Maximising Profits with Perfect Pricing

Determining the price that can attract prospects as well as existing customers for your service or product is in all probability one of the most daunting challenges when you are launching a new product or service. Unlike digital goods, pricing for physical goods is pretty simple.


For the omnipresent and effervescent digital goods with no cost of production, it is not a very simple exercise to determine the right price that can help you maximize revenues or make your profits looks shiny. Fundamentally, it is a known fact that the higher your prices, the lower the demand. But then again, if you price it too low, you won’t make a lot of money even though you might sell a lot. Similarly, if you price it too high, you won’t make a lot of money even though each unit sold brings you greater amount of money. This is the basic principle of price elasticity of demand.

But one thing we need to keep in mind is that every product has a price point at which revenues become maximum, If you try to act smart and price more than it, revenues will fall. And if you think you can price less than it, revenues will still fall. Of course, you can’t be sipping a cuppa and come up with this price-demand curve for your product. It has to be worked out upon. Your market determines this curve and A/B testing is an excellent way to find out which price-point maximizes the total revenue.

So how do we get around a price range for A/B testing?
The answer is: don’t go for guesswork or start rolling the dice. The moot point is to look for other similar products in the market and also determine the value your product is delivering. Set a price range with that in mind. Once you have a price range in mind (say $50-$150), the next step is to use A/B split testing to determine the exact price which maximizes revenues.

Is optimization the right way to make decisions?
Human brains are foreseeable enough and they can be mined for decision data and yield well-patterned insights. Amazon, Pandora and Google can smoothly and easily come up with the list of consumer’s next areas of interest and likely purchases without any prompting from the individual. The messages we receive from nearly everywhere are “optimized” because they are proven to most likely produce a positive reaction from us. Optimization is data science that has known to throw results. Pricing is the second step of optimization that concerns itself with how much a certain type of prospect will pay at that point in time through that particular channel.

Things to think about
Now this looks like an easy territory and a reliable way to make some very important pricing decisions. Even though this methodology has its merits and stands solid, before taking on an extensive pricing optimization effort it’s important to always take into consideration the following:

Trying to garner statistical significance could be too time consuming
The difference in performance between the different pricing levels could be insignificant or marginal, which essentially means that the amount of traffic required to achieve significance would be too high. Always make sure you are comfortable with the level of significance you want to achieve and the amount of traffic you need.

Continuously testing different prices could annoy and confuse users
This could be especially true for digital SaaS products whose purchase journey can have multiple touch points and prospective clients might be visiting the pricing page with some frequency. Narrow down the testing to certain geographical areas or in order to avoid this type of problems.

So, all set to take on price testing? In case you want to know more, we at Sellosphere are always ready with a solution. Get in touch and we’ll do it all for you!