We usually hear about a/b and multivariate testing hen referring to UX/UI improvements, user engagement and conversion optimization. These are obvious applications for these tools, allowing users to actually test changes in real time, or even before a crucial functionality roll-out.
However, for those cases in which there is no crm backing up the pricing strategy and generating some kind of pricing insight, we can actually use these tools to find out the right price range yelding the highest revenue per clic, thus helping us to maximize our ROI and online advertisement spending. I focus on this kpis because that very price, if referring to on/offline business, could not provide the same advantages in a offline environment: revenue per visitors refers strictly to the online side of the business, linking online advertisement spending to actual conversions.
In this chart we relate revenue per visitor with the price of the product adquired through the online channel. We tested different prices on a 2$ increase scale, and tracked down revenue per visitor. Considering that a lower price generates a higher conversion rate, revenue per visitor goes up, until elasticity to price seems almost irrelevant. Then prices still goes up, but the conversion rate decreases and so revenue per visitor.
Tracking the correlation in between price and revenue per visitor we can actually maximize overall profit, finding the right price (or the right price range) yelding the maximum ROI per visitor, hence giving us a pretty clear idea on the elasticity to price of our prospect.
Base price and upselling optimization
However this can turn pretty tricky if we consider a base price and and upselling price. The right price range for your base product might not be the same for the total upselling price. This case is pretty common in all those industry which strongly relies on ancillary revenues (travel and accomodation) and generic upselling (banks, insurance). In all these cases, there’s a different elasticity to price depending on product category and purchase intent, so this can affect directly your pricing strategy.
As for this chart, the optimal price range for the base product it is not the same as for the total upselling product. All this can be achieved pretty easily with a multivariate testing. At least a few hundreds of conversion might be necessary to achieve some statistical relevance, however most a/b testing tools already take care of this.