The LeanScale Podcast · Episode 44

Making Your B and C Players Sell Like A-Players

Ebsta's Adam Roberts on the data foundation behind revenue intelligence — relationship scoring, AI qualification, pipeline visibility, and bottoms-up forecasting

Adam Roberts · Commercial Leader, Ebsta · Ebsta Hosted by Anthony Enrico
Published Updated 00:39:52 36 min read 7,107 words
Executive Summary

The one-paragraph brief, extended

Why this conversation matters — and who should spend the hour.

Anthony Enrico hands the wheel to Adam Roberts, the commercial leader at Ebsta (reporting to founder-CEO Guy Rubin), for a live, screen-shared deep dive into what Anthony calls his favorite revenue intelligence platform. A few weeks earlier, Ebsta's founder had joined the show to unpack the state of B2B sales — much of that data drawn from the platform itself. This episode goes under the hood: how Ebsta actually works, and why the company believes the way to fix revenue is to fix the data first.

The problem Ebsta exists to solve is stark. Roughly 80% of reps miss quota, nearly 40% of deals slip somewhere in the pipeline, and the top 14% of sellers — the A-players — deliver about 80% of the revenue. Despite years of investment in more sales tech, that disparity persists, because it isn't a tooling problem; it's a visibility and insight problem. Signals are siloed across ten systems, and roughly 70% of the communication that actually influences a deal never makes it into the CRM. Ebsta's thesis is that if you consolidate every signal into one place — with Salesforce as the single source of truth — and lift the 85% of B and C players a little closer to A-player behavior, the aggregate performance gain for any decent-sized sales team is enormous.

Mechanically, Ebsta connects to the mail server, the conversation layer, and the CRM, then looks back in time across every closed-won and closed-lost opportunity to backfill history that reps never logged — often within the first week of engaging. On the opportunity record itself, that data renders as a relationship score and trend (a leading indicator of deal health), benchmarks for time-in-stage and deal age drawn from the organization's own won deals, and a deal score from 0 (lost) to 100 (won) that climbs by stage and reacts to positive and negative signals. Its call intelligence ingests recordings from Gong, Zoom, or Teams and auto-captures qualification — scoring frameworks like MEDDIC straight off the transcript — which removes the admin burden on reps and strips out the bias that creeps in when 20 to 150 different sellers self-report. Pipeline insight, coverage ratios, funnel analytics, and a bottoms-up forecast (with a manager override) roll it all up so a rep can self-correct before a pipeline review and a VP can read the whole business in thirty seconds.

The strategic through-line that Anthony and Adam land on is that all of this is really a data-foundation argument for the AI era. AI is only as good as the data it can reach; point a model at siloed, unstructured CRM data and it produces meaningless output. Both agree the durable advantage isn't which AI model or platform you pick — those are swappable — but the accuracy and structure of your underlying data, what Anthony calls the ontology. Get the foundation right and there will always be plenty of AI tools able to read it; skip it and any AI you layer on top will struggle.

Who should listen: RevOps leaders trying to benchmark what 'good' looks like without enough data to do it, sales managers and CROs who want their B and C players to close more, and revenue leaders wrestling with forecast accuracy and pipeline hygiene. The biggest takeaway is a sequencing lesson — start with the data foundation (emails, meetings, contacts, relationships, history), layer intelligence and forecasting on top, and treat clean, structured data as the single highest-leverage investment before adopting any AI in the go-to-market.

Key Takeaways

11 things worth stealing

The load-bearing ideas, each with the business implication and who should care.

01

The performance gap is a visibility problem, not a tooling problem

Despite heavy spend on sales tech, ~80% of reps miss quota and nearly 40% of deals slip. Adam argues the root cause is that sellers, managers, and leaders lack a real handle on the data points influencing revenue — signals are siloed across many systems rather than surfaced in one place.

Why it matters: Before buying another point solution, consolidate the signals you already have. The delta between top and bottom performers is largely an insight gap you can close with visibility, not more features.

RevOps LeadersSales LeadersRevenue Executives
02

Lift the 85% of B and C players — don't just polish the A-players

The instinct is to make top closers even better, but the A-players are only ~14% of the team. If you nudge the 85% of B and C sellers incrementally toward A-player behavior, the aggregate improvement for a decent-sized sales org is far larger.

Why it matters: Point your enablement, coaching, and tooling at the middle and bottom of the distribution. Emulating even a little of what A-players do, at scale, moves the number more than perfecting the few.

Sales LeadersRevOps Leaders
03

Make Salesforce the single source of truth and consolidate every signal into it

Ebsta connects the mail server, conversation intelligence, and CRM, then renders everything — communications, scores, benchmarks, pipeline views, forecasts — natively on the Salesforce opportunity, account, lead, and contact records, so no one has to log into ten systems to understand a deal.

Why it matters: Insight only gets used when it lives where reps and managers already work. Reducing the friction of finding information is itself a performance lever.

RevOps LeadersSales Leaders
04

About 70% of sales communications never reach the CRM — so backfill history, not just today

Reps rarely log every email, meeting, and contact; Ebsta typically finds ~70% of that information missing. It captures communications going forward but also looks back across closed-won and closed-lost history to enrich records — often within the first week of engaging.

Why it matters: A tool that only starts capturing today leaves you blind to the past. Retroactively enriching history is what makes relationship scoring and benchmarking possible on day one.

RevOps LeadersSales Leaders
05

Relationship score is a leading indicator of deal health

By aggregating communication frequency, depth, and stakeholder engagement — and trending it over time — Ebsta produces a relationship score that Adam calls one of the clearest indications of an account's, opportunity's, and relationship's likelihood to close.

Why it matters: Treat relationship health as a forward-looking metric, not a soft one. A declining relationship trend is an early warning that a stage or amount field won't show you.

Sales LeadersRevenue ExecutivesRevOps Leaders
06

Benchmark deals against your own won and lost history

Because Ebsta looks back over closed opportunities, it sets organization-specific benchmarks: a deal 24 days in stage when your won deals average 10, or 44 days old when winners are typically 20, isn't automatically dead — but the seller needs a narrative for why it's taking longer before the pipeline review.

Why it matters: Define 'what good looks like' from your own data, then surface deviations. Benchmarks turn a vague pipeline review into a specific, answerable question.

RevOps LeadersSales Leaders
07

Auto-capture qualification from call transcripts to kill admin burden and bias

Ebsta's call intelligence analyzes conversations (from its own recorder or Gong, Zoom, or Teams) and recommends qualification scores — e.g., MEDDIC metrics scored a three, with supporting notes the rep can accept or override. Across 20 to 150 sellers, this removes the negative/positive lean of individual reps and gives blanket consistency.

Why it matters: If you score qualification, you can measure it — spotting which reps under-qualify or miss implication of pain. Consistency of capture at scale is what makes the data trustworthy enough to coach on.

Sales LeadersRevOps Leaders
08

The deal score turns every signal into one number that should climb by stage

A 100 is a closed-won opportunity and a 0 is closed-lost; the deal score should increase as a deal advances and is driven by all the positive and negative factors Ebsta identifies against a 12-month benchmark of won deals — multi-threading, recent activity, future meetings booked, closed dates in the past.

Why it matters: A single composite score, drillable into its risk factors, lets a rep self-diagnose and a manager triage. Warning signs like a past close date or no next step become fixable before they cost the deal.

Sales LeadersRevOps LeadersRevenue Executives
09

Run bottoms-up forecasting with a manager override for a bigger, more accountable number

Ebsta believes in bottoms-up forecasting: reps submit a data-backed commit/upside forecast weekly, then managers submit an adjusted view — hedging a rep's commit to upside when qualification is thin. Adam argues bottoms-up produces a bigger number while keeping everyone committed and accountable.

Why it matters: Give the whole org access to the metrics that matter and forecasting becomes a shared, honest exercise rather than a top-down guess. The goal is a number that is both accurate and as big as it can defensibly be.

Revenue ExecutivesSales LeadersRevOps Leaders
10

Give reps self-serve pipeline hygiene before the pipeline review

Pipeline insight and smart-insight filters let a rep glance at engagement, close deals that are clogging the pipeline, and catch problems — close dates in the past, no future meetings, weak multi-threading — themselves. Often it's not negligence, just a rep managing a high volume who forgot to book a next step.

Why it matters: Empowering reps to catch their own issues before a manager does expands their effective book and makes reviews about strategy, not cleanup.

Sales LeadersRevOps Leaders
11

A clean data foundation is the prerequisite for AI — ontology beats model choice

Both Adam and Anthony argue we're moving fast into an AI-first world where AI is only as good as the data it can access. Disparate, unstructured data yields meaningless AI output. Anthony's framing: don't obsess over which AI tools to buy — those are swappable — obsess over data accuracy and structure, the ontology.

Why it matters: Invest the next 6-12 months in getting the foundational data right. Get it right and plenty of AI tools will be able to read it; skip it and anything you layer on top will hallucinate or underperform.

FoundersRevOps LeadersRevenue Executives
Frameworks Discussed

9 named models

Every framework Jimmy names, defined and time-stamped.

Salesforce as the Single Source of Truth

03:08

Consolidate every revenue signal — email and calendar from the mail server, conversation intelligence from calls, and CRM history — into the Salesforce opportunity, account, lead, and contact records, rather than scattering them across ten systems.

Adam's core product thesis: visibility only gets used when it lives where people work. By making the CRM the one place risk, relationship, and health render, Ebsta removes the barrier that keeps reps and managers from acting on insight.

Relationship Score & Trend

07:21

A score, tracked over time, that aggregates communication frequency, depth, and stakeholder engagement across an account or opportunity to indicate the strength of the relationship and the likelihood the deal closes.

Benchmarked against historical won and lost opportunities, the relationship score and its trend act as a leading indicator of deal health — the earliest, clearest read on whether a relationship is strong enough to win.

Benchmarking Against Won/Lost History

08:02

Use an organization's own closed-won and closed-lost deals to set benchmarks — time-in-stage, deal age, stakeholders per stage — then flag opportunities that deviate from what winning normally looks like.

A deal 24 days in stage against a 10-day won benchmark, or 44 days old against a 20-day norm, isn't automatically dead, but the seller needs a narrative for the delay. Benchmarks convert a vague review into a specific question a manager will ask.

AI Qualification Auto-Capture

12:20

Analyze call transcripts with AI to auto-populate a qualification framework (e.g., MEDDIC) — recommending a score per element plus supporting notes the rep can accept, edit, or ignore — without the rep manually entering it.

Because the AI captures consistently across 20 to 150 sellers, it removes individual bias (some reps lean negative, some positive) and standardizes data quality at scale, which in turn makes it possible to spot which reps under-qualify or miss implication of pain.

Deal Score (0-100)

21:19

A composite score where 0 equals closed-lost and 100 equals closed-won; it should rise as a deal moves through the pipeline and reacts to all positive and negative signals mapped against a 12-month benchmark of won deals.

Multi-threading, recent activity, booked future meetings, and past close dates all move the score, and each risk factor is drillable — letting a rep self-diagnose and a manager triage before problems cost the deal.

BAMFAM — Book a Meeting From a Meeting

22:48

A selling discipline of always securing the next meeting while you are still in the current one, so an opportunity never sits without a scheduled next step.

Adam invokes BAMFAM as the fix for the most common warning sign Ebsta surfaces — no future calls, meetings, or tasks on a deal. The platform prompts the behavior by flagging the gap right on the record.

Bottoms-Up Forecasting With Manager Override

24:34

Reps submit a data-backed forecast (pipeline / upside / commit) weekly; managers then submit their own adjusted view, hedging a rep's commit to upside when qualification is thin. Coverage ratios and pacing roll up by the Salesforce hierarchy.

Adam argues bottoms-up produces a bigger number because everyone is committed and accountable, and pairing it with a manager's licensed adjustment keeps the forecast both realistic and ambitious — accurate, but as big as it can defensibly be.

Required vs. Actual Pipeline Coverage

27:55

Compare a rep's actual pipeline coverage (e.g., 6.8x) to the coverage they historically require to hit target (e.g., 3.6x) to decide whether they need more pipeline or should focus on closing what they have.

The message changes with the ratio: a rep behind on the quarter but sitting on ample coverage should be told to close, not prospect. It focuses limited manager time on the reps and deals that will actually move the number.

Data Foundation / Ontology Before AI

37:55

Treat the accuracy and structure of your underlying data — the ontology — as the foundation for any AI strategy, because AI is only as good as the data it can access, and swappable models matter less than the data feeding them.

Point an AI at siloed, unstructured CRM data and it returns meaningless output. Get the ontology right and any number of AI tools can read it; Anthony's rule is to obsess over data structure, not which model you buy.

Best Quotes

14 lines worth clipping

Pulled verbatim. Copy or share any of them.

“Our top 14% of sellers, our A players, are delivering 80% of the revenue. And that isn't sustainable.”
Adam Roberts 02:29
“We firmly believe that Salesforce is and should be the single source of truth.”
Adam Roberts 03:08
“If you can get the 85% of your B and C players to just incrementally improve, for an organization that has a decent sized sales team, that's a massive improvement to overall performance.”
Anthony Enrico 03:52
“It's not like our B and C sellers are lazy or they don't want to win. We're all in sales to win and to close business and hit our numbers. It's a performance sport.”
Adam Roberts 04:29
“We typically see that when we work with clients, about 70% of that information is missing from CRM. Then businesses are blind. They're making decisions without the right information.”
Adam Roberts 05:42
“The relationship score and that relationship trend over time is one of the leading indicators of the health of an account, an opportunity, and a relationship.”
Adam Roberts 07:21
“If you score it, you can measure it, and therefore you can understand patterns and you can spot deficiencies across a large, broad group of sellers.”
Adam Roberts 12:20
“What the AI does when you're using this at scale is remove all the bias, remove all the subtle interpretations, and give you just blanket consistency of capturing information.”
Adam Roberts 14:56
“We believe in bottoms-up forecasting. If we do it bottoms-up, we get a bigger number.”
Adam Roberts 24:34
“Our job as revenue leaders is to make sure that we're forecasting accurately, but that number's as big as possible.”
Adam Roberts 25:13
“Within the space of 30 seconds, I've identified exactly how the business is pacing, and exactly which opportunities are going to move the needle for me.”
Adam Roberts 30:53
“Everything we do is relational. Sales is relationships. So understanding the health of those relationships, going back in time, that's super powerful.”
Anthony Enrico 35:24
“AI is only as good as the data that it has access to. And if your data's in different disparate silos all over the place, not structured, the AI is going to provide meaningless output.”
Adam Roberts 37:55
“You can swap in and out AI models, AI platforms all day long. Don't focus so much on which AI tools you should be getting. Focus more on the data accuracy and how you structure your data.”
Anthony Enrico 38:32
Practical Advice

What should you actually do?

The playbook, split by the seat you sit in.

RevOps Leaders

  • Consolidate email, calendar, conversation, and CRM signals into one system of record so no one has to log into ten tools to understand a deal — insight only gets used when it lives where people work.
  • Backfill history, not just new activity: reps typically leave ~70% of communications out of the CRM, and enriching closed-won/closed-lost history is what makes relationship scoring and benchmarking possible from day one.
  • Build organization-specific benchmarks (time-in-stage, deal age, stakeholders per stage) from your own won and lost deals so 'what good looks like' is grounded in data, not opinion.
  • Get the data foundation and structure — the ontology — right before layering AI on top; a model pointed at siloed, dirty CRM data will hallucinate.

Sales Leaders

  • Aim tooling and coaching at the 85% of B and C players, not just the A-players — incremental gains across the middle move the number more than perfecting the few.
  • Auto-capture qualification (MEDDIC, SPIN, BANT, or your own fields) from call transcripts to remove admin burden and strip out the per-rep bias that makes forecasts unreliable at scale.
  • Use warning signs — past close dates, no next meeting, weak multi-threading — to let reps self-correct before the pipeline review; practice BAMFAM (book a meeting from a meeting) to never leave a deal without a next step.
  • Read coverage ratios before you coach: a rep behind on the quarter but sitting on ample pipeline coverage should be told to close, not to prospect.

Revenue Executives

  • Adopt bottoms-up forecasting with a manager override — it produces a bigger, more committed number while keeping the forecast honest through the manager's licensed adjustment.
  • Use funnel analytics and forecast-change waterfalls to see where value slipped and why (no engagement, low deal scores, warning signs), then drill straight into the specific deals to act.
  • Focus limited leadership time on the deals in commit that are trending down — the fastest way to identify how the business is pacing and where to intervene.

Founders

  • Treat clean, structured, unified data as the single highest-leverage investment for the next 6-12 months — it is the prerequisite for any AI strategy in your go-to-market.
  • Don't over-index on which AI model or platform to buy; those are swappable. Invest in the ontology and data accuracy that any AI tool will need to read.
  • Start with the foundation (emails, meetings, contacts, relationships, history) before buying more point solutions — the performance gap is usually a visibility problem, not a tooling one.
AI Takeaways

How AI actually changes GTM

LeanScale's signature read on the AI-in-GTM question this episode wrestles with.

The thesis

The AI conversation in sales is really a data conversation. AI is only as good as the data it can reach, so the durable advantage is a clean, unified, well-structured data foundation — the ontology — not which model or platform you pick. Ebsta's bet is to consolidate every signal into Salesforce so any AI has a sanitized, trustworthy dataset to reason over.

Data foundation before AI

Point a model at siloed, unstructured CRM data and it produces meaningless output. The next 6-12 months of foundational data work is what decides whether AI helps or hallucinates.

AI removes rep bias at scale

Auto-capturing qualification from transcripts across 20-150 sellers strips out the negative/positive lean of individual reps and delivers blanket consistency, which is what makes the data coachable.

Capture without admin burden

Analyzing calls to recommend qualification scores and notes (which the rep can accept or override) gathers far more accurate data than asking busy reps to type it in — and keeps the rep's judgment in the loop.

Models are swappable; ontology is the moat

Anthony's rule: don't obsess over which AI tool to buy. Get data accuracy and structure right and plenty of AI tools will be able to read it.

Agent & automation ideas

  • A qualification-scoring agent that reads every call transcript, scores a chosen framework (MEDDIC/SPIN/BANT), drafts supporting notes, and flags gaps for the rep to confirm.
  • A relationship-health agent that ingests email/calendar/call signals to score and trend relationship strength per account and alert on decline before stage or amount fields react.
  • A pipeline-risk agent that benchmarks each open deal against won-deal history (time-in-stage, deal age, multi-threading, next-step) and surfaces prioritized warning signs before pipeline reviews.
  • A forecast-coverage agent that compares each rep's actual vs. historically required pipeline coverage and recommends close-vs-prospect focus for manager one-on-ones.
Operations Takeaways

By function

The same conversation, filtered for RevOps, pipeline/marketing ops, and customer ops.

Revenue Operations

  • One source of truth. Consolidate mail, calendar, conversation, and CRM signals into Salesforce so risk, relationship, and health render in one place reps already work.
  • Backfill the history. ~70% of communications never reach the CRM; enriching closed-won/closed-lost history is what makes scoring and benchmarking possible from day one.
  • Benchmark from your own data. Set time-in-stage, deal-age, and stakeholder benchmarks from your won and lost deals so 'what good looks like' is grounded, not opinion.
  • Free RevOps from data-gathering. Automating capture and benchmarking lets RevOps leaders stop pulling information together and do more critical, strategic work.
  • Leading indicators over lagging fields. Relationship score, deal score, and warning signs predict deal health earlier than stage and amount ever will.

Pipeline & Marketing Ops

  • Surface risk automatically. Flag stalled stages, missing multi-threading, past close dates, and no next step so reps self-correct before the pipeline review.
  • Deal score by stage. A 0-100 composite should climb as the deal advances; when it doesn't, the drillable risk factors tell the rep what to fix.
  • Slice and dice at scale. Pipeline insight and smart-insight filters let a rep or manager interrogate the whole book by hierarchy, product, or revenue type in seconds.
  • Coverage tells the story. Compare actual vs. historically required coverage to decide whether a rep needs pipeline or needs to close what they have.
  • Forecast change as the executive view. The forecast-change waterfall shows what was submitted at the start vs. end of period and why value slipped — drillable to the deals.
Metrics Mentioned

The numbers, with context

~80%
Reps missing quota

The market baseline Ebsta exists to fix — roughly 80% of reps miss quota despite heavy sales-tech investment.

~36-40%
Deals slipping in pipeline

Even after improvement, nearly 40% of all deals still slip in the pipeline market-wide.

Top 14% deliver ~80% of revenue
A-player revenue concentration

The disparity between top and bottom sellers Ebsta aims to narrow by lifting the B and C players.

~70%
Communications missing from CRM

The share of email, meeting, and contact data Ebsta typically finds missing from a client's CRM before it backfills history.

~3%
Win rate for badly slipped deals

When deals slip beyond three to six months, win rates fall to roughly 3% — per Ebsta's benchmark report.

24 days vs. 10-day won benchmark
Time-in-stage benchmark example

A demo opportunity sat 24 days in stage while the org's won deals average 10 — and was 44 days old vs. a 20-day norm for winners.

6.8x actual vs. 3.6x required
Pipeline coverage example

A rep (Josh) had 6.8x coverage against a historically required 3.6x — the signal to focus on closing rather than prospecting.

$300K across 11 deals
Commit deals trending down

In the pipeline-change view, 11 commit deals worth $300,000 were trending down — the leader's first-priority intervention.

$181K
Forecast value slipped

The forecast-change waterfall surfaced $181K of value that slipped, drillable to the exact deals and reasons.

20-150 sellers
Mid-market seller range

The team size where AI-standardized qualification capture pays off most, removing per-manager and per-rep bias at scale.

Entities

Companies, people & tools mentioned

Auto-extracted and linked into the knowledge graph.

Companies

People

Tools & software

SalesforceCRM

The CRM Ebsta treats as the single source of truth — every captured communication, score, benchmark, pipeline view, and forecast renders natively on the Salesforce opportunity, account, lead, and contact records so reps never leave the CRM.

GongRevenue Intelligence

Named as a supported external call-recording source: Ebsta ingests Gong transcripts (alongside its own recorder) to auto-capture qualification and surface key moments inside Salesforce, so managers don't have to scroll through hundreds of calls.

ZoomVideo Conferencing

Cited as a call source Ebsta's conversation intelligence captures, alongside Gong and Teams, to bring transcripts and key moments into Salesforce.

Microsoft TeamsVideo Conferencing / Collaboration

Cited as a meetings/call source Ebsta's conversation intelligence ingests, alongside Gong and Zoom, for auto-capture of qualification and key moments.

Methodologies referenced MEDDICSPIN SellingBANT
Frequently Asked Questions

Straight answers

Generated from the conversation, marked up for search and AI extraction.

What is a revenue intelligence platform like Ebsta?

A revenue intelligence platform consolidates the signals that influence a deal — email and calendar activity, conversation/call intelligence, and CRM history — into one place, in Ebsta's case natively inside Salesforce. It backfills communications reps never logged, then renders relationship scores, deal scores, benchmarks, qualification, pipeline insight, and forecasting on the opportunity record so sellers and managers can see the health and risk of every deal without logging into multiple systems.

Why do about 80% of reps miss quota despite heavy investment in sales tech?

According to Ebsta, it's a visibility and insight problem, not a tooling problem. Signals that influence revenue are siloed across many systems, and roughly 70% of the communication that shapes a deal never reaches the CRM, so sellers, managers, and leaders are making decisions without the full picture. The disparity between top and bottom performers is largely an insight gap that consolidating data — not buying more features — can close.

What is a relationship score and why does it predict deal health?

A relationship score aggregates communication frequency, depth, and stakeholder engagement across an account or opportunity, tracked as a trend over time. Because it's benchmarked against an organization's own won and lost deals, it acts as a leading indicator: a strong or improving relationship trend signals a higher likelihood to close, while a declining one is an early warning that a stage or amount field won't reveal.

How does AI auto-capture qualification like MEDDIC from sales calls?

Ebsta's call intelligence analyzes call transcripts — from its own recorder or from Gong, Zoom, or Teams — and recommends a qualification score for each element of a framework such as MEDDIC, along with supporting notes explaining the score. The rep can accept, edit, or ignore the suggestion. Capturing this consistently across 20 to 150 sellers removes individual rep bias and standardizes data quality, which makes it possible to spot which reps under-qualify.

What is bottoms-up forecasting and why does it produce a bigger number?

In bottoms-up forecasting, reps submit their own data-backed forecast (pipeline, upside, commit) every week, then managers submit an adjusted view — for example hedging a rep's commit to upside when qualification is thin. Ebsta argues this produces a bigger, more accurate number because everyone in the organization has access to the metrics that matter and is committed and accountable to their submission, rather than accepting a top-down guess.

How can you help B and C players sell more like A-players?

A-players are typically only about 14% of a sales team yet deliver roughly 80% of revenue, so the largest gains come from lifting the 85% of B and C players. Give them the same visibility top performers have — relationship and deal scores, benchmarks for what winning looks like, auto-captured qualification, and warning signs they can self-correct before a pipeline review — so even a small, consistent improvement across the middle produces a large aggregate gain.

Why is a clean data foundation critical before adopting AI in sales?

AI is only as good as the data it can access. If your data sits in disparate, unstructured silos, an AI layered on top will produce meaningless output. The durable advantage isn't which AI model or platform you choose — those are swappable — but the accuracy and structure of your underlying data, or ontology. Get the foundation right and many AI tools will be able to read it; skip it and any AI you add will struggle to help.

Full Transcript

The whole conversation

Broken into chapters, searchable, verbatim from the audio. Speakers inferred (not diarized).

00:00Intro: Ebsta, a revenue intelligence platform

0:00 I'm really, really pumped to go into the Ebsta platform today. Ebsta is my favorite revenue intelligence platform. We've been working with them for a few years and we have Adam Roberts here today to go through it. A few weeks ago we had their founder CEO Guy on talking about the state of B2B sales and all of the opportunities for improvement and a lot of that data is coming from the platform itself and today we have an opportunity to dive into the product and see exactly how Ebsta is helping sellers become the best versions of themselves. Adam, thanks

0:39 for being here. Really excited to go into the Ebsta platform and appreciate you taking out some time to do this. Yeah, thank you Anthony. It's great to be here. I'm very excited. Guy, guys told me how much fun he had on the show last time he was here. So yeah, really looking forward to diving into it. Shall I give a bit of an introduction for the audience for myself? Please, please do. Yeah, so I'm Adam. I'm the commercial leader here at Ebsta. I'm reporting to Guy. I've been in and around the Saas ecosystem for the last decade in various contributor manager leadership positions. I've always been in a position where I've helped

01:20Adam Roberts: a data geek at heart

1:20 businesses do more with their data. I'm a bit of a data geek at heart and really kind of hope that hopefully we can kind of share Ebsta in a really positive light today. So if you're happy, Anthony, I'll dive straight in. Yeah, let's do it. And there's a lot of powerful data that comes out of Ebsta. So I'm sure you have a lot of fun with it as well as leading the sales team with Ebsta. It's very meta to be able to do that. But yeah, excited to see what we have today. Yeah, absolutely. Absolutely. Look, you said yourself there's a ton of data, and I think that's probably a really

01:55The state of B2B sales: 80% of reps miss quota

1:55 good starting place. If I can just bring up some slides here for a second, hopefully it won't bore everybody by PowerPoint. That is my least favorite thing to do. What Guy would have talked about on the last show was the state of the market and the problems in the market. And we see that 80% of reps, nearly a missing quota. There's a huge delta in velocity between your top and your bottom performers. And even though the number of deals that are slipping across the market has dropped, we're still at like 36%, nearly 40% of deals, all deals slipping in the pipeline. And we see that

2:29 there's this huge inconsistency. That delta in velocity is an inconsistency of seller performance. You know, our top 14% of sellers, our A players are delivering 80% of the revenue. And that isn't sustainable. And while there are tons of tools out there in the market that provide lots of features and functionality, there are businesses that are spending money on lots of tech and lots of platforms, and yet this is still the case. There is still a huge disparity between top and bottom performers. And the way that Edster thinks about this is really more about, we see huge gaps. We see huge

03:08Closing the visibility gap inside Salesforce

3:08 gaps in the visibility and insight that sellers, managers and leaders have into their pipeline. They're able to understand signals, but they really don't have a real grasp and a handle on all of the data points that are influencing revenue. And what we strive to do here at Edster is bring all of that together into one place. We connect to multiple different data sources, and we firmly believe that Salesforce is and should be the single source of truth. And so what we've created in its rawest form is visibility and transparency in a single place inside Salesforce that is designed to transform the way that people view the risk and the health

03:52Lifting B and C players toward A-player performance

3:52 of their pipeline. It's really, really common for people to just notice the top A players, the top performers, the top closers, and really focus on just maybe making them better. But if you can get the 85% of your BEC players to just incrementally improve for an organization that has a decent sized sales team, that's massive improvement to overall performance. So I think it's a really strong mission to be on. And if we can just emulate a little bit of what those top performers are doing with the bottom performers, the results could be massive. Yeah, you're

4:29 absolutely right, Anthony. And look, it's not like our BNC sellers are lazy or they don't want to win. We're all in sales to win and to close business and hit our numbers. It's a performance sport. And sometimes we just need the proper guidance and the proper understanding. And when that information is siloed or I have to log into 10 different systems to really get an understanding of what's going on every opportunity, it's a barrier. It's a barrier for people using the insight and using the information. So what we've done and what I'm about to show you is hopefully you can see my screen, is an opportunity record inside Salesforce.

05:07The enriched opportunity record

5:07 And what episode has done in the background is it's connected to the mail server, it's connected to the conversations that we're having, and it's connected to CRM. It's looked back in time over the history of all the close won and close lost opportunities. And it's brought a ton of rich insight onto the opportunity record without anybody having to do anything that gives you real life visibility into risk, into relationship, into all the signals that are going on with this opportunity that are going to provide you insight into the health and the likelihood of

05:42Capturing the 70% of communications missing from CRM

5:42 this deal to close. So what I'll do is I'll start by scrolling down and I'll start at the bottom and I'll work my way up. So I mentioned earlier that we connect to the mail server. The mail server is the single source of truth for all the communication and it's a pain in the proverbial to get reps to log in to log every activity and every contact. And we typically see that when we work with clients about 70% of that information is missing from CRM. So when it comes to understanding things like how multi-threaded you need to be, which is the right stakeholders to be involved in an opportunity,

6:17 70% of that information is missing, then businesses are blind. They're making decisions without the right information. And so we fix all that and we can fix all that historically within the first week of engaging. And what that looks like is we bring this onto the opportunity record, account record, lead and contact record inside Salesforce and you get this visual representation of all the communication that's taking place. You can see the contact, you can see the direction of communication. If you want to read the email, you can see what's going on. If you want to add that

6:48 to Salesforce as an object, you can. Many of our clients used to use us to store all this information, but you can get a quick look as a manager, leader or seller as to all the communication that's taking place. You understand where your strongest relationships are. So of all the contacts that I'm talking with, I want to know who's the main contact there at a glance. I also want to make sure that my database, my marketing database is full. So if I want to add those contacts into Salesforce, I can. And I guess one of the key things that you'll see there, Anthony, is this

07:21Relationship score as a leading indicator

7:21 idea of a relationship score. This is something that we're really proud of. And then one of the differentiators from us is that we were able to provide you that relationship score and that relationship trend over time. And it's one of the leading indicators of the health of an account, an opportunity and a relationship that really gives you the clearest indication of the likelihood of this deal to close. And not only that, because we look back historically over all of your close won, close lost opportunities, we're able to start setting things like benchmarks. And that brings me

08:02Benchmarking against won and lost deals

8:02 really neatly on to the next set of information or suite of information that you'll probably see in front of you on the screen. Before you move on from that point, I want to highlight for those watching how important and foundational that is that not only are you saying, hey, we'll begin to capture emails and meetings that are coming in right now so you can start to begin to do a relationship score, but going back in time and going back in history and gathering all of that intelligence a lot. I know when you start an implementation, I don't know exactly what the

8:36 percentage is, but it's a high percentage of past contacts, past meetings, past activities that have happened that never made its way to the CRM, and you get to pull that in. It's absolutely game changer when you have that data finally enriched and finally in one place where you can benchmark it and see the relationship score too. Yeah, 100%. It is transformational. And I speak with lots of RevOps leaders about this, and they are trying to do these kinds of benchmarks. They're trying to understand who are the key stakeholders that we need at each stage and how long has this been in

9:14 the pipeline for and what does good look like for our organization. They simply can't do it because they don't have enough data in the system, and that's one thing that we can fix immediately and hopefully provide RevOps leaders the opportunity to get back and do more critical work than trying to pull together all this information. Yeah. So yeah, really, really good point. Really good point. And look, this is just an example, right? But what we can see here is that, again, we're connected to the mail server. We're looking into the calendar. We can see that there's no

09:44Time-in-stage, deal age, and slippage

9:44 future calls, meetings, or taskbooks. We've benchmarked this opportunity against historical business, and we can see that, look, it's been in the current stage for 24 days. And when we win business, the upper benchmark for us winning business is typically only 10 days. And we can also see that the opportunity is 44 days old, but typically the opportunities when we win, they're typically only 20 days old. Now, that doesn't mean that this is a disaster of an opportunity, but it does mean that as a seller, I need a narrative as to why this is taking longer

10:17 to close than usual when it comes to my pipeline review. And I probably should have a good answer, because I know that my manager is going to see this. And probably one of the first points that he's going to ask me about is, well, why is this taking so long? And we know that slippage and when slippage deals slip beyond three to six months, win rates at like 3%, you can check out the benchmark report, go to the episode website and download it. If you want to find out exactly what that number was, but a huge impact on win rate by deal slipping. And so by having this visibility, we're surfacing all that risk in the pipeline and allowing sellers

10:58 to make better decisions, make their next steps, have more constructive conversations with their managers. We can also see there's been no activities. And actually, the benchmark for Patrick here, his benchmark for closing deals is at 76, his relationship score is only 50. So he's got some things to work on. And really kind of benchmarking, understanding all of these factors that are influencing the deal is really, really important. And when you do this and you make it easy for reps to see what needs to be done and what's going on in their pipeline, it makes it much easier for them to take the necessary actions to self-serve, to fix these

11:36Qualification: MEDDIC and capturing data by stage

11:36 problems really, really quickly. The next thing that I'm going to touch on, other kind of bits of visibility, you can get more context over the activity that's been going on over the duration of the opportunity, if you want to visualize that activity in a different way. And another thing that we're really hot on here at Ebster is qualification. We med pick for us as our gold standard, but this is just a method of capturing information. And so we have businesses that use spin, that use band, that use med pick, even have their own custom qualification capture fields

12:20Auto-capturing qualification from call intelligence

12:20 baked into Ebster as well. I think a lot of the work we do at LeanScale too is helping people set up a qualification method, making sure capturing the data stage by stage. Is this where people will capture notes for this so the rep can easily enter in the information here? Yeah, exactly. So look, we believe in scoring, we believe in scoring med pick, right? And if you score it, you can measure it and therefore you can understand patterns and you can spot deficiencies across a large, broad group of sellers. And you can manually add data and tweak this as you want. One of the really cool things that Ebster provides with its core intelligence tool

13:06 is the ability to auto capture qualification from the conversations that we're having without actually having to do much work at all. So let me just share a different tab briefly. So again, we can do this with gone recordings, we can do this with your Zoom calls, with Teams calls, bring everything inside Salesforce so you have that kind of single source of truth of all of your data and all of your insights. And we bring these kind of key moments onto the opportunity record. But to touch on that qualification, I mean, look, you get all of the things that you'd

13:43 expect in terms of a call summary, in terms of the transcript, in terms of we can identify key topics. But the main use case here is really identifying and capturing that qualification status. So what the what the Ebster bots done here is it's analyzed the call. And it's recommending that look, metrics, we believe, you know, the AI believes that the metrics on this opportunity should be a three. And the rep can have a look at that, he can understand why. And it's also recommended some notes as to what's driving that score of a three. And the rep can update,

14:22 or he can ignore it. You know, it's important to give the rep license, you know, we, the rep has to build this overall qualification over multiple calls, multiple emails. And so, you know, we do want to give we don't want to remove the the kind of the license from the rep, but we're just trying to make it as easy as possible for the rep to capture as much information as is humanly possible. And I think like one of one of the big advantages for businesses, particularly those mid market businesses that have got you know, anywhere from 20 to 150 sellers, right, like your individual

14:56Removing rep bias at scale

14:56 managers, your leaders have got to interpret, you know, different, you know, biases and different, you know, different ways of doing things and capturing information from 150 different people, you know, some people lean negative, some people lean positive. What the AI does is when you're using this at scale is kind of remove all that, remove all the bias, remove all the, you know, in subtle interpretations and give you just blanket consistency of capturing information and of the quality of information you're getting. And it makes it it means it's really, really

15:30 powerful for kind of then diving into, you know, which reps are under qualifying, which reps are not proficient, you know, maybe implication of pain, you know, all of those kind of things that we bring that make that really easy for people to see. Because we're capturing that data consistently at scale. No, that's incredible. So I just want to I just want to clarify. So any call recorder they're using, you have a native one in EPSTA, if they want to use that one, but if they're using Gong or something else, you'll capture the transcripts, and then you'll automatically populate qualification methods from the transcripts of the call. That's yeah,

16:09 absolutely huge time saving. And like you mentioned, just getting it accurate and unbiased. Because who knows what reps are typing into these fields sometimes, or like, you know, I don't want to say anybody's doing anything with malintent, maybe they're just uninformed. But like, are they actually gathering the right qualification methods? Or the right qualification information? And are they putting in the right data? Or are they just thinking that they're qualifying and moving something along, when maybe they didn't actually have that conversation that needed to be had?

16:39Call intelligence: key moments and playback

16:39 Exactly. That's exactly it. You've hit the nail on the head. Let me jump back to the opportunity record here. And I'll wrap this bit up. And then we'll get into some of the more sexy stuff. The final piece of the puzzle we've just touched on call intelligence. Yes, we we're looking for insight from the conversations that we're having, in order to be able to inform our insight into the health of the opportunity. And so, you know, every company, every company, every customer of Ebstas is different. And so these key moments that we pick out of the conversations, we work with our clients to define those prior to deployment. And they might be

17:16 different from client to client, they also might be different from function to function. So, you know, those key moments, if you're, you're in CS, and you're working on a renewals basis, be very different to, you know, hunter gatherer in in net new. And so we can break it down right to that kind of really granular level. But what we're displaying here is the is the is the insight that we've picked out that matters most to the to the to the to the client on the opportunity. And so you can see here on the positive side, we've got a sense of urgency and readiness for

17:47 implementation. We've talked about timeline and the fact that they want dealing place by the end of the quarter. And the same on the negative side, we can see that a couple of competitors have been mentioned. And so, again, we really try to bring this insight out to inform decision and inform leadership of, you know, where the risk is, what's the likely thing. And the beauty of this is that if I want to go as a manager and a leader and have a have a quick look, I can hit playback and write on the opportunity record inside Salesforce. I can view that, you know, a minute and a half,

18:25 two minute clip on the key piece of information that I that I want to see. So that's really cool. That's so smooth, such a smooth process and giving information that you need right up front, organized really well. It's really, really intentional. Yeah, yeah. Now, honestly, like, you know, from a leader, the last, you know, I've used call intelligence tools before, you know, I think they're great. I one of the things that, you know, I just found I couldn't manage was like trying to scroll through, you know, managing six, seven, ten reps, scrolling through hundreds

19:00 of gone calls or, you know, other calls to to really kind of find out what are the key bits, where's the bits that I'm going to have the most impact or make the most difference to me? Or how do I how do I identify what those what those where those where those where that gold is? And this really does that for me. And it means I don't have to kind of leave Salesforce, which is which is brilliant. So I guess that that that that wraps up the the opportunity stuff, right? We've we've we've gone through a lot of information, we've we've sucked data from the mail server, we've

19:32Managing the pipeline at scale

19:32 sucked data from the conversations, we've pulled historical opportunity data from Salesforce to provide you all this insight. And this is on an opportunity record. But how do how do people manage this at scale? And that's, that's what I want to dive into next. So what, hopefully, you can see my change of screen there, Anthony. But what what we're looking at here is a pipeline insight tool. And what we've built is a visual representation of every opportunity that exists in the business. And you can slice and dice this in information in any way you want. You can look at deals that are

20:08 you know, that are current for this quarter, you can look at the height, you can log in by various different hierarchies or individual users, by product types, revenue types, you can use extra smart insights function, which will tell you all the deals that are stalling or they have warning signs. But it's a really easy way to manage a lot of opportunities at scale. We logged in as Wayne who's an individual contributor. And Wayne can manage his pipeline a lot, a lot more easily, right? He gets an initial glance of all his opportunities. And he can see, look,

20:39 he's got a ton of opportunities where he's really high engagement, this relationship score kind of acts as that first gut check. And you can see how that relationship scores trending as well. On the flip side of that, if he wants to interrogate the lower end of his pipeline, he can see it's got a bunch of opportunities in here where there's no engagement or very little engagement. And these deals are realistically clogging up Wayne's pipeline, then they don't appear to be real. And he should probably just close, lose these deals and move on and focus where he's got high engagement and he's got a real chance of winning these deals.

21:19The deal score and warning signs

21:19 That's kind of the first thing. The second thing you'll notice is this idea of a deal score. The deal score is, you know, a hundred deal score would be a close one opportunity, a zero deal score would be a close lost opportunity. And the deal score should increase as we move through the stages of the pipeline. But the deal score is impacted by all of those positive and negative factors. So all of those signals that we have identified by looking at all the data sources, by mapping those to how, you know, what the benchmarking of what good looks like when you

21:48 win over the last 12 months worth of closed opportunities. All of those signals, all of those positive and negative factors are going to influence the deal score. So you can have a look, I've got a good relationship score, I've got a good deal score. However, I've got some warning signs and you can click on those and it takes you into the sidebar tab that pops up. And you can see, look, I can see all my negative factors on these deals. I can see the risk factors. I can see what I'm doing well, which is great. My relationship scores increased. I've had activities in the last

22:17 seven days. However, I'm not multi-threaded. I'm not as multi-threaded as I should be. I need to get more colleagues involved. I've got closed days in the past. Unforgivable, if you ask me. But, you know, there's something that before a pipeline review, I'd probably going to want to address that immediately. And look, we've got no future calls, meetings or tasks, but again, that's a good next step for me as a rep on this opportunity. Go, look, get that next step in place. Get that, I mean, what they really should be doing is booking a meeting from a meeting. I spoke a lot about

22:48 BAMFAM in my time. But look, we haven't got a meeting in. Let's go get our next steps in place. So again, as a seller, it's guiding me what to do. As a manager, it's guiding me, what questions do I need to ask? What do I need to be looking for? You know, here's all the risk. You can see your qualification. You can see the contacts that you've got on the opportunity. And again, you can see that activity timeline, all from the insights tab. Click on any of these opportunities and you get that visibility. Yeah, this is great just to be able to see, especially the intelligence of looking at what's being qualified, be able to slice and dice it.

23:25 And this is such a better view for a rep to go through their own pipeline before getting ready for that pipeline review. Hopefully they catch opportunities with close dates in the past and things like that before they get in front of their manager. But it just gives you all the information you need right up front. Yeah, absolutely. And look at that. We've built, we've built this in mind for the rep, right? You click on the smart insights tab, you click on has a closed date in the past, and it's going to show you all the opportunities that have a closed date in the past. There's nine of them. Yeah. And if you're managing a

23:57 high volume of opportunities, sometimes it's not about negligence or something you just may not have realized like, Oh, it has been two weeks since I've reached out to them. Or, Oh, I forgot to book a call on the last call that we had. So just empowering them to manage a bigger book of opportunities to help expand their level of effectiveness. It's really, really important to help them close as many deals as possible. Yeah, absolutely. Couldn't agree more. So how does all this roll up into forecasting, right? Like we've provided you with this data. You can

24:34Bottoms-up forecasting with manager overrides

24:34 slice and dice it at scale. You can look at it, you know, it maps to the hierarchy that sits inside Salesforce. So if I'm a manager, I'm going to get a roll up of all my reps opportunities. I guess that what we say is that look, we believe in bottoms up forecasting, right? If you take this approach from bottoms up, we get the reps involved and committed to submitting a forecast week in week out, backed by data, backed by information. The managers will then come and submit their forecast. If we do it bottoms up, we get a bigger number, right? Our job as revenue leaders is

25:13 to make sure that we're forecasting accurately, but that number's as big as possible. And there are tons of tools in the market that will look at signals and go, you're going to hit that number. But if you do it this way, if you give everybody in the organization access to the data and the metrics that matter, we drive better performance. And we drive, we, you know, here at Epstein, we're able to guarantee that we guarantee that we will improve quota attainment. We guarantee that we will improve your forecasting accuracy. So yes, we want to get you to that accurate number,

25:39 but we want that number to be as big as possible. We want you to win more deals. We want your reps to achieve more of their quota. And we built this tool to help them do that. So as we segue from kind of that to forecasting, the idea here is that you come in as a rep, you have a weekly forecast cadence. I can check out my deals. I can see that I've got a good relationship score. I've got a reasonable deal score. I can see the warning signs. I can see how well qualified my deal is. And I'm going to make a forecast submission, right? I'm going to put this, this is going to stay in

26:15 pipeline. I'm going to move it to upside. I'm going to move it to commit. And on a weekly basis, we'd expect our reps to be making a forecast submission. And they can submit their forecast by very, very, very, very easily. They can submit their commit forecast. They can, they can make some notes. They can adjust the numbers. They can, you know, they can account for a pipeline that's created and closed in period or all of that kind of good stuff. But what we also say is that the manager needs to have license to forecast as well, right? So the manager might come in here and say,

26:47 look, you know, Wayne here, he's got decent relationship score. He's got a great deal score. But he's not, he's not qualified enough on the paper process. And he's got this deal in commit. So as a manager, I'm going to hedge that. And I think that's an upside deal. I'm going to do everything that I can to help Wayne close that, but I can't forecast that yet. And then the manager will submit their forecast at the same time. So how do we then view what, you know, view the forecast? How do we view the performance of the business as a whole? What you're looking at here is the

27:21The forecasting tab: coverage and pacing

27:21 Exodus forecasting tab. Again, everything's inside Salesforce. I'm logged in here as James Worthington, who's the VP of Sales. So he's responsible for the whole business. He can see his Q2 quota. He can see his attainment to quota today. He can see his all of his team members. And you can drill into this information. You can see how Helen's team members are performing, Jacob's and Josh's. James is going to look at this and say, hey, look, Helen is smashing it. I need to spend most of my time with Jacob and Josh and figure out how we get them to quota. You can see the commit submission for

27:55 the business. You can see the individual team commits submissions. You get two numbers here. So one is the rep submission and then one is the manager's adjusted submission. You can see the upside submission. And we pull some really cool insights around things like, you know, your required coverage and your pipeline coverage. So we've done some analysis historically because we know the pipeline really well. We can see that Josh here has got 6.8x coverage. And he typically, based on historical performance, he requires 3.6x to hit his target. So what the message to Josh is,

28:29 look, you're behind on Q2 relative to the rest of the team. You've got enough pipeline. I want you to focus on closing deals because we know that you've got enough pipeline to cover your gap. So really a quick glance, leaders in the business can see exactly how the business is pacing. Once you've got a visualization of exactly how the business is pacing, as a leader or a manager, you want to focus on what's going to move the needle, what's going to help us get there. And that typically we find is focusing on the problem deals. So I'm going to switch to our pipeline

29:05Pipeline change: focus on the problem deals

29:05 change tool. So the pipeline change tool here is really a visualization of what's trending in the pipeline. We can see that we started the period and where we're predicting on ending the period. And we can see that as a leader, certainly, I'm looking at this first up. I'm looking at the deals that I've got in commit. They're in commit for Q2. It's great that we've won some deals. It's great that we've got some deals that are trending up. So there's quite a few deals here that are idle. And that's probably my second priority. But first and foremost, I want to focus

29:42 on these deals. They're in commit for this quarter. There's 11 deals valued at $300,000 and they're trending down. And what I can do is I can click on that number. I can see immediately that I've got a bunch of these commit deals that have very low relationship score. There's a bunch of warning signs. We've got some OK deal scores and there are some good relationship scores. But what I want to do is I want to expand those deals out and then I can interrogate the pipeline in exactly the same way. So I can see that I probably need to be having a conversation with Lucy, Neil and James

30:17 because these deals are in commit. We've got a bunch of warning signs. There's no engagement. They need to come out and be closed, lost immediately. And then I would probably start with these deals. They might need some support and engagement. We might need to move those to upside and work on an engagement plan. But really, as a leader within the space of 30 seconds, I've identified exactly how the business is pacing. I've identified exactly which opportunities are going to move the needle for me, which are the problem opportunities. And I've now got a line

30:53 of sight to exactly the conversations that I need to address that ASAP. So easy for a manager to just lie in high level, see exactly where the forecast is, see where opportunities are moving, target. They have very limited time. So target the opportunities they need to and go figure out how they can help their reps. And this is what I think we're talking about at the beginning. How do we get those BC players to just perform a little bit better, get a little bit closer to that A player performance and the results can be massive if you can focus that manager's time on

31:24Funnel analytics and time-in-stage

31:24 where they should be. Yeah, absolutely. Absolutely. This is something that we really care about. So it's a really good segue actually to our funnel analytics tool. So we talked about benchmarking time in stage, time in pipeline. And you said there, how do we improve those B and those C sellers? This is one example of how we do that, right? But what you see here is basically the funnel performance for the business. We can see, and again, you can slice that by quarter, by opportunity type, by revenue type, but you get a very quick view of the velocity or team's

32:05 velocity, opportunities close, win rate, sales cycle, average order value in a very quick snapshot. And you can filter this by, you can drill down to individuals, but you can see what's moving through the pipeline and how things move through the pipeline. And obviously this is dummy data, right? This looks like a beautiful waterfall. I have never seen a client funnel look like this, look this pretty. Obviously mine looks like this, mine's perfect, but that's for another story. But look, one of those things is, look, again, you can drill into all of this information and

32:39 you can drill into pipeline insight so you can see those opportunities that slipped or dropped out. But one of the things that we're talking about around that benchmarking is look, time in stage. So immediately what we've done is we've got visibility of who's lagging, who's dragging, we see Jacob here, his average time in stage is 24, in average time in the value proposition stage is 24 days. However, when he close wins deals, his time in stage is only 14 days. So that tells me that Jacob's team is hanging on too much, hanging on too long to deals, because anything that's

33:17 over that long time in stage, anything that's over like 15, 16 days is probably not going to close. So that might mean that I need to work with Jacob and his team on better crafting value propositions, better communication of business impact and outcomes to clients. And it's really just a really good way of spotting trends and patterns in big data sets across the funnel and helping you kind of root out those problems. Before we wrap up Anthony, there's probably just one more thing that I think I would like to show you, but it's this forecast change. So this is

33:52Forecast change: where leadership lives

33:52 where a lot of the C level, a lot of the leadership will spend their time. They want to know what's happening to their forecast over the period and they want to know why. They want to understand what's been submitted at the start of the period versus at the end of the period and what happened in between. And again, you can see that in this case, we've pulled in a chunk from future quarters. We've got some deals that are new in forecast. We won a load that wasn't committed. At the same time, we've had 181K value slip. And again, all of this data is drillable. And within a very

34:41 few short clicks, I can identify all of those deals that slipped. They're in upside, but they've slipped. Why have they slipped? We've got no engagement. We've got low deal scores. We've got a bunch of warning signs on the deal. So again, it's another way of slicing and dicing this data. So I think from my point of view, Anthony, it's been a whistle-stop tour, hopefully a meaningful one. And hopefully it's added a ton of value. No, I think you covered a ton in a short period of time, and especially because the EPSTA platform is so powerful and robust. But just to reiterate some of the things that

35:24Recap: start with the data foundation

35:24 I think really stand out to me and our team, to remind people listening, we're doing revenue operations for high-growth, fast-selling companies all day long. You really hit the most important part, starting with the foundation, getting the data in, getting the emails, the meetings, the contacts, the relationships. Everything we do is relational. Sales is relationships. So understanding the health of those relationships, going back in time. You have lots of tools that can start picking up where you are today, but being able to go back in history and pull all

36:00 of that in now, that's super powerful. And that data foundation just gives you the opportunity to get started and is immense value day one if you go with EPSTA. Then integrating the call intelligence, auto-populating the qualification methodologies, getting the right information that a busy manager or busy rep needs to see on an opportunity. Single-click into something positive going on with the deal. Single-click into something negative that's going on with the deal. Then seeing your whole pipeline in one place with your qualification, your contact relationship

36:32 scores, being able to slice and dice your pipeline as a rep. So powerful. Then for managers and executives, the funnel analytics and funnel waterfall, huge insights packed into really, really easy to consume visualizations. And you make sure you have all the data there. So it's accurate and it's actionable. You can click right into any of those charts and start actioning any of that data. It's not living in some data warehouse plugged into a BI tool where the drill downs are tough to deal with. You can hop right in and start going from top level board room,

37:11 executive level reporting, diving deep into a specific conversation, a rep had with a specific deal. So that's why when people are in market for a revenue intelligence platform, Epstein, I think you really have put together all the components you need where a lot of them maybe meet one or two, but you really cover that full journey. And I love the mission. Let's get those BNC players performing at least a little bit more like your A players and then see the results that'll happen in your business. Yeah, I love it. Absolutely. Amazing summary. And look, the point is that you can't do the last thing you said, which is improve your BNC sellers without

37:55Data as the foundation for an AI-first world

37:55 data. And I'd also like as a side note, we're moving very, very quickly into an AI first world. And having that foundation of data is probably one of the most important things to getting businesses AI strategy, right? Because AI is only as good as the data that it has access to. And if your data's in different disparate silos all over the place, not structured, the AI is going to provide meaningless output. And it's one of the things that I think businesses really need to focus on in the next six to 12 months is to get that foundational data piece, right? Because if

38:32 they don't, any AI that they layer over the top of their business, unless it's within specific tooling is going to have a really hard time. Oh, totally agree. And we talk about this all the time at LeanScale. You can swap in and out AI models, AI platforms all day long. Don't focus so much on which AI tools you should be getting. Focus more on, we really like the term ontology. So focus on the data accuracy and how you structure your data in order to train a model. If you do that really, really well, don't worry. There's going to be plenty of AI tools on the

39:08Close: where to find Adam

39:08 market that will be able to read that data, but you have to start there. Yeah, couldn't agree more. Well, Adam, this has been awesome. Thank you so much for sharing so much in such a short period of time. We really appreciate having you on the LeanScale podcast, and we can't wait to see what you and the team at Epstein build next. Amazing. It's been an absolute pleasure, Anthony. If anybody would like to know more, find out. Hit me up on LinkedIn. I'm on there 97% of the time. So yeah, it's been great fun. I've really enjoyed it. And yeah, hopefully we can do something like this again. We'd love to. Thanks, Adam.