The LeanScale Podcast · Episode 6

Why Your Forecast Is Broken

Anthony Enrico and LeanScale engagement managers Bernardo and Cameron on why most forecasts are wrong — and the simple fixes that get you one step closer to the truth.

Anthony Enrico · Co-Founder · LeanScale A LeanScale solo episode
Published Updated 00:15:53 13 min read 2,631 words
Forecasting Revenue Operations GTM Strategy Sales Leadership
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Executive Summary

The one-paragraph brief, extended

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

Forecasting is one of those never-ending problems with a million ways to do it, so for this early LeanScale Podcast episode Anthony Enrico doesn't go it alone — he brings two LeanScale engagement managers, Bernardo and Cameron, for a three-man clinic on why most companies' forecasts are broken and what to do about it. The throughline is disarmingly practical: forecasting exists to give a business predictability, and predictability is what tells you when to make investments in marketing, sales, and customer success before the revenue actually shows up.

The panel is candid that the stakes cut both ways. Forecast too high and you over-invest ahead of actuals, hiring and spending against demand that never arrives. Forecast too low — the failure mode people underrate — and you over-promise to the market, bring on more customers than you can support, and never build the infrastructure to take care of them, with a ripple effect that can be catastrophic to the brand. And forecasting is genuinely hard, because you're predicting a future full of things outside your control: deals slip, org structures change, priorities shift. The team's reframe is to stop chasing penny-perfect precision and instead treat forecasting as an iterative exercise whose goal each cycle is to get 'one step closer to the truth.' A forecast that's 100% accurate every time isn't a triumph — it's usually a signal of sandbagging, slipping deals, or too little friction in the pipeline.

From there the conversation gets tactical. Start by making the inputs consistent: clearly defined lead and sales stages with explicit entry/exit criteria, so a deal only advances when specific things have actually happened. Watch three milestone stages — pipeline entry after real pre-qualification (intent plus budget), proposal/negotiation once commercials are on the table, and legal or executive approval once the deal leaves the champion's hands. Sharpen the nomenclature the LeanScale way, naming stages after completed actions ('Negotiation Completed,' 'Proposal Sent') rather than ambiguous nouns, so everyone knows exactly where an opportunity stands. Then segment the pipeline by the dimensions that matter — deal size and tier, geography, product or use case, industry — because blended conversion rates and sales cycles hide reality. Only once that foundation is set should you layer on technology, including AI forecasting tools, which amplify a good process rather than fix a broken one.

Who should listen: RevOps and sales operations leaders standing up a forecasting motion, sales leaders trying to make their pipeline stages mean something, and founders and revenue executives who need a forecast reliable enough to plan the business around. The takeaway isn't a magic model — it's a mindset and a sequence: get comfortable being one step closer to the truth, define your stages, segment your pipeline, and earn the right to add tooling.

Key Takeaways

10 things worth stealing

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

01

A wrong forecast is catastrophic in both directions

Forecast too high and you over-invest ahead of actuals — hiring salespeople and CS you can't ramp on day one against demand that may never arrive. Forecast too low and you over-promise to the market, bring on too many customers, and never build the infrastructure to support them, diminishing the brand.

Why it matters: Treat forecast accuracy as a two-sided risk, not just an upside target. Both over- and under-forecasting carry real, expensive downside, which is why the whole organization ends up invested in getting the number right.

FoundersRevenue ExecutivesRevOps Leaders
02

Forecasting exists to give the business predictability

The core value of a forecast is predictability: knowing what's coming down the pipeline and when tells you how much to spend on marketing and sales and how much CS capacity you'll need. In an economy where few companies can hire ahead on cash alone, that timing is everything.

Why it matters: Frame forecasting as a capacity-planning and investment-timing tool, not a scorecard. It's the input that lets you make the right investments at the right moment and stay ahead of the curve.

FoundersRevenue ExecutivesRevOps Leaders
03

Forecasting is hard because you're predicting the future

So much is outside your control — deals slip, company structures change, priorities shift on the customer side. Your forecast on day one of the quarter looks very different two weeks before close, so it's a constant exercise of ingesting new information and adjusting.

Why it matters: Accept that it will never be a perfect science and build a cadence for continuous re-forecasting rather than a single set-and-forget number.

RevOps LeadersSales Leaders
04

100% accuracy is a red flag, not a goal

If you hit your forecast at exactly 100% every time, it usually means you're sandbagging, deals are slipping to make the number, or you're not introducing enough friction to push deals to close. Nobody can predict the future perfectly — if you could, you'd be playing the stock market.

Why it matters: Stop treating perfect accuracy as success. Consistent 100% attainment should trigger an investigation into sandbagging and pipeline hygiene, not a celebration.

RevOps LeadersSales LeadersRevenue Executives
05

Chase 'one step closer to the truth,' not penny-perfect precision

A lot of people want to forecast down to the penny and obsess over getting it exactly right. The healthier goal is iterative improvement: layer on new information and new methodologies each cycle to get directionally closer to reality. Perfection isn't possible; getting better is.

Why it matters: Set the team's north star as directional progress. Being comfortable with 'one step closer' keeps the forecast improving instead of paralyzed by false precision.

RevOps LeadersFounders
06

Start with consistent, clearly defined stage criteria

The foundation of a good forecast is consistency in what you feed it. Define your lead and sales stages with explicit entry and exit criteria — don't let a deal enter a stage unless specific things have taken place — so there's science and structure under something highly volatile.

Why it matters: Before any tooling or math, standardize what each stage means and gate advancement on real evidence. That structure is what lets reps layer subjective judgment on top of a reliable base.

RevOps LeadersSales Leaders
07

Name stages after completed actions to kill ambiguity

This is the 'LeanScale method' applied to staging: instead of a plain 'Negotiation' or 'Proposal' stage — which could mean you're building a proposal or you've already sent it — use 'Negotiation Completed' or 'Proposal Sent.' The nomenclature makes every opportunity's position unmistakable.

Why it matters: Small wording tweaks to your sales stages pay dividends in forecast clarity. Completed-state naming removes the vagueness that quietly corrupts a pipeline.

RevOps LeadersSales Leaders
08

Watch the three milestone stages

The panel tracks three deal milestones: entering the pipeline after enough pre-qualification to confirm intent and budget; proposal/negotiation, which begins once you're talking commercials and is a good health signal; and legal or executive approval, when the deal leaves the champion's hands for compliance and sign-off.

Why it matters: Focus forecast attention on these transitions rather than every micro-stage. They're the moments that most reliably indicate whether a deal is real and moving.

Sales LeadersRevOps Leaders
09

Segment your pipeline — never forecast on blended rates

Companies too often blend their numbers — one SQL-to-close rate and one deal cycle across the whole business — when enterprise, mid-market, and SMB, and different geographies, products, and industries convert and move at very different paces. Segment by what matters and measure conversion and cycle within each.

Why it matters: Blended averages hide reality and produce a misleading forecast. Segment by the dimensions that anecdotally matter to your deals to get materially closer to an accurate number.

RevOps LeadersRevenue ExecutivesSales Leaders
10

Layer on technology only once the process is ready

LeanScale doesn't recommend adding forecasting technology until the underlying process is set. Once stages, criteria, and segmentation are in place, tools like QFlow.ai and BoostUp can use AI to run the many calculations behind an accurate forecast — but they enhance a solid foundation, they don't create one.

Why it matters: Sequence the work: process first, tooling second. Buying AI forecasting software before fixing the process just automates a broken input.

RevOps LeadersFounders
Frameworks Discussed

6 named models

Every framework Jimmy names, defined and time-stamped.

The Two-Sided Cost of a Wrong Forecast

02:54

Forecasting too high pushes you to over-invest ahead of actuals; forecasting too low leads you to over-promise to the market and under-build the infrastructure to support the customers you win. Both directions carry catastrophic downside.

The panel stresses that the under-forecasting failure mode is the one people underrate — it quietly manufactures churn and brand damage by bringing on customers you can't support. Framing accuracy as a two-sided risk is why the whole org invests in the number.

One Step Closer to the Truth

06:12

A forecasting philosophy that treats the forecast as an iterative pursuit of directional accuracy rather than penny-perfect precision — each cycle you layer on new information and methodologies to get one step closer to reality.

This is the recurring mantra of the episode. Perfection is impossible because you're predicting a future you don't control, so the goal is continuous improvement and comfort with 'closer,' not obsession with 'exact.'

The Three Forecast Milestones

08:59

The three deal milestones that most reliably indicate forecast health: (1) entering the pipeline after real pre-qualification (confirmed intent and budget), (2) proposal/negotiation once commercials are being discussed, and (3) legal or executive approval once the deal leaves the champion for compliance and sign-off.

Rather than obsessing over every micro-stage, the panel watches these three transitions as the strongest signals that a deal is genuine and progressing toward close.

Completed-State Sales Staging (the LeanScale Method)

10:18

Name every pipeline stage after the action that has been completed — 'Negotiation Completed,' 'Proposal Sent,' 'Marketing Qualified Lead' — rather than an ambiguous noun like 'Negotiation' or 'Proposal,' so an opportunity's exact position is never in doubt.

Anthony ties this to the 'LeanScale method' from a prior episode: small tweaks to sales-stage nomenclature pare down ambiguity ('am I building the proposal or did I send it?') and pay off directly in forecast clarity.

Segment Before You Forecast

12:16

Break the pipeline into meaningful segments — deal size/tier (enterprise, mid-market, SMB), geography, product/use case, industry — and measure conversion rates and sales cycle within each segment instead of using one blended rate for the whole business.

Blended averages (e.g., '20% SQL-to-close, six-month cycle') mislead because segments behave very differently. Segmenting by the dimensions that anecdotally matter to your deals gets the forecast materially closer to the truth.

Process Before Technology

14:08

Don't layer forecasting technology — including AI forecasting tools — until the underlying process (stages, entry/exit criteria, segmentation) is ready. Once the foundation is set, tooling can enhance accuracy; before that, it just automates a broken input.

LeanScale explicitly withholds tooling recommendations until the process is in place, then points to partner tools like QFlow.ai and BoostUp to run the heavy calculations behind an accurate forecast.

Best Quotes

15 lines worth clipping

Pulled verbatim. Copy or share any of them.

“If you forecast too low, over-promise to the market, bring on too many customers, you may not build out the infrastructure to take care of them — and the negative ripple effect of that could be pretty catastrophic.”
Anthony Enrico 00:00
“The biggest thing when it comes to forecasting really comes down to predictability.”
Bernardo Alves 01:33
“From a capacity planning perspective, forecasting is everything.”
Bernardo Alves 02:09
“Just making sure that we're not talking about backcasting — we're talking about forecasting. How are we looking forward into the future and creating a plan that fits for where you're projecting going?”
Cameron Legge 02:26
“Forecast too high, invest too much. Forecast too low, invest too little — you could diminish your brand.”
Anthony Enrico 04:14
“To put it bluntly, it's really hard. You're trying to predict the future, and you don't have full control of the situation.”
Bernardo Alves 04:14
“If you're forecasting with 100% accuracy, it's probably an indicator that you're either sandbagging in some way, deals are slipping, or you're not introducing enough friction somewhere.”
Bernardo Alves 05:35
“If you're hitting your forecast at 100% every time, you probably should be playing the stock market and be sitting on a beach in the Bahamas somewhere.”
Cameron Legge 06:12
“Don't be so obsessed over getting it right rather than getting it one step closer to that actual truth that helps push the business forward.”
Cameron Legge 06:12
“I think of forecasting as an iterative process. You layer on new pieces of information, new methodologies of forecasting that allow you to get one step closer to the truth.”
Bernardo Alves 06:51
“The beginning of it all is just making sure that the things you're putting into your forecast are consistent. Don't put this deal in this stage unless these things have taken place.”
Bernardo Alves 07:34
“We've seen BANT kind of phase out of priority over the last few years, with MEDDIC taking over as the predominant methodology people are using to qualify deals.”
Bernardo Alves 08:59
“Instead of just having a plain negotiation stage, I want a 'negotiation completed' stage — so every opportunity in that stage, you know exactly where it's at. Let's pare down the ambiguity.”
Anthony Enrico 11:01
“Segment out your opportunities and your pipeline based on the things that matter to your business. You out there know your business better than we do.”
Cameron Legge 12:16
“We don't recommend layering on technology until the process is ready. But once you have the foundation set, there are a lot of good tools out there.”
Anthony Enrico 14:08
Practical Advice

What should you actually do?

The playbook, split by the seat you sit in.

Founders

  • Treat the forecast as your investment-timing engine: it tells you when to fund marketing, sales, and CS ahead of the revenue actually landing.
  • Respect the downside of forecasting too low as much as too high — over-promising and under-building the infrastructure to support new customers can be more damaging to the brand than over-investing.
  • Sequence the work: fix the forecasting process before buying tooling. AI forecasting software amplifies a good process, it doesn't create one.

RevOps Leaders

  • Standardize lead and sales stages with explicit entry/exit criteria so deals only advance when specific things have actually happened.
  • Rename stages to completed-state nomenclature ('Negotiation Completed,' 'Proposal Sent') to remove ambiguity about where each opportunity really stands.
  • Segment the pipeline by deal size/tier, geography, product, and industry, and measure conversion and cycle within each segment instead of using one blended rate.
  • Set the team's goal as 'one step closer to the truth' — build a cadence for continuous re-forecasting as new information comes in, not a single locked number.

Sales Leaders

  • Watch the three milestone stages — qualified pipeline entry (intent + budget), proposal/negotiation once commercials start, and legal/executive approval — as your strongest deal-health signals.
  • Treat consistent 100% forecast attainment as a warning sign of sandbagging or slipping deals, not a win, and introduce enough friction to push deals to close honestly.
  • Give reps a structured base (clear stages and criteria) so their subjective judgment layers onto reliable inputs rather than replacing them.

Revenue Executives

  • Use the forecast for capacity planning: staff CS and support against the size and timing of deals in the pipeline, not against a blended average.
  • Insist on segmented conversion and cycle data before trusting a forecast — blended enterprise-plus-SMB rates will mislead your resourcing decisions.
  • Accept imperfection: your forecast will shift from day one of the quarter to two weeks before close, so manage to a range and a trend, not a penny-perfect point.
Operations Takeaways

By function

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

Revenue Operations

  • Consistency first. The forecast is only as good as its inputs — define lead and sales stages with explicit entry/exit criteria so deals advance only on real evidence.
  • Kill nomenclature ambiguity. Name stages after completed actions ('Negotiation Completed,' 'Proposal Sent') so every opportunity's position is unmistakable.
  • Segment, don't blend. Measure conversion and sales cycle by tier, geography, product, and industry; blended averages produce a misleading forecast.
  • Iterate toward truth. Run a continuous re-forecast cadence — layer on new information each cycle to get one step closer to the truth rather than chasing false precision.
  • Process before tooling. Only add forecasting technology (including AI tools) once the process foundation is set; tools amplify a good process, they don't fix a broken one.

Pipeline & Marketing Ops

  • Three milestones matter most. Qualified pipeline entry (intent + budget), proposal/negotiation once commercials start, and legal/executive approval are the strongest deal-health signals.
  • Gate stage entry. Don't let a deal sit in a stage unless the specific qualifying events have actually happened — that's what adds science to a volatile process.
  • 100% is a warning. Perfect attainment usually means sandbagging or slipping, not accuracy; introduce enough friction to push deals to close honestly.
  • Watch the whole funnel language. Completed-state naming applies earlier too (Marketing Qualified Lead vs. 'qualifying') — clarity upstream compounds into forecast clarity downstream.
Metrics Mentioned

The numbers, with context

100%
Perfect forecast accuracy

Consistently hitting the forecast exactly is presented as a red flag — a sign of sandbagging, slipping deals, or too little friction — not a success.

20% SQL-to-close
Example blended win rate

Cited as a sample blended rate that misleads because enterprise and SMB segments convert very differently — an argument for segmenting before forecasting.

6 months
Example blended sales cycle

Used alongside the 20% win rate as an example of a blended average that hides how different segments and geographies actually move.

Entities

Companies, people & tools mentioned

Auto-extracted and linked into the knowledge graph.

Companies

LeanScaleGTM Operations

The fractional RevOps and GTM operations firm behind the podcast. Anthony Enrico hosts alongside two LeanScale engagement managers, Bernardo and Cameron, and references the 'LeanScale method' of completed-state sales staging from a prior episode.

00:00 · 10:18Company →

People

Anthony Enricohost

Co-founder of LeanScale and host of The LeanScale Podcast.

Co-FounderProfile →
Bernardo Alvesguest

Engagement manager at LeanScale; came up through customer success into operations and business intelligence, focused on attribution and usage-based tracking.

Engagement Manager, LeanScaleProfile →
Cameron Leggeguest

Customer-success professional and early LeanScale Engagement Manager; a CS practitioner who argues customer success is a company's 'front porch' and most underrated growth engine.

Engagement Manager, LeanScaleProfile →

Tools & software

QFlow.aiRevenue Intelligence / Forecasting

Named ('Q flow') as a partner tool LeanScale points teams to — once the forecasting process is ready — to use AI to help produce a more accurate forecast.

Website ↗
BoostUpRevenue Intelligence / Forecasting

Named ('boost up') alongside QFlow.ai as a partner forecasting tool to layer on after the underlying process (stages, criteria, segmentation) is in place.

Website ↗
Methodologies referenced BANTMEDDIC
Related LeanScale Services

Where this shows up in the work

Forecasting & ARR Definition

Define a raise-worthy ARR and build a reliable consumption forecast across finance, data science, and go-to-market.

The episode is a foundational clinic on building an accurate, process-driven forecast — exactly the ARR-and-forecast work LeanScale delivers.

Fractional RevOps

Embedded, field-facing RevOps leadership and execution — the operator model Jimmy describes, delivered as a service.

Defining stages, entry/exit criteria, and pipeline segmentation is core embedded RevOps execution.

AI for GTM

Agents and workflows that multiply operator throughput — the 'make humans superhuman' thesis, operationalized.

The 'process before technology' close — layering AI forecasting tools like QFlow.ai and BoostUp once the foundation is set — maps to how LeanScale operationalizes AI for GTM.

Frequently Asked Questions

Straight answers

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

Why is forecasting so important for a business?

Forecasting gives a business predictability — a clear sense of what's coming down the pipeline and when — which drives capacity planning and investment timing across marketing, sales, and customer success. Because few companies can hire and spend ahead purely on cash, knowing when revenue will land lets you make the right investments at the right moment. Forecast too high and you over-invest; forecast too low and you over-promise and can't support the customers you win.

Why do companies struggle so much with forecasting?

Because you're trying to predict the future, and much of it is outside your control: deals slip, company structures change, and customer priorities shift. A forecast on day one of the quarter looks very different two weeks before close, so it's a constant exercise of ingesting new information and adjusting. It will never be a perfect science — the goal is to get one step closer to the truth each cycle, not to be exact.

Is a 100% accurate forecast a good thing?

No — consistently hitting your forecast at exactly 100% is usually a red flag. It typically signals sandbagging, deals slipping to make the number, or too little friction being introduced to push deals to close. Since you can't fully control the outcome, perfect accuracy every time is a warning sign to investigate, not a success to celebrate.

What does 'one step closer to the truth' mean in forecasting?

It's the mindset that forecasting is an iterative process rather than a penny-perfect prediction. Each cycle you layer on new information and new methodologies to get directionally closer to reality. Perfection isn't possible because you're predicting a future you don't control, so the goal is continuous improvement — being comfortable with 'closer' instead of obsessing over 'exact.'

What are the most important deal stages to watch for forecasting?

Three milestone stages. First, entering the pipeline after enough pre-qualification to confirm real intent and budget capacity. Second, proposal/negotiation, which begins once you're discussing commercials and is a good indication the deal is moving. Third, legal or executive approval, when the deal leaves the internal champion's hands for compliance and sign-off.

How should you name your sales stages?

Name each stage after the action that has been completed — 'Negotiation Completed' or 'Proposal Sent' rather than a plain 'Negotiation' or 'Proposal.' Ambiguous nouns leave it unclear whether you're building a proposal or have already sent it. Completed-state naming (the LeanScale method) removes that vagueness so everyone knows exactly where an opportunity stands, which pays off directly in forecast clarity.

Why should you segment your pipeline before forecasting?

Because blended conversion rates and sales cycles hide reality. A single '20% SQL-to-close, six-month cycle' figure can be dramatically wrong once you separate enterprise from SMB, or different geographies and industries that move at different paces. Segment your opportunities by the dimensions that matter to your business — deal size/tier, geography, product/use case, industry — and measure conversion and cycle within each to get closer to an accurate number.

When should you add forecasting technology or AI tools?

Only after the underlying process is ready. Once your stages, entry/exit criteria, and segmentation are in place, tools like QFlow.ai and BoostUp can use AI to run the many calculations behind an accurate forecast. But technology enhances a solid foundation rather than creating one — layering tools onto a broken process just automates the bad inputs.

Timeline

Jump through the conversation

Click any chapter to open it in the transcript.

00:00Cold open: the hidden danger of forecasting too low 00:56Meet the panel: Bernardo and Cameron 01:33Why forecasting matters: predictability & capacity planning 02:54Forecasting too high vs. too low 04:14Why companies struggle: you're predicting the future 05:35The 100% accuracy trap (sandbagging) 06:12One step closer to the truth 07:34Fix #1: consistent, clearly defined stages 08:59The three milestone stages (BANT → MEDDIC) 10:18Fix #2: completed-state sales staging 12:16Fix #3: segment your pipeline 14:08Fix #4: layer on technology when the process is ready 14:42Wrap-up
Full Transcript

The whole conversation

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

Transcript as markdown ↓ Whole episode as markdown ↓
00:00Cold open: the hidden danger of forecasting too low

0:00 I don't think people think about this problem as much, but if you forecast too low, overpromise to the market, bring on too many customers, you may not build out the infrastructure to take care of them, to manage them, and the negative ripple effect of that could be pretty catastrophic. Welcome to the LeanScale Podcast, where we talk about everything RevOps. Thank you for listening. Today we have a really full topic to go through. Forecasting seems to be one of those issues. It's never ending. There's a million ways to do it, so I thought it'd be helpful if I brought more than one guest this time. I brought the best of the best from LeanScale.

00:56Meet the panel: Bernardo and Cameron

0:56 I have two LeanScale engagement managers with me today. Bernardo, Cameron, thank you for helping me tackle this one today. Absolutely. Our pleasure. Three-man crew. Got to tackle it. Got to tackle it heavy. Forecasting, let's do it. It probably takes 100 people to figure this one out, but we'll give it our best with the team we have here today. For people listening, I wanted to tee it up a little bit. I think people know forecasting is important, but could you give me some reasons why it's so important? Practically speaking, why is forecasting so important for your business?

01:33Why forecasting matters: predictability & capacity planning

1:33 Yeah, I think the biggest thing when it comes to forecasting really comes down to predictability. Everything in the business, especially in today's economy where not everyone has cash just on hand to hire ahead of time and make the right investments in the business at any given moment. Forecasting gives you that predictability of when do I need to make investments, what's coming down the pipeline. It's going to influence how much marketing you're going to spend. It's going to influence how much sales investments you're going to make. Your CS team is going to be highly contingent on the deals that are coming in, the size of those deals, who

2:09 you need to escalate to work on those deals. From a capacity planning perspective, forecasting is everything. Now, more so than ever, having a clear sense of what's coming down the pipeline and when it's going to come in will allow you to better run your business and stay ahead of the curve in terms of being up to date on what you need down the line. I think Bernardo put it best and very beautifully. I think just making sure that we're not talking about backcasting. We're talking about forecasting. How are we looking forward into the future and creating a plan that fits for where you're projecting going? That's the basics of forecasting.

02:54Forecasting too high vs. too low

2:54 If I'm going to answer it in more layman's terms, that's why forecasting is just such a critical piece to follow the trends of the business and be able to plan out ahead quarters, years in advance to know where you are going to end up and what resources you need to put in place to continue on that growth trajectory. I can give a couple of examples that I've run into. If you're forecasting too high, some of the investments that you need to make take time. It's not like you can get salespeople ready to go on day one. If you need extra resources and customer success or customer

3:32 support, they're not ready day one. If you're forecasting very high, a lot of times you need to start making those investments ahead of time ahead of actuals coming into the door. You may be over investing if the forecast is too high. On the other side, if you forecast too low, and I don't think people think about this problem as much, but if you forecast too low, over promise to the market, bring on too many customers, you may not build out the infrastructure to take care of them, to manage them. The negative ripple effect of that could be pretty catastrophic. Both sides of the coin have really, really negative outcomes.

04:14Why companies struggle: you're predicting the future

4:14 Forecast too high, invest too much. Forecast too low, invest too little, you could diminish your brand. I think that's why there's so much attention to this. Everybody seems to be invested in the forecast and understanding where we're going to land, how we're going to land and how to make it as accurate as possible. Why do you think companies struggle so much with forecasting? To put it bluntly, it's really hard. You're trying to predict the future. You don't have full control of the situation. We know that on the customer or prospect side, there's going to be delays. There might be changes in the company structure

4:54 that might cause deals to slip. Their priorities might shift over time. There are so many things outside of your control that you just kind of have to accept that it's never going to be a perfect science. There's a lot of subjectivity into it. Especially when it comes to timing. Your forecast on day one of the quarter is probably going to look very different than two weeks before you're actually closing the period. It's an ever happening exercise where you're constantly making adjustments, ingesting new information and making decisions based on what you know to the best of your ability at any given time. It's never going to be

05:35The 100% accuracy trap (sandbagging)

5:35 a perfect science where you always have 100% accuracy on your forecast. If you're forecasting with 100% accuracy, it's probably an indicator that you're either sandbagging in some way, deals are slipping, or you're not introducing enough friction somewhere, pushing deals to close faster or anything like that. It would be slightly concerning if you were always 100% on the spot because it's not something that you can fully control on your side. Yeah. And just to add to that, I mean, if you're hitting your forecast at 100% and just hitting goal and making your call every time, you probably should be playing the stock market

06:12One step closer to the truth

6:12 every day. Maybe you are on the side and be sitting on a beach in the Bahamas somewhere because to be able to predict the future to 100%, I'd love to find that person and let them be my best friend. But I think, again, to follow up Bernardo there, I think it's about making progress to get one step closer to the truth. A lot of people want to forecast down to the penny and make calls. And it's important to set goals and make those calls. But don't be so obsessed over getting it right rather than getting it one step closer to that actual truth that helps push the business forward. That's really the most important

6:51 thing and you want to be comfortable with that, just that one step closer to the truth. Yeah, I agree with that a lot. I think of forecasting as an iterative process. So you start to layer on new pieces of information, new methodologies of forecasting that allow you to get that one step closer to the truth. Understanding perfection is not possible, but at least if you can get better, I think it's going to be better for your business. So we know it's hard. We know it's important. What do we do about it? What are some practical ways we can start enhancing our forecast accuracy and setting a good foundation for having the

07:34Fix #1: consistent, clearly defined stages

7:34 most accurate forecast possible? Yeah, absolutely. I think the beginning of it all is just making sure that the things that you're putting into your forecast are consistent. You want to maximize the amount of things that you can control and fill out the information for that and give reps the ability to be set up and empowered to make calls of what's going to happen in the future. So clearly defining things like what are the qualification methodologies that you're using? Don't put this deal in this stage unless these things have taken place. And that way you have a little bit of science and structure to something that is highly

8:14 volatile. So that is a great starting point having clearly defined lead stages, sale stages, what goes into your entry exit criteria and benchmarking things with that. Or then you to be able to come in and add that subjective perspective to it of, okay, based on the conversations that I've been having, these are the things that we need to consider. It opens up a lot of flexibility in the forecasting. For those listening, I think it'd be important to know what are the most important stages? Yeah, absolutely. I think some of the milestone stages that we're looking at, the first one is when it enters the pipeline, right? When

08:59The three milestone stages (BANT → MEDDIC)

8:59 you're committing to saying, I am going to be working this deal, there has been enough pre-qualification steps in here that we're confident that there's some level of intent as well as budget capacity. We've seen Bant kind of phase out of priority over the last few years with Medic kind of taking over as the predominant methodology that people are using to qualify deals within our existing customer base here at Lean Scale. Following that, I think some of the milestones that we're looking at is that proposal negotiation, obviously once you start talking commercials, that's generally a pretty good indication

9:39 of health, things are moving forward. And then getting into that, okay, we've cleared the commercial hurdles, let's go over into the legal or executive approval, where you have that internal champion or frontline decision maker kind of convinced, and now it's outside of their hands and more so into compliance, legal, whatever, higher power needs to get involved to sign this deal. Those are really the three main milestones that I'm looking at for a deal. It makes a lot of sense. Yeah, and I'll go one step further. I know we talked about one step closer to the truth of forecasting, so I'll go one step further on the sales staging

10:18Fix #2: completed-state sales staging

10:18 and something that I talked about on a previous episode was around the Lean Scale method. And so that's something that we all subscribe to here and really getting clearly defined on the sales staging side of the house and even making small tweaks to that sales staging and the nomenclature to create a clear window into opportunities are in what stage. So we like to get tactical here, right? We don't want to be high level and just tease out that change your sales staging, stop going with the basics of proposal negotiation, demo discovery. We add some language into our best practice sales staging that really speak to what stage

11:01 you've completed. So instead of just having a plain negotiation stage, I want to have a negotiation completed stage, right? So every opportunity within that stage, you know exactly where it's at because if I just have a plain negotiation stage or a plain proposal stage, what does that mean? Is that, does that mean I'm building a proposal? Does that mean I've sent the proposal, right? So let's pare down on the ambiguity, right? And vagueness of what's in that stage and really get clear, which is going to just pay off dividends on where you're forecasting. I'm a big fan of that. And what's funny is we actually see that

11:36 usually defined earlier in the funnel. So you'll see stages like marketing qualified lead sales qualified lead rather than marketing qualifying lead, like, or is it going to be qualified? Is it not qualified? So I think, yes, the, the language matters. And if you create your stages as something completed, it's way more clear for your reps because it doesn't get to proposal completed or proposal sent until you've sent it. So really cuts down that ambiguity. What else can we do? What other things should teams be looking for to be able to enhance their forecast accuracy?

12:16Fix #3: segment your pipeline

12:16 Yeah, I can, I can jump on that one. So I think a big thing and you're, you're going to out there, you're going to know your business better than we do, but we do have some basics in terms of how you want to segment your sales motion, right? And the opportunities that you have out there, um, segment those accounts by things that matter to your business. And you know, deals are going to look different, whether they're enterprise in nature or, you know, smaller tier, maybe you're looking at a mid market or SMB deal, uh, but really segment out your opportunities and your pipeline based on things like that, from a graphic segment,

12:51 uh, geographies matter, right? Product use case industry, understand what your conversion rates are in those, you know, different tiering and segmentations that matter to you, uh, as well as the overall, you know, sale cycle. Uh, I think those are both huge things to get to that one step closer to the truth when it comes to forecasting. Uh, and again, you out there, you know, your business better than we do. So segment by the things that matter, uh, that you know, anecdotally matter to deals that you've brought in or deals that you're pursuing, uh, to again, get closer to that forecast number that is accurate for

13:26 the business to plan ahead and look out into the future. I think that's so important. We see a lot of companies, they will blend those rates. So they'll say, you know, our sales qualified lead to close rate is 20% and our deal cycle is six months. And you know, that may be dramatically different if you're talking about your enterprise or your SMB segment. And if you're talking about different geographies that move at different paces. So I think that's really smart. If you can segment that, um, one, one last layer I think to put on is once you've had the foundations set, um, and, and, and we don't recommend

14:08Fix #4: layer on technology when the process is ready

14:08 layering on technology until the process is ready. But once you have the foundation set, once you have the processes set layering on some technology, there's a lot of good tools out there. Uh, you know, ones that we partner with or Q flow and boost up, you can use those tools to use AI to forecast for you. And I think that's a really important layer because there's a lot of calculations that take place to get an accurate forecast. And once you have the foundations and you're ready for that type of technology, it can, it can really enhance the accuracy of your forecast.

14:42Wrap-up

14:42 So let's, let's just wrap things up real quick. Um, forecasting it's important. If you forecast too high, if you forecast too low, there's really dramatic negative impacts in either scenario. Um, it's nearly impossible to plan for your business if you don't have an accurate forecast. It's hard. Bernardo mentioned that earlier. It's just hard. Um, you're trying to predict the future and most of us are not great at it, but there's some things we can do to make it easier. And some of the things that we talked about today, get very clear definitions around your staging around how things move into the funnel and segment your

15:21 conversion and cycle by the things that are meaningful for you and layer on technology once you're ready for it. Um, and if you do that, then you should be able to plan for your business better. Bernardo Cameron, thank you so much. Appreciate it. And thank you for listening. Thank you for listening to this episode. If you liked the discussion, please like share and subscribe to wherever you listen to podcasts so you never miss a new episode.