So, you want to make your customer success team more efficient through better data.
But data-driven customer success has always sounded too good to be true, right?
After all, if you’re truly data driven, the higher ups will notice how much revenue you’re not bringing to the table, correct?
Not really – remember, customer success has countless benefits beyond direct revenue. In fact, most CSMs’ efforts contribute in some shape or form to higher revenue. For example, simply having productive, honest conversations with clients can create relationships conducive to better upsells. Those elements can also be quantified and showcased, as I’ll show.
So – should you try for data-driven customer success? Yes. Not only is it the future, it’s also a good exercise in greater efficiency, particularly as we move to a more scalable, AI-assisted world. Today, to help you out, I’ll walk you through:
What exactly is included in data-driven customer success – the basic definition, requirements, and benefits so you know what you’re signing up for.
A detailed infographic containing all the essentials from this article so you have a visual aid to your implementation.
A list of common challenges to being data-driven in customer success. Believe it or not, there can be quite a few hurdles.
Some of the smartest metrics you can use to effectively gauge your CS efforts.
A downloadable guide to implementation – full of specifics on what to do, when to do it, and how to go about it.
What Is Data-Driven Customer Success?
Data-driven customer success is the practice of optimizing data analytics to the point where all datasets are accurate and all data-gathering tools are fully-set-up and interconnected. This enables truly actionable insights, boosts in productivity and efficiency, and greater customer goal alignment for the CS team and beyond. This sets the stage for the entire organization to work by the data and drive customer outcomes in a way that helps all parties grow together.
Or, to put it bluntly, data-driven CS lets you set your business and customers up for success in a measurable and demonstrable way. Sounds great, right? But how do you actually do it?
Also known as data-led CS, the practice is typically made real through a data-driven CS program or initiative, and championed by the CS team who must lead other customer-facing teams in customer success alignment and collaboration.
Requirements for Data-driven Customer Success
Beyond alignment, you’ll also need a few cut-and-dried things to get going. The following is an exhaustive list of what’s required. In the initial stages, you might find yourself spending a lot of time setting all these up. I recommend starting simple with a good CSP to have an initial, albeit broad and unhygienic, overview.
1. A good customer success platform (CSP)
The first step is to secure an appropriate place to centralize your data and ensure visibility for all your CSMs and other customer-facing departments. Also called a customer success platform, or CSP, it’s the place where every dataset goes to graduate into an actual insight. The best CSPs have a customer dashboard allowing for increased visibility of vital metrics, paired with automation capabilities that reduce the amount of repetitive work CSMs have to do.
2. Adequate integrations between all data sources and the CSP
But a CSP on its own is not nearly enough – you also need to secure proper onboarding and setup of the tool. That can usually be done by a CS Operations Manager, some form of personalized customer onboarding from the CSP’s side, or a combination of both.
3. Effective customer success operations and leadership
Speaking of CS Ops, you’ll need the appropriate experience in your team to properly set everything up. That means hiring or upskilling at least one CSM with CS Ops skills including analytics, automation, revenue forecasting, account management, reporting, data hygiene, and more.
4. A working data governance model and someone to own it
Your CS Ops lead must be in charge of Data Governance and create / adopt a model that works for your org. That means your teams speak the same language regarding what you track, where you collect it, how you define it, what type of insights you gain, and what the single source of truth is for every metric. More to the point, and specifically for SaaS, data governance implies a subset of practices including:
Regulatory compliance
Data integration and interoperability
Data ownership and accountability
Data quality assurance
Analytics and reporting
Data retention and archiving
Data security and privacy
Vendor management
5. Proper data hygiene and someone to safeguard it
According to the Compliance, Governance and Oversight Council, 60% of data collected during one day loses its business, legal, or regulatory value by the end of the same day. So one must employ a high level of diligence when it comes to data hygiene. I.e., the same CS Ops lead has to safeguard your data through a complex set of practices like:
Data audits, aka analyzing all the data your business gathers and auditing it in terms of quality, relevance, and security.
Setting a universal guideline for what you track along with their correct sources, names, and abbreviations
Determining the lifecycle of all data collected – from the moment it’s registered to when it’s used and when it becomes obsolete
Identifying the moment to move to a data warehouse and what needs to be done for that to happen.
6. Methods and metrics to derive value and insights from the data
Another essential requirement is the methodology for garnering useful insights. After all, these numbers are useless if you don’t use them well. So you should be proactive and create a set of metrics everyone looks at (based on what your business and your customers typically care about) and make certain all stakeholders have visibility.
7. People and automations that act on those insights / datapoints
Last but likely the most important point is that you need to act. You might get lost in the numbers, or in the sheer amount of things to do based on your analysis. Don’t fret or get stuck with choice paralysis – prioritize based on your OKRs and KPIs and get to work. You’ll need to determine both the people that do certain tasks and the automations that handle the rest (based on which triggers, what data sets, etc).
💡Remember: even if an objective is not measurable, it can still be very important. It’s the same distinction between effort-based and outcome-based results that I’ve talked about before in my article on OKRs. You can implement a weighted system that incorporates the effort based result (which can only be done / incomplete) into your overall scoring system. Check out my OKRs Template Spreadsheet for some examples of how to go about this.
Benefits of Data-Driven Customer Success
1. Make Informed Decisions and Improve CX
According to Gartner, more than two-thirds of companies now compete primarily on the basis of customer experience – up from only 36% in 2010! So, if you’re at this point in time when you share the same features, price and market fit as your main competitors, CX is the key.
Beyond this, having all the data in a clear-cut dashboard can ease your decision-making and reduce your likelihood of decision fatigue. When all the insights are right before your eyes, all you’ve got to do is follow the data trail and it will point you to success. And with a good CSP, your entire team will be right there next to you.
2. Promote Efficiency, Collaboration, and Alignment
Once you start leveraging your customer data for customer success, the next logical step is to integrate it within your entire organization.
By pushing to connect and integrate customer data into one source of truth for the entire organization, every aspect of the customer journey will be positively impacted. If all teams have access to the same customer context, support can be faster, handovers from sales to CS can be smoother, account managers and CSMs can keep notes and emails in sync.
Furthermore, with recent advances in AI integrated into your workflows, you’ll essentially cut down on repetitive tasks and work on more complex skills like leadership, creativity, and drive increased value for both your clients and the business as a whole (a fact corroborated by LinkedIn’s 2023 Future of Work Report).
3. Improve Products and Services
Improve products, make better product decisions, gather VOC feedback, pass on relevant info to Product, etc etc
Being data-driven allows for an overall greater alignment with customer goals. The data you gather will tell you how your users behave:
How they’re interacting with the product
If they’re achieving their goals or garnering value in some other way
What their path is towards their goals
If there are any bottlenecks, roadblocks, or bugs
Lastly, what kind of features would be good to have from a UX point of view
All this is to say, having better data leads to better products. The 2023 State of CX tells us that with higher budgets, better data and better data-driven CX decisions would be among the top things CX professionals would invest in, next to:
Better customer journeys and improved customer experience
More focus on customer loyalty and satisfaction
More automation, training, and help for customer support
So it’s fair to say that with budgets allowing for more data-driven work, the entire business offer would increase in quality, both in terms of product and services. However, if you’re still waiting for approvals on budgets or tools, one thing you can do at any point for better customer-focus and improve CX is to start with a small voice-of-the-customer analysis.
4. Enable Proactive Customer Engagement
What differentiates customer support from customer success is the impulse (and necessity) to act, rather than react.
In other words, instead of simply reacting to a customer issue like customer support specialists do, CSMs proactively look for ways to ensure customers aren’t stuck, can work the UI, understand what they need to do, and actually do it.
and what materials / tutorials / scripts to prep in advance
And this won’t just improve your customers’ experience with your brand. For example, CSMs can monitor product usage and make recommendations on ways to derive more value or reduce cost based on the client’s activity, segment, or account specifics. This, in turn, will drive loyalty, upsells, and more intangible business benefits. It will also allow you to stay relevant amongst your business peers. Here are five ways to start being more proactive in CS.
5. Become a Revenue Driver
We’ve all been there – for years, Customer Success was seen as a cost center. Not anymore!
With better, more actionable data and more demonstrable efforts, you’ll finally prove to everyone at the table that CS can be a powerful revenue driver and, moreover, a revenue unlocker (laying the groundwork for Sales and other departments to truly shine).
If you’re seen as a cost center, your higher ups will never commit the resources required for your team to work effectively and have peace of mind. In fact, it will end up cutting its budget, performance will drop even more, and more budget cuts will follow. Unfortunately, it’s an all-too-common vicious circle.
To avoid this, use the data you’ve been working so hard to obtain. Customer Success must spearhead and take credit for revenue expansion tactics such as upsells and cross-sells. According to our live webinar, 17% of CSMs say linking CS to revenue is the biggest challenge in demonstrating its value. We believe it’s high time more CSMs look at what numbers they can gather and use those to prove their efforts are worth it.
Common Challenges to Data-Driven Customer Success
1. Data Is Disorganized
Chances are, you already know how much data you have at your disposal, but you cannot use it effectively because it’s all spread out across different tools and platforms, such as your CRM system, your billing platform, your business intelligence tool, and so on.
This is not uncommon in organizations that don’t have a unified data strategy that is aligned with their customer success strategy.
For all that data to be useful, it should be united under the singular umbrella of customer success — both theoretically (analyzing and treating the data from the viewpoint of your customer success strategy) and practically (managing all the data with a single customer success platform).
2. Too Little Data
When the data-led setup is not complete, or you haven’t even begun it, too little data might mean you’re acting in the dark, following misleading metrics that might be tricks of the light and lead you to a wall.
The simple solution to this challenge is simply making more informed choices. Review your current data setup and determine which areas are most worth improving. That way, you can get more relevant metrics that won’t just tell you how your customers are doing or how your product is doing. You’ll also know the path forward, what needs to be set up next, and how that will help you.
3. Too Much Data
An abundance of data is not always a good thing — especially when it comes to customer success. The truth is, not all of the metrics you chose to track can be turned into actionable insights that will guide your data-driven customer success strategy.
On the contrary, a lot of the customer data that organizations possess is, essentially, just noise. And, as we saw earlier, close to 60% of your data can become obsolete within a day. So, how do you fish out the gold from all the sand?
Simple – work strategically, starting from revenue metrics. Why? 93.7%% of companies measure CS impact by looking at Gross / Net Revenue Retention, according to Daphne Costa-Lopes’ Customer Success Trends for 2024.
Then move on to your other objectives and key results, your key performance indicators for everyone on your team, and monitor those. Add metrics related to customer activity that can help you derive valuable insights about their activity. That should be more than enough to get you started on the correct path to data-driven customer success.
4. Unable to Act on Insights
Finally, to benefit from the insights that you get into your customers, you should be able to integrate these insights into your customer success strategy. Otherwise, those insights are not actionable and, therefore, not valuable, so all your hard work setting everything up has been very ineffective.
For example, consider one specific insight you get from one of your high-value customers — such as the number of meetings you had with them before you could confidently propose an upsell or an extra feature. Then, ask yourself:
Is that something you could add to your customer success playbook?
Can you apply the same logic to your other customers from the same segment?
Do you see a pattern with other accounts?
Does that pattern reliably lead to higher expansion revenue?
Do any of your OKRs or KPIs benefit from these types of efforts?
Questions like these are how you recognize an actionable insight. Scaling such a process will lead to a data-driven customer success program that prioritizes demonstrable, repeatable work that can bring you benefits for many years to come.
5. Not Enough Tools to Assist
Proper tools and software are the backbone of a good data-driven initiative. But a poorly managed tech stack can also be its downfall. How do you strike a good balance?
First and foremost, you will need:
A customer success platform, or CSP, capable of centralizing data and communicating with all the other tools in your tech stack – your email client, your CRM, your ticketing system, etc.
A customer relationship management software, or CRM, that your Sales department likes and that can send data over to your CSP and also communicate with your other tools.
Then, you could also use one or more of the following: an automated email tool, a good onboarding software, a product tour solution, and more. You can add anything else your team prefers, just make sure to back up its usefulness with numbers, lest it gets cut in the next round of budget slashes.
All that said, your data-driven CS initiatives can truly shine with the proper tools. There’s no way to do it as effectively otherwise.
6. Issues with Data Security
With all these tools and setups to be implemented, it’s frequently the case that security takes a back seat to being data-driven. The result? Every customer-facing team loves you, the lead CS manager, for all these wonderful new data points they can use. Conversely, your IT and legal departments likely hate you with a passion, as all these tools now require their oversight and a mandatory security checkup.
81% of IT professionals say they’re responsible for protecting sensitive data in SaaS apps
43% say they’ve added a new SaaS app that stores sensitive data in the last 12 months
But, more worryingly, 65% of SaaS apps added in 2022 were unsanctioned by IT, meaning people from the company adopted apps and chose not to communicate that with the IT department
There can be a whole number of reasons why that happens. Sometimes, IT can be a blocker to adding novel SaaS solutions – for example, if they say “No” for too many apps, then the next one won’t even reach their ears.
As the leader of the data-driven customer success program, you should facilitate communication between your CSMs and the IT department, and then make sure to take their security concerns seriously, particularly when it comes to sensitive customer data.
The Essential Customer Success Metrics to Monitor
We’ve covered the why and how of being data-driven in customer success. Now let’s look at some of the metrics typically monitored by customer success teams.
Generally speaking, these metrics fall into two categories:
Leading metrics. These allow CSMs to measure change and your progress in achieving your goals. Some examples include:
Moreover, these metrics are all related to different phases of your customer’s lifecycle. Here are the ones you should be monitoring during those phases:
The Elephant In The Room — Leveraging Data Effectively
The wealth of data that your organization has is only as good as the CSMs who will be leveraging that data. So, how do you make sure that your data is in good hands?
Instead of spelling out each action, your CSMs should prepare a step-by-step playbook for their efforts. They should focus on their OKRs, KPIs, while you focus on growth and finding individuals who have a high level of business awareness and know how to find, interpret, and act on data.
Your dream data-driven customer success team will take a while to put together. In the meantime, you’ll face doubts, hardships, and a lot of growth after a bit of work. However, once you build your dream team and processes, you will be able to zoom in on any customer in danger of churning and identify exactly when and why they went sideways.
An effective data strategy is the cornerstone of every good CS implementation, as it’s the only way we as an industry can move customer success from being a cost center to being a proven revenue driver and business growth engine.
As the CEO of Custify, Philipp Wolf helps SaaS businesses deliver great results for customers. After seeing companies spend big money with no systematic approach to customer success, Philipp knew something had to change. He founded Custify to provide a tool that lets agents spend time with clients—instead of organizing CRM data.
Scaling customer success is not a matter of hiring more CSMs. It’s about efficiency and doing more using the same resources. Read this article to find out how.
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