Optimize your customer experience strategy to drive more profit

Customer experience (CX) optimization is the process of analyzing and improving the journey your customers take when engaging with your business. The process involves:

Step 1 - Gathering data and conducting CX research to identify opportunities and problems.

Step 2 - Creating hypotheses and prioritizing them with the customer and business impact in mind.

Step 3 - Running experiments to scientifically validate customer experience strategy. Solutions could include changing business processes, designs, copy, UX, propositions, pricing, and product features.

Conversion rate optimization (CRO) tends to focus on website metrics such as conversion rates, click-through rates, return rates, average order values, etc. but it lacks wider context from the rest of the customer journey, before and after the website.

A CX optimization program measures user, visitor, and customer satisfaction as well as lifetime value, NPS, and metrics specific to other touchpoints in the journey.

Both practices are concerned with driving more revenue.

While they sound similar, there are key differences between CX and UX practices.

Customer experience is concerned with the overall satisfaction or impression people form based on a whole range of experiences and interactions with your business. 

User experience is a smaller part of the overall customer experience, referring specifically to the level of ease and clarity a customer has when they use your product, website, or app.

Almost half of companies who invest in experimentation see at least a 10% increase in revenue according to Optimizely. But without data, research, and experimentation, knowing which CX changes will be successful is anyone’s guess. Therefore a CX strategy is needed to systematically drive better results. We work with clients across a range of industries, from tech & SAAS to eCommerce. Check out our case studies to find out more.

There are many ways to improve your customer experience strategy, which include:

● Board-level buy-in and representation for customer experience. 
● A business culture that supports customer centricity, experimentation, and a test and learn approach. 
● A dedicated team of people with skills in data analysis, research, consumer psychology, design, UX, statistics, front end development, and quality assurance.
● Set processes for gathering and interpreting user data, prioritizing ideas, running experimentation, and sharing the learnings.

To be successful you need to align your objectives with the overall goals of the business–sometimes known as a ‘north star metric’.

North star metrics for companies are usually in three buckets:
1. Revenue
2. Customer experience
3. Operations and process

CX Optimization can focus on all three, but places special emphasis on customer experience and tying these objectives to measurable revenue uplifts. For example, your CX strategy objective might be to improve the satisfaction score of customers who purchase from you by 10%. If you meet this objective it will drive $1M in revenue.

A CX roadmap is a detailed plan of activities that are intended to improve customer experience. This should be a live document that includes all of the planned experiments (highest priority first) as well as the implementation work for winning tests or initiatives.

Once research has been conducted, you will have a prioritized list of test hypotheses, along with test plans. These will give you the predefined time and figures that your CX tests will need to hit to be concluded. You can then start to plan out the experiments into a CX roadmap taking into account other activities happening in the business which may impact your tests (e.g internal resources or external marketing campaigns).

Customer experience best practices are:

● Implementing the right methods to systematically validate ideas based on customer research and data. 
● Creating a strategy that doesn’t just focus on what and how you make improvements to the experience but also the business culture, internal processes, and tool stack. E.g. your strategy should outline change management initiatives to create a culture where business decisions are based on data not the highest-paid person’s opinion (HiPPO) or gut instinct. 
● Creating measurement mechanisms and scoreboards for measuring the impact of changes.

Poor customer experience affects your company’s revenue. Equally, a great customer experience can drive revenue. The Temkin Group found that companies that earn $1 billion annually can expect an additional $700 million in revenue within 3 years of investing in customer experience. Aside from revenue, your CX can impact a range of metrics such as brand perception, loyalty, returns rates, net promoter score (NPS), paid media performance, and average order value (AOV).

The better the research insights and the testing process employed, the faster you will see results (e.g quick turnaround of the full CRO process from hypothesis to live, less time stopping tests that haven’t been QA-ed, etc.) When it comes to ROI from your program we typically say within 12 months you’ll have 3,4,5x ROI.  

But it’s important to understand that not every experiment will win. But by running the experiment you have saved time and money you would have otherwise wasted implementing something that didn’t work. Or worse, harmed your business. And because Speero starts experimenting in the first month of working together you will see learnings straight away.

They fail in execution. To execute a successful CX optimization program it’s important to create a strategy that’s based on data and research, create the roadmap and tactical connection between activities, and measure key metrics. Some of the most common reasons why CX programs fail are because:

● Work isn’t aligned and connected with overall business goals. 
● People make changes without running a test to validate the idea first.
● Test hypothesizes aren’t based on real insights from data or research.
● No systematic process in place to maintain testing velocity to drive consistent ROI.
● Lack of senior buy-in/not a culture that supports failure or experimentation.
● Unreliable data or a lack of understanding of experiment statistics meaning tests are called or implemented when they shouldn’t be.
● Experiments aren’t bold enough or aren’t testing the main growth levers of the business.