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Bluenose | Driving SaaS Customer Acquisition with Success Metrics

As a SaaS business matures, the importance and value of SaaS metrics increase. Most SaaS businesses begin their journey down the SaaS metrics path by tracking recurring revenue in relation to customer acquisition costs. After building a ...

Driving SaaS Customer Acquisition w/Success Metrics

As a SaaS business matures, the importance and value of SaaS metrics increase. Most SaaS businesses begin their journey down the SaaS metrics path by tracking recurring revenue in relation to customer acquisition costs. After building a solid customer base, churn becomes a priority. These fundamental SaaS metrics are all apparent in the standard SaaS profit equation below.

SaaS profit =
current customers x ( avg recurring revenue – avg recurring cost )
– new customers x avg acquisition cost

However, it quickly becomes apparent that fighting churn requires a SaaS metrics toolkit that digs significantly deeper than simple financial metrics. Operational metrics are needed that connect day-to-day business reality to financial performance. It is this realization that gives birth to the new Metrics-driven SaaS Business as it discovers the goldmine of SaaS customer success metrics and predictive analytics that enable it to eliminate churn before it begins.

But what about the other half of the profit equation? Is it possible to apply SaaS customer success metrics to customer acquisition? The answer is most emphatically yes! Prospects are merely future customers, and their success lies in the purchase of your SaaS product. It’s a SaaS best practice to provide a seamless customer experience from visiting your website to trial to purchase to use, therefore the metrics used to describe this process should be seamless as well. In all cases, the goal is to help customers become happy users of your SaaS product, i.e., successful customers. SaaS customer acquisition is merely SaaS prospect success.

saas customer success metrics kpi dashboard

Customer success metrics are equally applicable to SaaS customer acquisition,
because prospects are merely future customers whose success lies in purchase.

This is the third post in a series inspired by my ongoing collaboration with Bluenose Analytics that explores the new Metrics-driven SaaS Business based on emerging best practices in SaaS customer success metrics. The last post discussed the promise of SaaS customer success metrics for churn reduction and upselling. This third post examines their use in SaaS customer acquisition.

Improving Trial Conversions

Marketing automation vendors built an entirely new software category based on the idea of facilitating purchase by helping B2B companies engage more effectively online with the New Breed of B2B Buyer. Unfortunately, marketing automation products focus all their attention on trying to get prospects to read your content, not use your product. Reading content is good, using product is better. Online trial is a foundational SaaS best practice and improving trial conversion is strategically important to many SaaS vendors. Fortunately, SaaS customer success metrics offer as much potential to increase trial conversion as they do to reduce churn.

saas customer success metrics root cause analysis

The same statistical methods used in customer success can be applied to customer acquisition,
only we are looking for drivers of purchase rather than drivers of churn.

Trial is the moment where the SaaS vendor establishes that always-on communication channel to the SaaS customer through the product. Prior to trial login, customers are just cookies and email addresses. After trial login, product usage can be monitored and every click can be associated with an individual customer. Trial forms also collect key customer demographics that can be used in predictive models. In fact, The Metrics-driven SaaS Business uses these same models to decide exactly what questions should be asked on a trial form, i.e., the ones that can be used to best predict and facilitate purchase! Using SaaS product usage data to create predictive purchase models is SaaS lead scoring done right.

Reducing Acquisition Costs through Metrics

Moving along our SaaS profit equation, we come to one of the most important SaaS financial metrics for every SaaS vendor: customer acquisition cost. Keeping customer acquisition cost is line is essential to the financial success of every SaaS business, because customer acquisition eats cash. The lower your customer acquisition cost, the sooner you get repaid for your upfront investment in acquiring a customer, and the sooner you can reinvest that money in acquiring yet another one.

saas customer acquisition is saas prospect success

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By now you should be able to guess exactly how SaaS customer success metrics can be applied to reduce customer acquisition costs, because they are essentially similar to the methods for improving customer success efficiency. First we need to identify the key predictors of purchase. For example, our analysis of product usage data may indicate that prospects that use the product every day of the free trial can usually be converted with a single phone call, whereas those that stop using it within the first week are twice as likely to convert if we can get them to attend an instructional webinar. Now what SaaS account manager wouldn’t love to have that little jewel of information?

As with our customer success organization, the key to reducing acquisition costs is to embed our predictive models into the daily sales activities. The process is the same. First we create descriptive models to identify the root causes of why prospects do and don’t purchase our product. Then, we use these models to create KPIs at the trial account level that reps can use to guide their daily activities. We can also create alerts when specific trigger events require a reps attention, such as increased or decreased product use of a particular kind. And, finally, we can automate communications within the product itself to facilitate purchase.

Improving Onboarding with SaaS Customer Success Metrics

Onboarding a new customer is where customer acquisition and customer success meet. Most SaaS customer success professionals will tell you that inadequate onboarding at the beginning of a contract is one of the key drivers of churn at the end of the contract. In addition, onboarding can be an expensive and time-consuming task for more complex SaaS products. The Metrics-driven SaaS Business understands the strategic impact of onboarding and applies SaaS customer success metrics to streamline the onboarding process and reduce onboarding costs.

Where are your new customers getting stuck? This is the primary question of the onboarding challenge, and I can think of no better indicator than product usage data. Whereas acquisition and churn KPIs were largely focused on predicting future events, onboarding customer success metrics are looking at the here and now, as in “Why is this customer stuck right here, right now?” By drilling down on this question, we can identify root causes and predictive indicators of onboarding failure that can be used by both customer success reps and product managers to streamline the onboarding process.

Driving Customer Advocacy and Viral Growth through Metrics

At the beginning of the SaaS customer lifecycle, SaaS customer acquisition and SaaS customer success first meet in onboarding, but at the end they meet again in viral growth, where life begins anew. Up until now, we’ve explored a number of ways SaaS customer success metrics can be applied to eliminate problems: stopping churn, reducing service costs, improving trial conversions, etc. At the top of the capability pyramid, The Metrics-driven SaaS Business uses SaaS customer success metrics to create opportunities, not just resolve problems.

The same techniques we used to identify customers who might churn can be used to identify customers that will never churn, customers that love us, and customers that will recommend us! By applying SaaS customer success metrics to identify advocates, we can improve our bank of potential sales references, customer testimonials, case studies and user group leaders. Many organizations use methods like Net Promoter to measure customer advocacy, and these are good ideas for SaaS businesses as well, but only SaaS businesses can complement these qualitative measures with quantitative customer advocacy metrics based on actual product use.

saas customer success metrics product usage data

By applying SaaS customer success metrics to identify advocates,
we can improve our bank of potential sales references, customer testimonials,
case studies and user group leaders.

Enabling viral growth through sharing is a common SaaS product design best practice. The main idea is to encourage customers to create work products, such as documents, charts, projects, etc. that are integral to the SaaS product experience, and then share those work products with non-customers with whom they need to collaborate. Understanding and facilitating the activities that lead to sharing is strategic goal of the Metrics-driven SaaS Business, because viral sharing leads to viral revenue growth.

The Promise of SaaS Customer Success Metrics : CloudAve

Over the past few years, the SaaS community has gained a solid understanding of SaaS financial metrics, as well as many of the operational principles required to achieve them. However, there has always been an obvious ...

The Promise of SaaS Customer Success Metrics : CloudAve

Over the past few years, the SaaS community has gained a solid understanding of SaaS financial metrics, as well as many of the operational principles required to achieve them. However, there has always been an obvious ...

Bluenose | The Promise of SaaS Customer Success Metrics

Over the past few years, the SaaS community has gained a solid understanding of SaaS financial metrics, as well as many of the operational principles required to achieve them. However, there has always been an obvious ...

The Promise of SaaS Customer Success Metrics

Over the past few years, the SaaS community has gained a solid understanding of SaaS financial metrics, as well as many of the operational principles required to achieve them. However, there has always been an obvious gap between what happens on the top line and what happens on the ground. It’s one thing to claim that a 50% reduction in churn will result in a 2X increase in recurring revenue, but it’s quite another thing to make it happen. Achieving that 50% reduction in churn is usually a tedious and unreliable process of trial and error. This is about to change. As the SaaS industry matures, we are witnessing the evolution of SaaS metrics beyond simple, historical financial measures toward sophisticated operational measures in the form of new SaaS customer success metrics and predictive analytics.

saas customer success metrics kpi dashboard

We are witnessing the evolution of SaaS metrics beyond simple, historical financial measures
toward sophisticated SaaS customer success metrics and predictive analytics.

This is the second post in a series inspired by my ongoing collaboration with Bluenose Analytics that explores the new Metrics-driven SaaS Business and its foundation of emerging best practices in customer success metrics. [Attention SaaS CFO’s and VP’s of Customer Success! Please see the exclusive invitation at the end of this post if you like this series and would like to explore more in person.] The first post discussed the unique qualities of SaaS that enable the Metrics-driven SaaS business to apply a more analytic approach to management than traditional licensed software. This second post drills down on the promise of customer success metrics to bring greater rigor to the processes of churn reduction, upselling and customer success management for increased recurring revenue and decreased recurring costs of service.

saas customer success metrics

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An Ocean of Customer Success Data

The promise of customer success metrics is immense. Unfortunately, so is the challenge of developing them. From the initial capture of a prospect’s email address to the final cancellation of a churning customer account, the Metrics-driven SaaS Business collects and analyzes customer data. At the very beginning of a SaaS customer’s lifetime, a cookie is dropped and the usage clock starts ticking as web visits turn into trial accounts. That initial email is complemented with profile information captured on sign-up forms and augmented by third-party databases. Sales and marketing kick in and engagement activities are recorded in CRMs and marketing automation systems. Finally, a purchase is made and the ecommerce engine captures the transaction and forwards it to the financial systems for future billing. Then, the real action starts. Customers log in to the product again and again. Every important click is recorded and every customer success activity is logged.

saas customer success metrics ocean of data

The SaaS customer success metrics challenge is a big data problem,
requiring powerful data collection engines and sophisticated statistical models.

Collecting the data, unfortunately, is not even half of the battle. The Metrics-driven SaaS Business must make good use of it, turning data into information and information into action. Compared to the SaaS metrics challenge of previous years where all we had to do was master a relatively short list of SaaS financial metrics, the SaaS customer success metrics challenge is truly daunting–a bona fide big data problem. There is just no way to make sense of these volumes of data without powerful data collection engines and sophisticated descriptive and predictive statistical models. Simply defining the relevant customer success metrics is a difficult problem onto itself. But for the very first time, we have the law of large numbers tilting in our favor and the benefit it offers for reducing churn and accelerating customer acquisition far outweigh the costs.

Driving SaaS Customer Success with Metrics

The SaaS profit equation from the previous post and repeated below shows the five key financial levers of SaaS businesses, the two volume drivers: current customers and new customers, and the three units of value: recurring revenue per customer, recurring service cost per customer, and acquisition cost per customer.

SaaS profit =
current customers x ( avg recurring revenue – avg recurring cost )
– new customers x avg acquisition cost

[ Note: For the accountants in the audience,
this should look a lot like activity-based costing. Because it is. 😉 ]

As SaaS executives, our financial goals are very simple: make business decisions that push these financial levers in the right directions to increase revenue and reduce costs. The challenge of maximizing SaaS profit is easily divided between the ‘current customer’ half of the calculation and the ‘new customer, half. SaaS business organizations and operating plans are often similarly divided into servicing current customers and acquiring new customers.

This second post in The Metrics-driven SaaS Business series focuses on the ‘current customers’ half. The next post in the series will focus on the ‘new customer’ half. As mentioned earlier though, pushing these financial levers is much easier said than done. Planning to increase revenue by increasing current customers with a 30% reduction in churn is easy. Reducing churn by 30% is hard. The following sections take a look at the first three financial levers: current customers (churn), average cost of service (customer success efficiency) and average recurring revenue per customer (upsells) and the principal role of SaaS customer success metrics in creating and executing operating plans that actually push them.

Leveraging Root Cause Analysis to Reduce SaaS Churn

By far the lowest hanging fruit of SaaS customer success metrics is their use in SaaS churn reduction. For a SaaS business of any reasonable size, churn uniformly represents the largest financial drain on SaaS growth and profit. Its simple math, ‘current customers’ is almost always the largest number in our SaaS profit equation above. SaaS churn is also a great place to start our exploration of SaaS customer success metrics, because at its heart, SaaS churn is a statistical concept, so modeling it operationally is fundamentally a statistical problem.

customer success metrics churn statistics

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[Note: If you tweeted the quote above, CONGRATULATIONS!
Welcome to the club of true SaaS metrics geeks! ]

SaaS churn represents the probability that a customer will cancel in a given period. That probability is determined by a number of factors: the value the customer sees in your SaaS product, the customer’s reliance on your SaaS product, the potential value of competitor offerings, and the internal priorities and politics within the customer’s organization. The Metrics-driven SaaS Business gathers and analyzes information on all of these predictors. Customer profiles in CRMs and accounting systems combined with direct product usage data go a very long way in describing the first two, whereas the less visible ones can be tackled through customer success surveys and expert ratings by executives, sales reps, support reps, and customer success reps.

saas customer success metrics root cause analysis

With an ocean of customer success data and the law of large numbers on our side,
we can apply well known statistical methods to identify the root causes of churn

Once we have collected the relevant information, we can apply well known statistical methods to identify the root causes of churn. There are a number of descriptive statistical methods that apply from simple cross tabulation of churn cohorts to more advanced methods like logistic regression and survival analysis. Statistics aside, we expect to find insights, such as customers in healthcare are more likely to churn than customers in financial services. If a customer has not logged in in the last 30 days, it is at severe risk of churn. Customers that use our reporting module frequently are our best advocates, and so forth. With the right data and the right analytics, root causes of churn can consistently be identified and addressed, a significant improvement over simply reducing churn from 15% to 10% in our financial forecast without having a clue as to how it will be achieved.

Predictive Analytics with SaaS Customer Success Metrics

Once we have a better understanding of why past customers churn, we can create models that predict the risk that a specific current customer will churn in the future. With sound predictions, the customer success organization can take action to prevent SaaS churn before it happens. At their heart, most of these statistical methods are simply scoring systems that estimate the probability of a given event, in the case of churn it is the probability that a customer will cancel. The predictors in our models and the models themselves can therefore be used to create key performance indicators (KPIs) for customer success that are tracked on a regular basis for each and every customer. For example, we may find that customers that stop using our product for a two week period are at a higher risk of churn, and that the risk increases the longer they do not use the system. This metric and the regression that produced it can both be used to create KPIs.

SaaS Customer Success Metrics and Product Use

Customer success metrics based on product usage data is the secret sauce within the Metrics-driven SaaS Business. In a sense, churn is simply the opposite of use. The more a customer uses your SaaS product, the less likely the customer is to churn. Not only does use indicate how much the customer values your product, prolonged use correlates strongly with switching costs. Customer success metrics that track inadequate use are key indicators of churn, while those that track deep and frequent use are strong indicators of customer advocacy. One of the smartest applications of customer success metrics based on product use is driving product use itself. By identifying customers that are struggling with your product, you can uncover opportunities to improve the user experience, offer help and education, and of course reduce churn.

saas customer success metrics product usage data

Product usage data is the secret sauce within the Metrics-driven SaaS Business.
In a sense, churn is simply the opposite of use.

Improving SaaS Customer Success Efficiency through Metrics

The same KPIs that we use for churn reduction can be applied to improve the efficiency of the customer success organization and thereby lower cost of service. They key is to go beyond simply monitoring and modeling customer success metrics to embedding them in the daily workflow of customer success reps. From the preceding example, if we know that customers that have stop using our product for two weeks are in need of immediate attention, then we can use this information to create dashboards and alerts for customer success reps. The primary goal is to direct the attention of customer success reps to customers where the reps can have the greatest impact on financial results. Conversely, the secondary goal is to not waste time on customer success activities that have no influence on the success of a customer.

The beauty of SaaS customer success metrics over SaaS financial metrics is that they apply at the individual customer level. Moreover, they can be rolled up along any dimension, such as time, customer type, product module, customer success rep, etc. to create a detailed picture of our customer success operation. At the individual account level, they can be used to create a scorecard or health index for every single account to help customer success reps monitor and manage their territories. At the aggregate level, they can be used to design the customer success territories themselves, so that customer success reps are deployed to customer accounts in the right numbers and with the right mix of skills. Customer success managers are usually familiar with a straightforward small, medium and large account approach to territory design, however, it might just be that your large accounts have the least risk of churn and the least potential for upsell! SaaS customer success metrics provide much stronger guidance and many more dimensions from which to choose for territory design.

Driving Upsells with SaaS Customer Success Metrics

SaaS customer Success metrics can also improve upselling to increase average recurring revenue per customer, the next financial lever in our SaaS profit equation. By applying the same types of statistical models we used in churn reduction to analyze past upsell purchases across customer demographics, product usage data, and so forth, we can develop predictive models and scores for upselling. Again, we can embed these models and KPIs into the daily activities of customer success reps or account managers to direct them to the accounts with the greatest upsell potential at any given time. Finally, we can use the predictive models within the product itself to automatically trigger communications with high potential customers and facilitate purchase.

Attention SaaS CFO’s and VP’s of Customer Success!

I will be speaking at an exclusive CFO only dinner sponsored by Bluenose Analytics this coming Tuesday, April 29 in San Francisco. Please email me directly at joelyork [at] chaotic-flow.com if you are interested in attending. This event is part of a larger, ongoing series designed to create an intimate setting for SaaS industry leaders (10-15 at a time at a nice restaurant) where they can discuss and evolve SaaS business best practices for finance and customer success. There are only a few spots left for next Tuesday, however, if there is sufficient demand, we will likely repeat it. There are also upcoming dinners focused on Customer Success operational best practices for VP’s Customer Success. If you are interested in these, please email me and I will send you the agenda. Bluenose is also considering expanding these dinners to multiple cities, so let me know even if you are not in the Bay Area.

Thanks again for following Chaotic Flow!

Cheers,

JY

PS Dinner is free!

Bluenose Enables the Metrics-driven SaaS Business : CloudAve

bluenose customer success metrics We are witnessing a dramatic change in the way SaaS businesses are managed. While SaaS financial metrics, such as recurring revenue, acquisition cost, service cost, churn, growth and ...

Bluenose Enables the Metrics-driven SaaS Business

bluenose customer success metricsWe are witnessing a dramatic change in the way SaaS businesses are managed. While SaaS financial metrics, such as recurring revenue, acquisition cost, service cost, churn, growth and lifetime value have dramatically increased our understanding of the economics of SaaS businesses, they have proven inadequate for managing them. As useful as they may be, SaaS financial metrics look at the past, not the future. They can tell you that you have a problem with churn, but they cannot tell you what you should do about it. Motivated by the need to better understand churn, many SaaS businesses have been independently exploring a new class of customer success metrics and have begun to embed them in SaaS customer success workflows with the hope of preventing churn before it occurs. We are witnessing the emergence of The Metrics-driven SaaS business.

Two weeks ago, I kicked off a new blog series on The Metrics-driven SaaS Business vision in collaboration with Bluenose Analytics, who I believe is going to help make this vision a reality. Over the course of the last five years, I’ve discussed the potential of SaaS customer success metrics with a variety of startups, but I didn’t feel anyone fully got it until I met the folks at Bluenose. There was always something missing, and that something was predictive analytics.

For those of us that work in SaaS, we feel the customer success metrics pain when we try to bend Web marketing tools like Google Analytics, Marketo or Eloqua to the purpose of SaaS customer success. Unfortunately, they are just not up to the task. They don’t integrate the critical subscription, product usage, and account engagement data required. More importantly, they don’t have the necessary analytical power to enable the Metrics-driven SaaS Business. At best they supply simple historical reporting and heuristic scoring systems that have little basis in reality. There is no short-changing the math. If you want real predictive analytics; you have to use real statistical methods.

Real Stats. Real Easy.

I started my software career at SPSS, a very successful Chicago-based software company acquired by IBM. SPSS made predictive analytics under the tag line: “Real Stats. Real Easy.” The reason I bring this up is that I think a lot of folks believe that real predictive analytics is something that is hard to do and even harder to apply to everyday business. Well it is hard to do, but it can be very easily applied to everyday business. A SaaS customer success manager doesn’t need to know a system is using logistic regression or survival analysis to produce health scores and churn alerts. She just needs to see the red light go on and get the alert in time to keep a customer from churning. Plus, statistical visualization methods can be incredibly intuitive and powerful, providing the ability to zoom out to see high level root causes and drill down to investigate account-specific issues.

Bluenose Analytics Enables the Metrics-driven SaaS Business

The Bluenose platform uses real statistics to create SaaS customer success metrics,
root cause analyses and predictive analytics that enable fact-based churn reduction.

I was lucky enough to get a preview of Bluenose back in November, and they got two things right that I have been waiting to see a long time: powerful statistical visualizations and predictive analytics. Of course the system has the baseline SaaS customer success capabilities, such as work flow management, surveys and broad data integration, but these are just a SaaS customer Success ERP module without the right analytics. Simple heuristic scoring systems just doesn’t cut the mustard. SaaS customer success metrics need real stats, real easy.

I think the Bluenose management team gets the potential of predictive analytics for SaaS customer success, because their roots are in serious big data analytics, specifically anti-virus software. That and I know they interviewed more than 50 potential customers in the development of their requirements. Having announced a significant pre-launch $11 million A round in December, the production release is imminent and the company is actively seeking and working with pilot customers. Anyone who follows my blog knows that I don’t do advertising and I rarely give product recommendations. For me, blogging is a labor of love, not commerce. The reason I am collaborating with the folks at Bluenose is that I think they get it, and I want to see the Metrics-driven SaaS Business become the standard in our industry.

Bluenose | The Metrics Driven SaaS Business

My first serious lesson in the criticality of SaaS metrics was about six years ago when I was unexpectedly stumped in a board of directors meeting. I had just presented the booking plan for the year and one of the Director's in ...

Bluenose | The Metrics Driven SaaS Business

My first serious lesson in the criticality of SaaS metrics was about six years ago when I was unexpectedly stumped in a board of directors meeting. I had just presented the booking plan for the year and one of the Director's in ...

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