Tuesday 25 August 2009

Mobile app stores - nice, but where's the money?


I love app stores! I used to (try to) use one back in the early days of mobile phones being able to run other applications - but it was hard! Now I'm a committed Apple iPhone app store customer!


But, even as I use these, I realized that many of the phone manufacturers and mobile carriers, as they rush to create their own app stores, may be missing a larger picture. An app store where you sell 99 cent applications and keep a few cents each -- unless you’ve sold over a billion from a selection of 65,000 , as Apple has recently done -- is a big ho-hum (or, at best, a “that’s nice”) to the CFO of a major carrier or manufacturer. Plus, one more online app store in the cloud is nice, but hardly revolutionary. But carriers, especially mobile operators, who are looking to implement both truly usable customer self-care as well as provide platforms for the “double sided business models” that we have all been talking about should look again at the app store platform. It is, as AT&T recently showed, an excellent way of distributing its customer self-care software (see http://www.billingworld.com/blogs/nextgen/blogdefault.aspx).


And, what better channel to offer services to consumers and a platform for transactions with the businesses they deal with every day? Then, combine it with mobile financial services and transactions – killer.

Saturday 22 August 2009

BSS/OSS Sales vs. Revenue During a Recession – A Simple Model and Explanation

As industry analysts, we at always do our best to forecast accurately the market size for various Billing and OSS markets and do so in our yearly Market Reviews and our special Outlook reports. Besides the normal issues of trying to be accurate in the present market size and shares of the software providers, we also forecast the growth (or diminution) of each sub-segment of the market. To do this, we take into account vendor-supplied data, macro-economic forecasts, CSP spending patterns, historical trends, and other, softer data. But what is it we really forecast? Revenue or sales? It’s actually revenue – we try to put everything in generally accepted accounting principles (GAAP) revenue terms. These are the kinds of numbers that are reported by public companies in their annual reports – they are what companies call the “top line.”


CFOs and product managers in software (and all other) companies spend a lot of time worrying about revenue, which is related to sales, but is not at all the same. Sales are “booked” when the customer signs a contract. Revenue is “recognized” in a way that is much more complicated – especially for software sales. During times of relatively smooth growth, yearly revenue (roughly, from sales of products this year + sales of products contracted for some time in the past, and now fully delivered + maintenance for that year + other professional services contracts completed this year) lags yearly sales (of new products, maintenance, and services) by some time – perhaps by a few months (for simple product sales that are delivered and accepted quickly) or perhaps by a few years (if it is a large, complex contract with multi-stage delivery, none of the revenue can be booked until ALL of the items have been delivered and accepted, and the customer has signed off on all services – in other words, everything in the contract has to be completed and signed off on before any of the revenue can be recognized).


So what does this mean? In the case where there is a recession, there is a flywheel effect on the revenue (or hysteresis, for the engineers and physicists out there) that smooths it out. Take a simple case of a company whose sales are growing by about 14% per year, before and after a two year recession. As seen below, the revenue is rising yearly, along with the product revenue and the total revenue. (I’ve assumed a simple model here where half the projects are completed within the year they are sold, and half the next year. Maintenance is assumed at 20% of purchase price.). Then along comes a recession, where sales drop to only 85% of the previous year’s sales and stay there for two years. Of course, the new sales curve drops. But the total revenue still continues to climb, although at a much smaller rate due to the smoothing effect of revenue recognition and the increasing maintenance base.


The smoothing effect is larger for larger companies, in general, since they tend to take on large, multi-year projects whose revenue is often deferred for some time. The effect is especially large for companies whose revenue includes a large fraction of services or custom work, like Amdocs and Telcordia (such companies also have the ability, during a sales downturn, to focus their development and delivery resources on completing contracts that may be lingering, boosting revenue) For smaller, very product-focused companies, who may be deriving 80% or more of their revenue in-year, a recession can be devastating to their “top line.” But, when times get good, their numbers recover more quickly, while the smoothing effect for the larger companies mean a longer recovery time for their numbers.

Why this complex relationship? Why does a company have to deliver it ALL to recognize ANY revenue? Because regulations were put into effect after the excesses of the dot-com era where some software firms were recognizing revenue when very little of the work had been done on the contract, then something would happen, and the contract was not completed. So now, a company may find itself in the strange situation of having delivered almost all of the components of a contract, perhaps even having been paid most of the money, but unable to “recognize” any of the revenue. Of course, revenue recognition is much, much more complicated than I’ve described here, and varies among the countries of the world, so if you are further interested, talk to your product manager or CFO.

If you want to play with this simple model of sales and revenue before, during, and after a recession, please e-mail me at mark.mortensen@analysysmason.com and request a copy of the spreadsheet that generated the chart above.