For the startups I help take to market, one of our most important projects is determining their optimal price. Unlike companies in established categories with high unit costs, optimal pricing for a software startup mostly relates to maximizing revenue. An optimal price allows the startup to grow at the fastest possible rate by maximizing profitable investments in customer acquisition programs and/or offering a free version to drive broad user adoption. Considering most software startups simply guess a price, determining your optimal price can become an enormous competitive advantage.
The optimal pricing project is part of the overall “optimization phase” I describe in my metrics driven go to market approach presentation.
There are three key factors to consider when determining your optimal pricing:
- Price sensitivity– You want to find the price that generates the highest yield per 1000 trials (or visitors, DLs, etc.). You can find this number by determining how many units you would sell at each price. For example, if you have a 10% conversion rate at both $8/unit and $10/unit, then $10 is obviously the better price for you. But let’s say at $20/unit demand drops to 8%. Despite lower demand, yield is higher at $20 so it would be a better price than $10 ($1600 per 1000 users at $20/unit compared to only $1000 per 1000 users at $10/unit). I estimate max yield pricing first through surveys and then through experimentation at several price points. Around launch your volume will be too low for a meaningful sample size, so be sure to launch with “introductory pricing” which should be at the low end of your expectations. Adjust the price when volume allows you to hone in on the optimal pricing.
- Marginal cost– For web services it’s important to understand your cost per unit to avoid pricing at a loss. This marginal cost is essentially a floor on your pricing. If you have bandwidth and storage costs that are $5/user/year, then your business would not be sustainable if you priced your service at $4/user/year. For most downloadable software, there is no marginal cost per user (beyond marketing costs).
- Growth strategy– I generally prefer one of the following pricing strategies for innovative products. One is a Market Builder pricing strategy where the majority of your users are coming through your demand generation initiatives. Demand generation is expensive (unless driven through viral tactics) and therefore requires premium pricing to create a high allowable user acquisition cost. An example of a company that took a Market Builder approach to grow the personal remote PC access category is GoToMyPC, which combined premium pricing with aggressive radio demand generation. An alternative strategy is a Market Drafter pricing strategy. Freemium pricing is ideal for a market drafter. Essentially as the Market Builder creates awareness for the category, the Market Drafter swoops in and offers a much better deal (SEM is a good place to focus for a Market Drafter). This strategy only works when a Market Builder is aggressively investing to grow the category. I prefer the Market Drafter position when possible (see this post for more details on why). In the long term, the Market Builder must focus on differentiation to justify its higher prices (or reduce prices)
Once the optimal price has been established, there are many tactics that can used to boost response rates. These include:
- Setting the price a bit higher than the optimal level and then frequently discounting it.
- Using a decoy super premium version to make the version with the “real price” seem cheaper.
My favorite pricing model for driving demand is Freemium, combined with carefully researched max yield pricing on the premium version of the product – then applying the response boosting tactics listed above. An insightful read on Freemium pricing is Josh Kopelman’s post “The Penny Gap.” It is an exploration of the “power of free” in driving customer adoption and suggests that elasticity of demand is not linear. At the price of zero, demand soars.
Dan Ariely also makes this point in his book Predictably Irrational. He concludes “Zero is not just another discount. Zero is a different place. The difference between two cents and one cent is small. But the difference between one cent and zero is huge.” He supports this point through the following experiment: He first offered a Lindt Truffle for 15 cents and a Hershey Kiss for one cent. Participants (who could only select one) purchased the Lindt Truffle 73% of the time and the Hershey Kiss 27% of the time. When they were both discounted an additional penny (making the Hershey Kiss free), demand for the Hershey Kiss shot up to 69% and demand for the Lindt Truffle dropped to 31%.
There are several other great pricing psychology nuggets in Predictably Irrational; I highly recommend reading it. It goes well beyond the three basic pricing factors presented above. Some useful points include:
- A higher price not only positions your product as superior, people may actually have a better experience using the product. He presents a fascinating experiment that shows people got more relief from a $2.50 pain killer than a 10 cent pain killer, even though they were both just vitamin C. He concludes “the perception of value, in medicine, soft drinks, drugstore cosmetics or cars, can become real value.”
- When we encounter a new product, we accept the first price that comes before our eyes as the anchor. This price has a long-term effect on our willingness to pay for the product from then on. He uses the example of black pearls. Initially there was no demand for them, but when they were anchored to the finest gems in the world with premium pricing, demand shot up.
- Differentiation gives more flexibility to increase price. His example here was that Starbucks differentiated the coffee shop experience allowing them to more than double the price of a cup of coffee compared to Dunkin Donuts.
Finally remember that technology prices tend to drop over time. Keep this in mind when determining allowable acquisition cost based on a user’s lifetime value. Lifetime value will probably be lower when considering future pricing pressure. It’s better to be ahead of the curve in driving prices lower, which often requires innovation that allows you to profitably offer the service at a lower cost than competitors (for web based services with marginal costs).
For the price sensitivity analysis, how do you calculate conversion rate? Is it the based on the number of page views, the number of visits or the number of unique visitors over a period of time?I see people throwing around conversion rates (1%, 3%,10%, etc) but the basis for the number.One other thing I am noticing is that appears that my conversion rate is far higher for people who come to my site infrequently than for my regulars. Perhaps this is an indication that I am doing something wrong. Is this phenomenon common?
The link to “metrics driven go to market approach presentation.” seems to be obsolete. I found this one: http://www.slideshare.net/Startonomics/creating-implementing-a-web-20-marketing-plan-presentation Is it the correct presentation? Seems older than the post.
The link to market drafting also seems to be incorrect: http://startup-marketing.com/2008/03/10/fremium-will-squash-premium.aspx
Would appreciate any help here. Can’t find it with a search on the blog either.