Comment testez-vous et expérimentez-vous de nouvelles idées et approches dans votre stratégie numérique?

Adding key goal metrics will help you identify if the experiment was a success or not. If the experiment is not going the right way, you need to take a quick decision to pause, reiterate or even end the experiment as per the performance metrics on a weekly or bi-weekly basis.

Don't just chase self-serving, impressive numbers. Formulating your hypotheses is so essential and so often overlooked. - They really help to keep testing and actions directed towards the key strategic objectives. - They ensure your efforts stay consistent with the SMART goals and the defined KPIs. - They serve as a bridge between action and its measurable outcome. The trifecta of defining goals, setting KPIs, and agreeing hypotheses forms a comprehensive, well-rounded approach to driving strategic improvements. It's not about chasing impressive numbers but about ensuring that those numbers highlight essential insights that act as an early warning sign of deviation from, or aid progress towards, desired business/project outcomes.

A/B testing is one of the best ways to identify which variation your audience likes. Remember to test only variation at a time to determine the best outcomes.

A/B testing is one of the most common testing methods I use to determine which variation of an element leads to an increase in the desired performance. It's essential to remember that successful A/B testing involves comparing two versions of one element - not two entirely different designs as this can make it challenging to pinpoint where the increase in performance comes from.

One must be careful of confirmation bias - the tendency to focus on information that confirms one's preconceptions, and selection bias - where the data selected for analysis is not representative of the population intended to be analysed. Ensuring the removal of biases from testing when analysing results is fundamental to maintaining the validity and reliability of the findings. Blinding procedures, where the individuals involved in data collection and analysis are unaware of the test conditions or hypotheses, can minimise both conscious and unconscious biases. Additionally, employing statistical controls, rigorous data verification, and repeating experiments to confirm results, are instrumental methods for minimising biases.

A great skill to utilize for experimentation is Human-centered design. HCD is a valuable approach for digital strategy experimentation. It focuses on understanding users' needs, preferences, and behaviors to create meaningful & effective experiences. HDC is solely focused on the 1 core element of your strategy. Humans! You can’t truly reach your audience unless you know your “why” and effectively communicate that to them. Here’s how you leverage human connection for results within your experimentation: Empathy. Begin by empathizing with your target audience. Conduct user research, interviews, & surveys to gain valuable insight. Your audience wants to connect with humans, not robots. Relatability is where you can start!