Series A SaaS Startup Benchmarks for 2018

Tom Tunguz

But the average MRR has increased substantially from the last time I analyzed the data. The usual caveats to this data analysis apply. How far along was the typical SaaS Series A in 2018? The median business was at $1.8M in ARR and growing at 250%.

Why “Closed Reason” is the Secret Weapon for High Performing Sales Teams


“Closed Reason” is one of the most valuable, yet underutilized, pieces of data that a sales organization can collect about its opportunities. To help you take advantage of this trove of intelligence, we’ve compiled some interesting results based on how our customers employ Closed Reason data.

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Reaching Rockstar Admin Status


Our data refreshes took a long time and we needed answers quickly. Plus, there was a lack of consistency across teams in terms of the reporting format and selection of data and metrics for measurement. “Slowness to change usually means fear of the new.” – Philip Crosby.

Data, Data Everywhere, Not a Second to Think

Tom Tunguz

More and more companies realize their proprietary data contains insights that drive tremendous competitive advantage. Enabling an organization to make data driven decisions is a long term process. Companies build or buying the tools and expertise to store and process that data.

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The Power of Open Source to Solve the Data Fragmentation Challenge

Tom Tunguz

Most modern data architectures employ many different data stores and processing engines. Data analysts looking to unearth insights within these data stores must move data back and forth between different systems and different data formats. As the number of new open source projects continues to grow geometrically, this data fragmentation is likely to splinter further. ” Arrow promises data engineers three things.

Winning with Data

Tom Tunguz

There’s a new class of company that wield data to create long-term competitive advantage. TheRealReal uses this morning’s sales data to inform this afternoon’s marketing campaigns. I first saw the impact of this type of data informed decision-making at Google.

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What to look for when hiring a data scientist

Tom Tunguz

But a more important driver has been the need to better understand how to qualify, evaluate and hire data scientists because data science is a massive competitive advantage. And many of the companies I work with are hiring data scientists. Data processing. Data analysis.

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Gorgias Improves Account Insight and ROI Attribution with Hull


Improve ROI attribution and intelligently measure channel performance by cleaning up marketing lead source data. Prioritize visibility and transparency throughout the company with data-driven dashboards and account-level insight. Data plays a significant role in the culture at Gorgias.

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Startup Best Practices 21 - Your Startup's Recruiting Scorecard

Tom Tunguz

For example, a data engineering role may require familiarity with data analysis tools. The same data analysis job would require an ability to learn new technologies and simplify complex data into comprehensible insights for the rest of a team. Last night, SaaS Office Hours at Redpoint welcomed Maia Josebachvilli , the VP of People and Strategy of Greenhouse. Maia is a thought leader in human resources.

How to Run a Regression Analysis to Forecast the Return on PPC Spending

SaaS Growth Strategy

Regression analysis was a tool that could help me. Based on your historical data, you can then forecast what the return will be at higher spending levels assuming that there’s a linear relationship between the two variables. Download Excel’s Data Analysis Toolpack.

Best Way To Successfully Launch A Digital Marketing Campaign Through Social Media


Developing the ability to utilize social data will put your brand at the forefront with all the other successful brands you can think of. This will make social data easier to analyze, too, as you will be looking for patterns and trends that are in line with your company’s vision.

How to Combat Inaccurate Data and Faulty Statistics When Making Decisions

Tom Tunguz

The conclusions were results of bad experimental design, biases in the data , and statistical tools used incorrectly. One of the major problems with data analysis are the imperfect methods we use. But p-values doesn’t answer the question to the answer most people care about: what are the odds the hypothesis about the data is correct? In addition to dissolving faith in the research process, bad data encourages wrong decision-making.

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The Technology that's Taking Data Science by Storm

Tom Tunguz

Amazon’s Redshift, an elastic data-warehousing solution launched in late 2012 is the most salient example. Redshift’s ability to process huge volumes of data is breathtaking. When running Redshift on solid state drives (SSDs), one team at FlyData queried 1 terabyte of data in less than 10 seconds. AirBnB’s data science team wrote about their experiences contrasting Redshift and Hive.

Startup Trends from YCombinator's Demo Day

Tom Tunguz

This increase in activity seems to be driven by advances in data analysis for drug discovery and novel sensors. If this data is any indication, we should see more commerce and consumer finance companies; and more vertical software businesses in the next few years I’ve been to many YC Demo Days and I always look forward to them. This year was no exception. There are so many wonderful ideas and companies founded by terrific entrepreneurs.

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Cohort Analysis for Startups: Six Summary Reports to Understand Your Customer Base (With Code)

Tom Tunguz

Cohort analysis provides deep insight into customer bases because cohorts expose how customer accounts grow, evolve and churn. Plus, cohort analysis provides a framework to evaluate product releases, marketing pushes and advertising campaign performance.

Summer is Here – Bring on the Customer Success Interns!


You could use this opportunity to have your intern built out a customer survey and manage the process and data analysis. To NPS or Not to NPS? – Does your organization collect NPS data? Summer is Here – Bring on the Customer Success Interns!

The Top 5 Customer Escalation Best Practices You Need to Know


Part of molding your business to your customer’s needs involves making customer data accessible to all team members. Gathering and sharing customer data across the company helps you take a more proactive approach. A quality customer success platform gives you access to the customer data you need to turn escalations into success. Signalization: Use this data to accurately determine a customer’s current situation and future needs.

Against All Odds in Startupland

Tom Tunguz

Win probability charts like the one above have become the icons of popular predictive data analysis. I love data, but let me whisper a heresy to you. The problem with predictive analysis like this is they never capture all the variables. Predicting the future is damn hard, and no matter how much data we jam onto disk, or how sophisticated our adversarial neural networks become, we still won’t be able predict the future accurately.

Why Personas Are Critical Product Development and Go To Market Tools for Startups

Tom Tunguz

When the data analytics team took the stage, I listened with great interest as the chief of the group described their internal struggles with data and the areas where startups might help them achieve their goals. I’ve summarized these personas below: The Three People That Matter in Data. Analyst Reveal trends in data Create and propagate data silos Visualization tools, Spreadsheets, BI. For example, I’d never heard the term data steward before.

An Exceptional Story with Exceptional Data

Tom Tunguz

Even if you’re not a soccer/football fan, the article is worth reading because it’s one of the finest examples of synthesizing data and a story to convey a point I’ve read in a very long time. Data is one of the most powerful ingredients to supporting a point of view. It’s one of the reasons I publish data analysis on this blog. But data alone isn’t enough - not nearly enough - to be compelling.

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Your Startup's Competitive Advantage

Tom Tunguz

A better chat experience ; a data modeling layer for data analysis, near-instant transcription of expenses. Startups fail when they run out of money. Startups run out of money when they lack focus. Without a maniacal focus on serving customer needs in a unique way, startups can flounder amidst competition. Without product market fit, the business is challenged to generate strong metrics and faces fundraising challenges.

SaaS Metrics Refresher #8: Data Literacy

Chart Mogul

It's more important than ever to ensure that your whole team is well equipped to work with metrics and data. Reading and writing skill levels are often defined by what people can or can’t accomplish in their everyday life—we must do the same for data literacy. Key data literacy terms.

3 Metrics to Determine if Your Subscription Model is Working or Failing


If your data analysis finds that you are in need of a change, use the tactics we discussed to identify your value metric and choose a new subscription model that fits with it. . Growing a SaaS company is an uphill battle, but it shouldn’t be impossible.

Feature/Product Fit

Casey Accidental

Most good product development starts with a combination of data analysis and user research. For example, when we launched the Grubhub mobile app, we saw in the data when people used the current location feature, their conversation rate was lower than people who typed in their address.

The 12 Things I Know About You

Tom Tunguz

But as I’ve learned writing this blog, experimentation and data analysis will lead authors to share those insights in the most generalizable way possible. I know 12 things about you. You have a great need for other people to like and admire you. You have a tendency to be critical of yourself. You have a great deal of unused capacity which you have not turned to your advantage. While you have some personality weaknesses, you are generally able to compensate for them.

Customer Relationship Management System Features: What a CRM Includes and What It Doesn’t


Keeping customers happy starts with obsessively gathering insights on your clients from data. From a lot of data. A customer relationship management system is a tool for managing the data associated with customer relationships. A quality customer success platform can: Create BI without exporting data to a separate data analysis application. Synchronized data sharing: A good customer success platform and CRM can transfer data between each other seamlessly.

Inbound Marketing: What Is A Lead Magnet?


The effectiveness of the site can then be deduced after data analysis of all its visitors. This data can be valuable to a decision maker.

5 Game-Changing Trends Shaping the Future of Sales [New Report]

Sales Hacker

The report also provides individual country and industry profiles using the following data points: Top Sales Technologies. Share of Data-driven Sales. 2) Data is the new common sense. 2) Data is the new common sense.

3 things your CRM needs today to empower your sales team for tomorrow

Sales teams using Close can integrate with other awesome communication, prospecting, analysis tools seamlessly which help them make sense of the lead—where the leads come from and how they need to act on it. Ability to deep dive into data.

The Evolution of CRM (And Where it’s Going) in the Future

Sales Hacker

Modern CRMs are capable of storing and analyzing large amounts of data and can be integrated with other omnichannel tools. Generate data-driven insight and reports about customers. This includes data entry and lookup to content recommendations and lead queueing.

The Optimal Price to Maximize Sales Efficiency for a SaaS Startup

Tom Tunguz

To eliminate bias, I whittled the dataset to a subset of the companies who had data in all three periods. In fact, within any of the three periods, and across the four different ACV categories, the data today shows that there is no optimal ACV that would enable maximum sales efficiency. There is another important conclusions from the data: sales efficiency is monotonically decreasing. But, I don’t have the data to prove that hypothesis.

Benchmarking Tableau's S-1 - How 7 Key SaaS Metrics Stack Up

Tom Tunguz

Today, we’ll examine Tableau, the market leader for data visualization software. The company went public in 2013 and we’ll use data from their S-1 through 2013 to benchmark the business. Tableau sells their desktop client to one or two data analysts within a company. Sometimes, teams buy a Tableau server license to collaborate internally on data analysis. And they have been investing at this rate for as long as we have data to measure it.

3 Landing Page Tips That Can Help You Convert More Customers

The Marketing & Growth Hacking Publication

Through landing page optimization and data analysis, you can convert more customers and increase your company’s revenue. For example, a lead magnet can be proprietary data that your organization has collected about a certain industry.

Product, Pricing, Marketing, and Data: Essential Building Blocks for SaaS Success

OpenView Labs

Learning from Experience: Data Analysis. In addition to being a tech geek, I’m also a data geek. We have actually built a big data warehouse that is informing many teams within our organization. I have always enjoyed the hands-on aspect of product development.

What to Look for When Hiring a Head of Marketing for Your Startup

Tom Tunguz

These teams are by nature technical, often performing significant data analysis to maximize return-on-investment of their marketing spend. This data informs the product and engineering roadmap. When a startup is confronted with the prospect of hiring a head of marketing, founders heads often spin. What should be the day-to-day tasks for this person? What skill sets are important?

The Best Sales Managers Don’t Chase Revenue: 6 Steps to Get Goal-Setting Right

Sales Hacker

To do their jobs, sales managers delve in data, technology, metrics, and forecasts. Sales forecasting and planning should begin with data on current performance. Use data in setting and assigning quotas. Success begins with setting goals.

What a Dog and a Monkey Taught Me About Management at Google

Tom Tunguz

It might have been a mishandled customer case, a forgotten internal data analysis or causing a car accident on the way to work. At all hands meetings on Tuesday afternoons, our 75 person AdSense Ops team reviewed the most important metrics for the business: top-two box customer satisfaction scores, revenue growth and customer churn. But unlike every other all hands meeting I attended, these meetings ended with a monkey and a dog.

Is your churn problem actually an onboarding problem?

Claudiu Murariu

In our analysis, we were looking mainly at activity churn, not payment churn. It all boils down to data analysis. For more articles on data analytics, you can subscribe to my weekly newsletter.

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The Best Pricing Tool for Your Subscription Business: Tips + Options


The following features are standard with most pricing tools: Analysis of historical data to inform your pricing decisions. Live data sets to get a broad overview of the current market. Positioning Pricing-tool data will help align your product to attract the right customers.

Strategies for increasing SaaS and ISV user engagement


The key to successful analysis of active users is to be clear on what DAU means to you. This is a process called cohort analysis. The Nickelled blog takes a thorough look at cohort analysis for a range of business types.

Grace Hopper: We are here


Amy-Willard Cross – Amy-Willard Cross is the Founder of the Gender Fair Index, which performs independent data analysis of industry-leading companies’ commitment to gender equality, allowing consumers to make educated purchase decisions.

PODCAST 67: How Data and Metrics Fuel Revenue and Company Growth w/ David Zwerin

Sales Hacker

We’ll walk through why sales ops are important to any organization, how data and metrics are driving growth (and how to manage those rapid changes), and we’ll take a special deep-dive into how a data-driven approach can be made accessible to any style of manager.

7 Principles To Mastering Growth Marketing

Brian Balfour

The fundamentals for growth are: Data Analysis - Understanding the meaning of data to identify, understand, and pinpoint new ideas and solutions. User Psychology - The data doesn’t matter unless you know how to influence the numbers in an authentic way.