Salesforce announced a pair of autonomous Einstein Sales Agents: Einstein SDR and Einstein Sales Coach. The products, built on Salesforce’s Einstein 1 Agentforce Platform, will be generally available in October.
Einstein SDR Agent, a chatbot, engages with inbound prospects and is not limited to pre-programmed responses. The multi-lingual conversational marketing agent manages top-of-funnel communications and hands leads off to sellers. The SDR Agent employs RAG to interpret and process information. It can answer questions, handle objections, qualify leads, and book meetings, with responses grounded by Salesforce CRM and Salesforce Data Cloud.
“Companies can upload existing sales materials like product FAQs, sales plays, and case studies for Einstein to use to generate a trusted and accurate response to lead queries,” explained the firm.”
Configuring Einstein SDR Agents.
Revenue Operations can customize the language, avatar, and tone. They can also set “guardrails for how often, what channels, and when Einstein should engage with inbound prospects.”
Einstein SDR Agent also lets RevOps set lead routing rules, log actions as Tasks, and monitor activity via existing reports and dashboards.
“Einstein SDR Agent makes decisions and prioritizes actions that align with the desired outcomes and analyzes a prospect’s question to autonomously determine what to do next — whether that’s answering product questions, handling objections, or scheduling a meeting,” stated Salesforce. “Each response is trusted, accurate, and personalized because it is grounded in a company’s CRM and harmonized external data using Agentforce’s retrieval augmented generation (RAG) service.”
When meetings are set, Einstein SDR Agent warmly hands off to reps with a pre-meeting summary that “critical lead information and previous interactions.”
The SDR Agent supports SMS and WhatsApp.
Einstein Sales Coach offers embedded role plays with feedback based on current scenarios.
The Einstein Sales Coach also employs RAG and text-to-voice to assist with sales role plays. Each roleplay is deal-specific, and Einstein provides post-roleplay feedback. RAG allows Einstein to customize sale scenarios based on Salesforce details about the deal, account, and prior correspondence.
The Sales Coach allows reps to practice pitching, handling objections, or negotiating before meeting with real customers.
“Using RAG, Einstein finds the relevant information in Salesforce — such as previous correspondence with that customer or external files provided, like buyer personas and buyer journey documents — and generates contextual responses in the buyer’s tone back to the seller during the role-play. For example, if a deal is in the negotiation stage, Einstein can create a role-play scenario sourced from the relevant context, including the opportunity record and buyer persona materials, to simulate the target buyer and engage in pricing negotiations.”
Einstein Sales Coach “incorporates insights from thought leaders and reputable publications when offering seller feedback.” Feedback includes next steps identified in Salesforce and overdue tasks and uploaded content such as sales methodologies and objection handling.
Einstein Sales Coach provides both positive and critical roleplay feedback for current deal scenarios. Next Steps are also called out.
During calls, Einstein provides real-time feedback, noting competitor mentions with competitive insights and providing negotiation tips.
“Every AI conversation needs to be an ROI conversation, and that will happen only when AI augments your team to accelerate growth,” argued Sales Cloud GM Ketan Karkhanis. “Every sales team needs more at-bats and more enablement to accelerate close rates, and that’s what these new autonomous sales agents will help drive. The sales team of the future is humans working with AI to drive sales success.”
Lavender applies AI to assist sales reps with drafting emails and recommending best practices.
Sales AI Email Coach Lavender rolled its V3 release into beta. The full release, expected later this summer, sports faster writing times through improved personalization tools and insights, and an enhanced coaching system with segmented and custom dynamic models.
By design, Lavender doesn’t want to replace sales reps but make them more effective, a sharp contrast with many SalesTech vendors. Lavender’s AI helps sales orgs understand why emails work and uses this understanding to help reps quickly write effective emails.
“Everything that we do is steeped in this concept of enablement and trying to get the user to think. There’s nothing I hate more than the concept of AI taking thinking away from sellers, because that’s where their growth is going to happen. The friction that we introduce is always intentional.”
COO Will Allred to GZ Consulting
The objective is to educate the sales rep on best practices that raise response rates and move deals forward. Blackboxing these practices doesn’t educate the rep and it ignores the wisdom of the rep.
While Lavender speeds up the composition process (efficiency), its true benefit comes in improving message quality through personalization and email best practices (as determined by AI analysis of client email data). Simply pushing out more quasi-personalized emails provides limited lift.
“If you actually customize emails to a one-to-one message, we’re seeing companies not only getting 25% plus response rates to their cold emails, but they’re generating a majority of their pipeline via those emails,” said Allred. “For example, a Forbes ‘Cloud 100’ company is seeing about 1900% more pipeline delivered via these emails.”
Sales reps struggle to provide “thoughtful personalization” as they need to search across their email, CRM, Gong conversations, and Sales Intelligence solutions. Reviewing and synthesizing this content can be quite time consuming. Furthermore, email platforms lack analytics, so are a “giant black box” when it comes to determining which emails are effective.
Sales reps have access to personal and conversational context when drafting emails.
Sales reps can select which context sources to employ when drafting an email, with Lavender recommending potential icebreakers and then generating the message.
Lavender addresses the issue of analyzing personalized emails, which creates a “complete blind spot” for Sales Ops. If each email is unique, RevOps can no longer perform A/B analysis of templates.
“In our dashboard, we’re helping managers understand exactly why reps are having success when it comes to email because it’s not an A/B comparison,” argued Allred. “There are hundreds of emails going out. They’ve all been personalized and customized. There are hundreds of variables that are at play here, and so we’re measuring all of them and pulling out what’s most important.”
Lavender’s email coach evaluates your message real time against your historical best practices to recommend changes (brevity, short sentences, non-spammy headers, questions) that raise response rates (2-3x on average) and continue deal progression.
Lavender’s email coach provides real time scoring. The AI detects reasons the email won’t get a reply and fixes them using a variety of LLMs.
Allred remarked that cold emails shouldn’t get all of the attention, arguing that the key to moving companies through various stages of the funnel is raising response rates. Unfortunately, one of the reasons that email threads die is because sales reps stop asking questions. So, Lavender builds in personalization and encourages questions in responses.
“If you actually customize emails to a one-to-one message, not only are the emails getting 25% plus percent response rates, but the pipeline that you’re driving from that is almost 2,000% more per email.”
Lavender analyzes data across the inbox (Outlook, Gmail) and SEP (Salesloft, Gong Engage, Outreach, Groove, Apollo) to create custom scoring recommendations. It also marries CRM (Salesforce, HubSpot), Conversational Sales (Gong) and third-party data to assist with drafting personalized emails with high response rates.
Lavender ingests activity from enterprise software platforms, analyzes the emails, and recommends edits based on best practices.
The AI can examine the emails and determine which phrasing, formats, styles, and wording are effective and which are falling flat. Lavender is diving deeper into AI analytics, looking to discern which emails are effective and why.
“Our approach to this [problem] is to stitch together an understanding of what’s happening in both inboxes, pull that together into a single pane of understanding, and then feed that back to the rep in real-time to give them the coaching they need. We pull all of that information to the forefront for the user.”
It’s a pure understanding of why an email works from an activity standpoint just like the metadata that we capture today, to the persona analytics, to the writing style data that’s been at the heart of Lavender since the beginning. Not just the style but the content itself.”
Lavender CEO Will Allred
The new release will also introduce a set of open-sourced email frameworks that help sellers tune their logic based on the scenario. For example, if a seller is trying to write an email to a prior closed lost opportunity, a framework would help the seller think through the message and subsequent follow-ups.
“We’re going to surface frameworks to help people think through better ways to write the message,” explained COO Will Allred to GZ Consulting. “We want our users to become better writers and better sellers. One of the things we found helpful in creating that was open-sourcing and bringing our frameworks to the surface so people could understand the logic behind what makes a great email.”
Lavender also added a ChatGPT style modal that allows the user flexibility to brainstorm, receive writing advice, or research via searching (or prompting) the web.
Lavender launched its service before the Cambrian Explosion of GenAI apps and subsequently built an “LLM stack” that includes its own models, Anthropic, OpenAI, Watson X, and more. This early mover advantage allowed it to build a solid customer base. According to the Chrome Store, Lavender has over 35,000 active users, a scale that exceeds its competitors.
“We have more daily active users than anyone in our space has downloads of their like products,” stated Allred.
ZoomInfo Copilot delivers account-based insights, buyer recommendations, and next best actions to sales reps.
ZoomInfo formally launched its Copilot service, which embeds GenAI capabilities within its GTM platform. The firm claims that Copilot “turns every seller into your best seller.”
Over 20,000 users have participated in the Copilot beta, and “their results and feedback have been overwhelmingly positive.”
ZoomInfo claims its users report being 60% more productive with Copilot. They also reduced time spent on research and manual tasks by ten hours per week, and 71% uncovered new opportunities at existing accounts.
Furthermore, ZoomInfo Copilot surfaced signals related to 45% of the open opportunities in their CRM.
ZoomInfo Copilot improves the efficiency and efficacy of sales reps. (Source: Q1 2024 $ZI earnings presentation).
“In today’s GTM environment, data by itself isn’t enough. Modern sales is becoming a science. It’s not enough to know who your buyer is — you need to know what they care about, exactly when they are in market, and what problems they’re facing right now,” posted ZoomInfo CEO Henry Schuck on LinkedIn. “And this information is out there in the form of digital buying signals. We have more information than ever about our buyers, but there’s too much noise.”
While signals such as sales triggers, visitor intelligence, and intent data sets continue to expand in depth and accuracy, delivering them in a coherent, holistic, and actionable way has proven difficult. Simply being told that somebody visited your website or there was an executive change at a prospect isn’t actionable because valuable signals get lost in the noise. Most individual signals do not assist with prioritization, identifying the buying committee, or writing an email that cuts through inbox noise. That is why sales assistants such as ZoomInfo Copilot will be warmly greeted.
“We built ZoomInfo Copilot to change that — to push these insights directly to sellers, teeing up outreach for the best leads at exactly the right moment,” continued Schuck. “Copilot turns ZoomInfo from a contact lookup tool into a platform that surfaces the key insights sellers need to take action against each day.”
Copilot also supports AI-guided prospecting that prioritizes sales rep activities on a redesigned home page. The ranked list of target accounts, based on historical deal analysis, employs sales triggers such as intent signals and executive scoops (ZoomInfo’s term for business events) to prioritize outbound activity.
CRM deal analysis identifies the “DNA of your best-fit customers and uses ZoomInfo’s leading go-to-market data to identify the best-fit accounts for you,” said CPO Dominik Facher. “Copilot generates natural language explanations of why an account has been prioritized so you have all the context to successfully engage.”
Copilot supports Salesforce and HubSpot for building target account lists, with additional CRMs planned in subsequent releases. Data is ingested from the Opportunity and Account record types.
To further assist prospecting, ZoomInfo surfaces buying committee members “who are most likely to engage,” which admins can curate and push out to their frontline sellers.
Sales Intelligence vendors have long said that their offerings helped reps know “who to call, when to call, and what to say.” However, this data was often raw information that needed to be analyzed by reps and messaged to prospects on an individual account basis. Not only does Copilot prioritize activities across their book of business and suggest the next best actions, but it can even recommend the channels on which individual buyers are most likely to respond.
“Marketers can access a ranked and prioritized list of the companies and buyers in-market, based on millions of signals analyzed and prioritized by ZoomInfo Copilot’s AI every day, directly from the homepage feed,” blogged Schuck. “ZoomInfo Copilot’s intelligent recommendations are presented in the language of today’s sellers to make taking action as easy and intuitive as possible. Users can explore each opportunity in more depth and engage with those opportunities directly from the Homepage Feed.”
Users can select between different persona definitions and filter by CRM presence and “likelihood to engage.”
Copilot sports an AI Email Assistant that employs GenAI to compose messaging around selected insights.
The Copilot includes an AI Email Assistant to simplify outbound, personalized outreach. Emails are generated based on the seller’s objective, previous account context, additional context offered by the sales rep, and ZoomInfo insights. The sales rep can select from three generated options and adjust the length or tone. Users have the option to email one or multiple individuals and can specify which insights should be used when generating the email.
Copilot assists with buying group discovery, “Pulling insights from websites, case studies, earnings call summaries, and many more real-time signals, ZoomInfo Copilot automatically creates buying groups of individuals who are most likely to engage and align with their ideal customer profiles.”
Copilot shortens the time to value for new users by automating personalization, including ICP and persona definition. Traditionally, new Sales Intelligence platform users spend hours customizing the platform, defining target personas, ICP, companies of interest, topics of interest, etc. Much of this process is automated by Copilot, allowing sales reps to immediately begin deriving value.
“When someone gets onboarded, we build out what we call a customer context database. We essentially go and do a lot of research on that company, what are their value props? What are their pain points? Who are their end users? And then we start to infer what topics, buying committees, types of companies that they’re interested in,” ZoomInfo VP of Data Strategy Brandon Tucker explained to GZ Consulting. “And then as they start to engage with the signals, or set some of those configurations on their own, they start to get even more relevant insights.”
ZoomInfo Copilot supports GenAI account queries.
A GenAI chat interface answers account questions, “giving users the answers they need quickly using conversational AI. Users can request answers on a range of account-level topics to get up to speed as quickly as possible.”
Copilot “meets you where you are,” including desktop, weekly email digests, Slack alerts, Chrome extension, and a mobile app.
“ZoomInfo Copilot also allows salespeople to seize time-sensitive opportunities in real-time with Breaking Alerts delivered through Slack,” blogged Schuck. “These alerts can be shared across multiple channels, allowing teams to quickly triage emerging opportunities and act decisively on high-quality intent signals.”
The Personalized Target Account Digest alerts users when prospects are “showing the right signals” and explains why now is a good time for reaching out to the prospect. Users can drill down into greater detail, compose an email, or export a prospect to the CRM.
Thus, “signals drive your actions,” said Facher.
ZoomInfo Copilot ingests ZoomInfo’s first- and third-party data to deliver “detailed overviews of specific accounts, including pain points and use cases, upcoming deals, important contacts, a summary of previous engagements, and more.”
“Finding the time to understand every account is a tall ask,” stated Facher. However, “Copilot effortlessly summarizes the need to know and the nice to know for any account, based on ZoomInfo, your CRM, and the engagements you’ve had with your customers, like emails or calls, to generate a holistic picture of an account’s health, its history, and the opportunity.”
Thus, reps can have a “detailed understanding” of any account “in seconds” and establish a “shared foundation” of account knowledge across the account team.
“Get briefed, get aligned, and get selling,” concluded Facher.
ZoomInfo contends that its Copilot has a significant advantage over other offerings due to the breadth and quality of its reference content and engagement data.
Account AI summarizes the account with multi-dimensional views.
“What sets ZoomInfo’s Copilot apart from any other solution in the market is that it is sitting on top of our AI-ready trusted data foundation that drives decisions, personalization, and confidence,” said ZoomInfo CEO Henry Schuck. “AI is only as good as the data it’s built on, and most solutions are layered on top of static CRM data.”
Schuck argued that ZoomInfo is well-positioned in the emerging Copilot space as it offers high-quality data for maintaining enterprise software platforms and grounding its Copilot. Among its verified data assets are third-party reference data, second-party intent data, and first-party engagement and conversational insights:
“Copilot takes signals like website visitors, spikes in job postings, earnings call transcripts, contract renewal dates, and expert calls that indicate spending or competitive threats, then uses advanced entity resolution and matching to combine them with customers’ first-party data,” stated Schuck on ZoomInfo’s recent earnings call. “It then applies AI technology to model and inform users immediately about which companies are in the market for their product and how and why you should engage with them.
“For our customers, understanding firmographics alone is not sufficient to understand whether or not your next buyer is about to be in-market for your product,” continued Schuck. “It’s only when you surround that core data with signals that you’re able to predict who your next customer should be.”
ZoomInfo feeds data and buyer signals into Copilot to identify the right buyer during the buyer research process.
Copilot looks to identify in-market accounts and buyers during the research phase, relying on a broad set of hidden buyer signals (e.g., intent, competitive research, job postings, earnings calls, website visits) before the buyer raises their hand. The research window is the period during which sales and marketing have the greatest opportunity to influence the problem framing and preliminary vendor list.
But cold-calling into the TAM absent signals is very wasteful, as roughly only 10% of the ICP is in the market.
“Very few of the buyers that you’re looking for are in-market during the time you’re looking at them, and the ability to pinpoint those isn’t very easy today,” explained Product Marketing SVP Jam Khan to GZ Consulting. “So you can use predictive analytics. You can make your best guess when you have a fairly broken MQL system. ABM vendors have tried to come up with a different point of view, but it doesn’t quite replace the MQL.”
Copilot looks to identify the “chasm of opportunity” between signal generation and the first point of contact.
“The bridge we’re trying to gap is the difference between being first in a deal and being second in a deal,” argued Khan. “You’re never going to have a crystal ball that lets you anticipate before a buyer ever even starts making their decision. But that short little window is the difference between winning and losing.”
Copilot looks to “solve for the chasm” and give sales teams a first-mover advantage. While this “window of opportunity is really small,” it is the difference between hitting .200 and .300, analogized Khan. “To the extent where you’re able to act on those, that’s the difference between hitting your number and missing your number.”
Thus, other GenAI or Copilot offerings that pull data from the CRM face a trio of problems when generating recommendations. CRM data is limited to what has been keyed into it. As this data is historical (and reps hate maintaining CRM data), it is likely to be outdated, stale, and inaccurate.
“Third, it lacks the outside signals and insights that drive modern go-to-market motions. ZoomInfo Copilot delivers a full picture built on the foundation of the world’s most accurate and up-to-date business data, publishes real-time insights, and turns that into personalized and relevant content,” stated Schuck.
“Copilot is one of the best pieces of software we built at ZoomInfo, across ease-of-use, end-to-end understanding of our customers’ pain points and product market fit. We have had leading AI models in production for years,” crowed Schuck. “We expect to monetize Copilot and we’ll roll it out in a thoughtful way, focusing first on the customers who are most likely to get significant value out of the advanced platform. Our go-to-market teams are excited to bring this to their customers, and I have a lot of conviction around the upgrade paths in our customer base.”
Earnings Scoops, the latest content set to be collected and fed into Copilot, extend ZoomInfo’s technology and business event Scoops into SEC filing analysis. The service ingests 10-Ks (annual), 8-Ks (Material announcements), international filings, and earnings transcripts. It then outputs a set of condensed topical summaries. Earnings Scoops leverage GenAI to analyze customer and prospect competitors, goals, initiatives, pain points, and SWOT elements. They are also fed into its beta Copilot service.
Earnings Scoops are assigned metatags from a set of 150 Scoops topics that assist with searching and Copilot customization. This additional tagging helps tailor Scoop presentation to each firm’s ICP and is displayed in a Scoops Topics column (see a subset of topics from Meta’s recent earnings on the right).
“Many organizations still struggle to provide frontline sellers with actionable go-to-market insights distilled from the myriad of available signals,” said IDC Analyst Roger Beharry Lall. “While AI can sift through mountains of data, solutions must be built on a foundation of fresh, accurate, and clean data in order to deliver meaningful intelligence. Suppliers like ZoomInfo that can combine robust data sets with novel AI capabilities will help customers lead their markets by enabling engagement to the right people with the right message at precisely the right moment.”
While Schuck is confident in Copilot and ZoomInfo’s ability to monetize it, Schuck does not believe it will significantly grow revenue in H2.
“It’s going to take longer than that. I have a lot of confidence because I personally pitched this product across dozens of our customers, across all segments, and all industries,” argued Schuck. “From a product market fit, I don’t think we’ve been ever so close to fit as we have been with Copilot, outside of the core company and contact data. And so, I have a tremendous amount of confidence that we’re going to be able to turn that enthusiasm into monetization, but I also expect it to happen over time.”
The Homepage prioritizes accounts with next best action recommendations.
I have been experimenting with posting LinkedIn Newsletters, so I have not been placing my content here for the past month. I posted a pair of articles about HubSpot, which held its annual INBOUND conference in Boston.
HubSpot Brings GAI to Its Hubs
Like many other RevTech vendors, HubSpot is all in on Generative AI (GAI) and infusing it across its Hubs. CEO Yamini Rangan and Product EVP Andrew Pitre emphasized GAI in their INBOUND keynotes, emphasizing GAI’s ability to foster customer connection and please the customer.
Rangan stated that the “most intelligent way to use intelligence is to drive customer connection,” which can be “scaled to hundreds or thousands or maybe even millions of your customers by observing their patterns, anticipating their needs, and offering them insights.”
A decade ago, vendors looked at SAP and Oracle as the second CRM they supported after building their Salesforce connectors. Five years ago, the momentum had shifted towards Dynamics following the 2016 launch of Dynamics 365 in the cloud. It seems that HubSpot is now vying with MSD to be the second-supported CRM.
HubSpot has long been viewed as a decent CRM for SMBs to start on before they grow up and require enterprise features, and research from Adam Schoenfeld at Keyplay backs up this impression.
IDC, which doesn’t include HubSpot in its CRM market share analysis, noted that HubSpot has been growing much faster than the overall market, with 31% growth in 2020 and 47% in 2021…
Dun & Bradstreet announced the launch of D&B.AI Labs to lead the co-development of AI solutions that apply Generative AI and Large Language Models (LLMs) against its proprietary data and analytics.
“Over the last few years, Dun & Bradstreet has gone through a dramatic transformation driving a culture of innovation and making significant investments in technology, data, and analytics, including adding 64% more analytics solutions, evolving its scores and indices to leverage AI, LLM and ML capabilities,” claimed the firm.
A team of data scientists, data engineers, and solution specialists with expertise in AI, LLM, ML, and advanced business analytics staffs D&B.AI Labs. Lab members will partner with clients to formulate solutions, build prototypes, and deploy solutions that leverage Dun & Bradstreet’s data and analytics.
“Powered by innovation and in support of the rapid changes across the business landscape, companies of all sizes need access to an environment where they can fuse our trusted datasets, responsibly apply AI, and tap into our expertise to quickly develop prototypes and solutions to advance their businesses. We believe there is no company better than Dun & Bradstreet to accomplish this,” said Dun & Bradstreet CEO Anthony Jabbour. “D&B.AI Labs creates an environment for us to work side-by-side with our unparalleled client roster, including 93% of the Fortune 500, to understand their pain points and help them to swiftly design and deliver innovative solutions specific to their needs.”
Along with generative AI expertise, D&B.AI Labs offers expertise in Dun & Bradstreet’s ESG, linkage (e.g., family trees), Master Data Management, and sales and marketing solutions. Dun & Bradstreet’s MDM solutions help connect data within organizations, cleanse and enrich records, and apply predictive analytics against customer datasets.
“In a world where LLMs are trained on mainly uncontrolled publicly available data from the web, the value of trusted datasets such as Dun & Bradstreet’s will increase significantly,” said Gary Kotovets, Chief Data & Analytics Officer at Dun & Bradstreet. “Our products and services are underpinned by validated, historical, and proprietary data, which allows us to deliver reliable and interpretable AI-created results that drive our clients’ most critical business decisions.”
Dun & Bradstreet also announced that its Sales Intelligence solution, D&B Hoover’s, is now available on the Google Cloud Marketplace. D&B Hoover’s via the Google Cloud Marketplace allows for a “dollar-for-dollar drawdown against Google Cloud commitments.”
“The demand for trusted B2B data intelligence is ever-increasing. D&B Hoovers continues to be a solution that organizations rely on to help boost sales productivity and strategic targeting to drive business growth,” said Karlos Palmer, SVP of Sales & Marketing Solutions Product. “Having D&B Hoovers available on Google Cloud Marketplace makes it easy to use incredibly valuable data to build sales pipelines, and since we’ve migrated D&B Hoovers to Google Cloud, customers have already reported significant performance improvements.”
Engage’s generative AI models help personalize customer engagement efforts while streamlining email composition.
Revenue Intelligence vendor Gong unveiled Gong Engage, its entry into the Sales Engagement space. SEPs have been adding Conversational Sales tools for several years, so Gong adding SEP functionality should not be surprising. After all, Gong began as a conversational sales solution before widening its scope to Revenue Intelligence. Also, one of Gong’s top rivals, Chorus, is owned by ZoomInfo, which has been building ZoomInfo Engage for several years.
“Gong Engage serves as a single solution to streamline sales engagement for revenue teams by delivering AI-based sales guidance from the first touchpoint with a prospect to deal close,” wrote the firm. “Engage helps teams create and accelerate pipeline by delivering increased productivity with AI-driven automation and guidance, high-quality outreach to engage entire accounts, and a single sales engagement solution to streamline revenue workflows. Gong’s proprietary AI delivers three times more accuracy than off-the-shelf AI models.”
Engage supports core SEP features, including a web-based dialer, workflows, and email templates. It also sports generative AI models for deal prioritization and extracting call highlights, action items, and outcomes. AI features include:
Call Spotlight, Gong’s generative AI functionality that composes call briefs, account highlights, and action items from sales conversations
Automated call outcomes and email response classification that automatically categorizes calls and emails and syncs these insights with the CRM
Assisted writing, which helps reps personalize their outreach based on persona
“Engage’s contextual, account-based approach for prospecting and selling exceeds the limitations of traditional lead-based tools that encourage mass outreach with little targeting. Instead, it delivers a complete understanding of an account, including all touchpoints, relevant CRM information, and conversational history,” stated Gong.
Gong emphasized that it didn’t jump on the generative AI bandwagon following the breakout success of ChatGPT but has been working to integrate generative tools into its platform for over a year. Furthermore, its focus on sales-focused NLP offers it a leg up on vendors marketing generic ChatGPT tools or lacking years of AI expertise and product development.
“Gong launched in 2016 to harness the power of AI to rid customer-facing people of their day-to-day drudgery. We have been working with LLMs for over a year and see it as a major breakthrough. But highly accurate, domain-specific AI that delivers a deep understanding of what’s going on within a sales organization and the next steps needed to win deals is more elusive,” said Gong CEO Amit Bendov. “AI is the core of Gong’s platform, and our new models exemplify the sales-driven advancements we’re bringing to market to help our customers transform their teams and accelerate revenue growth.”
Along with conversational sales expertise, Gong offers Smart Trackers, a set of AI-based customized signals. Launched last year, Smart Trackers help revenue teams “identify deal risks and opportunities, understand the effectiveness of strategic initiatives, replicate best practices, and get ahead of emerging market needs.”
Gong Recommended Contacts
Engage’s Recommended Contacts feature analyzes historical deal data to recommend contacts for outreach. It feeds contact data from LeadIQ, Cognism, and Apollo based on target personas. Recommended contacts may be added to flows (cadences), or sales reps can reach out via email, phone, or LinkedIn.
Engage also displays buying signals from over 120 Gong Collective integrations “to help teams stay on top of their pipeline, no matter where signals are coming from.”
Along with consolidating conversational sales, sales engagement, and revenue intelligence in a common platform, Engage supports partnerships that include LinkedIn Sales Navigator, Chili Piper, and Calendly.
Engage workflow and dashboard features include Pipeline Views for viewing key tasks and insights across open opportunities, an Analytics Dashboard for sales management, and team collaboration tools for sharing and assigning tasks across teams.
“Gong changed how revenue teams build pipeline with our customer-centric AI – now we’re changing how they engage with customers at every stage,” said Bendov. “We have leveraged our market-leading AI technology as the foundation for Gong Engage, which sets the entire revenue team up for success. Engage is the only solution that harnesses customer interactions at scale to drive high-quality engagement and ultimately, grow revenue.”
Sales Enablement vendor Showpad announced PitchAI, its response to the problem of overloaded managers that lack the time to consistently provide timely feedback and coaching around sales pitches.
“Delivering the perfect sales pitch is one of the most challenging sales skills to master—both for inside and field sellers. A sales pitch must be confident, impactful, and quick,” stated the firm. “It should grab a buyer’s attention, capture trust, and leave a curiosity gap. A great pitch doesn’t happen by accident—it takes practice, preparation, and feedback. Yet, sales managers and enablement teams aren’t always able to provide the thoughtful and attentive evaluations and feedback reps need to develop their pitch.”
According to a 2018 Gartner Leadership survey, only 42% of sales managers feel equipped to develop their staff. Furthermore, “sellers feel the shortcomings of managers as well. Just 38% of sellers report their manager helps them develop the skills they need for their role today, while only 34% report their manager helps them develop the skills they need for the future.”
PitchAI employs AI to record and analyze sales pitches, providing consistent, on-demand coaching. Feedback is instant, allowing the rep to iterate, refine, and build confidence.
Furthermore, PitchAI analyzes the seller’s “credibility, sincerity, and knowledge through non-verbal communication” and coaches reps on improving their messaging and presentation. Each pitch is benchmarked against top sellers across the industry “to provide context to PitchAI’s rating and performance scores.”
PitchAI evaluates reps across four dimensions:
Speed: How fast or slow the pitch delivery is and whether it’s understandable.
Body language: How trustworthy, friendly, and approachable the seller appears.
Silences: How and when to add or remove pauses.
Happiness: How enthusiastic the pitch comes across overall.
Tips are industry and language-specific. Showpad noted that “long silences make for a bad pitch in German, but a good one in Portuguese.”
PitchAI banner messaging includes advice on what adjustments to make.
Winn.AI captures real-time playbook insights and maps them to over twenty methodologies.
Israeli playbook vendor Winn.AI now offers a self-service registration that allows sales reps to trial the Generative AI service for a month at no charge. Winn describes itself as an “AI-driven no-typing CRM that aims to empower the next generation of sales teams.” It joins calls and offers real-time tracking, capturing, and CRM updating features for Salesforce and HubSpot.
Winn supports over twenty sales methodologies and is tuned to automatically capture playbook insights as they are organically discussed with the sales rep. Out-of-the-box methodologies include MEDDIC, MEDDPIC, Spin Selling, BANT, and Challenger. Additionally, playbooks are available for discovery calls, demos, and negotiations and may be customized.
Salesforce “State of Sales” report. Salesforce surveyed 7,700 sales reps in August & September 2022.
CEO Eldad Postan-Koren emphasized the administrative burden faced by sales reps, citing a 2023 Salesforce report that claims only 28% of sales rep time is spent selling. 72% of sales rep time is consumed by record keeping, CRM updating, virtual meetings, email follow-ups, data entry, and lead management. Winn.AI promises to claw back one-third of the 8.8% of rep time spent manually entering information. Furthermore, reps have long limited their data entry to the bare minimum, so Winn.AI will capture significantly more detail.
Other tasks, such as prioritizing leads/opportunities, researching prospects, and preparation and planning, also benefit from automated data harvesting and synchronization, helping reduce time on such tasks and improving their quality.
“Interfering with salespersons in our daily conduct is not good. Adding another system for them to enter and changing the way they are used to working is a negative type of interference,” argued Postan-Koren. “This is the main reason I built Winn.AI as an augmentation of other existing systems. This will effectively disrupt the sales force where disruption is needed and welcomed.”
Winn.AI automatically joins conversational platforms such as Zoom, Teams, and Google Meet and supports time warnings, attendee lists, and post-call meeting summaries. Editable summaries are displayed as soon as the call ends, adding them to the standard call workflow but removing most data entry. Salespeople can also send a personalized recap email to call participants. This review-and-notification process is generally completed within four minutes of each call’s conclusion.
The Winn-AI post-call meeting summary pops up on call completion and supports editing and matching against CRM records.
As playbook details are captured in real-time, reps can see which topics have been discussed and which ones remain open, helping ensure richer discussions. Following the call, reps are presented with a call summary, next steps, and playbook insights. Once reviewed, the information is synced with the appropriate CRM records.
Postan-Koren explained that Winn.AI employs NLU (Natural Language Understanding), which goes beyond NLP (Natural Language Processing).
“Understanding is not processing because I can ask you, ‘Michael, how many employees are you?’ And your answer will be 20 employees. But I can also ask you, ‘Mike, do you have 20 employees?’ And your answer will be yes. Or I can ask you ‘How many employees?’ and your answer will be 20. So here are three different ways to ask the same question to get the same answer, and the computer won’t understand it. Understanding context is the secret sauce here.”
What also differentiates Winn.AI is the combination of playbooks with real-time call coaching, data capture, and CRM syncing, allowing reps to be more present during calls. These elements are supported across many products, but I have not seen a company combine them into a single offering.
“The system is a personal assistant for the salespeople and relieves the burdensome administrative work of taking notes, having a list of answers, and entering information into the CRM,” stated Postan-Koren. “ It does so all at a level of detail that does not exist in other tools on the market, so much so that in real-time, it can exactly match a specific topic of conversation to the relevant field in the CRM. Also, the system knows how to follow the topics of the conversation in real-time, check the full coverage of the conversation’s agenda, and give instructions that will help improve performance. This is a new category in the SalesTech world, and Winn.AI aims to lead this category.”
The firm is not targeting specific verticals as it believes it has a compelling cross-industry solution that supports digital sales, customer success, and service departments.
The service is priced between $59 and $89 per user per month, with volume discounts available.
Winn.AI exited stealth mode last fall and now has a team of thirty. It has begun hiring in the Bay area. Postan-Koren noted that its $17 million September seed round provides it with a financial runway through the end of next year, allowing it to focus on go-to-market instead of hustling for funding in a weak VC market.
Winn.AI was named one of the “Top Israeli Startups To Watch in 2023.” The firm placed 16th on the list by Startup Stash.
Drift’s chatbot now offers suggested replies, based on company marketing materials, to sales reps.
Conversational Cloud vendor Drift announced it had integrated OpenAI’s API into its chat service. The GPT integration suggests chat replies to sales reps, helping them answer complex customer questions. Responses are based on a company’s website content, marketing material, conversational context, and GPT.
Reps can choose to send the response verbatim, edit it before responding, or request a refined response.
“The ability to generate suggested replies by repurposing any available content also provides real-time on-the-job training for new sales reps (which currently takes three months on average), teaching them how to respond accurately and in the right brand voice,” explained the firm.
Drift listed a trio of capabilities it will be releasing later this year:
Automated content creation and ongoing optimization of playbooks
Automated onboarding and AI topic training
Personalizing sales outreach and prioritizing target accounts
“The buzz around ChatGPT, and the widespread experimentation that followed, has only underscored that conversation-based AI is paving the way of the future. But it needs to be tailored to solve meaningful business challenges,” said Matt Tippets, SVP at Drift. “With Drift’s ability to use Conversational AI to contextualize responses that augment the efforts of human teams, we’ve created a feature that helps sales reps of all experience levels be more productive, particularly relevant now with pressure on during a more difficult economic climate.”
Drift’s approach offers a human-in-the-loop option for sales chat that ensures responses are accurate and not subject to hallucinations.
“We have years of experience and real-life use cases that are already being applied to how we manage and bring GPT capabilities to our customers,” blogged Drift Senior Director of Product Marketing Aurelia Solomon. “And we’re excited to continue embedding GPT into our products for marketers and sellers to automate more of their workflows to save time and help them create quality pipeline, faster.”
RingSense for Sales transcribes, summarizes, and analyzes meetings.
Cloud Communications vendor RingCentral entered the Conversational Sales space with RingSense for Sales. The new service employs generative AI to summarize meetings and calls. Initially, RingSense supports the sales function, but RingCentral plans to extend the service to other roles, including support and marketing.
“We’re going to be helping businesses make their voice, their meetings, their email interactions more intelligent, so they can automatically uncover new, actionable insights that help reduce errors, overhead, and improve performance,” said RingCentral COO Mo Katibeh. “I think of it as a force multiplier for every single employee.”
RingSense for Sales analyzes customer interactions with sales reps and surfaces insights and performance metrics. These insights are also delivered to sales managers to assist with training and mentoring.
“Today marks an important step forward in our journey. Generative AI is a game-changing technology that will fundamentally transform communications and collaboration. Natural language, and voice in particular, has always been a universal interface for information, intent, and emotion that has been largely untapped,” said RingCentral CEO Vlad Shmunis. “Now with RingSense, we have the opportunity to inject cutting-edge AI across the entire RingCentral portfolio and make communications a powerful resource for businesses to unlock new potential and quickly extract meaningful information and insights.”
RingSense for Sales supports:
Automated follow-ups to drive productivity: RingSense’s AI gathers interaction summaries, notes, and follow-ups. It then syncs them with the CRM.
AI-generated summary scoring: Interaction-level scoring and reporting call out which conversations should be prioritized by managers, helping them identify coaching opportunities.
Integrations with 3rd party apps: RingSense for Sales integrates with Salesforce, HubSpot, Zoho, Google Calendar, Microsoft Outlook, and call and video meeting providers.
Ability to track keywords and phrases (trackers): Sales admins can set RingSense to track keywords and phrases such as competitor names, objections, trends, or product features. RingSense can also focus on relevant concepts instead of just keywords and calls out positive and negative deal signals.
“Over the last few years, RingCentral has developed a rich set of AI models that delivers conversational speech analysis and emotional sentiment recognition to the RingCentral platform,” explained the firm. “Last year, RingCentral rolled out numerous AI-powered video meetings capabilities and was first to market with Advanced Meeting Insights and Summaries, which uses AI to enable a user to quickly catch up on a missed meeting or use the tool for automated note taking.”
RingSense for Sales captures topics, summarizes calls, and assists with deal coaching.
RingCentral also rolled out a set of AI APIs for accessing RingCentral transcriptions, summarizations, sentiment analysis, and interaction analysis for voice, video, and chat. These APIs will be available to their 85,000 registered developers and 350 third-party applications. RingSense, like RingCentral, is an open platform.
“Artificial intelligence (AI) is the fuel that will fundamentally transform how we think about digital transformation today. The era of deploying enterprise AI in isolation while wrestling with outcome uncertainty is over,” said Aragon Research CEO Jim Lundy. “RingCentral has created an enterprise-grade AI with a results-based design. Last year, they embedded this unique AI into their MVP platform to help make employees more productive. Now, with RingSense, they’ve taken it to a whole new level by focusing on bespoke use cases the industry truly needs.”
RingCentral has long partnered with incumbent Conversational Sales vendors such as Gong, Chorus, Wingman (Clari), ExecVision (Mediafly), and Outreach. Many of these vendors have been offering conversational sales tools for three to five years. In addressing their delayed entry into Conversational Sales, Senior Product Marketing Manager Keith Renison listed the following advantages of the RingSense offering:
RingSense is easy to purchase, deploy, and configure. Get ready for faster time-to-value without the expensive startup, platform fees, and model retraining costs.
AI-generated summaries, topics, and follow-ups are automatic. This accelerates the disposition of customer interactions creating huge efficiency gains.
Out-of-the-box “trackers” with higher quality pattern matching. Trackers can be customized or created from scratch. Tune your system to what you’re listening for and make adjustments along the way. AI does the rest.
AI-generated summary scoring at the top level helps find the important conversations without sifting through conversations manually.
RingSense is built by a trusted leader in cloud communications.
RingSense is not industry specific but can be leveraged broadly across industries for analyzing digitized sales calls and meetings, said Chief Innovation Officer Kira Makagon.
RingCentral, which has built a customer base of five million customers over the past two decades, can deploy RingSense to “empower person-to-person communications.” RingSense helps “make sense” and “find patterns in these interactions.”
Due to the large breadth of business conversations hosted on its digital platforms, RingCentral is “in a unique position to make sense of all of the wealth of information that is flowing through our platform,” said Shmunis. “By applying these ultra-modern AI techniques, we can really empower users of our system to make better sense as to what is happening in their own employee bases, whether it be sales agents, customer service agents, and, in certain cases, even just knowledge workers who are communicating and interacting with the outside World.”
Shmunis emphasized that RingSense is a platform with RingSense for Sales as the first offering. As an open platform, partners can build verticalized solutions. Shmunis suggested applications in legal, finance, and healthcare. Open RingSense APIs are already available.
Zeus Kerravala, Principal Analyst at ZK Research, argued that businesses suffer from poor data quality in their CRMs, with reps often limiting the information they key into Salesforce to “Met with customer.” Thus, AI’s most significant initial impact will be improving “data discipline” in organizations. The initial benefit of AI will be as an “input mechanism.”
“So, when I do a call with you, an AI can listen to it, summarize the meaning, put it in [the] CRM [and] update the record. You have contact center agents. You can do that with salespeople and customer success,” argued Kerravala. “You could actually use AI to create a better dataset and so, in theory, good AI leads to better AI.”
Kerravala sees AI improving the pre- and post-meeting experience due to its ability to create, manage, and refine meeting data. “Most of the vendors do a pretty good job mid-meeting. We have virtual backgrounds, transcription, and translation capabilities. But what happens when the meeting is over? Help me get the meeting minutes out to people and help me prep for meetings. That’s where I see this stuff having a pretty big impact.”
RingSense is available in three packages based on function: Observer for Marketers helps them track customer and competitive trends, Coach supports managers, and Professional provides the full complement of deal intelligence and CRM synchronization. RingCentral did not provide any pricing details.
RingSense seats may be allocated by role with Professional supporting the full complement of deal intelligence and CRM synchronization.
