Latest / AI Fire Daily / #571 Neil: How A $5,500 AI Automation Agency Deal Delivers $41,600

Transcript

#571 Neil: How A $5,500 AI Automation Agency Deal Delivers $41,600 · AI Fire Daily

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  Think about software built in just a few hours.
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  How does it justify a $5 ,500 price tag? Well,
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  because it completely destroys a $41 ,000 manual
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  bottleneck. Right. Welcome to this deep dive.
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  You know that imposter syndrome feeling? We all
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  feel it sometimes, especially when pricing our
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  own services. Oh, absolutely. It's a huge hurdle.
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  Today, we're exploring a massive mindset shift.
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  We are looking at value -based pricing, specifically
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  for AI automation agencies and independent freelancers
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  too. Yeah, such an important shift. Exactly.
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  So we're going to unpack the core pricing framework
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  today. We'll look at finding a project's true
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  worth. This happens during discovery. Then we'll
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  explore how to package that offer, and crucially,
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  how to protect your margins. You really have
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  to avoid scope creep. Right. This is a critical
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  road map. Like, anyone selling services today
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  needs this. OK, let's unpack this. We really
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  need to shatter the hourly billing illusion.
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  We will use hard numbers here. Yeah, we have
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  this perfect case study from the sources. So
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  imagine a client processes 20 leads a week. I
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  mean, that sounds like a very normal volume.
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  It's incredibly normal. Yeah. But look at how
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  they actually process them. A person just sits
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  at a desk. a new lead notification pings, they
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  stop what they are doing. They open the email,
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  they read the prospect's details, they copy all
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  that data, then they open a massive messy spreadsheet,
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  they paste the data in, then they draft a manual
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  email reply. That sounds totally mind -numbing
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  and honestly incredibly prone to human error.
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  Exactly. Each lead takes roughly one hour to
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  process. Their labor cost is $40 per hour. So
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  someone is doing this tedious work constantly.
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  Right. That specific bottleneck costs $800 every
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  week. Multiply that by 52 -inths. That totals
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  $41 ,600 annually. That is a massive expense.
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  It's just for a purely manual process. Right.
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  Then you introduce a custom automation. You price
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  that software at $5 ,500. That software delivers
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  a 7 .5 times return. That's just a staggering
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  return on investment. The sources mention another
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  digital agency, too. They fix a similar problem.
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  Yeah, they automated their entire onboarding
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  process. They did this using a tool called Zapier.
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  For those unfamiliar, Zapier is a tool that makes
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  different software apps talk to each other, which
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  is incredibly popular right now for good reason.
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  That workflow changed absolutely everything for
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  them. Their onboarding time dropped significantly.
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  It went from 45 minutes down to 10 seconds. That's
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  almost hard to comprehend. beat 45 minutes down
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  to 10 seconds. They saved 12 hours every single
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  month just by removing that one manual step.
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  The grocery brand Erwan did something similar.
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  They automated their customer service, but they
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  went a step further. They used Zapier alongside
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  ChatGPT. This handled incoming ticket requests.
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  What's fascinating here is the actual mechanism.
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  Zapier acts as the digital nervous system. It
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  catches the inbound email instantly. Then it
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  hands that email over to ChatGPT. And ChatGPT
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  acts as the digital brain. Exactly. ChatGPT reads
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  the email. It understands the customer's true
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  intent. It drafts the perfect refund or support
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  response. Then it hands it back to Zapier. Zapier
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  just hits send. All of this happens in about
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  three seconds. That mechanism saved them 1 ,500
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  labor hours. That time is worth $40 ,000 annually.
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  Yeah, they saw a 5 .5 times return. 70 % of tickets
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  required no human edits. The bots just handled
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  the problems perfectly. Charging for hours is
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  like selling the paper a map is printed on. You
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  aren't charging for the years of wandering the
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  map saves you. You know? That is a perfect analogy.
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  When you sell the destination, you change the
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  entire game. You aren't competing with the guy
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  down the street anymore. You know, the one charging
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  $10 less an hour. But I have a question here.
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  How dangerous is it to promise that exact 7 .5
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  times return to a client? It's quite dangerous,
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  honestly. The actual return depends entirely
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  on client execution. You cannot control how they
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  use the tool. You might build a flawless automated
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  sales funnel, but what if their sales team ignores
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  the leads? The revenue won't increase at all.
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  So, promise the potential, but the client controls
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  the final result. Precisely. You build the high
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  -performance engine, but they still have to drive
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  the car. Okay, that makes sense. We know why
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  we price based on value now. But calculating
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  that abstract number feels terrifying. I still
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  wrestle with pricing my own projects, to be honest.
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  Oh, it is really tough. Pulling a number out
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  of thin air feels like guessing. If I'm sitting
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  across from a business owner, how do I actually
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  calculate their specific value? Well, the framework
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  starts with a simple formula. You map their current
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  cost to the first year value. Then you determine
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  the project price from that gap. So you really
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  have to look at the hard data first. Right. Look
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  at Palo Alto Networks, for instance. This is
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  a massive enterprise scale example. They saved
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  $150 ,000 every single year. Wow. How did they
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  manage to save that much? They automated their
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  manual internal IT requests. Think about an employee
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  needing folder access. Manually, they submit
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  a ticket. An IT worker reads it. They message
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  back for clarification. It literally takes days.
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  Automation evaluates and grants access instantly.
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  That is great for massive corporations. But this
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  exact same math works for small, local businesses.
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  Take Corey Gannum's wedding venue example from
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  the text. Yeah, that is a perfect example of
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  revenue generation. It's not just time saving.
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  Their salesperson was constantly busy giving
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  physical property tours. So inbound leads waited
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  five to 10 hours for replies. So Corey built
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  them a speed to lead AI agent. An AI agent is
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  a smart program that performs specific tasks
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  on its own. Right. This specific agent responded
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  to new leads in just three minutes. Think about
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  a bride emailing at nine at night. The human
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  salesperson is asleep. The lead usually sits
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  there until the next morning. By that time, she
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  might have booked a different venue entirely.
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  Exactly. It is a highly emotional purchase. But
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  the AI agent changes that reality completely.
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  It was trained on their specific offers and FAQs.
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  It sent personalized messages immediately. It
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  proposed an in -person tour right then. It booked
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  two extra property tours every single month.
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  Their closing rate on those in -person tours
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  was 40%. Those extra book tours meant immediate,
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  tangible money. Whoa. Beat. Imagine generating
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  $11 ,000 a month in new revenue from a $30 piece
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  of software. It's wild. Two -sec silence. Companies
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  practically trip over their own feet doing this
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  today. They try manually doing what bots can
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  do instantly. The source talks about the five
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  pillars of business here. Intelligence, marketing,
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  operations, sales, and finance. Right. A traditional
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  operating system relies entirely on human effort.
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  Humans own the pillars and the underlying systems
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  inside them. An AI operating system completely
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  changes that structural dynamic. AI agents run
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  the individual systems automatically now. Right.
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  And humans just review the broader pillar level.
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  They direct the AI and course correct when necessary.
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  So let's talk about the actual pricing benchmark
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  for this work. The benchmark starts at 10 to
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  20 percent. That is a percentage of the total
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  first year value. Yeah. If you save them $50
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  ,000, you charge $5 ,000. That is the 10 % baseline.
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  20 % brings you to $10 ,000. Why is 10 % to 20
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  % just a starting range and not an industry standard?
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  Well, the AI services market lacks common benchmarks.
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  Outcome -based pricing is still the Wild West
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  out there. A website has an expected market price.
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  An AI agent replacing a sales team does not.
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  The market is too new. There's no single benchmark
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  yet. Exactly. You have to test your pricing constantly
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  against client reactions. But to plug numbers
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  into that formula, you need data. You cannot
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  price a bottleneck without finding it first.
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  Flawless discovery is absolutely critical here.
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  You need to ask much better discovery questions
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  up front. How long does a specific manual process
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  actually take today? How many people are involved
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  in that process on average? What happens when
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  that process is slow or fails completely? What
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  is one successful result actually worth to their
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  bottom line? You also need to know why they're
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  solving this now. Urgency changes the entire
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  value calculation for the client. P .U. gives
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  some fantastic advice on the source material
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  regarding this. Right. Businesses scatter their
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  data everywhere across different software platforms.
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  It's rarely clean or organized at all. Yeah,
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  you must map how the work actually happens first.
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  You have to find the real bottleneck, otherwise
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  you are pricing the wrong solution entirely.
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  It's like a doctor taking an x -ray before doing
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  a surgery. You have to see the scattered data
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  to fix the real disease. You cannot just treat
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  the visible symptoms and expect a cure. If we
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  connect this to the bigger picture, Beat, you
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  must look beyond just the raw time saved. Time
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  saved is great. But revenue impact is far more
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  compelling. You have to calculate the total revenue
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  impact of your automation. Like Corey Gannum
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  selling that simple speed to lead agent we discussed,
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  he sold it for $1 ,500 up front. because it directly
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  drove immediate sales for the client. The actual
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  build time was literally just a few hours, but
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  the revenue impact was massive for the business
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  owner. ROI becomes so much easier to measure
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  when sales increase directly. What if a client
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  is guarded and refuses to share their true revenue
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  data during discovery? Then you simply fall back
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  on labor costs and error reduction. You use the
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  specific data they are willing to share. You
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  calculate the hours they spend fixing mistakes.
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  Focus on labor and error costs if revenue data
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  is hidden. Exactly. You work with what you have
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  to build the business case. We're gonna take
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  a quick break right here. Yeah, stick around
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  for more on building the offer, sponsor, mid
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  -roll, sponsor break. All right, we are back.
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  Here's where it gets really interesting. We have
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  the value and we have found the real bottleneck.
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  Now we must present it without scaring the client
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  away. You should always offer three distinct
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  packages to them during the pitch. Call them
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  starter, growth, and scale. This fundamentally
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  changes the scope of the project. It gives the
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  client real choices to make regarding their investment.
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  The starter package covers the main automation
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  and the most important integration. It's the
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  smallest useful scope possible to solve their
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  immediate pain. Growth adds more complex workflows,
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  extra integrations, or advanced reporting features.
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  It creates clear extra value for a significantly
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  higher price. Scale covers the full comprehensive
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  system with broader automation and premium support.
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  Single automations might run one to three thousand
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  dollars typically. Multi -workflow projects easily
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  hit five to fifteen thousand dollars. There is
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  a crucial rule for dealing with low client budgets.
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  If they cannot afford you, you must adjust the
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  scope. You should never discount the project
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  price for the same work. You definitely must
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  verify the math before you send the proposal.
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  Psychologically, why do three packages work so
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  much better than a single flat proposal? It shifts
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  the client's mindset during the pitch entirely.
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  When you present one price, they ask themselves
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  if they should buy. When you present three, they
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  ask themselves which specific package they should
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  buy. It changes the question from yes or no to
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  which one. It's a subtle but incredibly powerful
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  sales tactic for agencies. Getting a yes on a
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  $10 ,000 package is great. but it's entirely
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  worthless if scope creep eats all your profit.
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  Scope creep is silent but deadly for your profit
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  margins. High -level actually cites scope creep
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  as a major agency pain point. Clients always
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  want just one more little thing added to the
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  project. Giving into scope creep is like letting
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  a client add extra bedrooms to a house. While
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  you're still pouring the foundation for the original
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  floor plan, it destabilizes the entire project.
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  That's exactly what ruins your profit margins
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  and your timelines. You can fix this easily with
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  a version 2 backlog. Imagine the client suddenly
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  wants a custom WhatsApp integration halfway through
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  the build. They just ask you to quickly add it
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  in. You simply move that request to phase 2.
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  You keep the current phase completely focused
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  on the agreed scope. You price the new WhatsApp
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  request completely separately for later. You
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  also must set clear, testable milestones tied
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  directly to payments. This protects you from
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  endless revisions and delayed payments. Can you
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  break down an example of a testable milestone
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  for us? Let's say a test lead goes into HubSpot
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  successfully. HubSwat is software used for marketing
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  and customer relationship management. Right.
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  So the lead enters HubSpot. Then a Slack alert
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  is sent to the team within one minute. The client
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  tests it and confirms it works exactly as agreed.
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  Once they confirm, the next payment becomes instantly
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  due. That keeps the project moving forward objectively.
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  You also must separate ongoing maintenance from
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  the actual build phase. What does maintenance
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  actually cover in this context? It covers fixing
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  broken integrations or handling unexpected API
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  changes. An API is a digital bridge letting two
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  software programs share data. Exactly. When those
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  bridges update, things break. Maintenance is
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  making small adjustments that keep the current
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  workflow running smoothly. Marty's open -claw
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  setup is a great example from the text. He deals
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  with constant gateway crashes and tricky routing
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  issues. Configurations can easily get lost after
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  random software auto -updates. Real production
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  systems need this kind of ongoing manual support.
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  But new workflows mean completely new scope for
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  the agency. They don't count as free ongoing
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  maintenance just because they are a client. Never
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  give away new features under the guise of basic
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  maintenance. So how do you tell an excited client
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  no to a new feature without killing the vibe?
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  You validate their idea enthusiastically, but
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  defer it to the backlog. You tell them it's a
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  brilliant idea for the next phase. Don't say
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  no, just say yes in phase two. Exactly. You keep
• 13:45
  the momentum going without losing your shirt.
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  So let's briefly synthesize the core takeaway
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  from today's deep dive. Pricing should never
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  reflect the hours you sweat behind a keyboard.
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  It absolutely must reflect the financial bleeding
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  you stop for the client. You are selling the
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  actual solution to a massive business problem.
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  If AI makes the actual time it takes to build
• 14:06
  software approach zero, then soon the only thing
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  you can charge for is your understanding of the
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  client's problem. Beat. So what problem do you
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  understand better than anyone else? Thank you
• 14:16
  for joining us on this deep dive at EuroMusic.