Deliver Better CX and Build a Data-Driven Culture With Experimentation

Experimentation is a powerful but underutilized tool for improving customer experience. That’s because even the strongest initial digital marketing ideas usually need some refinement for peak performance. Experiments give you a way to test those refinements and choose the ones that deliver the best results. And the most effective experiments go beyond the basic A/B test format. Optimizely found that experiments with multiple elements are at least 1.5 times as successful as an A/B test, and experiments that make “significant” customer experience changes outperform simpler tests by 25%.

How can brands expand their experimentation capabilities to better meet customer expectations? Well-designed experiments seeking the answers to clearly defined CX goals can help marketers identify friction points and test new engagement and conversion strategies. Besides these short-term gains in CX, experiments can also encourage teams and organizations to become more data-driven by delivering insights that lead to measurable results.

Key Takeaways

• Experimentation at different points across the customer journey can inform improvements to business metrics including conversions, customer satisfaction scores, customer engagement, and retention.

• Designing properly focused experiments that improve CX and support a data-driven culture starts with defining why you’re experimenting. Rightpoint has the experience and capabilities to help guide your experimentation strategy, get the most value from your digital experience testing tools and leverage data to inform decisions.

• Positive CX outcomes built on experiment results can foster a more data-driven culture within the organization, by illuminating the relationship between testing and actionable insights.

Ask “Why?” to Define Experiment Goals and KPIs

Successful experiments start with a clear understanding of why the experiment is taking place. For example, are you trying to fix low customer engagement, increase conversion rates, or something else?

Asking “why” can shape your goals for the experiment. It can also push you to consider other parts of the customer journey that come before the stage you want to experiment with, to think about how a prospective customer’s experience early on may be affecting what you see at the touchpoint in question. For example, if you’re asking why visitors to your Hawaii travel site are viewing hotels across multiple islands during a session, could it be that they need information about the differences between the islands before they reach that stage? And if so, what can we introduce to nudge them in that direction?

In the goal-setting step, you can also use analytics to see where visitors spend time on key pages, and to get a clearer view of the overall customer journey. Heat maps and other visual data tools can give you a page-level picture of how people interact with different elements on the site, where they spend their time and what visual ques inform how they navigate to different resources.

For example, if the most important content on a page is 3/4 of the way down, and heat mapping shows that most users only view the top 1/4, your goal might be to test ways to keep visitors moving down the page. This goal-setting approach also makes it easier to set the appropriate metrics for your experiments, to bridge the gap between high-level KPIs and actionable insights.

Design Your Hypothesis and Experiment

With your data-informed goal and metrics selected, it’s time to draft your hypothesis, a proposed explanation based on your data. You’ll get the most value from your experiments when you:

• Base hypotheses on data and research from your organization and third-party sources.

• Structure hypotheses for different phases of the customer journey.

• Challenge your team to think of several hypotheses, and ways to solve for each hypothesis.

Once you have a hypothesis, you can decide which experimentation techniques to use to test it.

A/B testing is a simple method for testing hypotheses with a single variable, and it can yield powerful results. For example, a vacation rental brand wanted to generate more engagement with their property page call to action. Rightpoint hypothesized that because the “book now” call to action came several steps before checkout, a more open-ended call to action might get better results at that point in the user journey. An A/B test found that “check availability” resulted in 35% more engagement than the original call to action message.

Multivariate testing evaluates several elements on a page in one experiment, so it requires more traffic and larger sample sizes than A/B testing. This approach can show which combinations of elements perform best, giving a clearer picture of customers’ behaviors across a page. Multivariate testing is useful for optimizing pages quickly, for example, if you need to make updates fast to support a short campaign.

Multi-armed bandit testing combines testing with machine learning (ML) to improve users’ experience on page during the test. As soon as the ML model detects statistically significant results, it can send more traffic to the winning path so fewer users see the poorer-performing options.

Identify the Right Tools and Resources

Designing and running experiments is easier with a digital experience platform that includes powerful experimentation tools. Optimizely’s web experimentation and feature experimentation models make it an ideal DEX platform for the kind of testing we’ve covered here.

Especially for companies that are just starting experimentation programs, we recommend working with a partner experienced in Optimizely’s capabilities as well as in experiment design and personalization strategies.

Work with Rightpoint to structure your experimentation program

Rightpoint’s experts can help you better define your experimentation and personalization program goals, identify areas where experiments may deliver quick CX wins, define your experiments, and engage stakeholders in the experimentation process. Our technical, strategic, and culture experience can help your organization build fluency in experiment design and execution while improving personalization for your customers.

If you’re ready to start experimenting or scale up your experimentation program, reach out to schedule a meeting with our team to explore how experimentation can drive smarter, data-driven decisions for your business.