Done right, the first step to doing good analytics has nothing to do with math or calculations or equations – these come later. The first step is taking time to understand the problem that is to be solved, who it’s being solved for, and what the context of the situation is. Don’t let the impatient quant inside of your head take over before you’ve taken time to set the parameters of the problem.
So, for an example, lets start out with a problem people in the business world confront on a regular basis – pricing. They need to know what the price point is that will maximize their profits, and this can often be a difficult thing to pin down – a prime opportunity for analytics to step in with a helping hand.
We’ve defined our problem: people need to know how to price accurately. Now, which of those people are we going to try and help with our analytics? People selling consumer products? People selling professional services? People selling entertainment? People selling heavy machinery?
What level of understanding do they currently have of pricing and analytical methods? Are they beginners, intermediates, advanced, or PhD level geniuses? We also want to know the scope and context of their pricing strategy – are they introducing a new product or selling an old one? Are they operating on a large scale, like a Proctor and Gamble type business, or are they selling garage sale items on eBay? How fierce is the competition for the product/service they are selling? The closer we can narrow down the segment to a definable person, the better we will be able to understand and serve them.
It’s critical that we understand this target person. If we don’t, we may find ourselves pontificating about the finer points of a predictive neural network pricing model to optimize a large scale skimming strategy to someone who’s never even seen a supply and demand curve. And that would be embarrassing.
Keeping all of the above in mind, it’s also important to consider your capabilities and the resources available to you for designing such an analytical product. What is your expertise? What are the capabilities of your organization? What interests you? How much can you invest in the project? To quote an ancient Greek aphorism, “Know Thyself.”
Then we must consider the scope and limitations demanded by the subject matter. Pricing analytics, for example, could include treatments of topics such as penetration vs. skimming pricing strategies, value pricing strategies, Conjoint analysis, the Van Westendorp Price Sensitivity Meter, commonly used pricing algorithms, data sources for pricing across the web, how to run robust pricing experiments, and so on and so on. Many of these topics could cover a few of the segments above at the same time (value pricing, for example, would help people selling products or services equally well). What about limitations? Pricing analytics is only as useful as far as the availability or ease of gathering relevant data to take action on exists; if the situation precludes this, the subject matter cannot help.
The key, then, is to convert the appropriate subject matter, covering the right segments, into a product that you are optimally capable of producing. Phew! I think we are discovering that setting the correct parameters for the problem to be solved is almost as much work (if not more so) as the actual data crunching part. To aid us in the understanding of this process, I’ve created a sophisticated circle chart below that provides, at best, marginal value. But I thought was clever all the same (I drew it in about 14 seconds):
So there you have it – because the world needs another acronym, we have created the OOPSS – Our Optimal Problem Solving Sector. (Pronounced, of course, as either “oops” or “oh oh, p.s.s.”, depending on the feeling you may want to convey at any given time)
Be there, and you and everyone you know will be happy.