I couldn’t have made that statement five years ago. What’s changed is a new generation of powerful, best-of-breed cloud products that delivers vertical capabilities — like data analysis, collaboration or customer support — all working together through open APIs. It’s like the cloud version of Lego blocks that can be combined into fascinating new shapes. The result? Accidental innovation, as SaaS partners and customers mix and match apps and functions at will.

Today’s announcement between GoodData and Box shows what I mean. Because of our open APIs, the new GoodBox Bash provides a common data platform that makes it easy for customers to analyze how effectively teams collaborate on Box. Companies can understand, for example, how team members use shared content, see successful habits, and easily measure the value and effectiveness of content. Customers can start using the new GoodBox Bash practically “out of the box.”

Compare that experience with the smoke-and-mirrors press releases of partnerships we’ve come to expect from legacy software vendors — lots of noise signifying nothing.

And that’s my underlying point. When we and other SaaS companies announce joint products or partnerships, it actually means something. This is the beauty of the new “cloud economy.” Imagine being able to combine and immediately deploy cloud-based apps from amazing SaaS companies — like Box, Zendesk for customer service, Zuora for online billing and GoodData for data analysis. I like to think of it as best-of-breed finally putting inbred out of its misery. (Or at least, ending of the misery of enterprises chained to the likes of Oracle, SAP and IBM.)

Just think of the ramifications for IT organizations! After more than 20 years of promise, the tech industry has finally liberated IT to focus on business priorities. That means quickly giving users powerful software that does exactly what they need, with the scale and reach of the cloud. Accent on “quickly” and “powerful” — two attributes that define the new generation of best-of-breed cloud apps and platforms.

I’ll use GoodData as an example. Our GoodData platform handles a mind-numbing array of big-data software built to handle tasks like data processing, data mining, programming, workflow and queries. And while these programs are incredibly powerful, they’re also amazingly complex. At GoodData, we handle all these programs at the back end so that our customers don’t have to. And that means our customers can quickly deploy powerful analytics across all their important data.

We aren’t alone in that philosophy. In fact, I’d say all great best-of-breed SaaS companies share the same mission and goal — which is to make it easy for business to consume technology.

Now, instead of worrying about flipping switches and keeping the lights on, IT folks take on new responsibilities related to the cloud — from governance, integration, capacity planning and security to vetting apps across all the lines of business. And that means the cloud economy satisfies another promise technology vendors have been making for years: Turning IT into a strategic partner that enables business value.

As I said: the cloud wins. Welcome to the new cloud economy.

Read Box’s blog or visit our website for more information on GoodBox.

The post The Cloud Economy: Putting an End to Inbred Software appeared first on GoodData.

That’s where we come in. At GoodData, we connect and analyze these data sets to ensure your company has the proper context to make decisions. Businesses in the Subscription Economy don’t need to be told that data is important. But between marketing data from Salesforce, expense reports, spreadsheets, and MRR, businesses often lose track of the big picture.

In the Subscription Economy, it’s sometimes hard to plan investments by the year because your customers are typically paying you on different cycles (by the month, by the quarter, every two years, etc). And your typical analytics solutions usually are laced with up-front expenditures. You need a business solution that is in sync with top and bottom line. At GoodData, we offer an end-to-end service that gives you a real-time perspective of your revenue and cost structure, enabling you to grow your business profitably.

GoodData aims to bring different data sources together and create rich visualizations so that business leaders can make effective decisions. GoodData allows customers to manage data from different data sources to get a more holistic view of their business. In this way, companies can monetize their subscription services in the most efficient way.

Let’s say you are a Subscription Economy business, using Zuora. By leveraging GoodData, you’ll be able to combine the rich subscription metrics in Zuora, the data from your expense management system, and your internal usage database. This will give you powerful insights into the health of your business with the full context of your expenses and the full context of how your customers are actually using the system.

Imagine how this powerful insight would influence critical business decisions: You no longer depend on a salesperson’s hunch to decide when to up-sell customers. Instead, your data tells you what you need to know.

Join us for a webinar to learn more.

The post Why Data Matters For Subscription Economy appeared first on GoodData.

Part One: Introduction to GoodData for Subscription Analytics

Part Two: Measuring Churn

Part Three: Loading & Mashing Data

Continuing in the GoodData for Subscription series, let’s take a look at slicing the data sets by subscriber cohorts and at computing customer lifetime value (CLV) metrics.  While cohort analysis and CLV computations are advanced topics, we will see that we can gain new insights into subscriber behaviors, without having to wade into the complexity of a dissertation-level treatment of the subject.

We’ll start with a simple example to illustrate the concepts of CLV and cohort analysis.  Let’s say that during January we sign up a 100 new subscribers.  This group of subscribers is our January cohort, also known as vintage. We will track this group’s behavior over time and separately from all other subscribers.  If our monthly attrition rate is 10%, then in February 10 subscribers will leave and not renew their subscription.   The following month, 9 more subscribers from this cohort will cancel, with 81 subscribers remaining, and so on, until about 2 years later when the last subscriber from the January cohort cancels their subscription.

In the following chart, we show a visualization of revenues by different subscriber vintages and we can clearly see the trend in declining revenues over time for each of the cohorts. Looking at our metrics by cohorts, rather than in the aggregate, we can better understand the effects of our marketing, product and customer satisfaction initiatives.

With the simplifying assumption of a constant attrition rate, it turns out that the formula for the average subscriber lifetime length is given by the inverse of the churn rate.  So in this case, our subscriber lifetime is about 10 months.  We can then approximate CLV (by multiplying the lifetime length by ARPU and our profit margin, ignoring the effect of time value discounting).  Knowing the expected CLV we can make more informed business decisions, like, budgeting our customer acquisition costs (CAC) to not exceed a fraction of CVL.

Schedule a Subscription Analytics demo and see for yourself just how easy and effortless analyzing your monthly subscription can be.

The post Part Four: Slicing & with GoodData for Subscription Analytics appeared first on GoodData.

Part One: Introduction to GoodData for Subscription Analytics

Part Two:Measuring Churn

A GoodData for Subscription Analytics project (or any project created in GoodData) consist of 3 layers: data, metrics and dashboards.  We begin our analysis and reporting with subscription billing data.  It is typical for the billing data to originate in specific billing systems and this data is usually extracted, transformed and loaded into GoodData on a daily basis.  Common billing data sources we have seen used are:

• Aria, Chargify, Zuora and other SaaS billing solutions
• Traditional billing and revenue management solutions
• Custom billing systems

Billing data lets us compute monthly recurring revenues, churn rates and other key performance indicators (as discussed in Part One.) But by itself, billing data can only give us a limited perspective on subscriber behaviors.  We can gain more comprehensive insight by correlating subscription billing metrics with data sets from financial, product usage, support, marketing, CRM or other operational systems.  Here are a few common data sources that are often integrated:

• Salesforce or other CRM solutions
• Zendesk or other support desk solutions
• Pardot or other marketing solutions
• Custom product usage tracking subsystems

At GoodData, we’ve found that simple mashups can often provide the most insight.  For example, by taking lead scoring and customer activity metrics from marketing automation or product and support usage data sets, more information can be gathered for a granular  look at how the metrics affect customer conversion and attrition.  With the mashup of data, we can better understand which trial customers are more likely to buy paid subscriptions, and which existing subscribers may be at a higher risk of cancellation or non-renewal.

The post Part Three: Loading & Mashing Data! GoodData for Subscription Analytics appeared first on GoodData.

In the following graphs we see that most of the churn is due to cancellations of subscriptions to the base product.

Presenting conversion and attrition rates based on subscriber counts tell us only part of the story.  We should account for the financial impact of changes in subscriber counts at different services tiers and include reports on our dashboard that show conversion and attrition by revenue.

This is especially important if the per user monthly recurring revenue amounts are broadly distributed, for instance, when subscribers can buy multiple user seats per service tier.  A cancellation by a “premium” tier subscriber with many seats would naturally have a larger financial effect than the churn of “base” tier subscriber with a single seat, even though the two cancellations would be counted equally in the subscribers churn metric.

If we sell subscriptions for different periods, such as monthly and yearly terms, the computation of attrition metrics becomes a bit more complex.  To avoid downplaying the actual attrition rate, we have to make sure to count only subscribers that are at risk of cancellation in the current period.

Stay tuned!  Our future GoodData for Subscription blog post will focus on customer lifetime value (CLV), cohort analysis and other hot subscription topics.  Are you ready to start your GoodData for  Subscription Analytics project?

The post Part Two: Churn Baby, Churn! GoodData for Subscription Analytics appeared first on GoodData.

Whether selling periodicals, communication services, gym memberships, or in recent years Software as a Service (SaaS), Subscription Analytics focus on two core areas of the business model: the acquisition of new customers and the retention of existing customers.  Typically, the lion’s share of revenue for most subscription-based firms is composed of the on-going subscription payments by existing subscribers and therefore newly signed up subscribers are only a fraction of the current revenue.  The rate at which existing customers cancel their subscriptions is referred to as churn or attrition and one of the most important metrics to track. An ever so small change in churn can significantly affect financial results. With the increasingly common practice of offering free trial subscriptions, paying attention to the conversion rate from free to paying subscribers, is also critical.

To show these metrics, let’s take a look at a sample dashboard created in GoodData. The dashboard summarizes the current month’s performance for:

• Monthly Recurring Revenues (MRR)
• Total number of subscribers
• Average Revenue Per User (ARPU)
• Conversion and attrition rates

To be continued: Today’s post just scratches the surface of Subscription Analytics.  Over the next few weeks we’ll take a look at data sources, mashups and provide more detailed dashboards and metrics of interest including Customer Lifetime Value (CLV) and Cohort Analysis.

What subscription metrics would you like to measure?  We’d love to to help you perfect your subscription reporting.  Either share your comments below or drop us and email.

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