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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
A blueprint to new levels of company growth leveraging your firm’s Partner Ecosystem
In Ecosystem-Led Growth: A Blueprint For Sales and Marketing Success Using the Power of Partnerships, veteran entrepreneur and tech leader Bob Moore delivers an intuitive and insightful guide to using your company’s Partner Ecosystem to unlock countless leads, break sales records, scale your organization, and build a once-in-a-generation business. In the book, you’ll discover why partnerships are no longer the domain of “partner people” schmoozing at conferences. Instead, they can be used to unlock vast amounts of data, new relationships, and scalable growth plays.
You’ll learn about:
- Transformational technologies that bring partner data to your fingertips
- Savvy companies and executives who convert that data into untapped growth opportunities
- Real-world examples of go-to-market leaders at dozens of leading tech companies implementing a powerful new perspective on growth
An indispensable roadmap to an exciting new strategy for scaling your firm, Ecosystem-Led Growth will earn a place on the bookshelves of managers, executives, founders, entrepreneurs, salespeople, marketers, and anyone else interested in taking their company to new heights.
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Seitenzahl: 307
Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Dedication
Acknowledgments
Introduction
PART 1: My $2.6 Billion Mistake
1 Muscle Memory and Scar Tissue
A Revolution in Business Intelligence
The Rollercoaster of Product-Market Fit
2 Disruption Is Cool Until It Happens to You
Amazon's Big Move
The Modern Data Stack Is Born
The Ecosystem Effect
3 How Partner Ecosystems Saved My Career
PART 2: The Ecosystem Revolution
4 Decoding the Confusing Language of Partnerships
Tech Partnerships
Channel Partnerships
Strategic Partnerships
Marketplaces
Partner Relationship Management
5 Why Legacy Partnerships Were Set Up to Fail
6 The Ecosystem Data Layer Arrives
7 Why Now? The Disruption of Growth as We Know It
Inbound Marketing and the Great AI Reset
Outbound Sales: A Negative-Sum Game
A Targeted Attack on Targeted Ads
Sales Intelligence: The Bundling Era
Product-Led Growth: A Different Animal
Another Door Opens
PART 3: Beginning Your ELG Journey
8 Is ELG Right for Me?
Value Proposition
Company Scale
The ELG Readiness Matrix
9 Getting Buy-In for ELG
Setting the Right Goals
Attributing Success to ELG
Eliminating Partner Team Baggage
10 Overcoming Security and Privacy Objections
Can I Trust My ELG Platform?
Does ELG Itself Expose Me to Risk?
11 Powering Up Your Account Mapping Matrix
PART 4: The ELG Playbooks
12 The ELG Playbook Map
13 Ecosystem Development: Populate Your Partner Ecosystem with Winners
Prioritizing Partners
Curating Data Access
The Partner Prioritization Matrix
14 Ecosystem-Led Marketing: Fill Your Funnel with Ecosystem Qualified Leads
The Ecosystem Qualified Lead (EQL)
Generating EQLs with Second-Party Data
Marketing Automation and Account-Based Marketing
Outbound: Upgrade Your SDRs to PDRs
Reinventing Event Strategy
Turning Investors into Pipeline Generation Engines
15 Ecosystem-Led Sales: Close Bigger, Better Customers Faster
Strategy and Buy-In: The ELG Sales Tetrahedron
Meeting Sellers Where They Are
Co-Selling
Hyperscaler Cloud Marketplaces
Training, Enablement, and Accountability
16 Ecosystem-Led Customer Success: Eliminate Churn and Grow Accounts
Driving Customer Success with Tech Partners
Driving Customer Success with Channel Partners
Conclusion: The Future of ELG
Glossary
Bibliography
Author Bio
Index
End User License Agreement
List of Illustrations
Introduction
FIGURE I.1 The account mapping matrix.
FIGURE I.2 The ELG playbook map.
Chapter 1
FIGURE 1.1 Artwork from RJMetrics website, ca. 2011.
FIGURE 1.2 RJMetrics revenue and customer growth: the bootstrap years.
FIGURE 1.3 In product-market fit, the market moves too.
Chapter 2
FIGURE 2.1 Amazon Redshift announcement release, November 2012.
FIGURE 2.2 SiSense benchmarking study outputs, 2015.
FIGURE 2.3
The Register
headline, April 2015.
FIGURE 2.4 When we lost the warehouse, we lost our way.
FIGURE 2.5 The modern data stack map by Valentin Umbach.
Chapter 3
FIGURE 3.1 Stitch launch blog post, August 2016.
Chapter 4
FIGURE 4.1 Hub and spoke.
FIGURE 4.2 Network graph.
FIGURE 4.3 Snowflake's high-density partner ecosystem via partnerbase.com.
FIGURE 4.4 DocuSign in Salesforce.
Chapter 5
FIGURE 5.1 The partner paradox: How can both be true?
FIGURE 5.2 The data Venn diagram.
FIGURE 5.3 The prisoner's dilemma.
FIGURE 5.4 The partnership dilemma.
Chapter 6
FIGURE 6.1 Crossbeam network graph, January 2019.
FIGURE 6.2 Crossbeam network graph, January 2020.
FIGURE 6.3 Crossbeam's k-factor during initial network surge.
FIGURE 6.4 Crossbeam network graph, January 2021.
FIGURE 6.5 Crossbeam network graph, September 2023.
Chapter 7
FIGURE 7.1 A Stitch “X to Y” SEO page, ca. 2018.
FIGURE 7.2 B&W photograph generated by the author via Midjourney.
FIGURE 7.3 The long tail of talent quality.
FIGURE 7.4
Predictable Revenue
.
FIGURE 7.5 Pavilion State of Sales Development Survey, fall 2022.
FIGURE 7.6 On-target earnings for sales development representatives.
FIGURE 7.7 News flash: no one opts into tracking.
FIGURE 7.8 GDPR affecting the ads industry.
FIGURE 7.9 DiscoverOrg (now ZoomInfo) home page, March 2013.
FIGURE 7.10 ZoomInfo home page, April 2023.
FIGURE 7.11 Clari home page, April 2023.
FIGURE 7.12 Gong home page, April 2023.
FIGURE 7.13 Outreach home page, April 2023.
FIGURE 7.14 PLG company operating costs are diverging in the wrong direction...
Chapter 8
FIGURE 8.1 The ELG readiness matrix.
Chapter 9
FIGURE 9.1 Partner attribution tagging in Crossbeam.
FIGURE 9.2 Pam and Bob at the Supernode 2023 Conference.
Chapter 10
FIGURE 10.1 The data sync selection tool in Crossbeam.
FIGURE 10.2 Simple match-based sharing logic.
Chapter 11
FIGURE 11.1 The exponential complexity of manual account mapping.
FIGURE 11.2 The account mapping matrix.
Chapter 12
FIGURE 12.1 The classic revenue funnel.
FIGURE 12.2 The ELG playbook map.
Chapter 13
FIGURE 13.1 The ELG playbook map: ecosystem development.
FIGURE 13.2 Classic J-curve shape.
FIGURE 13.3 Big partner, small overlap.
FIGURE 13.4 Small partner, modest overlap.
FIGURE 13.5 Peer-sized company with amazing overlap.
FIGURE 13.6 Lavoie's partner prioritization matrix.
Chapter 14
FIGURE 14.1 The ELG playbooks: ecosystem-led marketing.
FIGURE 14.2 EQLs are sourced from the bottom row of the account mapping matr...
FIGURE 14.3 Dynamic co-marketing lists in HubSpot update automatically with ...
FIGURE 14.4 SDR view of ecosystem overlaps in Salesforce.
FIGURE 14.5 Gainsight's ELG-driven attendee matchmaking.
FIGURE 14.6 Gainsight's target attendee makeup for co-hosted events.
Chapter 15
FIGURE 15.1 The fire tetrahedron.
FIGURE 15.2 The ELG sales tetrahedron.
FIGURE 15.3 The ELG tetrahedron: tooling transformation.
FIGURE 15.4 The ELG tetrahedron: democratize the data.
FIGURE 15.5 The ELG tetrahedron: enablement and buy-in.
FIGURE 15.6 Branch slide from Supernode 2023.
FIGURE 15.7 The Crossbeam Salesforce widget (simulated data).
FIGURE 15.8 The account mapping matrix: co-selling.
FIGURE 15.9 LeanData's ELG dashboard view (data redacted).
Chapter 16
FIGURE 16.1 Account mapping matrix for overlapping customers.
FIGURE 16.2 Net revenue retention (NRR) formula.
FIGURE 16.3 Integration adoption math.
FIGURE 16.4 LeanData's CSM dashboard for integration adoption.
Guide
Cover
Table of Contents
Title Page
Title Page
Dedication
Acknowledgments
Introduction
Begin Reading
Conclusion: The Future of ELG
Glossary
Bibliography
Author Bio
Index
End User License Agreement
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Ecosystem-Led Growth
A Blueprint for Sales and Marketing Success Using the Power of Partnerships
Bob Moore
Copyright © 2024 by John Wiley & Sons, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission.
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Library of Congress Cataloging-in-Publication Data:
Names: Moore, Bob (Entrepreneur), author.
Title: Ecosystem-led growth : a blueprint for sales and marketing success using the power of partnerships / Bob Moore.
Description: Hoboken, New Jersey : Wiley, [2024] | Includes bibliographical references and index.
Identifiers: LCCN 2023050164 (print) | LCCN 2023050165 (ebook) | ISBN 9781394226832 (cloth) | ISBN 9781394226856 (adobe pdf) | ISBN 9781394226849 (epub)
Subjects: LCSH: Strategic alliances (Business) | Business enterprises—Growth. | Sales. | Marketing.
Classification: LCC HD69.S8 M66 2024 (print) | LCC HD69.S8 (ebook) | DDC 658.8—dc23/eng/20231204
LC record available at https://lccn.loc.gov/2023050164
LC ebook record available at https://lccn.loc.gov/2023050165
Cover Design and Illustration: © Nick Beaulieu
For Dad,
Thanks for showing me how it's done—on and off the page.
Acknowledgments
My deepest gratitude goes out to the many writers, thinkers, and friends who contributed to the content of this book.
Thank you to the Crossbeam team for their tireless work in cataloging, documenting, and bringing to life the many stories that inspired and validated what you read in these pages. Jessica Rowe, Nick Beaulieu, Amy Rose, Jasmine Jenkins, Olivia Ramirez, Zoë Kelly, and Sean Blanda are among the many brilliant minds whose hard work helped bring this book to life. I'm also eternally grateful to my cofounder Buck Ryan for believing in our vision and making this all possible from day one.
To those who were kind and patient enough to offer feedback on early manuscripts, you are the real heroes. Thank you, Jake Stein, Martin Angert, Tom Basch, Allan Adler, Pete Cummings, Michal Filip Kowalik, Adam Kearney, Asher Mathew, Paul Campbell, Anna Weisman, and the others I surely forgot to mark down in processing the firehose of awesome feedback that helped shape this book.
I'd like to extend special thanks to the companies who agreed to share their stories, data, playbooks, and insights with us in order to pack the book with real-world examples and hard data. Crossbeam's tens of thousands of users have been our muses throughout this process.
You should also be asking every author in the world right now: “How much of this book was written by AI?” In this case, very little. ChatGPT was used for drafting and research in some sections of the book, but this content is first of its kind and informed by case studies and stories that not even the AI language models have seen before. These thoughts are original—for now.
While this book will discuss legal issues that may arise in connection with ELG, this should not be confused with legal advice. This book is not legal advice, is not a contract, and does not create any legal rights or obligations. You should never make decisions about sharing data externally without the consent and support of your company's privacy, security, and compliance stakeholders.
Introduction
Most business books could have been a blog post.
After nearly two decades in tech, I have reached a point of rolling my eyes at most “revolutionary advances” that come along. Most new product categories turn out to be little more than incremental features. Most new buzzwords are marketing-manufactured jargon that serve a company more than its customers.
And yet here I am, at the helm of a book promising all of those things. At long last, I'm witnessing a profound enough new movement that it overcomes my inner skeptic through raw evidence and sheer momentum.
It's called ecosystem-led growth (ELG), a revolutionary new go-to-market motion that focuses on partner ecosystems as the primary way to attract, convert, and grow customer relationships.
ELG turns your partner ecosystem into your company's most efficient and scalable source of revenue growth. As you'll see time and time again in the chapters ahead, the customer relationships it generates have higher contract values, close faster, see higher win rates, and expand more meaningfully over time. The companies embracing it are out-executing their competition at a blistering pace.
They are doing this by using modern account mapping methods (see Figure I.1), powered by ELG platforms such as Crossbeam, to unlock a powerful new data layer made up of intelligence, context, and next best actions from across your partner ecosystem.
This new wellspring of partner data and influence ripples into every stage of their revenue funnels:
You'll learn how
ecosystem-led marketing
is changing the way Stripe executives think about their funnel using ecosystem-qualified leads, Gainsight designs customer events by building curated audiences, and Okta Ventures moves the needle for its portfolio with qualified introductions (including a playbook that increased partner-influenced revenue from 3% to 80% of a company's new business in a single year).
FIGURE I.1 The account mapping matrix.
You'll get an inside look at how Braze, Fivetran, and many others have built an
ecosystem-led sales
motion inside of their revenue teams that use proprietary ecosystem intelligence, personalization, and co-selling playbooks to increase contract values, win at higher rates, and speed up deal cycles (including playbooks that have increased one company's close rates by 40%, grown its pipeline by 44%, and increased its average deal size by 50%).
You'll see how innovators such as RollWorks and Bombora have systematically infused
ecosystem-led customer success
to key parts of their post-sale customer experience, reducing churn and expanding revenue per account over time (including a playbook that decreased churn rates by 3.5x).
You'll learn how Gong, Intercom, and others have rolled out ELG playbooks to create a virtuous cycle of
ecosystem development.
These playbooks will show you how to prioritize partners, invest resources intelligently, and grow flourishing ecosystems at low cost and with lean teams.
The resulting map of ecosystem-led growth playbooks (see Figure I.2) will be our handy guide as we walk down your funnel, passing through your ecosystem at every step as it accrues even more scale and value to feed back into your company's growth.
The raw ingredients here are not new: partnerships, data, people, focus, and grit. What's new is the technology that pulls them together and the market moment that makes their potential so clear.
This book is being printed at a turbulent moment in economic history. In the early part of this decade, cheap capital and low interest rates from the pandemic era drove a bubble in the valuations (and burn rates) of growth-stage technology companies.
Just as rapidly, those valuations came crashing down, and those same companies were forced to quickly refocus on efficiency. The ensuing whiplash has led to the demise of many once-promising companies and made new superstars out of those that cracked the code of efficient growth. For most, however, unanswered questions about the best paths forward still remain.
Meanwhile, advances in artificial intelligence are undermining the strategies of old, making traditional growth playbooks commoditized at best and obsolete at worst. Add in the most complex regulatory, privacy, and security environment ever seen, and you have a market that will punish those who cannot adapt quickly.
FIGURE I.2 The ELG playbook map.
So what happens when the return on investment (ROI) on every growth playbook goes upside down, yet the market demands lean and profitable growth? You get a once-in-a-career market moment where only the strong will survive and the definition of every “best practice” for growing an enduring company will be rewritten.
Companies must now discover new avenues of growth that are proprietary to them and unable to be commoditized. These strategies must scale without a ceiling along with the company's long-term trajectory. And they must be lean and efficient in how they deliver their results.
These new growth playbooks must be ecosystem-led.
In the chapters that follow, we'll dive into the origin of the ELG movement, the technology that paved the way, the market dynamics that make it so compelling, and the play-by-play instructions for bringing ELG to life inside your organization.
You'll learn about how shifting paradigms of artificial intelligence, regulation, and data networks are rewiring old-school methods such as outbound sales, inbound marketing, sales intelligence, and targeted advertising.
You'll tap into never-before-shared data about how ELG networks have skyrocketed to global scale in the past five years, multiplying from nothing to tens of thousands of companies strong.
You'll learn how to overcome the objections of those who resist change and how to inspire confidence in the security and trust vectors that underlie any successful ELG strategy.
You'll see groundbreaking outcomes in sales efficiency, scalability, demand generation, churn reduction, and product-market fit, all told by the companies that experienced them.
I hope this book provides a glimpse into the excitement and passion that my team and I feel every single day as we work with tech leaders to bring ELG to life in their companies.
I'm thrilled to take you on this journey, and the best time to start is right now. So let's begin.
PART 1My $2.6 Billion Mistake
1Muscle Memory and Scar Tissue
When people ask me why repeat start-up founders are more likely to succeed, I say it's because they've developed a healthy mix of muscle memory and scar tissue—and they're willing to admit which is which.
The muscle memory comes from discipline, routines, and skills that are finely tuned over tens of thousands of hours of execution. The scar tissue comes from being wrong. A lot. It builds up during the countless times when, despite all that hard work, everything still goes sideways.
I happen to have a scar bigger than the GDP of Belize.
In June of 2019, a headline flashed across my phone, and I knew one of my most serious business missteps had finally been realized: Google had purchased Looker for $2.6 billion.
Back when Looker was founded in 2012, I was already four years into building a business intelligence software company called RJMetrics. Looker quickly became our main competitor in the space. They had a great product, and their team was first-class. Even so, I was disappointed at the outcome: we had all of those things plus a four-year head start.
We ultimately sold RJMetrics to Magento in a modest transaction that was orders of magnitude away from the $2.6 billion windfall earned by Looker. What was the difference between our companies?
It's easy to blame the “muscle memory” stuff—they had more experienced founders and marquee investors, and they just executed better. All true, but were those factors so much better than ours that it led to a 100x difference in outcome? No, there was something much bigger and more profound at play.
Understanding what happened, and the fundamental difference between RJMetrics and Looker, would take me on a fascinating journey of discovery and realization—one that led me to first encounter the ecosystem-led growth playbook and then laid the groundwork for Crossbeam itself.
A Revolution in Business Intelligence
This gets nerdy, but I promise the payoff is worth it. My journey in the world of business intelligence software is how the ecosystem-led growth playbook came to be.
Let's start at the beginning. If you rewind the clock just a decade or so, the analytics needs of a modern business operator were undergoing some major changes.
Take Dave Eisenberg, who in 2010 was chief of staff at an upstart e-commerce menswear shop called Bonobos. Like any smart leader, he wanted to understand his customer lifetime value (CLV) and calculate the return on investment (ROI) of his acquisition channels. To do this kind of analysis, he needed to use several sources of data:
Data from his ad networks to know how much Bonobos spent per click on different campaigns;
Data from his marketing platforms to know which emails were sent to which people at what times;
Data from his web analytics to tell him which visitors were referred from which sources;
Data from his shopping cart to tell him who bought what, when they did it, and how much they paid; and
Data from his payment provider to make sure he stripped out any discounts, refunds, or chargebacks.
None of these data sources could run the analysis Dave wanted on its own, because they were all “silos” that couldn't see into the other sources. Getting the answer meant that Dave needed to join all the data together in a separate, central location.
What Dave needed was called business intelligence (BI)software, a massive category that had existed for decades. Traditional BI software—which was the only real option for most of its history—consisted of three important components, often bundled up and sold together by a single vendor:
Software to
extract, transform, and load (ETL)
data out of all those pesky platforms and into a new, central storage location;
A
data warehouse
—the storage location in question—able to handle tons of data (“big data” in early 2000s parlance) and answer analytical queries quickly; and
A
reporting interface
where you could build charts and dashboards that display the resulting data and are accessed by your business users.
Any viable business intelligence offering consisted of all three of these pieces in some form or another (see Figure 1.1). Together, they were a singular stack of technology and data, all in one place from one vendor, that allowed a company to be run in a data-driven way.
Because of the scale of data involved, most enterprise deployments had this software deployed “on premise”—physically installed at the location of the business and its end users. Players such as Microstrategy, Cognos, and Business Objects ruled the day.
FIGURE 1.1 Artwork from RJMetrics website, ca. 2011.
But Dave was trying to build a modern, web-first company. Plopping an expensive server on premise and running desktop software to crunch his data felt like an anachronism. This was especially true in a moment when the shockwaves of the Great Financial Crisis were still affecting the start-up funding environment and upstarts such as Bonobos needed to squeeze as much capital efficiency out of their operations as possible.
It was use cases like Dave's that gave us the idea for RJMetrics. My cofounder Jake Stein and I dreamed of taking the classic on-premise BI paradigms and reinventing them in the cloud.
Your ETL scripts? We'll run them on our servers and have them extract your data from the most widely used online tools to allow for analysis on our platform.
Your data warehouse? We can spin up a free open source database called MySQL, host it for you on a server that we manage, and optimize it to run fast analytical queries.
Your reporting layer? Forget finicky desktop software that has to be installed on every client machine. You'll be able to log in from any web browser and create, manage, and distribute the charts and dashboards that make your business tick.
Jake and I quit our jobs in 2008, and RJMetrics was born. Dave at Bonobos became one of our first 10 customers, and hundreds more would soon follow.
The Rollercoaster of Product-Market Fit
Product-market fit (PMF) is the promised land for start-ups. It's that moment at which you have a product that lines up so perfectly with what the market wants and needs that the demand becomes organic and undeniable, yielding you the makings of unstoppable growth. PMF can't be faked, and no meaningful company can be built without first finding it.
In our early days at RJMetrics, we were inspired by start-up blogger Eric Reis, who preached the virtues of an efficient “build, measure, learn” cycle for navigating to success. (Reis would later coin the term The Lean Startup and author a legendary book of the same name.)
Our lean start-up journey took nearly two years of diligent work and iteration, but it paid off. By 2010, we found our product-market fit (see Figure 1.2).
The deals and the dollars came pouring in faster than we could process them. Our software was flying off the shelves, used by virtually every high-growth e-commerce company in the world. From Bonobos to Fab.com to Rent the Runway and hundreds of others, RJMetrics was ubiquitous among the jet set of next-generation commerce.
Until it wasn't.
We learned a key lesson about product-market fit the hard way. Achieving PMF is not just about iterating on your product; it's also about keeping your product in sync with the market. If the market moves, but your product doesn't move with it, you can lose your fit, and your growth will stop.
At RJMetrics, we got so blinded by the demand for our product that we didn't see a more meaningful evolution in our space happening right underneath our feet. In a span of just a few years, we went from ahead of the times to strong PMF to behind the times (see Figure 1.3). By the time we were ready to admit it, we had already lost.
FIGURE 1.2 RJMetrics revenue and customer growth: the bootstrap years.
FIGURE 1.3 In product-market fit, the market moves too.
So what happened? What could possibly change a market so radically that an emerging leader could lose its mojo in such a short period of time?
The era of the ecosystem came to our space. And we didn't get on board.
2Disruption Is Cool Until It Happens to You
Amazon's Big Move
At RJMetrics, by the time we realized we were losing, we had already lost. The same pace of market innovation that made our product a hit had continued under our feet so rapidly that it made us a dinosaur just as fast.
The first domino to fall came from Amazon Web Services (AWS). In late 2012, they announced a new product called Redshift (see Figure 2.1). It was a “fast and powerful, fully managed, petabyte-scale data warehouse service in the cloud.”
Remember that cool custom build of MySQL that we developed at RJMetrics? The one that sat right at the center of our technology offering and served as the central location where data was stored, processed, and analyzed? As it turned out, our data warehouse was the horse-and-buggy version of Redshift's Model T.
A study published by SiSense around that time (see Figure 2.2) summed it up plainly: “On average, Redshift was 500x faster than [traditional databases] for metrics like Daily Revenue, Daily Active Users, and Daily ARPU [average revenue per user].”
Yikes. These kinds of “metric queries”—ones about revenue, user activity, and average revenue per user (ARPU)—were precisely the kinds of metrics you could find in RJMetrics. Now you could get them 500x faster if you stored your data in Redshift instead.
FIGURE 2.1 Amazon Redshift announcement release, November 2012.
FIGURE 2.2 SiSense benchmarking study outputs, 2015.
Note: “RDS Postgres” is a cloud-deployed competitor to MySQL that was comparable to the RJMetrics data warehouse at the time.
We convinced ourselves that this new technology, while remarkable, wouldn't affect us. Why? Because the data warehouse was just one piece of the puzzle. BI software like ours had a lot more to it. Amazon didn't offer any data pipeline software to extract, transform, and load (ETL) data into its warehouse, nor did they offer a reporting layer for building out your analytics and presenting them to users. Those were big gaps to fill. Weren't they?
Well, as it turned out, a 500x performance improvement, combined with the ease of setup that came with AWS cloud deployments, proved to be enough to drive a major paradigm shift in how companies did analytics. In under three years, Redshift went from a new product to “the fastest growing service in the history of AWS” (see Figure 2.3).
Redshift proved most popular among engineers, data scientists, IT leaders, and professionals in the emerging field of “data ops,” all of whom saw tremendous value in keeping control over their company's data. By having a centralized data warehouse located in your AWS cloud and managed by your own ops team, you could exercise unprecedented control over data usage, consistency, cost, and more. It was an exciting proposition for enterprises on countless dimensions.
Cut back to RJMetrics. Here's what we started hearing on sales and renewal calls:
“Yeah, this looks great, but our data team is telling us that we already have all this data in a data warehouse over at AWS. Why should we also pipe all that same data outside of our cloud over into yours so you can analyze it?”
“We set up RJMetrics, but the results are just a little different from what our IT people see when they query our Redshift warehouse. Can you do an audit to get those reconciled?”
“Can we just use your charts and dashboards on top of our warehouse instead?”
I recently caught up with Vijay Subramanian, who was chief analytics officer and head of growth at Rent the Runway. They were a marquee customer of ours at RJMetrics who churned off of our platform in this pivotal era.
“RJMetrics was used to report on our key user and growth metrics, but as we started tracking other parts of the business, we felt the need to pipe it all into a common warehouse where we could manage more of the nuanced business modeling,” he reminisced. “RJMetrics struggled to serve our needs as it was an all-in-one solution.”
FIGURE 2.3 The Register headline, April 2015.
FIGURE 2.4 When we lost the warehouse, we lost our way.
Our data warehouse was a loser (see Figure 2.4). And when we lost the warehouse wars, it literally snapped our value proposition right down the middle. The other two ends of our product just couldn't hold up the product-market fit chasm in the middle.
The Modern Data Stack Is Born
Former Netscape CEO Jim Barksdale famously said that there are “only two ways to make money in business: one is to bundle; the other is unbundle.” Between 2013 and 2016, we experienced a radical unbundling in the way data was moved, processed, analyzed, and consumed by modern businesses. In the process, it forged one of the most powerful and valuable partner ecosystems ever to exist, exposing me to the ecosystem-led growth playbooks that would change my career forever.
Within a year of Redshift's release, the traditional business intelligence approach of an all-in-one analytics stack had been split apart. In its wake, a new generation of companies emerged that took on the rest of the stack piecemeal.
Depending on how you count it, somewhere between five and ten new categories of software and services seemed to emerge overnight. These companies each took over a slice of the old all-in-one stack and, when combined, gave end users a “modern data stack” that could be seamlessly integrated to provide more power, flexibility, and affordability than anything that came before.
Data warehouse innovation didn't stop with Redshift. Google and Microsoft soon answered with BigQuery and Azure Data Warehouse, respectively. Later, Snowflake's powerful data warehousing offering won it a dominant position as one of the fastest growing and highest valued SaaS companies ever with a peak market value of $118 billion.
ETL platforms such as Fivetran and Matillion arrived, whose main purpose was to move data between leading SaaS platforms and the warehouses listed above. In a 2021 funding round, Fivetran was valued at $5.6 billion.
Reporting platforms narrowed their scope to focus on nailing the data modeling and dashboarding capabilities. This is where Looker focused. In the modern data stack era, a wave of new vendors came to market in this space including Looker, Mode, Periscope, Redash, Hex, and Omni. Legacy application players such as Tableau also entered the space with more robust cloud offerings. In 2019, Tableau was acquired by Salesforce for $15.7 billion and Looker was acquired by Google for $2.6 billion.
Other layers surrounding these tools—such as transformation workflow solutions including dbt (“data build tool”)—have also emerged and come to meaningful scale. In a 2022 round of funding, dbt Labs was valued at $4.2 billion.
In addition to software innovation, an entire industry of services businesses, agency partners, and system integrators emerged around this market as well (see Figure 2.5). Especially with enterprise deployments, the value of human experts to pull these pieces together, train teams, and deploy best practices are more important than ever.
Just incredible. So many new markets that they're hard to count, each with new incumbents that didn't exist a decade ago—all valued at billions.
And with that, a new industry emerged with a bang, and RJMetrics would be sold off with a whimper.
The Ecosystem Effect
It's now clear as day how Looker grew to 100x our value in half of the time: they grew as part of an emerging and highly disruptive ecosystem while we flew solo.
