At early startup stages, customer success teams and other customer-facing teams are usually familiar with their customer base. A handful number of customers at this level means that customer success teams can identify most customers by name, describe their expected business outcomes, and predict at-risk and successful customers.
This relationship-driven engagement model becomes difficult as the customer base becomes larger and more diversified. While customer success teams have a positive impact, many companies struggle to scale them alongside a growing customer base.
The solution to this challenge is to switch to a proactive data-driven customer success engagement model that allows for better optimization of customer success efforts.
Why Should Customer Success Be Obsessed with Data?
Gainsight has identified four stages of the maturity curve for customer success teams. Each stage represents different levels of data usage that determine how scalable and effective a customer success team can be.
At this stage, the customer success team has data but does not put it to good use. CSMs usually call agitated customers directly to resolve issues on a case-by-case basis. This reactive model is driven by escalations, customer needs, and the importance of a logo.
2. Insights and Actions
The customer success team has invested in mapping out available data to discover actionable insights into the customer base. Prescriptive playbooks and CTAs are generated in line with the insights gained from the data. At this stage, data is translated into meaningful proactive action across the customer success team.
As a result of the insights and resultant playbooks, the customer success team continually drives business outcomes for their clients at scale and in a proactive manner.
This stage is the height of customer success maturity where the entire organization becomes customer-centric. All teams and departments are oriented about customer health and customer success.
So why obsess over data and strive for maturity? A survey of 100 customer success leaders, conducted by Gainsight in 2018, revealed that the maturity level correlated with the financial metrics of the company:
- Reactive; GRR is 80%, and NRR is 92%
- Insights and Action; GRR is 87%, and NRR is 106%
- Outcomes; GRR is 89%, and NRR is 113%
- Transform; GRR is 93%, and NRR is 125%
Source: Gainsight | The Science of Customer Success: https://www.gainsight.com/blog/gainsight-elements-science-customer-success/
Shifting Your Customer Success Team from Reactive to Proactive
Moving from reactive to proactive means your customer success team is not merely defending churn, but also growing expansions while driving business outcomes in a scalable way. Many organizations that I worked with make this shift by starting with the health score, graduating into actionable insights, analyzing customer cohorts, before developing ongoing advanced analytics capabilities using machine learning and artificial intelligence.
1. Health Score
Customer health score is more common by ten percentage points in companies that have a large long tail of customers. At the most basic level, health scores are designed to forecast renewals and indicate the risk of churn. They are manually updated based on human judgment and often result in code red escalations.
Mature companies used advanced health score calculations that are designed to provide strategic guidance to the CSM on how to turn at-risk customers to a best of breed customer that is likely to renew. Advanced health scores are always true despite the level of automation. It always includes well-defined and prescriptive playbooks for each customer’s health status and a success plan based on each category that feeds into the overall score.
Advanced health score includes a dashboard to track the CSM in charge of making the customer healthy, their progress, and the health trends of the account. It applies predictive analysis around the score for different customer attributes. It also includes risk-reason tracking, renewal forecasts, survey analysis, and many automation built around the scorecard.
2. Actionable Insights
Many of the organizations I’ve worked with already had access to data and lifecycle playbooks. Obtaining actionable insights entail harnessing the data to identify adoption trends and modeling playbooks to maximize product usage. Playbooks can be developed for:
- Adoption; Increase Usage (Depth and Breadth).
- Risk; Identify risk early for churn, down-sell, and onboarding.
- Expansion; Identify opportunities for up-sells, cross-sells, and add-ons.
- Value; Prove quantitative and strategic value delivered.
- Advocacy; Gain referrals, references, advisory, and online reviews.
All five customer success playbooks are important for data-driven methodology because they are triggered based on different trends in the data. At CSM practice, we help customers to harness data, extract actionable insights, and develop playbooks. A typical thought process and action plan for developing playbooks for risk are to:
- Plot the customer journey.
- Determine the moments of high risk that are crucial for clients.
- Identify triggers for those moments.
- Develop a strategy for identifying unhealthy clients during onboarding in the absence of data.
- Develop an onboarding score that can be updated for each client.
- Identify the appropriate time and channels to alert the CSM.
- Develop the right playbooks to manage the risk.
- Share risk playbooks with the team
3. Cohort Analysis
A cohort analysis is another powerful data-driven approach to gaining deeper insights into customer behavior by placing them in groups with distinguishable traits or actions. Specific metrics can be analyzed and compared between different cohorts, and the results can be leveraged to build a successful churn mitigation strategy using better playbooks. These attributes may be:
Renewal for the last quarter
- CSM in charge
- Number of years
- No Usage
- Low Usage
- High Usage
- Number of seats
- Disabled seats
- Untrained customers
- Adoption level
- Business sponsor
For our customers at CSM practice, we examine these attributes and others, such as login trends to determine the level of risk. We then develop better playbooks and prioritize action based on the cohort analysis.
4. Predictive Analytics and Artificial Intelligence
Predictive analysis uses historical data to predict outcomes based on patterns and trends. The key differentiator for customer success teams is whether they leverage data on a sporadic and ad hoc basis or an ongoing basis using solutions like Natero.
Advanced Analytics capabilities such as predictive analysis and AI are more common by 20 percentage points in companies that have low touch capabilities. This is due to the difficulty of manually analyzing a large long tail of customers on an ongoing basis and the availability of large data sets to feed into AI.
An Advanced Analytics Case Study
A case study involving one of our clients at CSM Practice revealed the true determinants of customer account expansion:
Using advanced analytics, the client wanted to discover the factors that impacted their ARR and their ability to grow revenue with their existing customer base.
The client had some data related to product capabilities, features in use, feature requests, onboarding process, client sentiment about onboarding, and the level of investment customers made. The assumption was that the level of investment would be the most important factor for customer expansion and renewal.
We examined all the data sources provided, including support, feature usage, and level of adoption. We conducted advanced analytics on these data sources to determine the true determinants of customer account expansion. We made the following findings:
- Support (by 22%), Features Used (by 20%), Adoption (by 43%), and Product Capabilities (by 17%) directly impact ARR.
- Level of Investment (by 50%) and Onboarding (by 53%) impacted the amount of support requested by their customers.
- Level of Investment (by 41%) and Onboarding (by 59%) impacted the number of products used by their customers.
- Level of Investment (by 31%) and Onboarding (by 59%) impacted adoption; the depth and breadth of product usage by their customers.
- Level of Investment (by 26%) and Onboarding (by 27%) impacted the demand on product capabilities by their customers.
- The Level of Investment (by 86%) directly correlates to the success of the Onboarding process.
4. Proposed Solutions
Adoption had the most substantial impact on ARR (by 43%) relative to other key drivers and Onboarding (by 60%) had a larger impact on Adoption. As a result, we realigned their resources towards better functionality expectation management and training during the onboarding process to increase the ARR.
Our advanced analysis nullified the assumption that the level of investment was the most important factor for growing their ARR. This case study goes to show the power of data-based decisions making over mere guesswork based on sentiment. Leading Customer Success teams leverage their data in this way to predict customer behavior and manage more customers with less effort and better results.
Scaling Customer Success Beyond Customer Success
The impact of customer success teams is not always increased by merely hiring more CSMs. After scaling customer success teams with data, the next step is to introduce the culture of success across the entire organization. You can scale the entire organization in the following ways:
1. Leverage Existing Teams
You can leverage existing teams to multiply your customer success workforce and create healthy internal competition among other departments around customer success metrics such as health score, product adoption, and the number of escalations. This can start with education about the value of customer success and its data.
2. Make Customer Success Data Accessible to All Teams
Put the data at the fingertips of existing teams and present clear visualizations of the data for cohorts, individual clients, and metrics for easier comprehension. Integrating data sources can make for faster, well-informed, and profitable customer-centric decisions.
3. Improve Communication
In most cases, other teams are unfamiliar with customer success data. The solutions include attaching a description for the data being presented, highlighting appropriate use guidelines, and establishing channels for further inquiries.
Providing context for the data enables other teams to grasp the importance of the data. As a result, they will have more informed conversations with the customers and in meetings around adoption, health, and other customer success topics.
4. Customer Intimacy Dashboard
A customer intimacy dashboard is another way to answer questions frequently raised by account executives proactively. It includes two views of the data. The first view presents the data as is and the second view answers executive-level questions about who is at risk and where more resources should be invested.
The dashboard categorizes customer accounts into quadrants based on a composite of factors so executives can differentiate between a best in class customer and an at-risk customer.
5. Data-driven Playbooks
Playbooks are updated for different teams based on customer status. Playbooks are designed to deliver clear action plans for different metrics.
The benefits of a proactive, data-driven customer success model are clear. To make the shift from reactive to proactive, SaaS companies need to evaluate their data to discover actionable insights into the customer base. Playbooks should be developed in tandem with those insights to create meaningful outcomes for the customer and the company. Scale customer success across the entire organization to improve customer success at all customer touchpoints.
Contact us at CSM Practice if you have any questions about scaling your customer success team with data, or if you have no data yet.