INDUSTRY INSIGHTS YOUR PEERS ARE READING
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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? MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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! MORE
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. MORE
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. MORE
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. MORE
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%. MORE
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. MORE
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. MORE
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. MORE
On the day of Tableau’s IPO, a company known for innovating in data visualization, I thought I would share the most impressive HCI concept I’ve seen in a long time. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
“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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
Data Analysis Related Topics Tom Tunguz
JANUARY 4, 2019
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%.
InsightSquared
MARCH 14, 2018
“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.
InsightSquared
JULY 10, 2019
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.
Tom Tunguz
JULY 11, 2013
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.
Tom Tunguz
FEBRUARY 16, 2016
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.
Tom Tunguz
MARCH 30, 2016
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.
Tom Tunguz
OCTOBER 28, 2012
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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Hull
SEPTEMBER 15, 2019
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.
Tom Tunguz
DECEMBER 2, 2015
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.
SaaS Growth Strategy
DECEMBER 15, 2016
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.
Backlinkfy
AUGUST 12, 2019
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.
Tom Tunguz
FEBRUARY 12, 2014
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.
Tom Tunguz
JANUARY 29, 2014
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.
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Tom Tunguz
AUGUST 19, 2015
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.
Tom Tunguz
AUGUST 11, 2013
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.
ChurnZero
JULY 2, 2019
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!
Totango
MARCH 28, 2019
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.
Tom Tunguz
FEBRUARY 4, 2017
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.
Tom Tunguz
OCTOBER 8, 2014
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.
Tom Tunguz
JULY 1, 2014
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.
Tom Tunguz
OCTOBER 22, 2016
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.
Chart Mogul
JANUARY 15, 2019
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.
Chargify
JULY 30, 2019
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.
Casey Accidental
APRIL 18, 2018
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.
Tom Tunguz
JANUARY 31, 2016
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.
Totango
JANUARY 29, 2019
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.
Backlinkfy
JULY 6, 2019
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.
Sales Hacker
JUNE 11, 2018
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.
Close.io
JULY 10, 2019
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.
Sales Hacker
AUGUST 8, 2018
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.
Tom Tunguz
SEPTEMBER 8, 2014
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.
Tom Tunguz
SEPTEMBER 21, 2014
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.
The Marketing & Growth Hacking Publication
FEBRUARY 21, 2019
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.
OpenView Labs
FEBRUARY 27, 2019
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.
Tom Tunguz
FEBRUARY 19, 2014
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?
Sales Hacker
DECEMBER 13, 2018
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.
Tom Tunguz
OCTOBER 20, 2013
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.
Claudiu Murariu
JUNE 8, 2018
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.
ProfitWell
AUGUST 15, 2019
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.
wepay
SEPTEMBER 12, 2018
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.
wepay
OCTOBER 10, 2018
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.
Sales Hacker
JULY 22, 2019
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.
Brian Balfour
OCTOBER 12, 2016
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.
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