2.5 Trillion Reasons for RPM
A couple of weeks ago, Marketo announced its research-based belief that its form of revenue performance management (RPM) could help grow global GDP by $2.5 trillion by 2015. I love it when emerging companies talk about big plans this way. It reminds me of the young plumber who upon seeing Niagara Falls for the first time says, “I think I can fix it!”
But there’s something to this proposal that ought to be taken seriously and when you talk about trillions of dollars you are presumably talking seriously. Global GDP in 2011 is predicted at $68.65 trillion by the International Monetary Fund and the Marketo announced figure was spread over three years. But that’s a lot of improvement no matter.
To put this into perspective you have to back up and ask about the assumptions involved and Marketo was kind enough to anticipate the questions and perform a little research. According to the announcement, Marketo did some analysis of its customers’ revenues as they took advantage of the company’s marketing automation, sales effectiveness and analytics tools.
Side note: No one’s crown jewels were harmed in the analysis. Having a big pile of relatively homogeneous data for analysis is a side benefit of multi-tenant cloud computing. Multi-tenant cloud computing could provide important analytic benefits like this to all users if we could only 1) Put down some ground rules governing the use of the aforementioned crown jewels, thus creating a data commons; and 2) Get over our hang-ups about maintaining the pristine nature of our data in clouds. Really, it’s like the five year-old who can’t stand seeing the peas touching the mashers on the plate. But I digress.
The three tools, marketing automation, sales effectiveness and analytics, combine to provide the tools a company needs to implement revenue performance management strategies. RPM is still a relatively new idea but other companies like Eloqua, with whom Marketo competes and Cloud 9 Analytics (a Marketo stable mate in venture capitalist Bruce Cleveland’s menagerie) are conspiring to give the idea critical mass.
In the nub, RPM is simply about using the data that is routinely given off by our business processes as fodder for the analytics engine. Too often the data goes unused or simple reporting engines choke on the abundance. But an analytics engine spits out all kinds of ideas like what to offer the customer based on its experience, or generally offering insight that a human eye might miss but which a statistical model would discover easily.
So, two and a half trillion bucks over three years averages out to a bit less than one percent a year. In percentage terms that is not much but the existence of all the zeros in a trillion will get your attention. After all, that’s growth and incremental improvements like this are how markets and economies grow.
More importantly, the ROI can be stunning. Given the fact that RPM would not be applied evenly across big corporations and lemonade stands, the places where it could make a difference would notice the change. Moreover, the cost of implementing RPM where it’s needed would be much less than the incremental gains, especially with modern cloud computing delivering the tools cost effectively.
I am not an expert on RPM, yet. I am more like the one eyed man in the land of the blind. But my thought is that we ought to get familiar with this idea, which is essentially applied analytics. Our economy is still climbing out of the recession and the jobs numbers that I have seen for May are disappointing. Every recession ends with some new product or idea taking off and leading the way. I haven’t seen the big new idea yet but maybe this is it. Regardless, a little investigation won’t cost anything.
Modeling in the Business Process
Modeling is a big idea and one that I started noodling on many years ago. In the last couple of weeks I’ve looked at the cloud computing model, or what it ought to be. Today I am looking at a broader paradigm. One of the great things about new paradigms is that there is no model per se, in a way a new paradigm is an opportunity to create the model; it’s an incipient model perhaps.
The most interesting models or modeling I can think of are those that take place in four dimensions. You will recall without much prompting that we live in four dimensions, time plus the three that define space and from this we get Einstein’s space-time. People who study the big bang speak of matter, space and time as “condensing” out of the event. That choice of word has always fascinated me.
There might even be more dimensions that we don’t know about or comprehend. How can that be? I don’t know the only analogy I can make is that my dog lives in the same four (or more) dimensions as I do but he’s only apparently aware of the ever present now.
Four-dimensional modeling is not hard to comprehend, but like a dog, we routinely fall back a dimension to deal with reality, especially in business. Let’s use the analogy of riding a bike. The bicycle stands against a wall in four dimensions like everything else though it can be comprehended in three very easily. But riding the thing is definitely a four dimensional experience. You can’t ride a bike unless you make a conscious effort to go through space-time balanced on those two wheels.
Learning to ride a bike requires modeling — either training wheels or the expert hand of an adult or perhaps an older sibling who models for you the feeling of keeping your balance. No amount of discussion before hand is very instructive for a first time rider because words fail to communicate the feeling in your stomach as you take your first ride.
In business our models are typically three-dimensional. The two that make the most interesting contrast for me are the accounts receivable (AR) report and the sales forecast. The AR report tells you in a two dimensional grid about what was done in the one dimensional past. It gives you an accurate representation of what is owed and what will come in barring some future four-dimensional miscue.
The sales forecast is much different. We treat is like the AR report but with far different expectations. The forecast is purely four-dimensional but we insist on treating it like the AR report which, though not perfect, is physics compared to the sociology of the forecast.
My point, and perhaps it is not a big one, is that good forecasting requires a four-dimensional model and that can’t be done with a report. You might disagree and the evidence is on your side, mostly. For decades we’ve used a standard forecast report to predict future revenues but honestly, it’s been far less than satisfying. We get our forecasts wrong quite a bit. My data invariably shows that sales forecasts rarely have a ninety percent confidence level. It’s a narrow range — fifty percent is as good as a dartboard so there isn’t a lot of room to work in. We complain about forecast accuracy with the same frequency we complain about the weather, but as Mark Twain wryly observed about the weather, we never do anything about it.
That standard sales forecasting via reports and spreadsheets has worked so well for so long is not a tribute to the method but an artifact of times when we were able to sell standardized products into huge markets. Demand was usually sufficient to backfill one opportunity with another when necessary.
But today’s markets are a bit different. We are doing far more cross- and up-selling than ever. Product lines are expanding but categories are relatively stagnant. Getting the forecast right has never been more important because margins are smaller and there are fewer deals with which to backfill.
The solution for the sociology of the forecast might be the same as the solution for the weather — a model moderated by computer processing power. Rather than focusing on a single report that amounts to a three-D snapshot in time, we need two things. First, a model that captures past information and integrates it into the present but also the model has to offer enough predictive value from prior experience to enable us to self-correct and avoid a crash.
A child (or a trained circus animal for that matter) on a bike can do this and it should not be terribly difficult for us big-brained adult humans to do so with a forecast. I have been impressed with the advances made by analytics companies in this area in the last few years though they often discuss everything in terms of analytics. I would rather they approach this in terms of a model or riding a bike though. Perhaps that would make the idea of using analytics less daunting.
