Businesses today understand the potential of data and analytics and how they can help generate valuable insights that shape more strategic and valuable decisions for better conversions.

However, most businesses still struggle to develop and cultivate a culture of data-driven experimentation in their organizations that fully recognizes and appreciates the analytical capabilities of data and evidence.

In a powerful culture of experimentation, data-driven decision-making is leveraged to gain a competitive advantage rather than simply relying on leadership’s opinion, intuition, and experience.

This blog aims to dive deeper into the concept of data-driven experimentation for conversion optimization and outline the steps businesses can follow in developing such a culture of data-driven experimentation.

How Businesses Can Develop a Culture of Data-driven Experimentation

a. Involve All Stakeholders

For a culture of data-driven experimentation to truly take shape, the first thing organizations must do is ensure it is accepted and fostered by the entire organization. All stakeholders from various teams, including sales, marketing, and others, must see the value in data and leverage it to improve on an iterative basis

Things businesses can do here include:

• Design a high-level strategy in line with the company’s goals and KRAs
• Prioritize data strategy in various company meetings
• Hire a dedicated head of data who can work towards influencing the culture

Following these actionable steps encourages varied teams to consistently test and experiment with new ideas, processes, and products to ensure they are making the best decisions based on real-time data.

Nevertheless, each stakeholder will contribute in their own unique way, so it is important to identify their roles.

For instance, a CRO specialist focuses on increasing conversion, while UX prioritizes usability. Both play critical roles in establishing an experimentation-oriented culture. CRO identifies immediate changes needed, while UX provides a starting point for improvement

b. Pick the Right Data

At this stage, organizations need to closely look at what data they currently have and assess what kind of evidence-based decision-making is possible. This should be followed by creating a long-term state analysis and identifying the existing gaps.

Organizations also need to consider the complete customer journey, and which aspects motivate a customer. Things businesses can do here include:

• Hold meetings with various key partners across the company, such as data analysts and analytics leads
• Identify the various data points and metrics that are most important
• Leverage different processes and customer journey maps
• Identify the ideal conversion path 
• Identify drop-offs in the conversion path

These steps encourage teams to gather valuable feedback from customers and stakeholders to ensure they are making the right decisions that will result in the best business outcomes.

c. Exceed the Conventional A/B Testing

The best data-driven companies today don’t just store and analyze data passively. Instead, they actively work towards generating actionable data by running various data-driven experiments.

“The limitation of traditional A/B testing is that it only provides temporary solutions. To fully optimize the customer experience, it’s necessary to go beyond traditional A/B testing and consider the overall customer journey. An experienced conversion rate optimization specialist can help you not only improve the CTA button but streamline the entire customer experience. A CRO specialist regularly revisits and updates previous tests as user behavior evolves.”

This kind of testing involves showing varied user experiences to different users, followed by measuring the impact of those differences. 

The obvious benefit of an A/B test like this is its ability to improve the product and enhance revenues. Further, A/B testing is also a sure way to get to the bottom of a business decision logically, without having to rely on anyone’s instinct or opinion.

d. Keep the Experimentation Culture in Sync With Business Goals and KPIs

While using experimentation for boosting conversion optimization rates and decision-making is a common practice these days, it does not necessarily guarantee that good business decisions are made.

Executing experiments accurately can be a very challenging task, and the data gathered from such an experiment is only as reliable as the execution of the experiment itself and can lead to unreliable decisions.

So how can companies ensure that their experimentation initiatives lead to good business decisions? 

The best way to navigate this is by ensuring that the experimentation culture is in line with the company’s values, goals, and respective KPIs. 

To achieve these goals and KPIs, businesses must make sure that the data-based experiment decisions are made through a standardized process that reflects the business’ values and helps

them eliminate subjectivity and biases from the process so that the decisions are both repeatable and trustworthy.

e. Propose a Solid Framework for the Hypothesis Ideation

Hypothesis-driven ideation or development in data-driven experimentation is largely about solving the exact problem at the right time.

Also known as data-driven hypothesis development (DDHD), it is a very effective approach when facing complex problems. It helps ensure that one’s understanding of the issue is verified before one starts working on the solution.  

Here’s how businesses can go about this:

• Identify the value proposition that the business is solving for

The key here is to identify and define the problem using data, thus bringing clarity to what the business is solving for. Companies can define the issue and metrics upfront and align them with their goals.

• Check if there is enough clarity for the user to achieve their macro and micro goals

When it comes to dealing with which problems to tackle first, businesses must check if there is enough clarity for the user to achieve their macro and micro goals and if the above values bring projected results optimally.

Macro and Micro Level Goals

The Website level goals

• To browse product specifications 
• To browse the product images
• To check other related products
• To purchase products

The goal for the Product detail page 

• Add product to cart
• Add product to wishlist
• To browse additional products
• To browse product specifications efficiently
• To browse the product images
  

• Check how the digital platform is persuading the user to reach the end objective.

Digital transformation here refers to upgrading traditional processes with advanced digital technologies, thus altering how a business operates, how value is delivered, and how customer needs are met in the end.

• Identify the challenges or roadblocks faced by the user in the path of conversion.

Among these include cultural issues stemming from professional relationships, lack of real-time support for on-the-go interaction, incompetency in A/B testing, incorrect budget, lack of CRO knowledge, and need for better processes for CRO.

f. Follow a Strategic Structured CRO Approach

Optimizing the business website to ensure high conversion rates is one of the fundamental aspects of any marketing strategy. This is where a powerful conversion rate optimization (CRO) strategy comes in.

The underlying idea of any CRO process is to optimize the conversion rate, leading to improved conversions and high retention rates. A strategic and structured CRO approach aims to make the business sales process much more efficient, thus generating better revenues for the business.

Elements that are essential in forming a data-driven experimentation culture ( Infographic )

To Conclude

If performed well, establishing a culture of data-driven experimentation allows businesses to assess and figure out what matters to most customers, within a defined timeline, for a limited cost, and with a risk-reward tradeoff that will work to their advantage in the long run.