10min read

The Ideal CRO Structure for Sustainable Growth

With experimentation, the goal is simple: find out what resonates best with your digital audience to create a relationship and drive business growth. But, how do you reach the point of success?

Experimentation opens the door to fresh insights that are only found through testing, compelling you to continuously refine different facets of your website for an improved digital experience across the board. Once you take your first steps down your experimentation roadmap, your path toward optimization evolves to the point where you can become a more prominent digital player.

However, experimentation success will introduce growing pains – especially if you’re a company starting its CRO journey. Allocating your CRO resources early and efficiently is important to set your business up for continued success, prosperity, and evolution.

A firm foundation and building good habits from the start is the best way to ensure that your growth won’t stop.

How to build out your CRO team following the centralized model

A successful CRO team needs to be well-equipped with the necessary resources to carry out their missions which include time, tools, people and technology.

The first step in creating your team is to focus on leadership. The leader of your team needs to set an example by prioritizing experimentation and making it a part of your organization’s values. Your leader needs to value and encourage experimentation by creating a safe environment for testing where failures are seen as learning opportunities. CRO organizations need to create a culture of collaboration and communication where everyone works together to achieve a common goal.

It’s important to keep in mind that experimentation requires a lot of collaboration. By having a vast team equipped with different skills, you’ll need to facilitate communication between different teams, such as designers, developers, marketers, and data analysts.

This means that everyone needs to be aware of the goals and deliverables of each experiment, the roles of each stakeholder, the project timeline, and certainly if there are changes to the roadmap. This requires constant and open communication to keep everyone prepared. Each team member needs to be able to trust their teammates to perform certain tasks and have confidence in their own individual role.

With open communication and frequent regroups to check progress and share ideas, you can ensure that everyone is aligned and working towards the same objectives. Sharing results builds trust between team members and gives everyone an opportunity to celebrate wins, support each other through the learning opportunities and create a positive environment where feedback is welcome.

What is the ideal CRO team structure?

When picking the ideal structure for your CRO team, you have to keep in mind that this will vary depending on your organization’s size, goals, and resources at hand.

A small CRO team following the centralized model will need to have individuals responsible for covering all core responsibilities – from ideation to implementation to examination. Ideally, this would include:

  • CRO Manager
  • UX/UI Designer
  • Data Analyst
  • Web Developer
  • Content Specialist

To continue CRO team expansion, a medium-sized or large team should adopt the positions above and some or all positions listed below:

  • Product Manager
  • Product Designer
  • Data Scientist
  • Content Designer
  • Content Writer
  • Conversion Rate Optimizer/Tester
  • Technical Web Analyst
  • Website Animation Specialist

The skills needed to perform CRO are vast. A person equipped to be a great addition to your CRO team will most likely have a background in one of the following areas:

  • Chief Data Officer
  • Full Stack Developer
  • Functional Designer
  • Digital Marketing Specialists
  • Data Scientist (Specializing in CRO)
  • Web Analyst

Keep in mind that a CRO team is typically a cross-functional team and team members may be involved in other projects simultaneously. As each organization is completely unique, there are no hard and fast rules for the “perfect” team. Your ideal structure may shift as you go, reminding you of the importance of flexibility.

Rapid CRO growth

To put the rapid growth of CRO teams into context, let’s take a quick look at one global leader in the premium cosmetics industry: Shiseido.

Even though Shiseido already had a CRO team in place, they wanted to grow and turn their constricted experimentation strategy into an intuitive and scalable optimization program. They went from running four tests per year to over 10 tests per month using AB Tasty and expanded their team accordingly to cover more ground and expand their experimentation goals. Growth can happen quickly when setting new priorities and adopting a new mindset. See how Shiseido revitalized its experience optimization strategy with AB Tasty.

Steps for successful CRO implementation

Mindset shift

Building a culture of experimentation is crucial for a successful CRO organization. There needs to be a mindset shift towards data-driven decision-making, embracing bold decisions and viewing failure as an opportunity to learn and improve.

One of the most significant obstacles in establishing this culture is the fear and apprehension linked to failure. CRO teams need to recognize that failure is a natural part of the experimentation process and that every failed experiment provides valuable insights and learnings. By embracing what doesn’t work, CRO teams can create a culture that encourages experimentation and embraces risks.

All data derived from tests is valuable for building out future steps. The sooner an organization can adapt to this mentality, the more stable its CRO foundation will be.

LOOKING FOR MORE about the culture of experimentation? 

Listen to the 1000 Experiments Club PodcastThe only podcast that interviews industry experts who have run over 1,000 experiments.

Set goals for your CRO team

CRO teams need to define exactly what they want to achieve through experimentation and how they will measure success. With this being said, data should be at the heart of all experimentation. Decisions should be made based on data collected and not only a gut feeling. By setting goals and assigning metrics to track progress, CRO teams can stay focused on their vision to achieve their objectives and track their progress.

Define the challenges of CRO implementation

There will be challenges to any success story. It’s important to address the potential challenges that may arise early on to keep your team prepared for any tough moments.

Barriers to continual success could include time restrictions, lack of adequate resources, employees with sub-par attitudes, pressure from HIPPOS, technology or anything that could potentially interfere with your roadmap.

After setting your goals and defining the next steps on your roadmap, it’s easier to outline the barriers that may prevent you from achieving those objectives, such as technical limitations or budget constraints.

Outline the team’s roles and responsibilities

Next, define the team’s roles and responsibilities. All team members should be aware of their personal objectives and how their work contributes to the overall success of the project (and their impact on the organization).

This includes identifying who will be responsible for testing, analyzing data, creating content, and making technical improvements to the website or app. Especially if team members have cross-functional roles where their time is divided, their responsibilities during each project should be clearly defined.

Standardize the A/B test process with your CRO Team

To standardize the A/B test process in your organization, there needs to be coordination of all digital teams around A/B tests and your overall CRO strategy. Your testing roadmap should outline the experiments your team will conduct, the hypotheses they will test, and the metrics they will use to measure success. By developing a testing plan, CRO teams can ensure that their experiments are aligned with their goals and that they are testing the right elements of the website or landing page.

With your new CRO team, it’s important to always start with identifying the most valuable tests at the right time. By brainstorming with your team to identify multiple elements, you will have various high-value optimization paths available to you when your team has the bandwidth.

When implementing a test, you must have a team ready to create the design and content for the test and another team available to put it all into production.

As a post-launch follow-up plan, you will need to develop an optimization plan to cater to the results.

  • Implement the winning variation – If your variation shows better results when compared to the original, plan for adequate time in your roadmap to incorporate any permanent changes.
  • Develop a new variation – Let’s say your variation wasn’t more influential than the original version. You’ve learned more about your audience that you can use in the future. If you’ve found what doesn’t work, leave room in your plans to go back to the drawing board to find a variation that resonates better with your audience.
  • Accept the original version – If you and your team are happy with the performance of the original version of your webpage, it’s time to move on to the next priority on your optimization list.
  • Re-challenge the winning variation – Consumer preferences are constantly changing. What worked 6 months ago might not resonate with your audience in the same way down the road. Plan time in your roadmap for more challenges to see continued success.

To promote communication, your experimentation roadmap and the results of each experiment should be accessible to everyone and promote transparency. This keeps your team aligned to standardize your process.

In CRO, you need to be adaptable. You won’t know the outcome of a test until it’s over (you don’t want to develop a bias by trying to guess the results either!). Based on the results, you and your team need to be ready to react quickly to follow the next steps of whichever path you choose.

A centralized CRO team built for sustainable growth

Developing a CRO team that’s built to grow and build a sustainable culture of experimentation is not the easiest task. There is always room for trial and error when figuring out what works best for your organization.

With a mindset shift, a well-equipped team, and a clear understanding of goals, barriers, and team roles, your organization will be prepped to carry out your winning strategy. With these elements in place, your organization can continuously test and optimize all digital e-commerce channels, leading to increased conversions, higher customer satisfaction, and ultimately, better business results.

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9min read

How AI Can Enhance Your Experimentation Roadmap

Artificial intelligence has been a recurring theme for decades. However, it’s no longer science fiction – it’s a reality.

Since OpenAI launched its own form of generative AI, ChatGPT, in November 2022, the world has yet to stop talking about its striking capabilities. It’s particularly fascinating to see just how easy it is to get results after interacting with this bot which is comprised of deep-learning algorithms for natural language processing.

Even Google quickly followed by launching a new and experimental project, Bard, to revolutionize its own Search. By harnessing the power of generative AI and the capacity of large language models, Google is seeking to take its search process to the next level.

Given the rapid growth of this technological advancement over the past few months, it’s time that we talk about generative AI in the context of A/B testing and experimentation.

Whether you’re curious about how AI can impact your experiments or are ready for inspiration we’ll discuss some of our ideas around using AI for A/B testing, personalization, and conversion rate optimization.

What is generative AI?

Generative AI is a type of artificial intelligence that doesn’t have programming limitations, which allows it to generate new content (think ChatGPT). Instead of following a specific, pre-existing dataset, generative AI learns from indexing extensive data, focusing on patterns and using deep learning techniques and neural networks to create human-like content based on its learnings.

The way algorithms capture ideas is similar to how humans gather inspiration from previous experiences to create something unique. Based on the large amounts of data used to craft generative AI’s learning abilities, it’s capable of outputting high-quality responses that are similar to what a human would create.

However, some concerns need to be addressed:

  • Biased information: Artificial intelligence is only as good as the datasets used to train it. Therefore if the data used to train it has biases, it may create “ideas” that are equally biased or flawed.
  • Spreading misinformation: There are many concerns about the ethics of generative AI and sharing information directly from it. It’s best practice to fact-check any content written by AI to avoid putting out false or misleading information.
  • Content ownership: Since content generated with AI is not generated by a human, can you ethically can claim it as your own idea? In a similar sense, the same idea could potentially be generated elsewhere by using a similar prompt. Copywriting and ownership are then called into question here.
  • Data and privacy: Data privacy is always a top-of-mind concern. With the new capabilities of artificial intelligence, data handling becomes even more challenging. It’s always best practice to avoid using sensitive information with any form of generative AI.

By keeping these limitations in mind, generative AI has the potential to streamline processes and revolutionize the way we work – just as technology has always done in the past.

10 generative AI uses for A/B testing

In the A/B testing world, we are very interested in how one can harness these technological breakthroughs for experimentation. We are brainstorming a few approaches to re-imagine the process of revolutionizing digital customer experiences to ultimately save time and resources.

Just like everyone else, we started to wonder how generative AI could impact the world of experimentation and our customers. Here are some ideas, some of them concrete and some more abstract, as to how artificial intelligence could help our industry:

DISCLAIMER: Before uploading information into any AI platform, ensure that you understand their privacy and security practices. While AI models strive to maintain a privacy standard, there’s always the risk of data breaches. Always protect your confidential information. 

1. Homepage optimization

Your homepage is likely the first thing your visitors will see so optimization is key to staying ahead of your competitors. If you want a quick comparison of content on your homepage versus your competitors, you can feed this information into generative AI to give it a basis for understanding. Once your AI is loaded with information about your competitors, you can ask for a list of best practices to employ to make new tests for your own website.

2.  Analyze experimentation results

Reporting and analyzing are crucial to progressing on your experimentation roadmap, but it’s also time-consuming. By collecting a summary of testing logs, generative AI can help highlight important findings, summarize your results, and potentially even suggest future steps. Ideally, you can feed your A/B test hypothesis as well as the results to show your thought process and organization. After it recognizes this specific thought process and desired results, it could aid in generating new test hypotheses or suggestions.

3. Recommend optimization barriers

Generative AI can help you prioritize your efforts and identify the most impactful barriers to your conversion rate. Uploading your nonsensitive website performance data gathered from your analytics platforms can give AI the insight it needs into your performance. Whether it suggests that you update your title tags or compress images on your homepage, AI can quickly spot where you have the biggest drop-offs to suggest areas for optimization.

4. Client reviews

User feedback is your own treasure trove of information for optimization. One of the great benefits of AI that we already see is that it can understand large amounts of data quickly and summarize it. By uploading client reviews, surveys and other consumer feedback into the database, generative AI can assist you in creating detailed summaries of your users’ pain points, preferences and levels of satisfaction. The more detailed your reviews – the better the analysis will be.

5. Chatbots

Chatbots are a popular way to communicate with website visitors. As generative AI is a large language model, it can quickly generate conversational scripts, prompts and responses to reduce your brainstorming time. You can also use AI to filter and analyze conversations that your chatbot is already having to determine if there are gaps in the conversation or ways to enhance its interaction with customers.

6. Translation

Language barriers can limit a brand that has a presence in multiple regions. Whether you need translations for your chatbot conversations, CTAs or longer form copy, generative AI can provide you with translations in real time to save you time and make your content accessible to all zones touched by your brand.

7. Google Adwords

Speed up brainstorming sessions by using generative AI to experiment with different copy variations. Based on the prompts you provide, AI can provide you with a series of ideas for targeting keywords and creating copy with a particular tone of voice to use with Google Adwords. Caution: be sure to double-check all keywords proposed to verify their intent. 

8. Personalization

Personalized content can be scaled at speed by leveraging artificial intelligence to produce variations of the same messages. By customizing your copy, recommendations, product suggestions and other messages based on past user interactions and consumer demographics, you can significantly boost your digital consumer engagement.

9. Product Descriptions

Finding the best wording to describe why your product is worth purchasing may be a challenge. With generative AI, you can get more ambitious with your product descriptions by testing out different variations of copy to see which version is the most promising for your visitors.

10. Predict User Behavior

Based on historical data from your user behavior, generative AI can predict behavior that can help you to anticipate your next A/B test. Tailoring your tests according to patterns and trends in user interaction can help you conduct better experiments. It’s important to note that predictions will be limited to patterns interpreted by past customer data collected and uploaded. Using generative AI is better when it’s used as a tool to guide you in your decision-making process rather than to be the deciding force alone.

The extensive use of artificial intelligence is a new and fast-evolving subject in the tech world. If you want to leverage it in the future, you need to start familiarizing yourself with its capabilities.

Keep in mind that it’s important to verify the facts and information AI generates just as you carefully verify data before you upload. Using generative AI in conjunction with your internal experts and team resources can assist in improving ideation and efficiency. However, the quality of the output from generative AI is only as good as what you put in.

Is generative AI a source of competitive advantage in A/B testing?

The great news is that this technology is accessible to everyone – from big industry leaders like Google to start-ups with a limited budget. However, the not-so-great news is that this is available to everyone. In other words, generative AI is not necessarily a source of competitive advantage.

Technology existing by itself does not create more value for a business. Rather, it’s the people driving the technology who are creating value by leveraging it in combination with their own industry-specific knowledge, past experiences, data collection and interpretation capabilities and understanding of customer needs and pain points.

While we aren’t here to say that generative AI is a replacement for human-generated ideas, this technology can definitely be used to complement and amplify your already-existing skills.

Leveraging generative AI in A/B testing

From education to copywriting or coding – all industries are starting to see the impact that these new software developments will have. Leveraging “large language models” is becoming increasingly popular as these algorithms can generate ideas, summarize long forms of text, provide insights and even translate in real-time.

Proper experimentation and A/B testing are at the core of engaging your audience, however, these practices can take a lot of time and resources to accomplish successfully. If generative AI can offer you ways to save time and streamline your processes, it might be time to use it as your not-so-secret weapon. In today’s competitive digital environment, continually enhancing your online presence should be at the top of your mind.

Want to start optimizing your website? AB Tasty is the best-in-class experience optimization platform that empowers you to create a richer digital experience – fast. From experimentation to personalization, this solution can help you activate and engage your audience to boost your conversions.

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