Month: March 2023

6sense Revenue AI for Sales

6sense Revenue AI for Sales helps prioritize account activity.

6sense released Revenue AI for Sales, its account prioritization platform that identifies in-market accounts and recommends contacts in an “anonymous world.”  The service assists reps with prioritization, research, multi-threading, and personalization.

“Information overload is killing sellers’ productivity.  In today’s rapidly changing business landscape, we need to give sellers the tools they need to reach new heights.  This means giving sellers sales technology that helps them spend more time selling and less time on unproductive activities,” said CEO Jason Zintak. “We’ve already transformed marketing teams by revealing and targeting accounts and engaging anonymous buyers.  Now we’re giving sales teams a massive upgrade from their legacy database vendors.  This puts 6sense in a new category where we can innovate and lead, just like we have with others like predictive analytics and ABM.  Early momentum from customers making the move to a better selling experience demonstrates the potential for impact.”

The Persona Map identifies engagement activity by role and level.

Revenue AI for Sales illuminates the “Dark Funnel,” where 97% of B2B research is conducted anonymously.  6sense ties new buyer intent signals with account and contact intelligence.  Recommended Actions prioritize engagement and suggest which contacts to acquire and reach out to.  6sense offers first-party (visitor), second-party (G2 and TrustRadius), and third-party (Bombora) intent signals.  It supports both topic and keyword intent alongside buying stage.

A Persona Map provides a visual map of the buying team, helping promote multi-threading and suggesting unknown buying team members.  The map includes contact details, activities, and talking points.

People and company pages highlight company hierarchies, job insights, technographics, and psychographics.  Contacts and accounts can be sent to CRMs, pushed to SEPs for engagement, and contacted via email, phone, or LinkedIn.

6sense insights are supported by a Chrome extension, allowing reps to prospect on the open web and identify potential buyers.

Prioritization Dashboards highlight in-market accounts and insights.  6sense also alerts reps to important account activity, recommended actions, and new buyer intent signals via email and Slack.

“Challenges exist on multiple fronts today as sellers have to navigate through a constant barrage of information and noise, plummeting productivity and increasing frustration,” said CTO Viral Bajaria. “With our AI-driven solution, sales teams will be able to focus on what they do best – building relationships and closing deals – while our technology takes care of the rest.  6sense Revenue AI for Sales uses the power of AI, big data, and intelligence to give sellers confidence in their ability to close more deals and be the trusted advisor their customers want and need.”

6sense maintained its momentum last year, growing revenue by 70%.  The company attributed its ongoing growth to its “strategic focus on new product introductions, market expansion, and ecosystem growth.”

New product introductions included the October release of Conversational Email.  The module, which leverages Generative AI, intent, and pre-intent data, dramatically enhances productivity when sending marketing and sales emails and fielding responses.  Conversational email composes “relevant and hyper-personalized emails to qualify and convert leads at scale.”  6sense claims its customers enjoy a 50 percent reduction in deal-cycle time, a 150% increase in average deal size, and $900K of new pipeline activity in four weeks.

“The results of the past year’s performance are a major achievement that demonstrates how the team’s hard work has paid off,” said Brian Ascher, Partner at Venrock.  “As 6sense continues executing against commitments and the product roadmap, we expect to see this upward trend maintain its momentum.  This is what the larger investor community loves to see, and we are proud of the 6sense team delivering these results.”

Other enhancements include ongoing investment in the Slintel database and the release of 6sense Pipeline Intelligence based on Fortella.  Both Slintel and Fortella were acquired in H2 2021.

“6sense offers billions of data points of market-leading account and contact data along with best-in-class curated data, enhancing a customer’s own first-party data sources and tech stacks to deliver powerful B2B go-to-market strategies, insights, and orchestrations,” claimed the firm.

6sense continued to grow its partner ecosystem with new integrations, including HubSpot, Microsoft, and Integrate.  In addition, its integrations with Dynamics and HubSpot CRM support prescriptive sales dashboards and buyer insights.

“Throughout the past year, we’ve built upon our growth in meaningful ways and continue to provide our customers with tangible value that impacts their bottom line,” said 6sense CEO Jason Zintak. “Using our own platform is essential to our success and puts us in a unique position where 6sense Revenue AI is a competitive advantage – both in our own category and where our customers compete.  Looking across our customer base, we see revenue generation is 120% more effective when using 6sense, their deal size doubles, and win rate increases 4X.”

The firm has expanded internationally over the past year with an increasing presence in EMEA and APAC.  The London office grew to 45 full-time employees, and APAC has over 400 employees in India and Singapore.

6sense employment growth (Source: LinkedIn)

Outreach Explore+ Announcements

Sales Execution Platform Outreach unveiled a series of product enhancements and dashboards at its Explore+ web conference earlier this month.  New features include Smart Email Assist with Generative AI, a Create Pipeline Calculator, Buyer Topics and Reactions in Kaia, Deal Grid, Deal Overview, Success Plan Methodologies, and Data Sharing with Outreach.

Outreach emphasized the breadth of its Sales Execution offering that began with Sales Engagement and has expanded to include Conversational Sales, Digital Sales Rooms, Success Plans, Coaching, Generative AI Emails, and Revenue Intelligence.  This full-funnel approach addresses sales teams’ top two issues: pipeline coverage and pipeline closing.

“The industry has never had a single place to generate and manage pipeline, run sales cycles from creation to close, coach reps, and forecast – until now,” said the firm.

Over the past decade, sales teams have acquired a set of SalesTech solutions that create a “hairball” of point solutions that work poorly together and suffer from siloed data and regular system switching.  Furthermore, a unified data platform supports advanced workflows, AI models, and account insights for sales coaching and deal management.

Outreach has enjoyed solid adoption of its new platform since launching it ten months ago.  Multi-product adoption is strong, with over 400 customers using two or more products.  Furthermore, multi-product adoption is driving platform ARR, which has grown by over 100% in the past two quarters.  Since the platform was launched, Outreach’s new logo deal size has increased by 16%.

Outreach repositioned itself as a Sales Execution Platform as it expanded beyond Sales Engagement (Source: Outreach Analyst presentation).

“Today, Chief Revenue Officers are facing two major problems: pipeline coverage and conversion.  They need to create an adequate amount of pipeline, and close it at a greater rate,” said CEO Manny Medina.  “That’s why Outreach has been on a journey to expand our offerings to solve our customers’ biggest problems today.  Our goal is to provide sales leaders with a single platform to manage all of their deals – from creating more pipeline to closing more deals.  Today’s announcements at Explore+ are an important milestone in our platform journey, and we look forward to continue innovating for the 30 million B2B salespeople around the world to help them unleash their selling potential.”

Outreach Smart Email Assistant

The Smart Email Assistant generates automated email replies that go beyond email templates.  AI factors in previous conversations between the buyer and seller when generating responses.  By automating email responses, “sales reps can focus their time on editing and personalizing the AI-generated content, instead of drafting these emails from scratch.”

A new Pipeline Calculator recommends prospecting activities to fill pipeline gaps.  The calculator utilizes historical pipeline data to determine the number of prospects that should be added to sequences to meet their quota.  In addition, the historical conversion rate assumptions are displayed and adjustable.  Thus, the assumed conversion or win rates can be adjusted to accommodate market shifts or new processes or messaging that boost historical results.

Outreach Pipeline Calculator

Outreach continues to develop Kaia, its conversational sales module, with the addition of Buyer Topics and Reactions.  AEs and sales managers can revisit meeting recordings and review the buyer’s reaction to fourteen relevant sales topics, such as budget, legal, or support.

“Using AI, Outreach is able to understand the contextual utterance of relevant sales topics in any meeting or email – ranging from pricing to product to next steps to support – and can understand when the buyer raised an objection at any point in the meeting,” explained the firm.  “It delivers invaluable insight into what is really happening in meetings, down to each moment, and at scale across all meetings.”

Success Plans now support popular sales methodologies, including MEDDIC, MEDDPICC, and SPIN Selling, helping reps “consistently and continuously qualify deals and align with champions to mitigate deal risk.”

Outreach added a single-pane-of-glass opportunity viewer called Deal Grid.  Reps can view their deals sorted by health score and value to focus on their best opportunities.  They can also edit fields such as Close Date, Amount, Stage, and Forecast status (e.g., omitted, commit, best case, most likely) with information synced to the CRM and forecasts updated.

Opportunity Viewers are a common feature of Revenue Intelligence platforms (e.g., Clari, RevenueGrid, People.AI), but with Sales Execution and Revenue Intelligence platforms expanding into each other’s domain, Deal Grid was an anticipated new feature.  Opportunity viewers help reps review their deal status, update the CRM, and prepare for meetings with sales managers.  They solve the problem of serially jumping between Opportunity records in Salesforce (which the firm has moved to resolve with similar functionality).

Outreach released several new reports and dashboards:

  • Create and Close Dashboard: Provides AEs and sales managers with a high-level forecasted revenue summary of the existing pipeline and highlights pipeline gaps and risks.

    “The insight-laden dashboard shows the forecasted revenue from existing pipeline, and highlights pipeline coverage gaps for the current and future quarter, which helps reps proactively mitigate risk earlier and drive to success,” said Outreach.
Outreach Pipeline Calculator
  • Deal Overview: An overview of open opportunities with a deal summary, an engagement timeline, deal health, sales methodology insights, next steps, and the shared plan.  The engagement timeline displays all sales activities and a heat map detailing customer engagement trends.
Outreach Deal Overview
  • Pipeline Dashboard: Displays all “relevant pipeline details to life in a single, sortable view, allowing sales managers to stay on top of their quarter.”  The dashboard includes a pipeline activity summary by stage, projected finish, revenue to date, quota, and top deals with deal health scores.
Outreach Pipeline Dashboard

Outreach also announced bi-directional syncing with HubSpot.  Earlier this month, it unveiled expanded Outreach Data Sharing with Snowflake.

Despite recent layoffs, Outreach continues to build its customer base.  FY 2023 revenue (FYE Jan 2023) passed $200 million across 6000 customers.  Outreach’s scale benefits its clients as it records over 25 million action/outcome pairings per week, helping refine its machine learning insights and recommendations.

Einstein GPT

On the same day that Microsoft launched Copilot, Salesforce announced Einstein GPT, its generative AI service that combines Salesforce’s own AI models with external models such as OpenAI’s.  Einstein GPT supports personalized content creation across all of Salesforce’s clouds and Slack.  For example, Generative AI functionality can write personalized sales emails, author customer support responses, compose targeted marketing collateral, and auto-generate code for developers.

“The world is experiencing one of the most profound technological shifts with the rise of real-time technologies and generative AI.  This comes at a pivotal moment as every company is focused on connecting with their customers in more intelligent, automated, and personalized ways,” said CEO Marc Benioff.  “Einstein GPT, in combination with our Data Cloud and integrated in all of our clouds as well as Tableau, MuleSoft, and Slack, is another way we are opening the door to the AI future for all our customers, and we’ll be integrating with OpenAI at launch.”

Einstein GPT for Sales

Einstein GPT is the next generation of Salesforce’s Einstein AI capabilities.  Einstein GPT supports natural-language prompts that “trigger powerful, time-saving automations and create personalized, AI-generated content” within Salesforce.  In addition, each application maintains a human-in-the-loop that reviews and edits client communications before they are sent out.

Einstein GPT reduces “the friction in sales reps wanting to move fast to meet their quota, having to leave Salesforce to send customer communication or do prospecting research, and spending too much time finding information stored in various parts of the CRM,” wrote Salesforce Ben.

New functionality includes:

  • Einstein GPT for Sales: Auto-generate sales tasks like composing emails, scheduling meetings, and preparing for the next interaction.  It can also provide external news for prospect research, add contacts not already in Salesforce, and generate additional collaboration channels on Slack.
Einstein GPT can identify event triggers and recommend whom to contact.
  • Einstein GPT for Service: Generate knowledge articles from past case notes.  Auto-generate personalized agent chat replies to increase customer satisfaction through personalized and expedited service interactions.  Einstein GPT for Service also auto-generates case summaries and knowledge articles from past case notes.
Generating a case article.
  • Einstein GPT for Marketing: Dynamically generate personalized content to engage customers and prospects across email, mobile, web, and advertising.  The service can generate content with brand-compliant images and layouts.  Marketing content can then be uploaded to Experience Builder.
Einstein GPT for Slack writes copy with brand-compliant images and formatting.
  • Einstein GPT for Slack Customer 360 apps: Deliver AI-powered customer insights in Slack (e.g., smart summaries of sales opportunities) and surface end users’ actions.  The Slack service supports writing assistance, background research on accounts, and instant conversation summaries.
  • Einstein GPT for Developers: Improve developer productivity with Salesforce Research’s proprietary large language model by using an AI chat assistant to generate code and ask questions for languages like Apex.                                         

“Salesforce can be a powerful multiplier of generative AI experiences because Einstein GPT blends public data with CRM data, and when several million of our customers are all using Einstein GPT, the model gets refined with each instance and becomes more accurate,” explained Salesforce’s SVP of AI and Machine Learning Jayesh Govindarajan.  “It’s a cumulative effect and is really a huge differentiator for Salesforce.”

Salesforce is also looking to establish an AI ecosystem, with OpenAI as the first integration.

“Einstein GPT will infuse Salesforce’s proprietary AI models with generative AI technology from an ecosystem of partners and real-time data from the Salesforce Data Cloud, which ingests, harmonizes, and unifies all of a company’s customer data,” announced Salesforce.  “With Einstein GPT, customers can then connect that data to OpenAI’s advanced AI models out of the box or choose their own external model and use natural-language prompts directly within their Salesforce CRM to generate content that continuously adapts to changing customer information and needs in real-time.”

As part of the announcement, Salesforce established a $250 million venture fund to develop “responsible, trusted, and generative AI.”

OpenAI CEO Sam Altman said using ChatGPT in CRM services “allows us to learn more about real-world usage, which is critical to the responsible development and deployment of AI—a belief that Salesforce shares with us.”

“It will be fascinating to watch how this plays out,” opined Fortune editor David Meyer.  “On the one hand, we’re now in the territory of serious businesses using generative AI for serious things, as opposed to playing around to see how long it takes to get a chatbot to say something offensive.  On the other hand, some of these applications involve customers who may have some curveball questions.  And it’s worth remembering that generative AI technology like OpenAI’s ChatGPT will occasionally ‘hallucinate,’ that is, basically make up fake information.”

“In theory, Microsoft’s and Salesforce’s new offerings should be safer to use because they only draw on information from companies’ own websites and internal databases—the customer-facing elements will in that sense be a bit like those Google search boxes in websites,” continued Meyer.  “But that won’t necessarily make these AIs immune to occasionally emitting bogus information.  Companies will find out soon enough how carefully they need to monitor their new copilots.”

Einstein GPT is in closed pilot. 

A ChatGPT for Slack app is in beta.  The ChatGPT app was built by OpenAI on the Slack platform and “delivers instant conversation summaries, research tools, and writing assistance directly in Slack.”

ChatGPT for Slack, built by OpenAI, was released this week.

Microsoft Dynamics Copilot

Copilot summarizes Teams meeting notes.

Microsoft announced Microsoft Dynamics Copilot, an interactive, AI-powered assistance tool for sales, marketing, customer service, operations, and supply-chain management.  Microsoft is positioning Copilot as an AI that helps businesspeople “create ideas and content faster, complete time-consuming tasks, and get insights and next best actions.”

CEO Satya Nadella promised that Copilot will “transform every business process and function with interactive, AI-powered collaboration.”

In the short run, Scott Guthrie, Microsoft’s Cloud & AI EVP, believes that “the fastest way to get some of this AI value” will be via “finished app” integrations like Copilot.  Microsoft can tailor integrations into apps that “people are already trained on.”  This augmentation strategy lets Microsoft “move faster,” so there is a “huge opportunity.”

Few people outside of the AI community had heard of ChatGPT, much less experimented with it, until a few months ago, but now it is being widely tested by end users, noted Guthrie.  “People are looking for solutions that integrate with their workflows that they already have and help them kind of accelerate even more.”

“And then, I think, we’re also going to see the next generation of apps that are going to be built on the raw APIs and the services around it that are going to re-envision pretty much every experience that we see,” continued Guthrie.

Copilot operates within Dynamics CRM and ERPs to reduce mundane tasks such as manual data entry, content generation, and note-taking.

And such tools are welcome by front-line workers.  According to a recent Microsoft survey, nearly ninety percent of workers hope AI will reduce repetitive tasks.

Dynamics 365 Copilot offers generative AI to automate tedious tasks and “accelerate their pace of innovation and improve business outcomes.”

Copilot is natively built into Microsoft Dynamics 365 Sales and Viva Sales.  AI helps sales reps respond to customers and includes email summaries of Teams meetings along with action items, follow-up dates, and voiced concerns.  Additionally, summaries are available for different meeting types, including multi-participant and internal calls.

“The meeting summary pulls in details from the seller’s CRM such as product and pricing information, as well as insights from the recorded Teams call,” wrote Charles Lamanna, CVP Business Applications and Platform.  “With sellers spending as much as 66% of their day checking and responding to emails, this presents a significant business upside to give the seller more time with their customers.”

Email replies are generally available in Viva Sales, and customizable emails will be added on March 15.  “For example, a seller can generate an email that proposes a meeting time with a customer, complete with a proposed meeting date and time based on availability on the seller’s Outlook calendar,” blogged Emily He, CVP Business Applications Marketing.

Sellers will also be able to rate generated content with a thumbs up or thumbs down to help refine replies.  And if the response needs to be tweaked, sales reps can provide a follow-on prompt that updates the response based on the additional context.

Generating marketing text with an emphasized feature.

Copilot in Dynamics 365 Customer Insights and Dynamics 365 Marketing simplifies data exploration, audience segmentation, and content creation.

Marketers can curate “highly personalized and targeted customer segments by having a dialogue with their customer data platform using natural language,” wrote Lamanna.   Marketers do not need to be SQL experts or wait for an operations specialist to build the query.  Instead, they can build segments in near real-time using Copilot’s Query Assist feature.

“With a few clicks, Copilot produces the results, along with information such as the customers’ average age, product preferences, or average purchase price,” wrote He.  “These insights can then be configured into a segment to support a campaign.”

Copilot also suggests additional segments.

Using Copilot in Dynamics 365 Marketing, marketers can describe their customer segment to a query assist feature and look for inspiration for fresh email campaign content based.  “Copilot makes suggestions based on key topics entered by the marketer, the organization’s existing marketing emails, as well as from a range of internet sources to increase the relevance of generated ideas,” blogged Lamanna.

Marketers can enter up to five bullet points in Content Ideas, which generates an email via Azure OpenAI Service.

“Unique content can be used as a starting point when composing marketing emails,” explained He.  “It can analyze the organization’s existing emails, in addition to a range of internet sources, to increase the relevance of generated ideas.  With Copilot, marketers can save hours of time brainstorming and editing while keeping content fresh and engaging.”

Copilot in Dynamics 365 Customer Service drafts contextual answers to queries in both chat and email.  It also supports an “interactive chat experience over knowledge bases and case history, so this AI-powered expertise is always available to answer questions.”

Copilot for customer service drafts emails and chats for customer service agents.

Other generative AI tools support product descriptions and supply chain disruption forecasts based on weather, financial, and geopolitical news.

Copilot is in preview across Dynamics 365 and Viva Sales.

Resources:

LinkedIn Sales Navigator Q1 Release


LinkedIn Sales Navigator rolled out its Q1 product, focusing on relationships, personas, and enhanced buyer intent functionality.

The new Relationship Explorer surfaces “hidden allies” and best paths into accounts, helping sales reps avoid cold outreach and “spam cannon techniques.”

“Instead of a blanket approach where you target everyone at an account, you can laser in on the people who are most likely to take a meeting with you based on their persona and what connection they have to you,” explained LinkedIn Senior Director of Product Mitali Pattnaik.  “You can also use it to multi-thread deeper into accounts by finding the next-best person to reach out to.  This creates a more efficient experience for buyers and sellers alike.”

Sales Navigator has long supported introductions and TeamLink (colleague) suggestions, but it has never fully leveraged the value of its economic graph for warm communications.  The Economic Graph supports 900-million-member profiles across 61 million companies, along with current and prior employment, educational background, posts, etc.

Sales Navigator has a second advantage: its profiles are maintained by its members, ensuring that profiles are kept up to date and contain rich data around education, interests, skills, employment history, etc.

“Teams have relied so heavily on cold outreach largely because they’re leveraging sales intelligence tools that are limited in showing how to get a foot in the door of an account.  These tools are chock-full of stale data: everything from incorrect contact info to the wrong person in the wrong role.  With reliance on tools full of stale data, reps end up spamming all potential prospects with a spray-and-pray strategy, leading to an abysmal 1-2% response rate,” argued Pattnaik.  “Looking forward, sellers are going to need to be smarter and reach out with a more personalized approach.”

Relationship Explorer recommends prospects at an account, leveraging the interactions and trends across its professional network “to provide sellers with optimal paths to connect with their target personas at their target accounts.”  As a result, Relationship Explorer saves time prospecting, cross-selling, and upselling at accounts, helping reps find the best contacts at target accounts.

The feature offers up to eight “of the most relevant individuals” based on their target persona and relevant, actionable insights (called spotlights by LinkedIn) based on interactions between members and organizations.  Spotlights highlight both biographic and dynamic information, including recent job changes, LinkedIn postings, and past customers.  As such, they provide timely reasons to reach out and content to include in their outreach.

Relationship Explorer suggests the best contact at an account based on the user-defined persona.

Relationship Explorer is available in all Sales Navigator editions.  However, while it displays a dozen spotlights, not all are available in each edition.  For example, Past Customer spotlights are only available in the Advanced Plus edition.

Personas help users identify their target audience by function, seniority level, geography, and current job title.  They are available on the Homepage, Search, Relationship Explorer, and Account pages.

Users can define up to five personas which act as templates for homing in on ideal prospects.

Persona definitions on the homepage.

Pattnaik suggested several use cases for personas:

  • Creating highly targeted Personas matching target customer profiles.
  • Leveraging Personas in Search, Homepage, or Account Pages to identify the most relevant opportunities.
  • Identifying warm paths and decision-makers at targeted accounts with Relationship Explorer.
  • Using insights from Account Pages, including Persona growth, to prioritize accounts composed of leads matching Personas.

Persona functionality is available to all users.

Over the past few releases, Sales Navigator has built buyer intent into its service.  Its latest intent-based feature is Product Category Buyer Intent, which identifies buyers searching for products in their category.

Product Intent Categories

Previous Sales Navigator intent was based upon research into a vendor.  Product Category Intent identifies prospects researching a product category but may not know a vendor or its offerings.  The two types of intent data can be compared to understand the level of interest in the company versus the interest in the company’s product category, informing sales and marketing strategy.

“Categories are created with AI by combining related keywords into one central category, which is then tied to products using publicly facing product descriptions.  For example, “fintech” and “financial tech” are individual keywords, which the AI model can combine into a single category,” explained Pattnaik.  “Intent is then connected using buyer’s members’ profile as well as recent buying activities on LinkedIn.com to help sellers find the buyers who are likely looking for a solution like theirs.”

LinkedIn is rolling out several new Buyer Activities that will be displayed on Account Pages and the Buyer Intent Account Dashboard.  Additional intent categories are rolling out over the next quarter:

  • LinkedIn Ad Engagement: Clicks and view activity data.  Both of these data points are private, so sellers will only be able to see the general profile of the buyer.
  • InMail Acceptance for a colleague: Displays the public identity of individuals who have accepted InMails from other sellers on the same contract.
  • Company LinkedIn page visits: Clicks on the company page.  Page visits are a private activity, so the buyer is anonymous.
  • LinkedIn profile visits to colleagues and leadership: A new activity that shows sellers when a potential buyer visits the profile of a colleague on the same contract or company leadership.  This is also a private activity.

Buyer Intent is available in the Advanced and Advanced Plus editions of Sales Navigator.

Users can now search against any account list or use an account list as a suppression list.  Other new search filters include:

  • Past Customer (Advanced Plus only)
  • Past Colleague
  • Executive TeamLink – leverages the networks of a company’s executives (Advanced and Advanced Plus only).
  • Viewed Your Profile
  • Product Category Buyer Intent

LinkedIn also enhanced its Sales Insights (LSI) service with the improved matching of companies to CRM accounts and Adjustable Growth Time ranges.

LinkedIn admitted that its previous LSI matching logic may have been inaccurate as it only matched against a few standard CRM fields.  LSI now supports CRM custom ingestion that improves match rates with customer-defined match fields.  There is also an option to force matches based on LinkedIn Ids or URLs.

LinkedIn Sales Insights Field Mapping

Adjustable Growth Time Ranges can be set to 3, 6, 12, and 24-month increments.

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

Corporate Visions Acquires Primary Intelligence

Sales Research and Advisory firm Corporate Visions acquired win/loss analytics firm Primary Intelligence.  Primary Intelligence’s TruVoice platform conducts surveys, online interviews, and live phone interviews.  In addition, buyer feedback is displayed in Competitive Intelligence platform Crayon and available in battlecards, newsletters, and dashboards, providing “more depth, context, and insight from the buyer’s perspective.”

TruVoice can also be used for customer experience, competitive analysis, and churn analysis.

Primary Intelligence has collected win/loss intelligence through manual post-mortem deal interviews for twenty years.  Automating the process both lowers the cost of intelligence collection and widens the scope of intelligence collected.  Survey response rates are between 25% and 35% due to the request coming from the sales rep.  Online surveys run ten to fifteen minutes and are dynamically adjusted based on the responses.  TruVoice collects both qualitative and quantitative responses.

Primary Intelligence Buyer Insights provide a roll-up of win/loss insights at an account. Users can drill down to “direct evidence” from the interviews.

“We’ve taken the live interview model that we have honed over our entire existence and turned it into an online experience, where we call it an online interview.  But it really is something that a respondent can do in ten minutes on a mobile device.  There are some quantitative questions for them to record voice responses, which we then transcribe and…publish that data.  And then we still do the live interview.”

“The value of win-loss-no decision analysis at scale is that you have continuous, near real-time feedback on a higher percentage of accounts for improved insights and confident strategy adjustments across all of your revenue teams,” said Primary Intelligence CEO Ken Allred.  “But the biggest breakthrough is having the ongoing rep-by-rep, deal-by-deal intelligence to drive situational training and enablement.  This eliminates the bias of reps providing their own feedback or requiring managers to review hundreds of hours of call recordings.”

Gong and Crayon Partnerships

Primary Intelligence recently partnered with conversational sales platform Gong to integrate in-cycle deal intelligence.  Primary Intelligence delivers a daily log of competitive mentions to sales reps, providing them with battlecard links and links to call transcripts that help them write follow-on messaging that parries competitive statements.  The Gong integration ensures that conversational intelligence from deals is available alongside post-deal analysis, providing a clearer view of why deals are won or lost, comparative strengths and weaknesses versus competitors, and improved messaging.

“Our approach in the last five years or so has been [asking] ‘How can we take the operational lift and automate as much as possible?” explained Primary Intelligence President Nick Siddoway to GZ Consulting.  “Now it’s a process that begins in Salesforce or whatever CRM you’re using.”

The Performance Gaps view displays competitive strengths and weaknesses as seen by buyers and displayed by priority.

Primary Intelligence also recently partnered with Competitive Intelligence Platform Crayon, marrying competitive and win/loss intelligence in a common platform.  The joint solution “helps teams better understand their competitors.”

“In today’s competitive climate, competitive intelligence and win-loss analysis are essential for B2B companies looking to increase win rates,” stated the firms.  “While win-loss interviews and surveys with buyers are filled with critical competitive insights, getting these insights updated into competitive intelligence deliverables, such as battlecards, has traditionally been a slow, manual process — until now.”

Crayon and Primary Intelligence highlight competitor offerings’ relative strengths and weaknesses, providing improved positioning for sales and marketing teams.  A separate pricing module helps debunk sales rep claims that deals are regularly lost on price, providing a more accurate view of deal loss.  By helping differentiate offerings and identify why deals are won or lost, vendor offerings become less price sensitive. 

Primary Intelligence also provides views at the rep level, providing insights into the strengths and weaknesses of reps and where they would benefit from coaching.

“The future of sales enablement is providing custom, rep-specific coaching in the flow of work.  Ideally, those recommendations are based on actual performance feedback from real customers,” said Erik Peterson, Chief Executive Officer of Corporate Visions.  “The acquisition of Primary Intelligence will enable us to make invisible problems visible and then provide personalized coaching to individual reps and revenue teams based on how buyers and customers respond.”

“What better evidence that your strategies and spend are actually working as intended than actual customer feedback connected to wins, losses, and no decisions, as well as renewals and expansion cycles?” Peterson added.

B2B DecisionLabs

The acquisition provides Corporate Visions with 100,000 buying decisions spanning twenty years, which will be incorporated into its B2B DecisionLabsresearch and advisory business.  Corporate Visions, which positions itself as a Decision Science company, calls Primary Intelligence its “fourth lab.”  B2B DecisionLabs incorporates behavioral research, brain studies, and field trials, alongside customer feedback.

TruVoice customer feedback is Corporate Vision’s latest B2B DecisionLabs laboratory.

“We will engage our B2B DecisionLabs research director, Dr. Leff Bonney, co-founder of the Florida State University Sales Institute, to effectively leverage the incoming data points into insights using all applicable and appropriate academic research-based approaches, tools, and techniques,” explained Tim Riesterer, Chief Strategy Officer at Corporate Visions and Chief Visionary at B2B DecisionLabs, to GZ Consulting.

“Our expectation is that this steady flow of buyer-driven deal insights will completely distinguish B2B DecisionLabs among other research and advisory firms who rely on their subscriber clients to provide data snapshots and self-reported survey responses to formulate their industry insights,” continued Riesterer.  “This will be in addition to our completely unique brain study lab and ongoing field trials with actual clients.”

The Primary Intelligence dataset provides a deep set of historical and cross-industry data that captures deals both in progress and after closing.  This research complements its other three research laboratories.

“This will give our advisory clients even more confidence in the B2B DecisionLabs recommendations compared to opinion surveys and moment-in-time snapshots of data,” said Riesterer.  “It will also mean we can provide more reliable tools than you otherwise get from peer communities that only curate unexamined personal experiences and unsubstantiated claims of expertise.”

“This ongoing flow of customer-sourced data will also be used to continually expand and enhance our revenue growth services to ensure Corporate Visions’ clients always have access to the industry’s best and most updated intellectual property,” Riesterer added.

Corporate Visions offers science-backed revenue growth services for sales, marketing, and customer success.  Along with hosting conferences and training, Corporate Visions helps firms “articulate value and promote growth” in three ways:

  1. Make Value Situational by distinguishing your commercial programs between customer acquisition, retention, and expansion.
  2. Make Value Specific by creating and delivering customer conversations that communicate concrete value, change behavior, and motivate buying decisions.
  3. Make Value Systematic by equipping your commercial engine to deliver consistent and persistent touches across the entire Customer Deciding Journey.

By April, Corporate Visions plans to combine automated win-loss-no decision customer feedback with automated skills coaching and customer messaging content from Corporate Visions.  Riesterer intends to launch the “first fully automated, situational enablement solution that identifies rep-by-rep weaknesses based on actual customer feedback, to direct specific, custom coaching videos to help address these challenges – in the rep’s flow of work.”

This vision shifts sales rep training from “just-in-case” event-based generic classroom training to “just-in-time” situational coaching and enablement that is customized to each rep and deal.  This training will be “always on, deficit-based situational coaching and enablement” that does not require managers to “listen to a bunch of calls or read a lot of feedback and then formulate a custom coaching plan.”

Data Anonymization

Corporate Visions has already considered which data can be employed for aggregate analytics.  Research protocols are subject to Institutional Review Board (IRB) review and approval.  Data will only be available for aggregate analysis with the consent of customers.  Unique identifiers are stripped from the data and replaced with arbitrary data identifiers, and no individual customer’s data will be published. 

B2B DecisionLabs has “partnered with Florida State University as the primary means of data analysis and have taken the steps as outlined in GDPR protocols regarding ‘Information Processors’ to ensure that data is passed to FSU without any unique respondent identifiers,” explained Bonney.  “To decrease any risk of inadvertent identification of a customer in the data, Primary Intelligence will assign the ‘ID number’ to customer data and then pass [it] to the B2B DecisionLabs and FSU research team, who will have no input or insight into how data ID numbers are assigned.  Additionally, Primary Intelligence will remove any data fields that may be used to ascertain the identity of any one customer.”

The FSU IRB will review Primary Intelligence’s anonymization protocols.

Real-time Coaching

Managerial deal coaching “just doesn’t happen and won’t happen at scale,” remarked Riesterer.  Furthermore, “because the system continues to run and generate customer deal feedback, you will be able to monitor, measure, and modify enablement interventions on the fly to see the impact and make continuous appropriate adjustments.”

Thus, the merged company will combine neural research concerning purchasing behavior and buyer studies, with in-the-moment situational coaching tailored to each rep and deal.

Primary Intelligence’s TruVoice platform

LinkedIn: Using AI Responsibly

LinkedIn posted its AI principles today. These are all high-level which is a good starting point, but implementing rules and policies requires more details.

AI is not new to LinkedIn. LinkedIn has long used AI to enhance our members’ professional experiences. While AI has enormous potential to expand access to opportunity and transform the world of work in positive ways, the use of AI comes with risks and potential for harm. Inspired by, and aligned with our parent company Microsoft’s leadership in this area, we wanted to share the Responsible AI Principles we use at LinkedIn to guide our work: 

  • Advance Economic Opportunity: People are at the center of what we do. AI is a tool to further our vision, empowering our members and augmenting their success and productivity. 
  • Uphold Trust: Our commitments to privacy, security and safety guide our use of AI. We take meaningful steps to reduce the potential risks of AI.
  • Promote Fairness and Inclusion: We work to ensure that our use of AI benefits all members fairly, without causing or amplifying unfair bias.  
  • Provide Transparency: Understanding of AI starts with transparency. We seek to explain in clear and simple ways how our use of AI impacts people. 
  • Embrace Accountability: We deploy robust AI governance, including assessing and addressing potential harms and fitness for purpose, and ensuring human oversight and accountability.
“Using AI Responsibly,” LinkedIn In the Loop Newsletter (March 2023)

As with every new technology, it can be used for either the betterment of society or malign purposes. Setting out principles helps frame product management and engineering in building their models, promoting trust, and setting guidelines to reduce negative effects (e.g., recapitulating bias, spreading misinformation and disinformation).

Transparency helps reduce negative effects as well. If it isn’t known why a recommendation was made, how can it be trusted? Furthermore, how does one know that the AI isn’t recapitulating somebody’s IP; gathering information from incorrect, malign, or outdated sources; or making incorrect assumptions? Thus, black box AI should be avoided.

Microsoft is the early leader in implementing Generative AI, a category of AI “algorithms that generates new output based on data they have been trained on” (Gartner). The best known of these is ChatGPT which generates text and carries on chat conversations. Microsoft recently invested $10 billion in OpenAI, the developer of ChatGPT and other generative AI tools. It is quickly moving to integrate it into Bing and other products.

On Monday, I will post about ChatGPT being integrated into Microsoft’s Viva Sales product.

GZ Consulting Resources