It's a little awkward, so we'll get straight to the point: This Friday we humbly ask you to defend Wikipedia's independence. We depend on donations averaging about $16.36, but 99% of our readers don't give. If everyone reading this gave $3, we could keep Wikipedia thriving for years to come. The price of your Friday coffee is all we need. When we made Wikipedia a non-profit, people warned us we'd regret it. But if Wikipedia became commercial, it would be a great loss to the world. Wikipedia is a place to learn, not a place for advertising. It unites all of us who love knowledge: contributors, readers and the donors who keep us thriving. The heart and soul of Wikipedia is a community of people working to bring you unlimited access to reliable, neutral information. Please take a minute to help us keep Wikipedia growing. Thank you.
Once you have done the analysis, determined your goal and formulated your hypothesis, it’s time to set up your test. The best way is to do an A/B test. An A/B test compares version A (the original version) to changes made in a version B while all other conditions are kept the same. If you send the test to a big enough group of recipients and all conditions (e.g. time of day, day in the week, type of audience etc.) are kept the same, an A/B test will let you conclude whether the results can be attributed to the made changes or whether they happened due to chance.
You will have a greater chance of gaining access to their mobile number by following these guidelines. You can collect their numbers using a sign-up sheet at your register, asking them when talking on the phone, allowing them to submit a form on your website, or by giving them a number that they can text to subscribe to your SMS messages. Whatever you do, keep a copy of their permission to market to them via SMS, if you run into problems you will be glad you did.