Customer Success models for SaaS businesses

~ Don MacLennan

I come across a wide variety of SaaS vendors in the marketplace  and, in many cases, we see Customer Success leaders struggling to define what our friend Nils Vinje calls the “Four P’s”:

  • Purpose: Does the Customer Success team exist to drive renewal revenue? Customer satisfaction?
  • People: What type of person is best suited to the needs of the team?
  • Process: What are the core processes the team must support?
  • Platform: What automation will enable these people and processes?

Answering these questions is key for determining which Customer Success model is right for your SaaS business.

Start With your Selling Model

One way to approach these questions is to start with how your products are sold and deployed in the first place.

Are you selling online without sales assistance? This implies a relatively inexpensive product on a unit basis and a low-touch provisioning process. Your post-sale experience will probably be low-touch as well. What will the role of your Customer Success people play in this case? Perhaps they will operate systems like online communities and marketing automation tools to enable “digital touches” only.

For the sake of contrast, imagine instead that you’re selling a complex product using outside sales resources. Your annual contract value is closer to $100,000 and your Professional Services team does the implementation. In this case, your Customer Success team is probably at the center of the post-sale experience. They’re delivering a high-touch, consultative experience to a low number of customers per Customer Success Manager.

Of course, many SaaS vendors sit between these polarities. In this case, a mix of people-driven and digital touches might be right for you. Which leads to another way to look at the problem.

Charting the SaaS Vendor Growth Journey

Another way to define the Customer Success team’s mission is to consider where you are in the growth journey.

We’d argue that a young SaaS vendor is operating a single, low- or no-touch selling motion. Your product is a basic version of what it will become, and your capital constraints mean that expensive sales resources aren’t affordable yet. Your Customer Success model is probably low-touch or no-touch in turn.

As you reach later years of growth, your customer base has become heterogeneous. Small customers continue to come through the front door, because who would stop using an efficient, online sales model to acquire new customers? However, your outside sales team is now landing larger customers too. And some customers grow year-over-year thanks to upselling. The Customer Success model in this case is multi-tier. Digital touches are used for the lowest tier while people-driven touches are used in the highest tier. A combination of the two is used for customers in the middle.

Last, I see some larger SaaS vendors start to look homogenous again. Their product has evolved into an Enterprise offering with high-touch selling and high Annual Contract Values (ACV). Small customers are no longer attractive to the acquisition sales team, and this determines the high-touch Customer Success model that follows.

Summing it up

As your define your Customer Success team according to the “Four P’s”, the first question to answer is whether you’re aligned to the selling model. If not, what are the gaps and how does your organization close them?

Second, has Customer Success evolved along with the business to support a multi-tier model? Has your business become a single-tier model again, thanks to very large customers? Are there gaps to fill?

My take: The most prevalent gaps are when Customer Success teams are focused solely on the highest-value customer tier. This is a somewhat understandable approach but is also a recipe for churn. Successful organization find a scalable way to gain a deep understanding of every end user, as well as a programmatic way to reach out to multiple accounts.

Quantifying the benefits of your Customer Success team

~ Don MacLennan

TLDR: I’ve been reflecting on the business value that Customer Success leaders are trying to deliver to their companies, as well as how to explain what my startup Bluenose does to enable the same. While Customer Success started off as a way reactive way to battle churn, the discipline is quickly evolving, so it’s increasingly important to be able to quantify the benefits of Customer Success.

We came up with a simple framework to quantify the benefits of Customer Success by describing the sources of value: impact and reach. So, the mission of Customer Success is to maximize the value that comes from each. The diagram below outlines a few Customer Success activities that fall into certain segments as it relates to impact and reach. The upper right quadrant is the ideal state for a modern Customer Success organization.

Impact

Key question for quantifying Customer Success value: For the customers that you engage, what is the impact you’re making?

I’d argue that the impact ranges from low to high as you move from being reactive to proactive.

At the reactive end, Customer Success teams are typically “fighting fires” by triaging valuable customers who are threatening to churn. In fact, hiring firefighters is often the genesis of the Customer Success team.

The impact is low because once you’re in “save mode” you’ll save some customers but not all; some are so dissatisfied they can’t be recovered.

As your Customer Success team becomes more proactive, you can generate more value in three ways:

  • Reduce churn by spotting and engaging at-risk customers sooner. There will always be customers at risk; the challenge is to engage them soon enough that you can fix what’s making them unhappy before it’s too late.
  • Increase upsell and expansion revenue by ensuring widespread product adoption in accounts that you engage
  • Create advocates in the customer base that accelerate your new customer acquisition.

What’s needed to become more proactive?

  • An Early Warning System that uses data to spot at-risk customers and drives interventions to prevent churn.
  • A customer journey. Each customer should be measured according to the stage of the journey they’re meant to be in. If they’re not in those stages, what customer touches are triggered to get them there?

Reach

Key question for quantifying Customer Success value: What percent of your customers are you impacting?

As SaaS vendors grow, their customer base often stratifies into tiers, such as Tier 1/2/3 where Tier 1 customers are the largest contracts. Customer Success teams are often created to protect revenue in these Tier 1 customers. This of course makes sense given the revenue at stake. However, servicing customers with your people is an expensive endeavor. Thus, the Customer Success team will often get stuck only serving Tier 1 customers because of the people cost.

Sometimes, we see Tier 2 customers served, but with a “coverage model” of one Success person to 50, 100 or even 300 customers. You could argue that with this ratio, the role is all about customer “saves” and not much else. Last, the lowest tier is often called “unmanaged” because Success teams have no involvement.

As your Customer Success team extends its reach, you can create value in several ways:

  • Reduced churn in lower tiers.
  • Nurture lower tier customers into higher tiers through upsell / cross-sell.
  • Improve NPS® or CSAT survey results through proactive service.

What’s needed to increase reach? Customer Success must work programmatically and at scale. Many of the concepts of acquisition marketing apply here: campaigns that target people with a specific and relevant call to action. This programmatic approach happens as a complement to the people resources deployed in your Success team.

Some examples:

  • If an end-user has a licensed seat but has never used a product, the call to action could be to watch a brief video on getting started with your product.
  • If an end-user has adopted your product, then the call to action could be to explore new features or even enroll in an advocacy program.
  • If an end-user has replied negatively to a survey, then the call to action might be to speak to a “special” support person to help overcome their issues.

A caveat: targeting users appropriately is essential. If the advanced user is sent an email about getting started, it’s spam because it’s not relevant.

Summing it up

For Customer Success leaders, it’s vital to maximize your impact and reach. I’ve found that you can vastly improve your reach by moving from firefighting to a proactive mode. To improve impact, work programmatically and at scale. How you achieve both of these are topics that I’ll dive into in further blogs.

What should go into your customer health score?

~ Don MacLennan

There’s a religious debate going on in the community of Customer Success vendors. My turn to weigh in.

On the one hand, some tools vendors are evangelizing the value of predictive analytics driven by product usage data. Other tools vendors are evangelizing the value of rules-based scoring driven by various factors including usage stats, survey scores, support tickets, and more.

While each vendor’s approach can be “right” under certain circumstances, each is generally wrong to characterize a health score as being driven by one approach versus the other.

My take: You need both.

Before I explain why, let’s get to first principles. Customer Success teams consistently say that they need to get away from “firefighting” mode to proactive customer engagement. Think of the health score as an “Early Warning System” that causes a Customer Success person to engage the customer at the earliest possible sign of caution, though direct contact or even a campaign.

Early warnings can come all along the customer journey, including during product trial, implementation and of course, as a precursor to renewal or retention.

The question then becomes, how to construct a score that satisfies key criteria:

  • Runs continuously so that each customer is monitored all the time.
  • Is suited for each stage of the customer lifecycle.
  • Is suited for each type of customer in your customer base.
  • Is trusted so that your team is inclined to react when a low score is generated.

So back to why neither group of vendors is “right.” In order to satisfy these criteria, you need multiple scoring methods. Some examples of different scoring methods for different lifecycle stages include:

  • During a trial, you know little about a user. The only signal you might have is usage. So, a score for trial users needs to be driven by usage stats or a predictive algorithm to spot the users likely to convert or not.
  • During onboarding & implementation, usage might be sparse. Especially if your product is complex and you deliver implementation services. You might, however, have a lot of support tickets as a signal that indicates if the customer is struggling to configure or adopt the product. Or, you’d look at early usage and tickets together.

Some examples that pertain to different product types and Customer Success models:

  • You service a high-value customer tier. The health score should take into account total adoption of your product in each account and all the facets of your high-touch relationship with the account.
  • You service a customer in a lower-value tier with a low touch service model. For example, one Customer Success Manager to 100+ customers. Usage data and maybe support tickets might be all you have to construct a score that spots at-risk customers.

So are predictive analytics a good thing? Of course. They can make for a better health score. However, let’s be clear about when and how they can useful.

First, the purpose of a predictive algorithm is to spot variances. In this case, your “most healthy” and “least healthy” users. If your product is not well instrumented, and you collect just a few usage event types, then an algorithm will be less reliable because it won’t spot variances in usage patterns. Conversely, usage data that contains more than 15 or more different event types will probably tease out interesting differences between users.

Second, if you don’t have a historical set of usage data collected, the algorithm will be less useful. You’re looking at correlations between usage patterns and outcomes such as churn, retention and renewal. One month’s worth of data won’t cut it.

Last, algorithms are most useful when you don’t have other facets of a customer relationship to rely upon. If you’re a Customer Success Manager with 20 assigned accounts, it’s doubtful you’d be surprised by an unhealthy customer nor would usage alone explain the health of that relationship.

In summary, for many Customer Success teams, customer health scores must take into account usage data plus something else. Also, if you want predictive algorithms to drive your score in part or in whole, be sure to instrument your product fully and build up a historical repository. Last, consider a health score driven by usage data alone only when you don’t have any other data to work from.

Happy scoring!

Inspired by the “Pay It Forward” people

~ Don MacLennan ~ Leave a comment

6510934443_8bd2942b79_bI caught up with Randy Womack yesterday.  He’s one of those “pay it forward” people.  As in, helpful for the sake of being helpful.  No strings attached.  Just wanting to see people succeed in life and realize their dreams.

He’s not the only one who’s been so kind to me.  There’s Wayne Willis, too.  And Kent Godfrey.  And John Keenan.  And Nate Williams #1 (I have a couple Nate Williams in my life).  And too many others to recall and give credit here.

I get so inspired when I meet these people.  Their positive energy is like a drug.  Not only do I feel freshly motivated to work on my own startup, but it reminds me of the effect I might have on others when paying it forward in turn.  I’ve tried to be helpful to others in the past couple years.  Hopefully folks like Kathleen and Matt caught the fever too.

But yesterday’s meeting was a reminder: you can’t do enough of it. Pay on, my friends.  Pay on.

Looking to de-code churn drivers? Look for hot-spots, not averages

~ Don MacLennan ~ 6 Comments

OK, I admit it. I’m recycling parts of a blog post from 2 years ago. 

The Normal Distribution, or “Bell Curve”

Our brains are wired somehow to think of everything in terms of a Normal Distribution, aka the Bell Curve. It’s a trap that can obscure important patterns in data such as churn drivers.

The shape of the curve means that we think of populations of data (such as customers) as being a somewhat homogeneous group if only we could compute the average. For example, how many minutes per day “on average” a user spends in your product. Or, the percentage of customers “on average” who renew their subscription.

The problem is that populations of people and customers almost never behave in a normal distribution. Instead, the more prevalent pattern of behavior is a Power Law, or Pareto Distribution.

The Pareto Distribution, or “80/20 rule”

The Pareto distribution is also known as the 80/20 rule. Except that in online worlds, the ratio can be even closer to “95/5″. So, it’s very likely that the 5, 10 or 15% of your customers that churn each year actually have a lot in common, and differ materially from the “average” customer.

Let’s de-construct this. Start by asking yourself a few questions.

Is churn the same across every acquisition channel?

Probably not. You’ll see variances between channels such as outside sales, inside sales, online channels, and even within the various online campaigns that can beget customers (email marketing, search engine marketing, display advertising, etc.).

Is churn the same across every customer segment you sell into?

Probably not. If you’re B2B and you sell into various industries, then you probably have some industries with better retention than others. You will also see differences between small, medium and large customers according to their company size (their annual revenue and/or employee count). If you’re B2C and you sell into various demographics, then you probably have some demographic segments with better retention than others (think age, household members, etc.)

Is churn the same across every region you sell into?

Probably not. Different countries have different retention characteristics. Sometimes, even across different states or provinces.

Is churn the same across product usage patterns?

Probably not. There are probably usage patterns such as feature use and frequency that correspond to higher and lower churning customer segments. Also, think about usage across a population of users inside an account. Some accounts will have a couple of very engaged users and the rest no so much. Others may have widespread, evenly distributed usage. You wouldn’t see these differences if you were working with “averages”. Yet, these different segments will probably have different retention rates.

De-coding churn is about the search for “hot-spots”

The game of de-coding churn is about finding variances from the average, instead of focusing on the average itself. There’s probably a high-risk segment in your customer base that can be described by a combination of multiple factors including acquisition channel, region, customer demographics and usage pattern. Think of this example: “churn is higher in the U.S. through the reseller channel by 25%, than the regional average” If you knew that, what would you do? You’d probably have a conversation with the person who recruits and trains re-sellers in the U.S. about doing a better job of it. You just found a “hot-spot”.

So why is the search for hot-spots so hard? It requires all the data about your customers to be in one place and fully dimensionalized (think “data cube”).

For most companies, getting data into this type of structure is done using Pivot Tables, which is time-consuming, error-prone and hard to maintain. This is why Big Data and analytics systems are becoming commonplace.

Happy hunting!

Mining for gold: 3 sources of product usage data

~ Don MacLennan

Update: the vendor landscape described here has evolved considerably since writing this. But the basic concepts still apply.

In a prior blog, I talked about ways to drive revenue growth with product usage data. Including reducing churn, spotting up-sell opportunities, increasing trial conversions etc.

In a follow-up blog, I discussed the four dimensions of product usage that you need to analyze:

  • Frequency: how often a user interacts with your product
  • Features: which features are used
  • Volumetrics: how often a feature is used, or a data record is created
  • Configuration settings: ways a user configures the app to suit their needs

By now you’re probably interested to unlock the value of product usage data for your company. Which begs the question: where do you get this data in the first place?

There are three sources of usage data. Generally speaking, usage data comes from:

  1. Clickstream data
  2. Log events
  3. Database queries

We’ll review each in succession.

Clickstream data

Clickstream data is generated by an end-user’s interaction with your product interface. For example, logging in to a web browser. Or performing an action in your mobile app.

This type of usage data can help you understand frequency of use (usually but not always) and coarse-grained feature usage, depending on how you “tag” usage events from the browser.

Several products are good sources of clickstream data for your browser-based app:

  • KISSmetrics
  • Mixpanel
  • Segment.io, a popular Javascript plug-in that feeds lots of other clickstream tools

An aside: why not Google Analytics for product usage? Unlike these other tools, Google’s terms of service prohibit you from collecting user-specific data of any sort. Thus, it becomes very difficult to understand which user or even company is accessing your application and how. Stick to the products above.

Getting usage data for Mobile apps can come from several of the packages above, plus some that are purpose-built for mobile:

  • Flurry. Note that Flurry monetizes by driving ad placements, so it’s not for everyone
  • Tapstream

Pros of clickstream data

  • Easy to deploy
  • Little to no engineering team involvement
  • Good for basic engagement metrics

Cons of clickstream data

  • Not detailed enough to reveal important features and user segments
  • Brittle to maintain

Log events

Depending on how well your engineers have instrumented your server-side code, they may be generating usage data as log events. For example, your Web or App Servers might be generating Apache logs that contain details about the user’s actions, especially feature usage.

Pros of log data

  • Log events can be very specific and accurate in depicting feature usage, compared to page-level clickstream data from a browser

Cons of log data

  • Log events need to be parsed, which can be challenging if you’re doing it for yourself in a database or Excel file
  • Log events can contain types of events that aren’t meaningful to you, because they describe system behaviors not user behaviors (think error logs)
  • Your engineers need to be involved to do a good job of log instrumentation

Database queries

On the server side of your application is some sort of database (such as MySQL, Hadoop, MongoDB, etc.). Each user action may have a corresponding “transaction” or record in the database that forms a picture of usage. For example, if a user started a new “Project” in your Project Management app, then that record was created in the database at a specific time by a specific user.

These records can be queried from your database to produce events or counts of events (such as daily summaries).

Other functions of your application may behave as “set it and forget it” where your app is automating processes without requiring a user action each time. In this case, usage events are generated even though the user hasn’t logged in lately. Database queries may be the only means to collect this type of usage event.

Pros of database queries

  • The most comprehensive picture of usage

Cons of database queries

  • Your Engineering or Operations team has to retrieve the data for you

Conclusion

There’s no “silver bullet” to getting usage data.

In many companies, you have easy access to one type of usage data and not the others. And, no single source of usage data depicts your application’s usage in a comprehensive way.

Think of it as a journey. It’s best to get started with the data on hand. As you learn to make sense of it, and drive business results, you’re armed with the justification to get other types of usage data. Sometimes this means further instrumenting your product. In other cases, it’s simply about getting another team to help you access their data.

But the journey is well worth the effort, because usage data is the foundation of understanding the health of your customer.

“Net Churn” is a bullshit metric

~ Don MacLennan ~ 4 Comments

TLDR: Certain subscription-based vendors have conceived of a metric called “Net Churn”. The concept seems to have originated in publicly-traded SaaS companies. It’s probably in response to Wall Street analysts’ enduring attempts to decode the underlying measures of financial health in their renewal revenue streams.

Here’s how Net Churn is calculated:

  • You take the renewal revenue pool for a given month; let’s say it’s $1,000,000 of potential revenue
  • You take the actual renewals PLUS actual up-sells/cross-sells that you get from those customers at the same time, PLUS actual cancellations value PLUS down-sells; let’s say these sum to $950,000
  • Divide actual over potential and voila! Net churn rate

By using this math, “Net Churn” can be a number as high as 95% even over 100%.

This is financial chicanery.

An organization should measure each metric separately. Each is a unique and important financial lever. Combining measures into Net Churn blurs how well (or not) you’re doing at each.

For example, in a B2B business with annual subscriptions, consider these metrics:

  • Logo churn – this is the count or rate of customers in the renewal pool who actually renew. The calculated rate is always 100% or less
  • Revenue churn – this is the count or rate of renewal revenue in the pool that was actually renewed. The calculated rate is always 100% or less
  • Cancellations – this is the count or rate of cancellations in the period between renewals
  • Base renewals – this is the count or rate of revenue from customers who renew at the same financial commitment as the prior period
  • Up-sell/cross sell – this is the additional revenue captured beyond the renewal potential
  • Downsell – this is the renewal revenue from customers who lower their financial commitment for the same product for the renewal period versus their prior level (downsell is also referred to as partial churn in some organizations)

Ideally, you measure and optimize on all levers:

  • Logo churn – useful to see churn patterns across your whole customer base
  • Revenue churn – useful to see churn especially from high-value customers
  • Cancellations – a particularly concerning outcome worth understanding in depth
  • Base renewals – useful to see the customers who are steady-state. These customers may end up as future at-risk customers because they aren’t growing
  • Up-sell – useful to see the performance of premium versions of your product offerings (if you have them). Your marketers spend a lot of time tuning your offer hierarchy and this is a great way to validate their efforts
  • Cross-sell – useful to see the performance of complementary products in your portfolio (if you have them). Many vendors diversify their product portfolio, at great expense. The payback is often measured by how well your customer base consumes the new offerings
  • Downsell – useful to see customers who lessen their commitment, which may present clues to overselling license capacity in a prior period

Leaving aside the games that are played with Wall Street analysts, focusing on these metrics individually will quickly highlight several drivers of financial performance. Each metric reflects a unique opportunity to drive revenue growth.

Six best practices for expectations management in Customer Success

~ Don MacLennan ~ 2 Comments

TLDR: The role of Customer Success might be one of the most connected and inter-dependent jobs in your company. In addition to working with your customers’ people, you have lots of internal stakeholders to work with too. Including executives, sales teams, marketing teams, finance teams, product teams and support teams.

With so many people involved and so much communication flying around, the art of expectations management becomes an essential tool for survival and success in the role.  I’d like to share our six best practices from many years of managing many relationships.

1. Consistency & timeliness

  • Regular communications beat sporadic ones. A clue to a problem in your current situation: somebody requests an update that you didn’t otherwise plan for
  • Consistent updates beat variable ones. Suggestion: find out what people want to be kept informed about, and make this the basis of the regular flow of communication

2. The power of “no”

Some of us hate to say no.  Yet it’s powerful when used appropriately:

  • “No” is required to build trust and credibility.  Saying “yes” to everything creates skepticism that you’ll deliver everything, and rightly so
  • If you like to under-promise and over-deliver, “no” is part of how you do it
  • Be careful to say “no” for a reason. Try using “no, because…” to justify your position, and negotiate if needed

3. Timely follow through

The longer somebody waits on us, the more likely they are to believe something’s gone wrong:

  • Any commitment should be met in days, ideally 1-3. Suggestion: if a commitment takes longer, de-construct the deliverable and commit to interim steps that last 1-3 days (e.g an update call to share progress, or an interim deliverable)
  • “Constant improvement beats delayed perfection”

4. Bad news must travel the fastest

Take a cue from the professionals who do crisis communications:

  • Be pro-active and control the message. Bad news + delay = elevated emotions by other parties

5. Active listening

Active listening is a way to assure the other party that they are being heard and understood:

  • Play the message back before responding. Suggestion: a person says something. Instead of responding, first play back what you heard: “what I heard you say was….”. And, use the word “because”. An example: “What I heard you say is that item #2 is most important, because you committed this to a customer”. Once you have confirmation, then respond.

6. Document the plan

Artifacts have a powerful way of aligning expectations.  Feedback is often easier to get by having someone react to something that’s written or visual (versus verbal only):

  • Write down and share information so that expectations and deadlines can be easily referenced later
  • Prior documentation can be excellent way to review progress and get credit for commitments you delivered on

The metrics-driven SaaS business

~ Don MacLennan ~ 1 Comment

I founded Bluenose in part because there are major changes happening across technology-based industries. In this post, I’ll review those changes and what it means for technology vendors. In particular, I’ll discuss the importance of adopting new ways to manage businesses, even as the old ways still hold true.

There’s a business model revolution going on.

Our friends at Zuora coined it the “Subscription Economy®”. It’s fundamentally changing the patterns of consumption:

  • in music content, we’ve gone from buying a CD for $18 to buying a song or subscribing to a monthly streaming service
  • in movie content, we’ve gone from owning the DVD for $49 (ouch!) to on-demand viewing or a monthly streaming service
  • in desktop software, we’ve gone from buying a boxed CD at the computer store to a monthly subscription. And lots of freemium mixed in.
  • in business software, we’ve gone from perpetual licenses with up-front payment to monthly or annual SaaS subscriptions
  • in mobile phones, we’ve gone from long-term contracts to shorter ones, and in many cases pay-as-you-go
  • my favorite example: GE Aircraft Engines used to sell you an engine for a few million dollars. Now, you purchase “flight hours” and your billing is metered accordingly

It seems like everywhere the internet touches our lives, the business model has been transformed in turn.

A new dynamic between customer and supplier

When suppliers got up-front payments for their products, their attitudes toward customers often sucked. You have a software problem? Call tech support, where you’ll be rushed off the phone as soon as they can. After you were on hold for too long.

After all, at that point you as a customer are a cost. Repeat after me: a cost, as in to be minimized.

In recurring revenue models where you consume and pay over time, it’s a new game. The supplier must keep loving you, or you and your revenue stream go away. Study the organizational design of a SaaS businesses, and you will encounter new departments called “customer success”, “customer advocacy”, “customer experience management” or “customer retention”.

What’s going on?

Follow the money. The new shape of revenue is causing a new way of thinking about the customer.

By the way, we as customers are liking this quite a lot. We’re holding the leverage now, and are enjoying the newfound attention as a result. For that reason, I think this model is here to stay. Marc Andreessen said “software is eating the world”. Maybe it’s more like SaaS is eating the world.

Time for new metrics

As suppliers, lots of the old ways to measure success are still there. We still care about revenue, profits, cash flow, access to cheap capital, competition, etc.

But this new business model forces us to master some new metrics in turn, like Lifetime Value (LTV). Or Customer Acquisition Cost (CAC) to LTV ratio. Or Churn Rate. Or Renewal Rate.

The new metrics that I find most interesting are ones rooted in the measure of a customer relationship over time. And, the linkage between relationship health and the associated financial outcomes: renewals, churn, up-sells, cross-sells, etc.

“Subscription Economy”® is the registered trademark of Zuora, Inc.

6 ways to increase revenue with product usage data

~ Don MacLennan ~ 1 Comment

TLDR: Analyzing product usage data can lead to several revenue generating opportunities. While most SaaS companies have some form of usage measurement, few have achieved a complete view of usage.

Let’s review all of the revenue levers at stake.

One: increase revenue by increasing trial conversions

Most trials convert because the user achieves rapid time-to-value, such as minutes or hours from the time you provision their trial account.

The conversion rate drops off significantly when provisioned users don’t actually engage with your product until days into the trial. In fact, most provisioned users who don’t access your application in the first few days probably don’t convert ever. So, usage data gives you an “early warning” about provisioned users who aren’t active in the first hours; you can react by stimulating their access with emails, phone calls and such.

As you master usage analytics, you’ll also discover the features that are most correlated to conversion. You can then engage trial users with a purpose: nurture them to experience the “best” features for conversion.

To summarize, the blueprint for successful conversion is about getting users to achieve rapid time-to-value, and to specifically experience the features that lead to conversion.

Two: increase revenue by spotting up-sell opportunities

Highly engaged users tend to adopt your application faster than others. In fact, the velocity of adoption is a critical measure for your business. These users are open to expansion revenue propositions and should receive attention when exhibiting “high velocity” behavior.

Even in the “average” user segment, progress toward some license limit should be the trigger to capture expansion revenue. Some examples:

  • users who have used 80% of their licensed seats
  • users who have consumed 80% of their capacity (as expressed in data volume or some other metering method)

The other important consideration is to present the up-sell opportunity at the earliest moment of need. Too often, vendors use an artificial event such as a renewal date to propose a higher-priced offering. But what if the customer had a need months ago? Selling at the moment of need is the best way to maximize this revenue stream. Pay attention to velocity and license limits as a result.

Three: reduce churn by spotting declining usage

Just as increasing adoption is a very strong positive signal, decreasing adoption is a strong negative signal. One could even argue that if usage is not growing, trouble lies ahead.

When a customer is at-risk as evidenced in usage data, the time to intervene is now. Whatever is causing customer dissatisfaction needs immediate attention. This “moment of opportunity” shouldn’t be confused with the renewals cycle. Customers can and do “churn” before their contract expires, by ceasing their usage. You’re highly unlikely to receive a renewal if you address their dissatisfaction at renewal time instead of the time they began to disengage.

Four: increase revenue by finding and mobilizing evangelists

Evangelists are a far more effective means to acquire new customers than any other means thanks to the power of recommendation and social proof. The challenge is that evangelists are often hard to spot. You have to find, enroll and activate them instead of waiting for them to naturally behave this way.

Usage data is a goldmine here. The heaviest adopters, and users of your most differentiated features, are hidden in your usage data. The programs you have to activate evangelists are super-charged when you’re identifying all of the potential recruits in usage data.

Five: increase revenue by improving usability

If we believe that adoption begets customer retention, then barriers to adoption must be systematically found and fixed. Usage data enables you to spot areas in your application that have usability issues.

Some examples of how to find usability issues in usage data:

  • No actions beyond login. Is the user having trouble getting started? Does the product have a good “first-use” mode?
  • Patterns of navigation that suggest users are getting “stuck”: a user is on the cusp of performing a task, then goes to a product help page, then goes back to the task and never successfully completes it
  • Users abandoning a guided process partway through, such a wizard. Is the process designed in a way that they can’t, or don’t want, to complete it?

Six: increase revenue by increasing prices

When you understand the correlation between usage and retention, you understand the sources of value creation in your product. Did you price your product on this basis? In other words, are you leaving money on the table by not linking your price to the features that generate value? Most organizations set prices for many other reasons than their unique selling points.

Where to go from here: getting and using product usage data

Now we have a framework for the value drivers of usage data. And they can add up to major revenue lift.