In this business era, there’s a new type of feedback survey required. It’s different than traditional ways of collecting customer feedback.
So.… you’re thinking about a Customer Success or customer analytics solution for your team. And you know that the quality of the data you integrate into your Customer Success system will determine the benefits you get from it.
I’ve seen three common “gotchas” in customer data quality. Spend some time on these upfront and you will greatly accelerate your Customer Success system time to value.
Get your data from the “systems of truth”
So much of deploying a Customer Success system is accessing useful customer information as it lives in source systems.
Follow a simple premise:
- your best financial data is in your financial systems
- your best support ticket data is in your support CRM
- your best usage data is in your back-end and/or your clickstream tool
- and so on…
Beware of using copies of data or derivative data that you get from a system other than the source. I’m talking about you, Salesforce CRM. Too often, in order to put things into Salesforce, the data is somehow aggregated or manipulated to the point where it loses its detail and accuracy. Bypass Salesforce and go to the truth.
Pay attention to “data curation”
Data that is machine-generated is great, if only because it’s inherently accurate. Think usage data, for example.
However, a lot of your customer data is input by you, the vendor. This data is “curated” by your employees. Some curated data is very accurate, because there are strong incentives to make it so:
- support ticket data is generally good because status and backlog are closely inspected by the head of support
- revenue data from the finance team is good because it drives financial reporting. Nobody wants to report inaccurate data to the Board or the Auditors
- sales opportunity can be good if there’s strong VP Sales inspection and/or commissions are driven from opportunity records
Conversely, beware of data that must be maintained but for which there is no incentive to do so. The result is a lot of missing data or inaccurate data. Some examples:
- account fields in Salesforce like Tier, Stage, custom fields
- support ticket fields like “reason code”
- anything else where the data isn’t closely inspected by leaders all the time
If you really want a field of data to be accurate, work to create the incentives and inspection that would make it so. Or, forget it and focus on the art of the possible.
Keys must join your data
The elusive “Customer 360” enables a Customer Success Manager to know exactly how a customer is doing.
By definition, a Customer 360 is created by joining data from various sources like your Sales CRM, Support CRM, usage data, billing data, survey data, etc. This means you need a “key” to match up those records to a common customer record. A key is unique identifier, often a long string of letters and digits. For example, an Account I.D. in your Salesforce in instance looks like this: “999bb7c9999f27d11d09a5e”
Done well, you would know that a support ticket created by “MegaCorp” is the same company that has a sales opportunity under the Account name “Mega Corporation” in Salesforce CRM. Why? Because they share the Account I.D. in common.
Do you care about the people in your customer base? Then you’ll need to associate those people and their data to their respective companies. One approach is to embed account I.D.’s into your user / contact records. Or, use email addresses to match the URL domain at the company for whom they work.
Nobody’s data is perfect. And never will be. However, you will probably need to invest in data quality in order to maximize the benefits of your investment in the Customer Success team and the tool they use. Pay attention to the three gotchas and you will remove most of the impediments to success.
TLDR: I come across a wide variety of SaaS vendors in the marketplace. I often see their customer success teams struggle to align themselves with the specific needs of the business they are in and understand what their Customer Success model should be.
The symptom can also be seen by one’s (in)ability to describe what our friend Nils Vinje calls the “Four P’s”:
- Purpose: why 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?
First clue: start with your selling model
One way to answer 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 and a low-touch provisioning process. Your post-sale experience should (must?) be low-touch as well. What will the role of your Customer Success people play in this case? Perhaps to 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 CSM.
Many SaaS vendors sit between these polarities. In this case, your Customer Success team might need to support a multi-tier customer success model, delivering a different level of service for each tier. Or, you might employ approaches that blend your people resources with programmatic outreach through marketing campaigns and the like.
Which leads to another way to look at the problem.
Second clue: charting your growth journey
Another way to define the Customer Success team’s mission is to consider where you are in the growth journey of your company.
Most young SaaS vendors are operating a single, low- or no-touch selling motion. Your product is a basic version of what it will become, and your money 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 up-selling. The customer success model in this case is multi-tier. Digital touches are used for the lowest tier. 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 later. Their product has evolved into an Enterprise offering with high touch selling and high ACV’s. 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 to close them?
Last, 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?
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.
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.
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?
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.
- 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.
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.
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:
- Clickstream data
- Log events
- Database queries
We’ll review each in succession.
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:
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
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
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
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
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.
Customer analytics for SaaS companies is about reducing churn and increasing lifetime value of your existing customers. While every company looks at acquisition metrics, SaaS vendors must do more because the customer lifecycle begins at the point of web conversion.
A customer that enrolls in a trial or subscription is beginning their relationship with your company. However, your SaaS offering will need to be constantly delivering the value your customer is seeking or you will fail at customer retention.
How do you know if you’re meeting customer expectations?
There is a recent body of research called behavioral economics. In a nutshell, it states that what a customer does, versus what they say, is a truer measure of their satisfaction and intent. This means that a customer who says they are happy in a survey might not actually be happy. Even worse is the customer who never tells you they’re unhappy, and they simply churn.
Customer analytics unlocks the power of this research by enabling SaaS vendors to understand their customers’ actions, focusing on product usage combined with everything else you can know.
How to get started? Make an inventory of all of the data you have about your customers, and ways it can be used.
- Traffic and volume metrics: Attributing customers to the channel that they came through will enable you to find patterns of valuable or less valuable customers.
- Product usage: Logins, modules used, processes run, usage frequency. This tells if your customers are using your product and what parts of it they are engaging with.
- Sales CRM: (when you have a sales-assisted acquisition model)
- Support CRM: Issues your customers needed help with. Use it to spot your customer’s frustration with your business.
- Purchase & product plans: What your customer purchased and how much they paid. Identifies customer tiers, potential up sells, upcoming renewals and payment status.
- NPS ® (Net Promoter Score ℠): Surveys taken on a subset of your customers to gauge satisfaction. Allows your customers to tell you qualitatively vs. quantitatively if they are happy with your service.
- User comments: In emails, on support forums, or even Tweets. A second data source of customer sentiment that can be tied with NPS and Support CRM.
- Customer intelligence: Key changes within your customer’s organization that could signal potential changes in the stability of your relationship.
Most SaaS companies review metrics from each of these sources independently. Customer Analytics happens when we analyze our customers’ data across multiple touch points. This reveals valuable insights you would have no way identifying if the data remained apart.
Look across all of your customer data, and consider what it means across the lifecycle. The result? Better products and increased revenue.