How do you test and experiment with your B2B marketing funnel to find the best practices?

Ah, the elusive quest for the holy grail of B2B marketing funnel optimization! Well, my friend, the secret lies in a combination of rigorous testing and daring experimentation. Embrace the scientific method, try different strategies, analyze the data, and be ready to adapt. Remember, Thomas Edison didn't invent the light bulb on his first try either (and we all know how bright that idea turned out to be!). So go forth, test boldly, and may your marketing funnel shine with success!

To test and experiment with your B2B marketing funnel: Define clear metrics for each stage Utilize tools to track and analyze funnel data Employ A/B, multivariate, and split testing Iterate and optimize based on test results and analysis

Firstly I would highly recommend focussing on the B2B data coming into your marketing funnel. Your conversion rates are entirely dependent on the quality of B2B data that you target. AB Testing such metrics as Industry Sector, Number of Staff, Turnover, Company Growth, Geo-Targeting, Competitor Analysis and Job Title are critically important factors to be measured and optimised.

Use A/B testing at each stage of the funnel to see how best to optimise the customer journey. Do this with a small segment of your customer base e.g. 20% before rolling out the winning option to the rest.

?? Selecting the appropriate testing method is critical for optimizing funnel performance. A/B testing compares two versions of a single element, providing insights into specific variations' effectiveness. Multivariate testing assesses multiple element combinations to identify the most impactful configuration. Split testing divides traffic into segments to evaluate different funnel versions' performance across distinct audience subsets. Consider factors like test duration, sample size, and statistical significance when choosing the method. Additionally, prioritize iterative testing and continuous refinement to uncover insights and enhance funnel efficiency over time.

1. Formulate growth hypotheses that are Specific, Measurable, Attainable, Relevant, and Time-based. This helps structure them in a way that is clear and realistically feasible. Here’s an example: If we change the offer on the landing page from “Service Demo” to “Free Lead Generation Audit,” then conversion to a meeting request will raise by 10% in a month. 2. Always base your hypotheses on credible and precise data. For the hypotheses above, you’d need to collect user activity data on the demo page (visits, conversions, bounce rate) and calculate the conversion change, significant for your specific case. Use Evan Miller’s sample calculator for it. Good luck!

Hypothesize with Precision: Start by forming clear, testable hypotheses about what you believe will improve your marketing funnel. Ask yourself, "What can we change to make this better?" A/B Testing Delight: Embrace A/B testing to pit variations against each other. It's like a friendly competition between different elements in your funnel – what works best wins! Data Detective Mode: Dive deep into your data, looking for clues and insights. Be the Sherlock Holmes of your marketing funnel, analyzing numbers to unearth success secrets. Never Stop Tweaking: The optimization journey is continuous. Keep tweaking, testing, and adapting because what works today might need a makeover tomorrow. Stay agile and ahead of the game!

Drawing from my experience, I've found that the most effective hypotheses are grounded in solid data and research, combined with industry best practices. For instance, I once hypothesized that personalizing email subject lines would boost open rates, and it did by 15%. When crafting your hypotheses, aim for them to be specific, measurable, and realistic. This approach not only gives you clear direction but also sets you up for more accurate and actionable results.

It's crucial to adhere to the scientific method, which involves testing one variable at a time while keeping other factors constant to isolate the impact of the change. Furthermore, conducting experiments over an adequate duration with a significant sample size is essential to achieve statistical significance and valid results. Utilizing tools like Google Optimize, Optimizely, and Unbounce can streamline the experimentation process, making it easier to implement and analyze tests, ultimately enabling data-driven insights to enhance performance and outcomes.

The fourth step is to execute your experiments meticulously, adhering to the scientific method. Test one variable at a time—such as changing the call-to-action button color—while keeping all other factors constant. This precision allows you to isolate the impact of each change. Ensure your experiments run for a sufficient duration and with an adequate sample size to achieve statistical significance and validity. Utilize advanced tools like Google Optimize, Optimizely, or Unbounce to streamline this process. By systematically collecting data and analyzing results, you gain actionable insights that drive strategic decisions, enhancing your sales funnel's performance and maximizing conversion rates effectively.

When analysing your results, please don’t forget to see if the test data is significant. Have seen so many A/B tests point out a clear winner, for it to fail when implemented full scale, because the results were insignificant. Search for a A/B test significance calculator (I use Evan Miller’s) and plug in your data. You will understand the data you need for your test to be significant.

It involves comparing metrics before and after the experiment, validating hypotheses, and identifying patterns or trends that reveal the effectiveness of changes. This process helps refine strategies and make data-driven decisions. Tools like Google Data Studio, Tableau, and Excel provide valuable assistance in visualizing and interpreting data, enabling businesses to gain actionable insights and continuously improve their marketing efforts.

I'd also recommend that you organize your experiment results in a Google Sheet or other method so you can build up a library of knowledge. This will also help you identify patterns and tell stories about the data the experiment generated. It's great when experiments are successful, but I find that apart from asking what works, it's just as important to understand why. Unless you know why an initiative has or hasn't been fruitful, you can't drive scalable, repeatable success. Pro tip: after experiments, always do a team retrospective to talk through your findings and share the learnings from the experiment. Talk about: what went well and wrong + what you need to do more of and less of next time.

Organise every data and result you have collected to have a clear view of it all. Try to identify patterns or common traits and inter-dependabilities between various variables of the results. Take note of what's evolving, and try to implement your results with that. This is where we will eradicate the metrics that do not contribute to the progress. This process can be documented and shelved for future references. And for future testings so there is room for evolving.

To effectively test and experiment with your B2B marketing funnel, start by setting clear objectives & defining key performance indicators KPIs. This step is crucial because it provides a benchmark against which you can measure the success of your experiments. Once your goals & KPIs are in place systematically run your tests, comparing the results against these predetermined metrics. This structured approach allows for objective evaluation of what changes are improving funnel performance & which aren’t. Remember, the effectiveness of your experiments hinges on how well they’re aligned with your set objectives & KPIs. Iterative testing, guided by these benchmarks, will lead you to uncover best practices tailored to your specific B2B context.

Iterate and optimize: Based on your analysis, make adjustments to your marketing funnel and continue testing until you find the best practices. It's essential to keep iterating and optimizing your marketing funnel to ensure that you're always improving your performance.

When starting out with funnel experimentation, it's easy to get overloaded with ideas making it hard to decide which experiment to start with. This is where prioritisation frameworks come in! ICE is the one I've found to be most useful. For each experiment idea you have, give a score out of 10 (1 = low, 10 = high) for it's potential Impact, your Confidence, and the Ease to run. Multiply the numbers together and you get an ICE score for each idea. Then rank them highest to lowest and start at the top. This will help you prioritise which tests to focus on and start with.