So, essentially, you're building an A/B testing tool specifically for pricing? Thea complexity you've alluded to in your various descriptions start to get real

So, essentially, you're building an A/B testing tool specifically for pricing? Thea complexity you've alluded to in your various descriptions start to get really hairy once you scratch the surface, for example: You mentioned trading off lower prices for more users. Whether the tradeoff is worth it depends not just on the difference in income, but on the marginal cost of serving another user (which itself may be variable). You mentioned more users leading to more WOM. That is a huge assumption, you have to actually measure your viral coefficient (which might actually change with different prices) to know whether that hypothetical WOM actually leads to more users. You may need to optimize for cash flow instead of just income. As one example, you could try a higher monthly price combined with a larger discount (two months free instead of one) for paying up front for a year. That may lower the total number of signups but a greater % pay for a year, improving your cashflow (and with fewer signups, you may have better profit margins on a similar income). You can add an additional expensive plan to act as an 'anchor' that no-one ever picks but makes the regular plan seem like a better deal. There are so many ways to adjust pricing, and every part of your conversion funnel (and business model) may be affected, sometimes with counterintuitive results. Figuring out what some change to your pricing plans is really doing for (or to) your business can be really hard. Instrumenting your funnel and business model so you can actually see what a pricing change would really do to the businesses finances over time would be extremely useful.