reps and prospects. Reps spend less time setting up meetings, are better focused during meetings, and can review and share call snippets afterwards. Furthermore, calls are likely to run more smoothly and quickly.

Sales Engagement vendor SalesLoft has moved quickly into building meeting management after acquiring NoteNinja last year. Key SalesLoft features include:

  • Automated Calendaring: The email template includes a calendar link which allows the prospect to view open meeting windows and setup the call without they typical back and forth that wastes time. Rescheduling functionality is also supported.
  • Video Meetings: Calls are supported by Zoom and other video meeting platforms.
  • Automated Recording: A bot automatically attends the call and records it, transcribes it, and indexes it. Reps can search by word or topic and review a call timeline color coded by topic and speaker.
  • Improved Presence: Because the sales rep is no longer charged with note taking, he/she can focus better on the conversation. This allows for fewer delays, better call management, and better comprehension.
  • One-Click Notation: During the call, the sales rep can click on a set of pre-defined buttons for reviewing a topic post call. Thus, next steps, open questions, issues, pricing discussions, etc. can be quickly found after the call. These are presented alongside the automated indexing.
  • Sharing: Excerpts of the transcript can be shared with managers, technical staff, customer support, etc., allowing them to hear the voice of the customer. This removes bias and errors on the part of the sales rep. For example, if a sales rep feels she poorly handled a situation, she can forward the excerpt to her manager for a coaching session during their next one-on-one.
  • Training: The call timeline helps the rep see whether the meeting was conversational or a set of monologues, whether too many filler words were employed (to help reduce them), or whether next steps were set. More broadly, the training department (or sales operations if there isn’t a training department) can assemble a set of best practice meeting excerpts providing a library of short videos around value proposition, objection handling, competitor discussions, new capabilities, etc.
  • Best Sales Practices: Longer-term, machine learning will be applied to the transcripts, helping identify best practices and evaluate deal health.

This functionality is also coming to mobile devices to assist field sales reps and reps that need to place calls during commutes. In March 2019, SalesLoft announced an app for placing digital calls through its platform and Chorus.ai announced a similar app:

“The Chorus Mobile app, paired with proprietary Smart Playlist technology, surfaces critical moments from sales calls that a manager should review, and allows them to provide personalized feedback to their teams. Using the mobile app, sales reps can now better prepare for customer interactions by reviewing sales calls, key moments and manager feedback while on-the-go. The solution proactively identifies patterns within conversations, and automatically curates playlists of relevant calls utilizing Chorus.ai’s proprietary AI-driven technology. With the Chorus Mobile app, users are no longer tethered to a computer and can take coaching and call preparation with them anywhere.”

Chorus.ai press release, March 26, 2019

Meeting Management is an exciting new set of functionality for sales reps and managers. It is likely to spread to other departments and be employed internally, particularly for remotely staffed companies.

What is Fit Data?

A Subset of the D&B Hoovers location selects with regional filters for the US and UK.
A subset of the D&B Hoovers location selects with regional filters for the US and UK.

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).

Firmographic variables such as geography, industry, and company size are commonly used for specifying target accounts (Source:
Firmographic variables such as geography, industry, and company size are commonly used for specifying target accounts (Source: “The 6th Annual B2B Marketing Data Report,” Dun & Bradstreet, Sept 2018).

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.

LinkedIn Sales Navigator offers a set of unique variables for building lists. Unfortunately, the variables are not exportable.
LinkedIn Sales Navigator offers a set of unique variables for building lists.

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.

What Is Intent Data?

Bombora Intent Data Collection Model
Bombora Intent Data Collection Model

I am beginning a monthly series entitled What Is where I provide an overview of one of the underlying sales and marketing intelligence technologies or processes being deployed at B2B firms.  I will begin with Intent Data.

Intent Data is one of the three informational elements of B2B Lead scoring (the other two are Fit and Opportunity).  Intent data consists of first, second, and third-party elements and identifies when companies are actively researching specific product categories.  First-party data is captured in your marketing automation systems and web logs.  Typical first-party intent data includes

  • Web Logs
  • Webform Submissions
  • Email Clicks
  • Downloads
  • Page Views
  • Webinar Attendance
  • Trade Show Booth Visits

In short, if somebody is viewing your website, reading your collateral, meeting with you at a tradeshow booth, or attending your webinars, then he or she is displaying purchase intent.  Of course, not everybody doing so is a potential purchaser, but a high percentage of individuals digitally interacting with your firm are somewhere in the buyer’s journey for your products and services.

“The case for intent data is clear. If only 3 percent of the potential buyers for any given product or service are in the market at any given time (while 40 percent are poised to begin and 56 percent aren’t interested), identifying and focusing on those buyers, and those close behind them, is the key to efficiency and effectiveness in revenue growth. That’s been the Holy Grail of marketing and sales for years. After all, how many times have you heard a sales rep say, ‘If I’m sitting at the table, I win more than my fair share of deals. Just get me to the table!’

That’s the promise of intent data. And practice shows it’s more than just a theory. Fifty-percent increase in close rates and an 82 percent reduction in sell-cycle have been attained.”

“Buying Guide: From the Black Box to Revenue Metrics – Translating Buzz into Results,” IntentData.io.

Unfortunately, intent data is often anonymous.  Unless the individual submits a web form, you are most likely limited to an IP address.  As B2B visitors are usually accessing your platform from a corporate IP address, it is possible to tie the IP address to the company and at least associate the activity with a company.  Companies such as DemandBase, Bombora, KickFire, Clearbit, IntentData.io, Zoominfo, and Dun & Bradstreet offer Visitor Intelligence services to map IP addresses to companies.  Along with the company name, they enrich the visitor intelligence with firmographics such as location, size, and industry. Some vendors include technographics as well.

Real-time visitor intelligence can assist with the user experience. By providing immediate firmographics, websites can be immediately customized based upon size, location, or industry.

As visitor intelligence is beginning to feed chatbots, it is possible to prioritize customer support and sales queries. As bots become more intelligent, they will digest the firmographics and customize the conversation. Likewise, ABM customers and prospects can be given priority over non-targeted prospects. If these teams are verticalized, chats can be routed to specialized teams.

External third-party intent data is provided by vendors such as Bombora, The Big Willow, and True Influence.  External intent data is gathered from B2B Media websites that evaluate topics of interest across their network and determine which topics are of interest to companies.  Interest is gauged by articles viewed, white papers downloaded, searches performed, case studies read, etc.  Generally, each company is baselined by topic with interest determined with respect to the baseline.  A surge of interest takes place when short-term interest in a topic is well above the baseline for the company.  Intent data is generally delivered as a numeric score by topic with companies licensing the topics of interest.  As intent is determined at the corporate level, it works best in lead scoring. One limitation of third-party data is you don’t know which individuals are researching specific topics, but this ensures that the data is GDPR- compliant.

TechTarget Priority Engine provides technology-specific second-party intent at the individual level along with contact information, buying stage (early or late based upon content viewed and downloaded), and key influencers (companies of interest).  TechTarget is focused on Technology topics across its 140 media sites and its BrightTALK webinars and virtual event service.  TechTarget is considered second-party intelligence because it owns the content directly, and contacts have opted in, making them GDPR-compliant.  It also offers first-party intent data through KickFire. 

G2.com (FKA G2Crowd) is another well-known source of second-party intent data. G2.com is a technology review site, so site traffic is highly associated with company and product research, making it a very strong source of early-stage demand intent. Competitors include TrustRadius and PeerSpot.

Additional Resources: