Along with hosting conferences and training, Corporate Visions helps firms “articulate value and promote growth” in three ways:
Make Value Situational by distinguishing your commercial programs between customer acquisition, retention, and expansion.
Make Value Specific by creating and delivering customer conversations that communicate concrete value, change behavior, and motivate buying decisions.
Make Value Systematic by equipping your commercial engine to deliver consistent and persistent touches across the entire Customer Deciding Journey.
“I have been a fan of Corporate Visions and their work since the beginning of Outreach. Their commitment to research-backed solutions for sales challenges and solid track record of implementing solutions across GTM teams that lead to results is unmatched. Their work has resulted in increased revenue and profitability for many of the world’s biggest companies,” said Outreach CEO Manny Medina. “That’s why Outreach is partnering with Corporate Visions to empower Chief Revenue Officers to unlock sales productivity so they can efficiently create pipeline and predictably close more deals.”
The partnership aims to increase deal velocity, improve sales quota attainment, and generate higher win rates. Corporate Visions will offer a quartet of Outreach-based services around tech implementation strategy, sales messaging optimization, skills coaching, and sales forecast process & measurement.
“By leveraging Corporate Visions’ best practices as organizations implement Outreach’s platform, enterprise sales leaders, from the CRO down, will ensure that every action their team is taking consistently builds enough pipeline coverage for the next quarter without sacrificing today’s deal velocity or pipeline conversion rates,” wrote the firms.
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.
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.
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.
“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.”
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.
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.
“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:
Make Value Situational by distinguishing your commercial programs between customer acquisition, retention, and expansion.
Make Value Specific by creating and delivering customer conversations that communicate concrete value, change behavior, and motivate buying decisions.
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.”
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.
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.
Sales Engagement vendor Groove introduced its Plays service to the market this morning. Groove observed that most sales engagement vendors tout flows (aka cadences and sequences) but that sequenced, linear processes fail to capture the increasingly complex nature of modern enterprise sales. Furthermore, flows were initially designed for SDRs and appointment setting but are inadequate for meeting the broader needs of the revenue team.
Along with the introduction of Plays, Groove is shifting from sales engagement to a broader vision of “Connected Sales Execution” that unifies team, strategy, and technology.
“When my co-founder Austin and I founded Groove, we were sales leaders facing the exact challenge that Groove Plays solves,” said CEO Chris Rothstein. “We knew that in order to digitally transform sales as a profession, we had to start by building a foundation in advanced data capture and linear-process automation. With Groove Plays, we are introducing the next generation of Groove to solve the biggest untapped market in sales.”
Forrester recognized this transformation in its Q3 2022 Sales Engagement Platforms Wave report, noting that Groove’s activity capture and interaction management are “top-notch.” Groove collects and aggregates signals from interactions and scores from Salesforce and external sources such as Clari, Seismic, 6sense, and Snowflake. “This information is used to connect buying group members and make suggestions based on broad data sets. Groove specializes in industry-specific and customer-specific suggestions and signals.”
“We’re launching Groove Plays as a way to take your playbook finally out of your head and put it into software so that you can assist reps at the right time, rather than after it’s too late,” explained Rothstein to GZ Consulting. “And then the second huge benefit: if you can constantly see what’s being done, what’s not being done, and what’s correlated with winning, then you can evolve and constantly get better.
Plays are designed for complex, non-linear sales processes. Sales Operations set up Groove Plays to monitor accounts for risks and opportunities. Plays are triggered when specified conditions are met (e.g., stalled deals, single threading, missing participants by role).
Groove Plays also monitors rep activity to see whether plays contributed to positive outcomes. Thus, sales managers and operations teams know whether sales reps follow company playbooks and which ones are effective. Play analytics are broken into outcomes without intervention (the playbook was followed), with intervention (the playbook was followed but after a reminder), or ignored.
Furthermore, by monitoring activity, Plays prevent reps from failing to follow critical steps (e.g., sending a follow-up message after a call, quickly turning around meeting action items).
Alerts are fed to Groove’s Master Review List, which is displayed in its Chrome extension and visible across Salesforce, Groove, and email. In addition, timers can be set to prevent plays from automatically firing, thus reducing the likelihood that reps are overwhelmed by automated triggers.
Plays provide proactive coaching instead of waiting until account reviews or forecasts. Delayed recommendations are generally reactive instead of proactive. “At that point, it’s too late. And then you react way too late. Our goal is for you to put the rules in the system, so it’s assisting you at the right time when there are signals…so you can be more proactive and consistent,” stated Rothstein.
Plays recommend actions when specific criteria are met. For example, a play can be built for deals with negative sentiment concerning price and slowing engagement. The play could then recommend an ROI calculator to a prospect, helping shift their thinking from cost to ROI.
Plays can also be built around handoffs, ensuring that crucial transition steps are not skipped.
Plays are also integrated into Groove’s conversational intelligence service and generative AI, providing meeting follow-up emails based on insights. Reps can choose to regenerate the email or add snippets.
Plays can also be designed around deal risk, suggesting actions if key buying committee members are not engaged. Likewise, plays can be setup if MEDDIC steps have not been completed, the primary contact has not responded to a renewal message, or internal approval timelines are not being met.
Groove’s RIO AI engine consists of three underlying engines:
NLP: Analyzes emails and generates insights for coaching.
Association: Ties actions to outcomes across the tech stack.
Guidance: Suggests actions based on sales plays and generates personalized content and best engagement times.
Groove supports “Connected Sales Execution” across sales, marketing, and customer success. RIO ingests account and activity histories with feedback loops to refine plays and recommendations. Thus, Connected Sales Execution spans teams, processes, and technology.
“We’re a platform to help you execute your sales strategy,” argued Rothstein. At its heart, Groove employs AI, processes, rules, and sensors (e.g., email capture, calendar capture, logging, phone calls) that analyze activities and generate insights.
“We’ve always been a company that connects all these things: the technology and the process, the team and the process,” stated Rothstein. “Where we can help is getting everyone on the same page, executing the playbook in real-time, and seeing what’s working and [what’s] not.”
Groove Plays is in Alpha with a planned Q2 beta. Groove Plays will be available to all customers at no additional cost when it GAs this summer.
Lavender, which markets an AI-powered sales email coaching platform, closed an $11 million Series A, raising its total funding to $13.2 million. Norwest Venture Partners led the round with participation from Signia Venture Partners. The funding follows strong growth in 2022, with revenue rising 865%.
Funds will be deployed towards expanding its team and introducing “new AI-powered features that help revenue teams not just understand why their messaging is falling flat but also provide actionable coaching to improve productivity and generate faster responses.”
“Lavender’s new investment helps it build out a generative AI solution at the intersection of email marketing, sales enablement, and news and information,” wrote Outsell Lead Analyst Randy Giusto. “It shows where sales and marketing intelligence vendors must head next.”
Lavender integrates with a user’s email workflow, helping reps improve response rates. It also delivers prospect company and contact intelligence. Lavender scores emails and recommends steps to improve response rates. It also “coaches sales reps on how to build meaningful relationships and close more deals.”
Along with raising response rates, email composition time is significantly reduced. Lavender claims that rep time writing an email drops from fifteen minutes to one while raising email response rates fourfold to twenty percent or more.
“Using Lavender is like giving every seller on the team a dedicated coach, making them more effective, more efficient, and more confident in their job,” stated CEO William Ballance. “This funding quickly accelerates our ability to build the best email experience for sellers around the world. Most importantly, we’re creating new jobs as our team of #EmailWizards rapidly expands.”
Lavender recommendations are initially based on “millions of successful sales emails and your historical emails.” However, it continues to adjust recommendations based on “what works best for you.”
Lavender evaluates the subject and body to improve open rates. It will identify subjects that are too long, not in title case, or contain numbers and punctuation. For the body, it looks at the length, layout, spelling, and grammar. For example, long sentences and paragraphs are difficult to read on mobile devices, so they are discouraged. According to Lavender, 80% of buyers are viewing emails on their phone, “so making emails easily scannable on a mobile device is imperative.” A mobile preview window displays the email as it would appear on phones.
“We recommend things across multiple categories, including formatting, phrasing, tonality and emotional intelligence, mobile optimization, personalization, and more,” explained Lavender COO Will Allred to GZ Consulting. “The data is always shifting though, and as trends shift in sales emails/what works well for that user and/or their team, Lavender’s scoring and recommendations dynamically adjust accordingly.”
Other Lavender features include a coaching dashboard, AI for Sales emails, and a personalization assistant. The coaching dashboard provides individual and team email scores, open rates, reply rates, and writing time. It helps managers determine which reps require additional coaching, what is working, and why it is working.
Lavender includes a “Start my Email” function that employs generative AI to “draft impactful outgoing email messages” based on seed bullet points from the rep or as email responses based on the email thread.
The personalization assistant displays recipient context to assist with personalization. For example, lavender surfaces social data, personality insights, news, events, job listings, funding announcements, and other intelligence within the composition workflow. Lavender AI also recommends “personalized intros to tailor your email and make it relevant to the recipient.”
Lavender is integrated with Gmail, Outlook, Outreach, and Salesloft. Lavender Anywhere, a Chrome extension, supports email composition for HubSpot, LinkedIn, Groove, Apollo, Engage, Outplay, and Mailchimp. As Lavender Anywhere is not directly integrated with these platforms, users must cut and paste the resulting text into the communication window. Lavender Anywhere is available with Pro and Teams licenses.
“Lavender is delivering exceptional value to our customers. Their integration provides real-time email assistance to help Salesloft customers build and deepen their relationships with prospects through better emails,” said Salesloft VP of Global Alliances Devin Schiffman. “We share in the mission of helping sellers be loved by the buyers they serve.”
Customers include Twilio, Sharebite, Sendoso, Segment, Lucidworks, and Clari. Allred said Lavender sells into a “large range” of companies, “but our sweet spot tends to be mid-market tech or tech-enabled companies.”
“Lavender is marketed toward ‘sales emails’ – but many things are a sale,” continued Allred. “Our users use Lavender for B2B sales, recruiting, customer success, marketing, and many more.”
While output is English only, features such as the email generator or summary tools can ingest foreign language input. Thus, Lavender “works great for ESL (English second language) selling into English speaking markets,” said Allred.
“Lavender’s platform goes beyond basic AI-generated writing to augment—rather than automate—sales outreach and humanize every interaction. It supercharges sales reps by reducing their time spent writing emails so that they can focus on building relationships and selling products,” said Scott Beechuk, partner at Norwest Venture Partners. “We were blown away by the ‘customer love’ for Lavender’s product, which is a testament to the founding team’s deep understanding of their end user and the tight-knit community of sales leaders it has already built. We’re excited to partner with this team on the journey ahead.”
The firm has benefited from the recent interest in generative AI and ChatGPT. “We were using GPT-3 long before ChatGPT was a thing, but ChatGPT has definitely increased interest in Generative AI more broadly. Users have created UGC (user-generated content) of them using Lavender to edit ChatGPT–generated emails,” explained Allred. “But before ChatGPT was released, thousands of users were already getting the benefit of it within Lavender.”
“We view the process of emailing as four parts: research, creation, editing, and learning,” continued Allred. Lavender assists in all four. Generative models can assist along the way to streamline things for our users.”
Lavender is sold on a freemium basis. Free users receive email analysis and personalization for five emails per month.
Reps can license an Individual Pro license for $29 per month that provides unlimited emails and recommendations. In addition, the Pro service includes Lavender Anywhere, multi-inbox support, analytics, and Gmail and Outlook 365 integrations.
For $49 per user per month, companies can license a Team edition that includes Team AI coaching, Team Insights, a Manager’s Dashboard, and SEP integrations for Salesloft or Outreach. Lavender offers a seven-day free trial and free premium licenses for job seekers, students, and bootstrapped entrepreneurs.
Attention, an AI-powered sales assistant, exited stealth mode and closed a $3.1 million seed round led by Eniac Ventures. Other participants include Frst, Liquid2 Ventures, Maschmeyer Group Ventures, Ride Ventures, and the founders of Ramp, Level AI, Truework, CBInsights, and Zoi. The new funds will be deployed towards advanced AI capabilities and market growth for the New York City-based startup.
Attention helps sales teams “overcome inefficiencies at every stage of the sales cycle,” including CRM hygiene, sales acceleration, and revenue growth. Attention accelerates sales rep ramp-up, drafts follow-up emails based on customer statements, improves forecasting, and automatically enriches Salesforce and HubSpot with post-call deal intelligence.
Attention maps discussions to custom CRM fields specific to standard sales methodologies such as MEDDIC and BANT.
Attention also provides real-time suggestions and question responses, “resulting in all sales representatives having higher rates of success and closed business.” Recommendations include product responses to technical questions and objections handling. The Attention AI determines common unsupported questions and builds battlecards based on sales responses. Battlecards can also be developed or edited by the sales enablement team.
One novel feature is the ability to query the meeting transcript with a question, allowing reps to summarize or revisit the discussion around key topics quickly.
Sales reps can share call snippets with managers or SMEs via Slack, allowing them to forward open questions in the voice of the customer.
“Attention is a game-changer. We’ve rarely seen any product like this in terms of efficiency gains and ramp-up acceleration. We’re also blown away by how fast they’ve been releasing new capabilities.” said Peter Santis, head of sales at RocketChat. Santis both licensed the service for RocketChat and participated in the seed round.
Founders Anis Bennaceur and Matthias Wickenburg founded competing AI software startups before joining forces in September 2021 to build Attention.
“We’re thrilled to partner with Anis and Matthias as they leverage the latest developments in AI generation and natural language understanding to superpower sales organizations,” remarked Hadley Harris from Eniac Ventures. “We love working with repeat founders and couldn’t be happier with the strong pull they’re already getting from the market.”
Attention supports communications platforms, including Gmail, Outlook, Slack, Teams, Meets, Zoom, and Zapier.
Attention’s initial customers are in the technology sales space, most commonly with 50 to 100 licensed sales reps.
“The acquisition extends Bigtincan’s lead in AI-driven revenue intelligence by improving B2B sales organization’s ability to scale by delivering insights and recommendations directly to sales reps for better decision-making to increase revenue,” the firm told investors. “The SalesDirector.ai technology links people, activity, and engagement across the buyer’s journey to derive insights, including opportunity risk and relationship strength, and then makes intelligent recommendations. By capturing all sales, marketing, and customer success activity the technology drives actionable revenue insights required to make the right business decisions.”
Bigtincan stated that SalesDirector’s technology would improve its AI-powered insights capabilities and forecasting accuracy.
SalesDirector ingests data from GSuite/Gmail, Slack, Microsoft Office, and Microsoft Exchange and feeds contact data (e.g., Title, Department, Level, Phone) to Salesforce and Microsoft Dynamics. SalesDirector also captures Engagement Signals and writes them back to CRMs as custom fields:
Executive Engaged on Opportunity / Account
Single or Multi-Threaded Opportunity Relationship
Last QBR Complete / Next QBR Scheduled
Partner / Sales Engineer Engaged on Opportunity
Next Step / Meeting Scheduled
AI tools include stakeholder identification, individuals who are supporters or detractors, disengaged stakeholders, account risk scoring, sentiment analysis, and deal health.
“Every organization has to be more productive,” said Bigtincan CEO David Keane. “With the Sales Enablement market shifting towards a more holistic approach encompassing Revenue Enablement, we can deliver more value to our customers by providing full-cycle sellers with AI-driven recommendations on the next best actions based on intelligent sales analytics from SalesDirector.ai.”
SalesDirector pricing begins at $29 per user per month for activity capture. Revenue Insights pricing is not published.
Bigtincan will incorporate SalesDirector’s functionality into its product suite and phase out the SalesDirector brand.
“Traditionally, when we do small tech-focused acquisitions, they become part of the Bigtincan product suite, so that capability gets added to the existing platform we have,” said Keane. “We don’t tend to run these things as separate businesses, mostly because we miss out on some of the benefits for our shareholders of taking it all together and taking that technology and really getting in the platform. This is no different than our existing strategy – embed, connect it all together and make it something that our customers can choose to add. We do believe that it is all about choice, and we want people to be able to buy these technologies from us as additional value-added options. I think that’s going to be the case here as well.”
Bigtincan paid $1.2 million in cash and equity for SalesDirector.
The entire SalesDirector team is joining Bigtincan.
Bigtincan, listed on the Australian Stock Exchange, has acquired a series of RevTech companies, including ClearSlide, Brainshark, and VoiceVibes.
Dale earned a BA and MA from Valparaiso University with a concentration in ethics. He also received an MBA from the Kelley School of Business at Indiana University.
What experience have you had developing AI tools?
As the SVP of Product Management at Salesloft, I am working with our team to bring Rhythm, Salesloft’s AI-powered signal-to-action engine platform, to life. Rhythm ingests every signal from the Salesloft platform as well as signals from partner solutions via APIs, ranks and prioritizes those signals, and then produces a prioritized list of actions. The action list gives sellers a clear, prioritized list of actions that will be the most impactful each day, along with an expected outcome prediction. In addition to simplifying a seller’s day-to-day, it helps them build their skills by providing the context about why each action matters.
AI is becoming increasingly important in RevTech, with many of our interactions being mediated by AI. Where do you see AI having the biggest impact on Sales reps between now and 2025?
AI will enable significant improvements in both seller efficiency and effectiveness. The most obvious impact will continue to be automating away low-value, repetitive work. What will surprise people will be the rapid advance and adoption of AI to suggest next best actions to take and content to use in those interactions with buyers. A typical workday for a seller will see them greeted by a recommended list of actions to take each day. Each action will be prioritized based on where the seller sits in relation to their targets, with each action accompanied by suggested content where appropriate. For instance, I might see a suggestion to respond to an email from a champion in an in-flight deal. The recommendation will include suggested text for the response as well as a resource to attach to the email. That’s a future we are actively investing in at Salesloft, which is at the heart of our soon-to-be-released Rhythm product.
Same question, but looking further out to 2030…
As AI becomes more commonly deployed across the sales profession, buyers will experience a more consistent sales experience in each buyer-seller interaction. As this becomes more common, it’s going to raise the bar on what buyers expect from a sales experience today. That will put more pressure on sales teams to deliver consistently in ways that today may seem unreasonable but will be possible with AI assistance.
One of the key ways to raise the seller performance bar will be high-impact, tailored coaching. Manager time is a constrained resource, and seller coaching augmented by AI provides a path to realizing performance improvement without manager time constraints. We should fully expect AI to help coach sellers to hit their goals based on each seller’s unique profile. We can expect AI to evaluate the seller’s entire game (activities, conversations, and deal management) to identify the highest leverage areas each individual seller should focus on to improve. Some of the coaching will be provided by AI at the point of execution, like on a call or when writing an email, with the rest provided throughout the workday as recommendations.
What are the most significant risks of deploying AI broadly across the Sales Function?
Two areas come to mind. First, AI used without clear boundaries in a sales process can lead to problems. If you employ AI and automation capabilities, it should be to allow the user to be better armed to make a decision, not make it for them. AI tools should not replace the human touch but rather augment it. There’s a lot of pseudo-science tossed up around the topic of AI, but ultimately, humans understand the nuance of relationships better than machines. One of the ways to address that concern is to deliver models that not only provide a recommendation but can provide the insights that led to it; humans will better trust the model when making decisions based on those recommendations as well as know when to ignore the recommendation.
Second, there’s a privacy component as well. Companies may create AI models that share data about a particular buyer with other companies’ sales teams without said buyer’s knowledge. The buyer may know they shared their data with one company but have no idea that multiple other customers at this company are using that same data. Creating models with this type of function puts companies and sales teams in a high-risk zone that can tread on the unethical. It isn’t clear that building models in that way may be considered legal in the future. If you plan to deploy AI in a sales org, it’s important to understand how data is collected and used.
AI Models are only as good as the underlying training data. How concerned are you about biased models recapitulating discrimination? For example, emphasizing sales skills that are gender or racially biased when evaluating sales rep performance?
It is a legitimate concern. AI products are based on probabilities, not certainties. The recommendations you receive or workflow automations that fire happen based on the probability that the given recommendation or action is right. Not the certainty that it is right. In a good product, the model is correct more often than a human would be when faced with the same decisions. At times, this is because the model can evaluate a larger set of factors, and in some cases, it is simply that machines can apply rulesets at a higher level of consistency than humans.
One of the key determinants of the AI model’s value is the dataset upon which it was trained. If the dataset does not properly represent the real world, the model will produce results that are either biased or provide poor recommendations. We’ve already seen several examples of that with image editing software that didn’t include black-skinned people in the training dataset. This led to either poor outcomes or worse dehumanizing results when the AI product was used in the real world. If you plan to deploy AI in your business, you should ask the provider what precautions they take to prevent bias in their models. We are very intentional about removing factors that could lead to bias in our training datasets. Still, it isn’t something I see most technology companies paying attention to in the revenue tech space.
How do you curb racial and gender bias when performing sentiment analysis?
We take great care at Salesloft to remove things that would lead to discriminatory factors. For example, for our Email Sentiment model, one of the ways we prevent bias is by removing all mentions of people’s names within the email because that could provide clues to their gender, race, or ethnicity. We do that kind of preprocessing with any data we use in an AI model before we build our models.
One of our assets is our scale. We’re fortunate that we operate globally and are the only provider in our space with offices in the Americas, Europe, and APAC. As a result, we work with organizations of all sizes globally, including many of the world’s largest companies. That means when we build models, we have one of the largest datasets in the world for sales execution. This enables us to train models based on datasets with both breadth and depth. When we build a model, it is easier to train it in a way that fairly represents reality and includes safeguards to avoid racial or gender bias.
AI will increasingly be deployed for recommending coaching and mediating the coaching. What concerns do you have about replicating bias when coaching?
As with any AI product making a recommendation, the potential to make a recommendation with bias is a concern that needs to be addressed when building models.
We take our responsibility to avoid bias in any product we release very seriously. The revenue technology industry as a whole hasn’t demonstrated a similar commitment to avoid harmful bias as of yet. I don’t hear other companies talking about proactive steps to avoid it, but I think that will change. We’re monitoring potential governmental action in both the US and EU that will require companies to raise their standard in this area. It is only a matter of time before laws are passed that require companies to prevent unlawful bias in their AI products.
Sales activities are becoming increasingly digitized, a boon for revenue intelligence, training, and next best actions. What guardrails do we need to put in place to ensure that employee monitoring does not become overly intrusive and invade privacy?
Let’s start by recognizing it is reasonable for an employer to have insight into what work is getting done and how it’s getting done. On the other hand, getting a minute-by-minute record of how each seller spends their day is unreasonable, as is dictating every action the seller takes from morning until nightfall.
We have to start with the right first principles. I think we can all agree that humans have inherent worth and dignity. They don’t lose that when they go to work. The challenge is that we have some companies in the technology industry that forget that fact when developing solutions. When you forget that fact, I believe that you actually harm the customer that you’re trying to serve. That harm happens in two ways.
First, you lose the opportunity to realize the true potential of AI, which is to serve as a partner that enables humans to do what they do best…which is to engage with and relate to other humans. AI should not be used to make final decisions for humans or to dictate how they spend every minute of their day. Good AI solutions should be thought partners and assistants to humans. It’s Jarvis to Tony Stark’s Iron Man.
The second way overly intrusive technology harms companies that employ it is via employee turnover. It’s no secret that industries that offer low autonomy to employees suffer from high turnover. Most humans fundamentally desire a base level of autonomy; if that’s threatened, they leave whenever a good option opens up.
In short, if the seller is working for the technology instead of the inverse relationship, we’re on the wrong path.
In 2018, Salesforce CEO Marc Benioff argued that the best idea is no longer the most important value in technology. Instead, trust must be the top value at tech companies. How does trust play into ethical applications and AI?
We get to build the future we want to realize. We can either build a future that perpetuates the things we don’t like about today’s world, or we can build a future that elevates human potential. AI can be used to take us in either direction. That means what we choose to build with AI and how we build it should be a very value-driven decision.
We can absolutely build highly effective AI-powered solutions that elevate the people who use them and deliver tremendous business value. The people that believe otherwise simply lack the imagination and skill to do it.
What I love about our team at Salesloft is that we exist to elevate the ability of the people we serve and to enable them to be more honestly respected by the buyers they serve. In sales and life, the way you win matters. It matters to the people you serve on your revenue team, and it matters to your customers.
An emerging category of AI called Generative AI constructs content (e.g., images, presentations, emails, videos). It was just named a disruptive sales technology by Gartner. They stated that “By 2025, 30% of outbound messages from large organizations will be synthetically generated.” What risks do you see from this technology?
There are two immediate risks that come to mind. First, the messages need to be reviewed by a human before they are sent. The technology has made extraordinary leaps forward. I’ve spent a fair amount of time playing around with some of the tools released by OpenAI and others. The output is impressive and also, at times, very wrong. This goes back to the fact that the output is based on a probability that the answer provided is correct. You can get a very professional, persuasive email, or you can get something that approximates a professional email but won’t land well with your intended customer.
Second, it has the potential to make every outbound message sound the same. Generative AI doesn’t replace the need for human skill. It changes the areas of focus for that skill. Specifically, the opportunity for humans is to use Generative AI to help generate a higher volume and variety of ideas and then to edit and refine the output. The returns available to creativity are always high, but they become even higher when everyone is doing the exact same thing in the same way.
Having said that, I see tremendous potential in the technology and think if used properly it will be very valuable to revenue professionals.
Kyle is absolutely right. At the end of the day, a sale happens when a seller connects with a buyer to help them solve a problem. You can’t do that without authentic connection and trust. Generative AI should not replace that human connection, and I don’t think buyers want it to replace human connection. A close friend of mine was a sales leader at a now-public PLG-driven SaaS company. They added sales reluctantly. When they did, the company learned that buyers both bought more from them and were happier customers. That company now wishes it had added sales much earlier. How we interact with one another can evolve as technology evolves, but it doesn’t change the fact that humans are wired to connect with each other. I think emerging tools like Generative AI will help us be more productive, but they won’t replace the need for authentic human connection and trust.
Seismic and Microsoft announced an integration partnership that will embed Seismic’s sales enablement workflows within Microsoft Viva Sales. Seismic will provide “content production, collaboration, task automation, and engagement intelligence for Viva Sales users across the meeting experience to help drive deals and relationships forward.” The goal is to “streamline” buyer engagement at relationship-based sales teams.
Microsoft Viva Sales became generally available on October 3. Viva Sales is “a new seller experience application that brings together any customer relationship management technology (CRM), Microsoft 365, and Teams to provide a more streamlined and AI-powered selling experience.” The new solution is designed for the hybrid work environment where reps leverage video conferences, chats, emails, and documents to close deals.
“We’re united with Seismic in our commitment to empowering sellers through relevant content and an improved seller experience. Our plan to integrate Seismic with Viva Sales will help sellers have more personalized customer engagements whether they are in the office or on the road, with a helpful assist from the AI-driven insights and content,” said Lori Lamkin, CVP, Dynamics 365 Customer Experience Applications.
Viva Sales customers include Adobe, Crayon, and PwC.
Conversational data capture has been a popular theme this month with announcements from People.AI and Nektar. Winn.AI, a Tel Aviv-based AI Assistant and playbook vendor, came out of stealth mode this month to offer AI-based playbook coaching during digital sales calls. Winn.AI also captures real-time conversations and syncs account intelligence with Salesforce and HubSpot.
“Winn.AI relieves salespeople of…administrative busywork so that they can focus on their customers — not their keyboards. Its magic lies in its unique ability to monitor, interpret, and document sales calls,” said CEO Eldad Postan-Koren.
Winn.AI also supports playbooks and tracks key topics during calls, helping salespeople stay focused on their prospects. Winn.AI captures all relevant data during sales calls, such as pain points, timelines, competitors, and team size. Product, competitive, and technical information are also displayed during calls, reducing the frequency of “I’ll get back to you” responses and presenting competitive parries.
“One of our core competencies is that we’re training our AI in real-life sales meetings. This greatly helps us make it more accurate versus standard training resources,” said Postan-Koren. “Furthermore, the more a customer uses the product in their own sales meetings, our AI capabilities will improve to match their exact needs.”
To speed up playbook definition, Winn.AI includes a set of templates based on common sales methodologies.
Winn.AI joins conversational platforms, beginning with Zoom. Teams, Google Meet, and additional conversational platforms (meetings and dialers) are on the roadmap for Q4.
Other features include 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 call conclusion.
Winn.AI does not create new records at this point but updates and enriches current Account, Contact, Lead, and Opportunity records. Winn.AI does not yet support inbound calls or SDR outbound prospecting performed on dialers.
Playbook coaching reduces onboarding time and gives reps confidence that they have proper messaging, competitive knowledge, and technical details at the ready.
Like most SalesTech companies in 2022, Winn.AI positions itself around productivity and effectiveness during lean times.
“The funding round timing made perfect sense. During turbulent times, when salespeople’s productivity and effectiveness are more critical than ever, a tool like Winn.AI provides the additional edge sales leaders and individuals are seeking,” Postan-Koren said. “The Winn.AI real-time assistant acts as an extra pair of hands during meetings, giving salespeople the freedom to focus their attention entirely on the customer.”
Winn.AI is backed by $17.25 million seed funding from Insight Partners and S-Capital. The firm has 25 employees and plans to double its headcount by the end of 2023. The funds will be invested in “improving our deep technology” and building a sales and marketing organization. All employees are in Israel, but they will be hiring in the US over the next two quarters.
Near-term roadmap goals are expanding the set of supported conference platforms and dialers, broadening the set of supported customer-facing roles (e.g., customer success, support, SDRs), adding playbook measurement and optimization tools, and implementing multiple, dynamic playbook branching (e.g., competitive handling, verticalized playbooks, technical discussions).
Postan-Koren offered GZ Consulting a compelling vision of “automated knowledge and personalization” feedback loops with the sales enablement team. Closing the loop helps the enablement team maintain playbook recommendations. Winn.AI will collect common responses and discussion tracks and feedback this intelligence to sales enablement for review and adjustments. This human-in-the-loop approach facilitates playbook and coaching refinement and filling in coaching gaps. It also identifies potential training needs as markets evolve.
“Winn.AI’s innovative and intuitive technology has identified a solution that addresses the pain points of Busy work. By allowing salespeople to focus on making the sale, Winn.AI enables organizations to Improve performance and increase the quota,” said Hagi Schwartz, Managing Director at Insight Partners.
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.”
Winn.AI is currently in beta with plans to formally launch in early 2023. 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.