Category Archives: Pricing Your Product

December 29, 2011

Great Guidance on Pricing from Zoosk CEO

There is a way to think about that. The model that I have in mind is a graph where the X-axis is the price and the Y-axis is the revenue. At a price point of zero, you make zero money. A ridiculous price or a very high price point, again you make zero money because no one buys your product. This curve starts from zero and then goes up and then comes down. There is a peak, the revenue maximizing price point. Theoretically it is there whether you know it or not. It depends on your product and your demographics, etc., but if everything else is fixed, there is a revenue-maximizing price point. If you actually know the revenue-maximizing price point, you can do say, okay, that’s the top of the peak. However, I prefer to make 10 percent less money but have 20 percent more customers. You want to stay a little bit to the left side of the peak. It is around 90 percent of the revenue maximization point. The way I think about it is a little bit different. I don’t look at it as a continuous thing. I would try to pinpoint the revenue-maximizing price point and then find the nearest round number right before. If my revenue maximizing price point is somewhere between $20 and $30, I would shoot for $19.95. I can tell you that there is at least 20 to 30 percent additional profit you can get by optimizing your product packaging and your product pricing. If you can figure it out, you can go from a company
Shah,Tarang; Shah,Sheetal (2011-11-16). Venture Capitalists at Work: How VCs Identify and Build Billion-Dollar Successes (p. 64). Apress. Kindle Edition.

A lot of people have asked me about how to determine optimal pricing for a product or service.  This morning I read the following statement from Alex Mehr, the founder/CEO of Zoosk, and thought it was the best explanation I’d ever seen.  It’s a great articulation of the theory behind the process I’ve used for years.

Alex Mehr, the founder/CEO of Zoosk on pricing: “There is a way to think about that. The model that I have in mind is a graph where the X-axis is the price and the Y-axis is the revenue. At a price point of zero, you make zero money. A ridiculous price or a very high price point, again you make zero money because no one buys your product. This curve starts from zero and then goes up and then comes down. There is a peak, the revenue maximizing price point. Theoretically it is there whether you know it or not. It depends on your product and your demographics, etc., but if everything else is fixed, there is a revenue-maximizing price point. If you actually know the revenue-maximizing price point, you can do say, okay, that’s the top of the peak.

However, I prefer to make 10 percent less money but have 20 percent more customers. You want to stay a little bit to the left side of the peak. It is around 90 percent of the revenue maximization point. The way I think about it is a little bit different. I don’t look at it as a continuous thing.

I would try to pinpoint the revenue-maximizing price point and then find the nearest round number right before. If my revenue maximizing price point is somewhere between $20 and $30, I would shoot for $19.95. I can tell you that there is at least 20 to 30 percent additional profit you can get by optimizing your product packaging and your product pricing. If you can figure it out, you can go from a company.”

Shah,Tarang; Shah,Sheetal (2011-11-16). Venture Capitalists at Work: How VCs Identify and Build Billion-Dollar Successes (p. 64). Apress. Kindle Edition.

April 2, 2009

The Right Business Model for Your Startup

The right business model is critical to sustainably drive scalable adoption of your startup’s product or service.  Typical business model choices for software, web services, and online media startups are advertising or direct monetization (licensing, subscription, virtual goods, ecommerce, etc).

I generally avoid customer development roles with advertising supported startups because it is very difficult to self-fund (via arbitrage) early growth.  I faced this challenge at Uproar in the mid 90s when building an ad supported business was arguably easier.  We had created very engaging online games that we were certain would eventually attract a large user base.  In the first month after launch I presented the games to the big Madison Avenue advertising agencies and they were initially excited about the integrated advertising opportunities.  However, when I explained we only had a few thousand users interest quickly faded. 

These guys had multimillion dollar monthly advertising budgets.  Even if we could offer a strong ROI on their advertising investment, it wouldn’t be worth their time setting up and managing the campaign.  Our potential contribution to overall results was a rounding error on their typical campaign.  And considering the custom integration work, it wasn’t going to appeal to anyone but the most “visionary” advertiser. 

It was at this point that I realized the life savings I invested in Uproar was in serious jeopardy.  I asked our CEO for the opportunity to focus on user growth so that we could eventually attract big budget advertisers.  We managed to generate a substantial audience (becoming the world’s biggest game site), but even then still suffered from rapidly dropping ad rates that plagued the entire web.  It seemed each time we doubled traffic, the online advertising rates cut in half.   

What I like least about an advertising supported business is that it is almost impossible to always do the right thing for your customers.  Your two primary customer groups have opposing needs.  Each time you try to please your advertisers, you damage the user experience – and vice versa. 

Of course it is possible to build a valuable advertising supported company that overcomes this challenge – just look at Google.  Google reconciled the needs of advertisers and users, improving the user experience and advertiser results with perfectly targeted advertisements.  In fact Google’s advertising results were so good that later as an advertiser I was able to scale a profitable marketing spend to millions of dollars without ever speaking to a sales person (the results sold the ads). 

Today most online marketers buy on tracked ROI.  So if you are considering an advertising model, I highly encourage you to develop one that delivers results that will minimize the need for a sales team.  I do not envy the salesperson that has to make a case on the abstract branding value of their web property.  As tracking continues to improve, it going to be a much harder to incubate a startup with advertising.  Long-term success will require years of high burn.

In my experience, it is much easier to build a lean startup using a direct monetization model such as subscription, software licensing or ecommerce.  With these business models, your customer acquisition can be self funded from the beginning because it works at a very small scale.  For example, if your users have an average lifetime value of $100, your breakeven acquisition cost is $100 less any direct costs of serving this customer (such as storage or bandwidth).  Of course if you can acquire the user for $50 and there is no marginal service cost, then you’ll generate a $50 marginal profit on this user.  With a good arbitrage model, it becomes much easier to sustainably build a customer base from day one keeping burn at a minimum.  And eventually enough marginally profitable users offset fixed costs – creating an overall profitable business. 

Arbitrage supported customer acquisition can even work on a freemium model, but your allowable acquisition cost for a free user will be much lower when you average revenue across the whole free user base.  Still, over time you can add additional monetization channels to boost your allowable acquisition cost and expand the number of viable acquisition channels.  Ultimately freemium businesses become more defensible than “premium only” businesses, because you’ve built the premium portion of your business to compete in the toughest economic scenario.  I’ve blogged about freemium several times already, but have a lot more thoughts to share as I’ve helped several additional startups implement the model since my last freemium post.  Look for a more comprehensive post soon, but in the meantime here is a link to my earlier freemium posts.

January 3, 2009

How to Determine the Optimal Price for Your Web Service

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:

  1. 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.
  2. 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).
  3. 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).