Why you need experimentation to continuously improve customer engagement
Building a culture of testing and learning for better outcomes
Key Takeaways
- Systematic experimentation reveals what actually works versus what we assume works
- Customer behavior changes over time, requiring continuous testing and adaptation
- Small improvements from multiple experiments compound into significant gains
- Experimentation reduces risk by validating approaches before full deployment
- A culture of experimentation drives innovation and competitive advantage
In customer engagement, what worked yesterday may not work tomorrow. Customer preferences evolve, economic conditions change, and new channels emerge. Organizations that rely on static strategies based on past success inevitably fall behind. The solution is systematic experimentation—a disciplined approach to testing, learning, and continuously improving engagement strategies.
The Limitations of Intuition and Experience
Many customer engagement strategies are built on intuition, industry best practices, or past experience. While these can provide useful starting points, they have significant limitations:
Assumptions Go Untested
Without experimentation, organizations never validate whether their assumptions about customer behavior are actually correct. What seems logical may not reflect how customers actually respond.
Missed Opportunities
Incremental improvements that could significantly boost performance go undiscovered because no one tests alternatives to the current approach.
Changing Contexts
Strategies that worked in the past may become less effective as customer preferences, technologies, or market conditions change—but without testing, these shifts go unnoticed until performance degrades significantly.
What Systematic Experimentation Reveals
Organizations that embrace experimentation consistently discover insights that contradict conventional wisdom and dramatically improve outcomes:
Messaging That Works
Small changes in message framing, tone, or content can produce surprising differences in response rates. Testing reveals which approaches resonate with different customer segments.
A financial services company tested two payment reminder messages. Version A emphasized avoiding late fees; Version B highlighted maintaining good credit standing. Version B improved payment rates by 18% among customers with good credit history, while Version A performed better for customers with poor credit history. Neither messaging strategy was universally superior—effectiveness depended on customer context.
Channel Preferences
Customers increasingly interact across multiple channels, but preference varies by demographic, urgency, and context. Experimentation identifies which channels drive the best outcomes for different customer segments and situations.
Timing Effects
When you reach out to customers matters as much as what you say. Testing reveals optimal timing patterns that vary by customer type, day of week, and stage of delinquency.
Process Friction Points
Each step in a customer journey represents potential friction. Experimentation identifies where customers drop off and which process changes reduce abandonment.
The Compounding Effect of Continuous Improvement
Individual experiments often produce modest improvements—a 5% increase in response rate here, a 3% reduction in call center contacts there. However, these improvements compound over time:
- Multiple small wins across different touchpoints combine into significant performance gains
- Learnings from one experiment inform hypotheses for future tests
- Organizational capability in experimentation improves with practice
- A culture of testing and learning attracts talent and drives innovation
Over months and years, organizations with strong experimentation programs pull ahead of competitors relying on static approaches.
Reducing Risk Through Testing
Experimentation also serves as a risk management tool. Rather than deploying new strategies across entire customer populations and hoping they work, testing enables validation on small samples first:
- Identify approaches that backfire before they damage relationships at scale
- Build confidence in new strategies before full implementation
- Understand which customer segments respond well to changes versus those who don't
- Refine approaches based on real results before widespread deployment
Building an Experimentation Framework
Effective experimentation requires more than occasionally trying something new. It demands systematic approach:
1. Define Clear Hypotheses
Good experiments start with specific hypotheses about what will improve and why. "We believe that [change] will improve [metric] for [segment] because [behavioral insight]."
2. Design Rigorous Tests
Proper experimental design ensures results are valid and actionable:
- Randomized control and treatment groups to isolate effects
- Sufficient sample sizes for statistical significance
- Appropriate test duration to capture full effect
- Clear success metrics defined upfront
3. Implement Reliable Measurement
Accurate measurement of outcomes is critical. This requires:
- Robust data collection and tracking systems
- Clear attribution of outcomes to specific interventions
- Monitoring of both primary metrics and potential unintended effects
- Statistical analysis to distinguish signal from noise
4. Learn and Iterate
The goal of experimentation isn't just to find winners—it's to build understanding:
- Document learnings from both successful and unsuccessful tests
- Share insights across the organization
- Use results to inform future hypotheses
- Build models of customer behavior based on accumulated evidence
5. Scale What Works
Validated improvements should be implemented broadly:
- Deploy winning strategies to broader populations
- Incorporate learnings into standard operating procedures
- Continue monitoring performance after implementation
- Be prepared to adjust as conditions change
Common Experimentation Opportunities
In customer engagement, particularly around collections and payment processes, valuable experimentation opportunities include:
- Message testing: Language, tone, framing, personalization
- Channel optimization: Email vs. SMS vs. voice vs. digital channels
- Timing: Time of day, day of week, days past due
- Frequency: Number and spacing of touchpoints
- Payment options: Default amounts, plan structures, flexibility
- Process design: Number of steps, information required, cognitive load
- Incentive structures: What motivates action for different segments
- Segmentation approaches: Which customer attributes predict response
Overcoming Barriers to Experimentation
Despite clear benefits, many organizations struggle to build experimentation capabilities. Common barriers include:
Fear of Failure
Experiments sometimes produce disappointing results. Organizations must reframe "failed" experiments as valuable learnings rather than mistakes.
Technical Limitations
Experimentation requires systems capable of creating test/control groups, delivering different treatments, and tracking outcomes. Investment in infrastructure may be necessary.
Cultural Resistance
Shifting from opinion-based to evidence-based decision-making can be uncomfortable. Leadership must champion experimentation and celebrate learning.
Resource Constraints
Designing, implementing, and analyzing experiments requires time and expertise. However, the ROI typically justifies the investment many times over.
The Competitive Advantage of Learning Faster
In customer engagement, competitive advantage increasingly comes not from having the best current strategy, but from learning and improving faster than competitors. Organizations with strong experimentation capabilities:
- Adapt quickly to changing customer preferences and market conditions
- Discover insights competitors miss
- Compound small improvements into substantial performance advantages
- Attract top talent who want to work in data-driven, learning environments
- Build organizational capabilities that are difficult for competitors to replicate
Moving Forward
Customer engagement will continue evolving. New technologies, changing demographics, shifting economic conditions, and emerging channels ensure that what works today will need refinement tomorrow. Organizations that embrace systematic experimentation position themselves to thrive in this dynamic environment.
The question isn't whether to experiment—it's whether you're experimenting systematically enough to stay ahead.
