Sales Execution Platform Outreach unveiled a series of product enhancements and dashboards at its Explore+ web conference earlier this month. New features include Smart Email Assist with Generative AI, a Create Pipeline Calculator, Buyer Topics and Reactions in Kaia, Deal Grid, Deal Overview, Success Plan Methodologies, and Data Sharing with Outreach.
“The industry has never had a single place to generate and manage pipeline, run sales cycles from creation to close, coach reps, and forecast – until now,” said the firm.
Over the past decade, sales teams have acquired a set of SalesTech solutions that create a “hairball” of point solutions that work poorly together and suffer from siloed data and regular system switching. Furthermore, a unified data platform supports advanced workflows, AI models, and account insights for sales coaching and deal management.
Outreach has enjoyed solid adoption of its new platform since launching it ten months ago. Multi-product adoption is strong, with over 400 customers using two or more products. Furthermore, multi-product adoption is driving platform ARR, which has grown by over 100% in the past two quarters. Since the platform was launched, Outreach’s new logo deal size has increased by 16%.
Outreach repositioned itself as a Sales Execution Platform as it expanded beyond Sales Engagement (Source: Outreach Analyst presentation).
“Today, Chief Revenue Officers are facing two major problems: pipeline coverage and conversion. They need to create an adequate amount of pipeline, and close it at a greater rate,” said CEO Manny Medina. “That’s why Outreach has been on a journey to expand our offerings to solve our customers’ biggest problems today. Our goal is to provide sales leaders with a single platform to manage all of their deals – from creating more pipeline to closing more deals. Today’s announcements at Explore+ are an important milestone in our platform journey, and we look forward to continue innovating for the 30 million B2B salespeople around the world to help them unleash their selling potential.”
Outreach Smart Email Assistant
The Smart Email Assistant generates automated email replies that go beyond email templates. AI factors in previous conversations between the buyer and seller when generating responses. By automating email responses, “sales reps can focus their time on editing and personalizing the AI-generated content, instead of drafting these emails from scratch.”
A new Pipeline Calculator recommends prospecting activities to fill pipeline gaps. The calculator utilizes historical pipeline data to determine the number of prospects that should be added to sequences to meet their quota. In addition, the historical conversion rate assumptions are displayed and adjustable. Thus, the assumed conversion or win rates can be adjusted to accommodate market shifts or new processes or messaging that boost historical results.
Outreach Pipeline Calculator
Outreach continues to develop Kaia, its conversational sales module, with the addition of Buyer Topics and Reactions. AEs and sales managers can revisit meeting recordings and review the buyer’s reaction to fourteen relevant sales topics, such as budget, legal, or support.
“Using AI, Outreach is able to understand the contextual utterance of relevant sales topics in any meeting or email – ranging from pricing to product to next steps to support – and can understand when the buyer raised an objection at any point in the meeting,” explained the firm. “It delivers invaluable insight into what is really happening in meetings, down to each moment, and at scale across all meetings.”
Success Plans now support popular sales methodologies, including MEDDIC, MEDDPICC, and SPIN Selling, helping reps “consistently and continuously qualify deals and align with champions to mitigate deal risk.”
Outreach added a single-pane-of-glass opportunity viewer called Deal Grid. Reps can view their deals sorted by health score and value to focus on their best opportunities. They can also edit fields such as Close Date, Amount, Stage, and Forecast status (e.g., omitted, commit, best case, most likely) with information synced to the CRM and forecasts updated.
Opportunity Viewers are a common feature of Revenue Intelligence platforms (e.g., Clari, RevenueGrid, People.AI), but with Sales Execution and Revenue Intelligence platforms expanding into each other’s domain, Deal Grid was an anticipated new feature. Opportunity viewers help reps review their deal status, update the CRM, and prepare for meetings with sales managers. They solve the problem of serially jumping between Opportunity records in Salesforce (which the firm has moved to resolve with similar functionality).
Outreach released several new reports and dashboards:
Create and Close Dashboard: Provides AEs and sales managers with a high-level forecasted revenue summary of the existing pipeline and highlights pipeline gaps and risks.
“The insight-laden dashboard shows the forecasted revenue from existing pipeline, and highlights pipeline coverage gaps for the current and future quarter, which helps reps proactively mitigate risk earlier and drive to success,” said Outreach.
Outreach Pipeline Calculator
Deal Overview: An overview of open opportunities with a deal summary, an engagement timeline, deal health, sales methodology insights, next steps, and the shared plan. The engagement timeline displays all sales activities and a heat map detailing customer engagement trends.
Outreach Deal Overview
Pipeline Dashboard: Displays all “relevant pipeline details to life in a single, sortable view, allowing sales managers to stay on top of their quarter.” The dashboard includes a pipeline activity summary by stage, projected finish, revenue to date, quota, and top deals with deal health scores.
Outreach Pipeline Dashboard
Outreach also announced bi-directional syncing with HubSpot. Earlier this month, it unveiled expanded Outreach Data Sharing with Snowflake.
Despite recent layoffs, Outreach continues to build its customer base. FY 2023 revenue (FYE Jan 2023) passed $200 million across 6000 customers. Outreach’s scale benefits its clients as it records over 25 million action/outcome pairings per week, helping refine its machine learning insights and recommendations.
On the same day that Microsoft launched Copilot, Salesforce announced Einstein GPT, its generative AI service that combines Salesforce’s own AI models with external models such as OpenAI’s. Einstein GPT supports personalized content creation across all of Salesforce’s clouds and Slack. For example, Generative AI functionality can write personalized sales emails, author customer support responses, compose targeted marketing collateral, and auto-generate code for developers.
“The world is experiencing one of the most profound technological shifts with the rise of real-time technologies and generative AI. This comes at a pivotal moment as every company is focused on connecting with their customers in more intelligent, automated, and personalized ways,” said CEO Marc Benioff. “Einstein GPT, in combination with our Data Cloud and integrated in all of our clouds as well as Tableau, MuleSoft, and Slack, is another way we are opening the door to the AI future for all our customers, and we’ll be integrating with OpenAI at launch.”
Einstein GPT for Sales
Einstein GPT is the next generation of Salesforce’s Einstein AI capabilities. Einstein GPT supports natural-language prompts that “trigger powerful, time-saving automations and create personalized, AI-generated content” within Salesforce. In addition, each application maintains a human-in-the-loop that reviews and edits client communications before they are sent out.
Einstein GPT reduces “the friction in sales reps wanting to move fast to meet their quota, having to leave Salesforce to send customer communication or do prospecting research, and spending too much time finding information stored in various parts of the CRM,” wrote Salesforce Ben.
New functionality includes:
Einstein GPT for Sales: Auto-generate sales tasks like composing emails, scheduling meetings, and preparing for the next interaction. It can also provide external news for prospect research, add contacts not already in Salesforce, and generate additional collaboration channels on Slack.
Einstein GPT can identify event triggers and recommend whom to contact.
Einstein GPT for Service: Generate knowledge articles from past case notes. Auto-generate personalized agent chat replies to increase customer satisfaction through personalized and expedited service interactions. Einstein GPT for Service also auto-generates case summaries and knowledge articles from past case notes.
Generating a case article.
Einstein GPT for Marketing: Dynamically generate personalized content to engage customers and prospects across email, mobile, web, and advertising. The service can generate content with brand-compliant images and layouts. Marketing content can then be uploaded to Experience Builder.
Einstein GPT for Slack writes copy with brand-compliant images and formatting.
Einstein GPT for Slack Customer 360 apps: Deliver AI-powered customer insights in Slack (e.g., smart summaries of sales opportunities) and surface end users’ actions. The Slack service supports writing assistance, background research on accounts, and instant conversation summaries.
Einstein GPT for Developers: Improve developer productivity with Salesforce Research’s proprietary large language model by using an AI chat assistant to generate code and ask questions for languages like Apex.
“Salesforce can be a powerful multiplier of generative AI experiences because Einstein GPT blends public data with CRM data, and when several million of our customers are all using Einstein GPT, the model gets refined with each instance and becomes more accurate,” explained Salesforce’s SVP of AI and Machine Learning Jayesh Govindarajan. “It’s a cumulative effect and is really a huge differentiator for Salesforce.”
Salesforce is also looking to establish an AI ecosystem, with OpenAI as the first integration.
“Einstein GPT will infuse Salesforce’s proprietary AI models with generative AI technology from an ecosystem of partners and real-time data from the Salesforce Data Cloud, which ingests, harmonizes, and unifies all of a company’s customer data,” announced Salesforce. “With Einstein GPT, customers can then connect that data to OpenAI’s advanced AI models out of the box or choose their own external model and use natural-language prompts directly within their Salesforce CRM to generate content that continuously adapts to changing customer information and needs in real-time.”
As part of the announcement, Salesforce established a $250 million venture fund to develop “responsible, trusted, and generative AI.”
OpenAI CEO Sam Altman said using ChatGPT in CRM services “allows us to learn more about real-world usage, which is critical to the responsible development and deployment of AI—a belief that Salesforce shares with us.”
“It will be fascinating to watch how this plays out,” opined Fortune editor David Meyer. “On the one hand, we’re now in the territory of serious businesses using generative AI for serious things, as opposed to playing around to see how long it takes to get a chatbot to say something offensive. On the other hand, some of these applications involve customers who may have some curveball questions. And it’s worth remembering that generative AI technology like OpenAI’s ChatGPT will occasionally ‘hallucinate,’ that is, basically make up fake information.”
“In theory, Microsoft’s and Salesforce’s new offerings should be safer to use because they only draw on information from companies’ own websites and internal databases—the customer-facing elements will in that sense be a bit like those Google search boxes in websites,” continued Meyer. “But that won’t necessarily make these AIs immune to occasionally emitting bogus information. Companies will find out soon enough how carefully they need to monitor their new copilots.”
Einstein GPT is in closed pilot.
A ChatGPT for Slack app is in beta. The ChatGPT app was built by OpenAI on the Slack platform and “delivers instant conversation summaries, research tools, and writing assistance directly in Slack.”
ChatGPT for Slack, built by OpenAI, was released this week.
Microsoft announced Microsoft Dynamics Copilot, an interactive, AI-powered assistance tool for sales, marketing, customer service, operations, and supply-chain management. Microsoft is positioning Copilot as an AI that helps businesspeople “create ideas and content faster, complete time-consuming tasks, and get insights and next best actions.”
CEO Satya Nadella promised that Copilot will “transform every business process and function with interactive, AI-powered collaboration.”
In the short run, Scott Guthrie, Microsoft’s Cloud & AI EVP, believes that “the fastest way to get some of this AI value” will be via “finished app” integrations like Copilot. Microsoft can tailor integrations into apps that “people are already trained on.” This augmentation strategy lets Microsoft “move faster,” so there is a “huge opportunity.”
Few people outside of the AI community had heard of ChatGPT, much less experimented with it, until a few months ago, but now it is being widely tested by end users, noted Guthrie. “People are looking for solutions that integrate with their workflows that they already have and help them kind of accelerate even more.”
“And then, I think, we’re also going to see the next generation of apps that are going to be built on the raw APIs and the services around it that are going to re-envision pretty much every experience that we see,” continued Guthrie.
Copilot operates within Dynamics CRM and ERPs to reduce mundane tasks such as manual data entry, content generation, and note-taking.
And such tools are welcome by front-line workers. According to a recent Microsoft survey, nearly ninety percent of workers hope AI will reduce repetitive tasks.
Dynamics 365 Copilot offers generative AI to automate tedious tasks and “accelerate their pace of innovation and improve business outcomes.”
Copilot is natively built into Microsoft Dynamics 365 Sales and Viva Sales. AI helps sales reps respond to customers and includes email summaries of Teams meetings along with action items, follow-up dates, and voiced concerns. Additionally, summaries are available for different meeting types, including multi-participant and internal calls.
“The meeting summary pulls in details from the seller’s CRM such as product and pricing information, as well as insights from the recorded Teams call,” wrote Charles Lamanna, CVP Business Applications and Platform. “With sellers spending as much as 66% of their day checking and responding to emails, this presents a significant business upside to give the seller more time with their customers.”
Email replies are generally available in Viva Sales, and customizable emails will be added on March 15. “For example, a seller can generate an email that proposes a meeting time with a customer, complete with a proposed meeting date and time based on availability on the seller’s Outlook calendar,” blogged Emily He, CVP Business Applications Marketing.
Sellers will also be able to rate generated content with a thumbs up or thumbs down to help refine replies. And if the response needs to be tweaked, sales reps can provide a follow-on prompt that updates the response based on the additional context.
Generating marketing text with an emphasized feature.
Copilot in Dynamics 365 Customer Insights and Dynamics 365 Marketing simplifies data exploration, audience segmentation, and content creation.
Marketers can curate “highly personalized and targeted customer segments by having a dialogue with their customer data platform using natural language,” wrote Lamanna. Marketers do not need to be SQL experts or wait for an operations specialist to build the query. Instead, they can build segments in near real-time using Copilot’s Query Assist feature.
“With a few clicks, Copilot produces the results, along with information such as the customers’ average age, product preferences, or average purchase price,” wrote He. “These insights can then be configured into a segment to support a campaign.”
Copilot also suggests additional segments.
Using Copilot in Dynamics 365 Marketing, marketers can describe their customer segment to a query assist feature and look for inspiration for fresh email campaign content based. “Copilot makes suggestions based on key topics entered by the marketer, the organization’s existing marketing emails, as well as from a range of internet sources to increase the relevance of generated ideas,” blogged Lamanna.
Marketers can enter up to five bullet points in Content Ideas, which generates an email via Azure OpenAI Service.
“Unique content can be used as a starting point when composing marketing emails,” explained He. “It can analyze the organization’s existing emails, in addition to a range of internet sources, to increase the relevance of generated ideas. With Copilot, marketers can save hours of time brainstorming and editing while keeping content fresh and engaging.”
Copilot in Dynamics 365 Customer Service drafts contextual answers to queries in both chat and email. It also supports an “interactive chat experience over knowledge bases and case history, so this AI-powered expertise is always available to answer questions.”
Copilot for customer service drafts emails and chats for customer service agents.
Other generative AI tools support product descriptions and supply chain disruption forecasts based on weather, financial, and geopolitical news.
Copilot is in preview across Dynamics 365 and Viva Sales.
LinkedIn posted its AI principles today. These are all high-level which is a good starting point, but implementing rules and policies requires more details.
AI is not new to LinkedIn. LinkedIn has long used AI to enhance our members’ professional experiences. While AI has enormous potential to expand access to opportunity and transform the world of work in positive ways, the use of AI comes with risks and potential for harm. Inspired by, and aligned with our parent company Microsoft’s leadership in this area, we wanted to share the Responsible AI Principles we use at LinkedIn to guide our work:
Advance Economic Opportunity: People are at the center of what we do. AI is a tool to further our vision, empowering our members and augmenting their success and productivity.
Uphold Trust: Our commitments to privacy, security and safety guide our use of AI. We take meaningful steps to reduce the potential risks of AI.
Promote Fairness and Inclusion: We work to ensure that our use of AI benefits all members fairly, without causing or amplifying unfair bias.
Provide Transparency: Understanding of AI starts with transparency. We seek to explain in clear and simple ways how our use of AI impacts people.
Embrace Accountability: We deploy robust AI governance, including assessing and addressing potential harms and fitness for purpose, and ensuring human oversight and accountability.
“Using AI Responsibly,” LinkedIn In the Loop Newsletter (March 2023)
As with every new technology, it can be used for either the betterment of society or malign purposes. Setting out principles helps frame product management and engineering in building their models, promoting trust, and setting guidelines to reduce negative effects (e.g., recapitulating bias, spreading misinformation and disinformation).
Transparency helps reduce negative effects as well. If it isn’t known why a recommendation was made, how can it be trusted? Furthermore, how does one know that the AI isn’t recapitulating somebody’s IP; gathering information from incorrect, malign, or outdated sources; or making incorrect assumptions? Thus, black box AI should be avoided.
Microsoft is the early leader in implementing Generative AI, a category of AI “algorithms that generates new output based on data they have been trained on” (Gartner). The best known of these is ChatGPT which generates text and carries on chat conversations. Microsoft recently invested $10 billion in OpenAI, the developer of ChatGPT and other generative AI tools. It is quickly moving to integrate it into Bing and other products.
On Monday, I will post about ChatGPT being integrated into Microsoft’s Viva Sales product.
Groove Plays are triggered when one or multiple conditions are met.
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).
Groove Play Outcomes analyzes the efficacy of Plays
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.
Groove suggests “ideal email content based on insights gathered from earlier in the deal process via Groove Conversations and advance activity capture.”
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.”
The Lavender Team Dashboard
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 employs a freemium pricing model.
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.
Happy New Year. While off on vacation last week, I published an interview with Salesloft SVP of Product Management Frank Dale concerning Ethical AI. He joined Salesloft in November 2019 when Costello, the opportunity management firm he founded, was acquired by Salesloft. He has served as either CEO or COO at several investor-backed software companies, including Compendium, which Oracle acquired.
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.
SalesLoft CEO Kyle Porter has long emphasized authenticity and personalization in sales conversations. Do you see Generative AI potentially undermining trust?
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.
