Customer satisfaction and loyalty are dependent on your ability to continuously improve the way you interact with your customers. Running experiments can help you improve customer interactions while also achieving better results, such as improved click-through and conversion rates. Failing to conduct comprehensive experiments may lead to inefficient and ineffective communication, which can be costly in time and resources, and result in lost opportunities. As the presence of digital fatigue and decision fatigue continues to increase, experimentation becomes essential for keeping up with changing behaviors.
What do we mean by experimentation?
Experimentation involves testing multiple variables, such as different behavioral concepts, messaging and cadence with randomized customer groups. Many enterprises use A/B testing to identify variables, such as different subject lines, that work better than others. A/B testing is a great place to start basic experimentation but, if done incorrectly, you may only gain insight into what is working rather than why it’s working. “Without understanding the ‘why’ behind what is working, you’re restricted in your ability to apply findings to additional contexts and scenarios,” explains Andrea Ranieri, PhD, Behavioral Scientist at Symend.
Once you’ve mastered basic experimentation that captures the why behind your customers’ behavior, you can begin to experiment with more variables to fine tune every interaction.
Understanding the “why”
To begin to understand the “why”, every experiment should be built based on a hypothesis, which is a statement that can be tested by scientific research. When creating a hypothesis, Symend’s behavioral scientists consider many factors:
- The hypothesis should be based on what you already know about your customers – use this as a starting point to test a different approach. You can also base your hypothesis on validating how you expect your customers to interact.
- Look at your current customer interactions and ask what can or should be improved.
- Brainstorm reasons why you’re seeing certain results and then come up with ways to test new ideas.
- If you are doing split testing, ensure you’re randomly dividing your customers and that the groups are big enough to achieve statistical significance.
As an example, say you’re finding that click-through rates on your communications decline sharply past 3:00 pm. You may suspect that the time of the day is leading to poor performance and shift all your communications to be sent before 3:00 pm. However, for messages that require significant evaluation, content may be the main factor that is impacting performance. Communications that don’t require much consideration may perform very well after 3:00 pm. Understanding the why may make a difference in your strategy moving forward.
When developing your experiments, it’s important to identify the action that you want your customer to take with each communication that goes out. Having a clear understanding of your ideal customer journey creates a guide to follow during the experimentation process.
Keep in mind that no two customers are alike. The more personalized you can be in your communications, the better your chance of interacting with your customers. When you’re putting together your hypotheses, consider context and ensure you’re tailoring the experiment as much as possible.
How experimentation contributes to long-term customer satisfaction and loyalty
The benefit of experimentation is that, over time, you’ll be able to anticipate and adapt to changing customer behavior faster. As you continue to test different hypotheses and analyze the results, you will uncover findings that you never expected or may have previously missed, setting you and your customers up for success now and in the future.
For example, your call volumes may skyrocket during a local crisis. You learn how to best interact with those customers during this time by testing different outreaches to confirm what is most effective to calm customer anxiety. These learnings will be helpful if a similar situation occurs again, either in the same area or perhaps elsewhere with similar circumstances.
“In this example, anxiety is the psychological experience that is affecting customer satisfaction for a specific subset of your customers. This means that if you reduce anxiety for these customers, you can simultaneously increase satisfaction. This is the type of deep insight you can apply to new, similar situations,” says Tara Giller, PhD, Senior Manager of Behavioral Science at Symend.
Learn at the pace your customers change
Experimentation is often reactive to a negative outcome, such as declining open rates. However, the insights you receive from continuous experimentation are essential to maintaining and improving customer engagement. No matter how much data you’ve gathered on your customers, experimentation is essential to matching the pace of changing behavior.
Find out why decision fatigue is making experimentation a more essential practice.
