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Austin Hay (Reforge, Ramp, Runway)13 August 2023

The ultimate guide to Martech

7Frameworks
14Insights

Frameworks in this episode

Insights & moments

The myth-busts, hot takes, explainers, and tools worth keeping.

Myth Buster· 1

Myth Buster56:30

Most Companies Chasing MMM Actually Just Need MTA

Austin pushes back on the rush toward media mix modeling (MMM). When he evaluates whether a business is ready, he asks if they have the data and whether it will actually change their marketing decisions. Most of the time the answer is no: they don't really need MMM, they need better multi-touch attribution and better probabilistic modeling.

  • Many companies gravitate to MMM without understanding when it's useful
  • Key questions: do you have the data, and will it change decisions?
  • Most businesses aren't ready for MMM; they actually mean MTA
  • Better probabilistic modeling is usually the real need

most of the time businesses are not ready for mmm they actually just mean MTA and they need better probabilistic modeling

Austin Hay · 56:30
#attribution#mmm#mta#measurement

Hot Take· 2

Hot Take19:30

Why the Martech Person Is the Real Quarterback

Austin argues the most important trait in a martech hire isn't how many people they've managed, but how well they manage upward, laterally, and downward. Because the role sits between so many departments, it survives on persuasion and salesmanship rather than headcount. He calls the martech person a true cross-functional quarterback.

  • Martech teams are small, so cross-functional skill beats management experience
  • They must persuade rev-ops, product, and data leaders to make changes
  • The job is a game of persuasion and salesmanship, not authority
  • They call plays across departments like a quarterback

I almost view them like a true quarterback every like says people are quarterbacks but really marketing technology because it lives between so many departments…

Austin Hay · 19:30
#leadership#cross-functional#martech#influence
Hot Take1:23:00

Austin's Hot Take: Twitter Ruins Careers

Asked where to find him online, Austin says he's on LinkedIn and Threads but deliberately not on Twitter. His reasoning is a blunt hot take: he's seen multiple careers ruined by Twitter because some people don't know when to stay quiet.

  • Austin is on LinkedIn and Threads but not Twitter
  • He's seen multiple careers ruined by Twitter
  • The core issue: people who don't know how to shut their mouth

I think Twitter like ruins people's careers I've already seen multiple careers run by Twitter some people just don't know how to shut their mouth

Austin Hay · 1:23:00
#social-media#twitter#careers#opinion

Explainer· 5

Explainer04:30

What Martech Actually Is (and How It's a PM Role)

Austin defines marketing technology as an amorphous, cross-functional discipline sitting between product, growth, engineering, and marketing. He frames the martech person as essentially a product manager whose product is the marketing systems and platforms themselves. Crucially, martech isn't only third-party tools; as companies scale it includes first-party homegrown systems built on top of them.

  • Martech lives at the crossroads of product, growth, engineering, and marketing
  • Think of a martech person as a PM whose product is the system/platform
  • It spans both third-party tools AND first-party homegrown solutions
  • The role's shape is a function of company size and stage

marketing technology is like this very amorphous cross-functional discipline that lives at the crossroads of product and growth and engineering and marketing

Austin Hay · 04:30
#martech#definitions#product-management#growth
Explainer25:30

Martech vs Marketing Ops: The Key Distinction

Austin distinguishes marketing technology from marketing operations. Martech has 'tech' in it, so it's usually an engineer or someone with an engineering background architecting and building systems. Marketing ops is semi-technical work like setting up campaigns, sending email blasts, debugging, and running SQL queries, and you typically see the mar-ops function in B2B.

  • Martech is an engineering-based role: architecting and building systems
  • Marketing ops is semi-technical: campaigns, email blasts, SQL, analytics
  • Mar-ops people may be systems/business analysts, not engineers
  • The mar-ops function shows up most often in B2B

marketing technology has Tech in it so it's usually an engineer or somebody with an engineering background doing that function marketing operations is usually not…

Austin Hay · 25:30
#martech#marketing-ops#roles#b2b
Explainer31:30

How Cheap Warehousing Killed the Classic CDP Stack

Austin walks through the b2c stack 'then and now.' From ~2016-2020 the CDP sat in the middle collecting user data and piping it to ad networks, email, and analytics tools. Around 2020 the cost of warehousing dropped sharply, making it cheap to store everything in something like Snowflake, which gave rise to reverse ETL and let companies effectively build their own CDP.

  • 2016-2020: the CDP was the centralized hub of the b2c stack
  • Around 2020 the cost of ownership of warehousing became much cheaper
  • Cheap Snowflake-style storage enabled the rise of reverse ETL
  • Simplicity-first businesses buy a CDP; cutting-edge eng teams use reverse ETL

the cost of ownership of warehousing became much cheaper

Austin Hay · 32:30

now you can actually build your own CDP and lots of businesses already have

Austin Hay · 33:00
#cdp#reverse-etl#data-warehouse#stack
Explainer52:00

The End of the Deterministic Matching 'Golden Years'

Austin describes how ad attribution has fundamentally shifted. From 2010 to 2020 marketers enjoyed deterministic matching, tying an install to a device's IDFA and PII with precision. That era is over, so ad networks have become more complex while marketers understand their spend less. He now spends most of his time on probabilistic modeling that extrapolates from ~30% of a population to 100%.

  • 2010-2020 were the 'golden years' of deterministic matching
  • You could tie an app install to an IDFA and PII with precision
  • That's no longer possible; opt-in rates make it very hard
  • Probabilistic matching/attribution is now a core skill marketers need

from 2010 to 2020 we had the golden years of deterministic matching where you know it was very easy to run an ad and understand…

Austin Hay · 52:00

so so probabilistic matching and probabilistic attribution I feel like is a skill set that more marketing technologists and marketers should just like get familiar…

Austin Hay · 53:00
#attribution#privacy#probabilistic#ad-networks
Explainer54:00

Why Your Paid Traffic Now Shows Up as Organic

Austin explains two forces making web attribution harder. Browsers are stripping URL parameters, so a growing share of paid clicks arrive with their tracking truncated and get counted as organic. Separately, cookie blockers keep users anonymous throughout their journey until they log in, so you lose the pre-conversion source data entirely.

  • Browsers truncate URL parameters, so paid clicks get miscounted as organic
  • Third-party cookie blockers keep users anonymous until they log in
  • Anonymous journeys mean you lose pre-conversion source information
  • Blocked signups 'might as well be organic'

browsers now are stripping out those URLs we talked about so you're just seeing a bigger and bigger percentage of people being counted as organic…

Austin Hay · 54:00
#attribution#cookies#browsers#tracking

Story· 1

Story22:00

The Intern Who Emailed a Million People

Austin illustrates why permissioning and access control matter with a real failure mode: give someone HubSpot access and a test email goes out to a million people, embarrassing the company on Twitter. He says this has happened to him. The lesson is to design automated systems that grant access based on role, tenure, and department rather than manually clicking approvals.

  • Loose tool access can send a fake test to a million real users
  • This kind of permissioning failure has happened to Austin firsthand
  • The fix is automating access decisions by role, experience, and tenure
  • Admin/permissioning work is unglamorous but high leverage at scale

you give added access to somebody who wants HubSpot and they send a fake email test to like a million people and now you're like…

Austin Hay · 22:00
#permissions#operations#martech#risk

Q&A· 1

Q&A1:00:30

The Interview Question: 'How Did You Prepare?'

Austin's favorite interview question is simply asking how the candidate prepared for the interview. It reveals how a person thinks, plans, and takes things seriously without asking them directly. If you have to prompt them to list what they did, they're not a systems thinker; a rich, unprompted answer signals a more interesting candidate.

  • Ask candidates how they prepared for the interview
  • It surfaces how they think, plan, and take things seriously
  • Needing to be prompted for details signals a non-systems-thinker
  • The more interesting the answer, the more interesting the candidate

how did you prepare you're really asking like how does the person think how do they plan how do they take things seriously or not

Austin Hay · 1:01:00

if you have to prompt them to tell you all the things they did then like they're just not a systems thinker

Austin Hay · 1:01:00
#hiring#interviews#questions#systems-thinking

Tool· 3

Tool36:00

Reverse ETL Tools: Census and Hightouch

Asked for concrete reverse ETL products, Austin explains reverse ETL is a capability (moving data from a warehouse to a tool) found both inside CDPs and as standalone products. Segment, mParticle, and Rudderstack have reverse ETL functions, while Census and Hightouch are the two standalone options. He discloses he's an investor in Hightouch and uses it at Ramp.

  • Reverse ETL = moving data from a warehouse out to a tool
  • Census and Hightouch are the two standalone reverse ETL products
  • Segment, mParticle, and Rudderstack also ship reverse ETL functions
  • Austin is an investor in Hightouch and uses it at Ramp

census which was Back Bay 16z and high touch are the two Standalone reverse etls and like I said I'm an investor in high touch

Austin Hay · 36:00

you should pick tools because they help solve problems not because of like anything else

Austin Hay · 36:00
#reverse-etl#tools#hightouch#census
Tool1:15:30

Cal.com as a Cheaper, Cleaner Calendly Alternative

Austin's recently discovered favorite product is cal.com. A longtime Calendly user, he found Calendly expensive, clunky at syncing multiple business and personal calendars, and dated in its interface. Cal.com gave him a more Notion-like experience with a command-K interface and integrations that just work.

  • Austin was a longtime Calendly user but found it expensive
  • Calendly struggled syncing multiple business/personal calendars
  • He wanted a more Notion-like command-K interface
  • Cal.com delivered and he strongly recommends it

I've been a big calendly user for a long time but countley is pretty expensive

Austin Hay · 1:15:30

cal.com has not failed me it has been awesome

Austin Hay · 1:16:00
#tools#scheduling#cal.com#productivity
Tool1:18:30

Austin's 'Golden Stack' of Martech Tools

At the end of the episode Austin delivers his promised 'golden stack.' For b2c: Amplitude as CDP and product analytics, Customer.io for email (upgrading to Braze later), Snowflake as the data warehouse, Hightouch for reverse ETL, and AppsFlyer (or Branch) for mobile attribution. For B2B it's roughly the same, but Branch for web-only attribution and everything connected into Salesforce.

  • b2c: Amplitude (CDP + analytics), Customer.io email, Snowflake warehouse
  • Hightouch for streaming data out; AppsFlyer or Branch for mobile attribution
  • B2B: similar stack, but Branch for web-only and data connected to Salesforce
  • Email path: start on Customer.io, move to Braze later

if I was a b2c business I'd buy amplitude for my CDP I'd buy customer i o and maybe I'd upgrade to braze in the…

Austin Hay · 1:18:30
#stack#tools#amplitude#snowflake

Takeaway· 1

Takeaway07:30

The 100-150 Person Tipping Point for a Martech Hire

Austin explains that early startups can run tools with a 'village' approach where everyone chips in, but this breaks down at scale. Around 100 to 150 people, someone has to own how data flows through tools, the schema, and the procurement/legal contract exposure. Pain (not just efficiency) is usually what finally forces the hire.

  • Below ~30-40 people, everyone chips in to manage the CDP and tools
  • Around 100-150 people is the critical mass where the village approach fails
  • Unmanaged SaaS contracts create near-infinite liability exposure
  • Pain drives the decision more than a desire for efficiency

usually around I would I would call it like 100 to 150 people is the critical mass where you can't just have a village approach…

Austin Hay · 07:30
#hiring#scaling#martech#org-design