DLG Project: hypothesis, goal & success metrics

Great! Now we know the problem we're solving and it's impact. Next, let's define the finer details of the experiment:

1. Problem statement & overview

2. Hypothesis, goal setting & success metrics

3. Experimentation design: Part 1

4. Experimentation design: Part 2

5. Post experiment learnings & next steps

6. Stakeholder management

For example:

If we show the product demo & CTA in first fold

then our website visit to signup rate will increase by 5%

due to clear value prop delivered & CTA being the only focus

For an E-commerce website:

Running an experiment with the objective of improving session conversions in a particular category would probably align with the overall business goal of revenue rather than; running endless smaller optimisations that lack focus and don't contribute meaningfully to the overall business objective.

Always break down the business objective to input levers and run experiments only on those input levers.

For example;

Worst case scenario: absolute increase in conversion by 1.5%.

Best case scenario: absolute increase in conversion by 5%

Even in the worst case, if the experiment is successful, we will scale the experiment.

What that means is; you’ve already done the cost, effort & impact math to know that the 1.5% increase if worth scaling the experiment.