Customer success is fundamental to the growth and sustainability of any B2B company. If you’re not putting aggressive focus on retaining the revenue you fought so hard to win, you’re running in a hamster wheel forever.
The real challenge lies in demonstrating value quickly and efficiently to each customer, tailored to their unique journey. This is where innovative techniques for experimentation in customer success come into play.
By adopting a systematic approach to experimentation, you can identify what works best for your customers, enhancing their experience and driving your company towards its revenue goals.
Experiments aren’t just for the marketing or product teams – in fact, you could argue that your customer experience IS your real product. Competitors can replicate the commodity aspects of your software like its interface and data sources, but they can’t replicate the feeling customers get when they interact with you. They can take your ideas but they can’t steal your vibe!
The Importance of Experimentation in Customer Success
Experimentation is not just about trying new things; it's about using a structured approach to test hypotheses and measure outcomes. In customer success, this means experimenting with different strategies to improve customer onboarding, engagement, satisfaction, and retention.
Why is this crucial?
- Experimentation provides data-backed insights, reducing guesswork and giving empirical basis to your decisions.
- Regular experiments identify areas for continuous improvement.
- You’ll discover new ways to tailor your services to meet the specific needs and preferences of your customers.
Basic Experimental Methods
A/B Testing
A/B testing involves comparing two versions of a process to see which performs better. For example, test two different onboarding processes to determine which one leads to higher customer satisfaction.
How to implement:
- Identify the Variable to Test:
- Choose one variable to test at a time to isolate its impact.
- Example: Onboarding email sequence.
- Formulate a Hypothesis:
- Clearly define what you expect to happen.
- Example: "Personalized onboarding emails will increase user engagement by 20%."
- Create Two Versions (A and B):
- Version A (Control): The current process.
- Version B (Variation): The new or modified process.
- Split Your Customer Base into Two Groups:
- Randomly assign customers to two groups ensuring they are comparable.
- Ensure group sizes are statistically significant using a free tool like Surveymonkey’s Sample Size Calculator.
- Implement the Test:
- Run both versions simultaneously to control external variables.
- Ensure that other variables (e.g., timing, frequency) remain constant.
- Measure the Outcomes:
- Track key metrics like activation rates, engagement levels, and feedback scores.
- Analyze the Results:
- Compare the performance of both versions.
- Determine if the variation (Version B) outperforms the control (Version A).
- Make Data-Driven Decisions:
- Decide whether to implement the new process based on the results.
- Document findings and iterate as needed.
Multivariate Testing
Multivariate experiments test multiple variables simultaneously to see the combined effect. For example, you can test different combinations of onboarding messages, tutorial formats, and follow-up strategies.
The implementation is very similar to an A/B test:
- Identify Multiple Variables:
- Choose several elements to test together.
- Example: Email content, tutorial format, follow-up cadence.
- Formulate Hypotheses:
- Define expected outcomes for each combination.
- Example: "A combination of video tutorials and frequent follow-ups will increase activation rates."
- Create Multiple Variations:
- Develop different combinations of the variables.
- Example:some text
- Variation 1: Text email + PDF tutorial + Weekly follow-up.
- Variation 2: Video email + Interactive tutorial + Bi-weekly follow-up.
- Variation 3: Personalized email + Webinar tutorial + Monthly follow-up.
- Segment Your Customer Base:
- Randomly assign customers to different groups, ensuring comparability.
- Ensure group sizes are statistically significant using a free tool like Surveymonkey’s Sample Size Calculator.
- Implement the Test:
- Run all variations simultaneously to control external factors.
- Keep other variables constant.
- Measure the Outcomes:
- Track key metrics like user adoption, satisfaction, and retention.
- Analyze the Results:
- Identify the best-performing combination.
- Use statistical methods to determine the significance of results.
- Make Data-Driven Decisions:
- Implement the best combination based on the analysis.
- Document findings and iterate as needed.
Statistical Analysis Tools for Non-Data Scientists
Once you’ve got your data, you’ll need to conduct some basic statistical analysis to find out which test variation was most successful. Don’t worry, if you’re rusty on stat 101 then you’re living in the best possible time to have data imposter syndrome: there are tons of tools that make it super simple for non-specialists to analyze data.
You can use these tools to analyze both A/Bs and complex multivariate setups. Here are a few we like:
- Dynamic Yield Bayesian Calculator
- A free tool that helps you assess which variation in an experiment is the most successful one, and the loss or penalty you incur if you place the wrong bet on a particular variation.
- Julius.ai
- Chat with your CSV and Excel files and ask questions in natural language to get insights instantly.
- Create visuals, animations, and reports from the most relevant data points.
- As Julius puts it, “get expert-level insights without the complexity.”
- Formula Bot
- Similar to Julius, Formula Bot lets you converse with your spreadsheets to ask complex questions and get simple insights back
- They also offer a plug-in to add their analysis capabilities directly into Google Sheets or Excel where you’re used to working
Best Practices for Successful Experimentation
- Document Everything
- Keep detailed records of all experiments, including hypotheses, methodologies, and outcomes. This helps in understanding what works and what doesn't over time.
- Start Small
- Begin with small-scale experiments to validate your hypotheses before rolling out changes on a larger scale.
- Focus on Key Metrics
- Identify the metrics that matter most to your business, such as revenue retention or churn rate. Ensure your experiments are designed to impact these metrics. If you’re using Praction, make sure to pay special attention to these risk factor metrics in the Revenue Guide and use the relevant playbooks to make sure you have full ops coverage.
- Iterate Quickly
- Don’t wait for perfect results. Use the data from initial experiments to make quick adjustments and continuously improve your strategies.
- Involve Your Team
- Collaborate with your sales, marketing, and product teams to ensure alignment and gather diverse perspectives on customer success initiatives.
TL;DR
Experimentation in customer success is a continuous journey and a specific mindset. Why let product and marketing have all the fun? This approach enhances customer satisfaction and retention and drives sustainable revenue growth, positioning your company for the next stage in your journey.
At Praction, we specialize in helping B2B businesses implement these strategies through our automated Chief Revenue Officer platform and consulting services. Our tailored solutions are designed to increase revenue, refine GTM operations, and enhance customer value, accelerating your path to an exit, acquisition, or IPO.
Ready to scale? We’d love to meet you.