In order to achieve a better pricing in commerce, the whole initiative should start by realizing that prices themselves are irrelevant. Only the pricing strategy itself matters, that is, the logic that crunches all inputs such as the purchase prices and all the other relevant variables in order to produce the final price values.
When asked about the first step to get better prices, many retail practitioners answer: knowing the prices of your competitors. Rubbish. The first step consists of transitioning from implicit pricing strategies to explicit strategies, because only the latter are subject to measurable improvements.
Unless you’re quite familiar with the concept of pricing, this might sound very confusing.
The most difficult challenge of pricing is that you can’t replay the past. Once you’ve set a price, you will never know how many sales you would have got if you had put another price on display.
Oh yes, you can still change the price now and observe the sales for the next month, but are your sales growing because the price is going down or because your web traffic is going up or because your new product picture is more attractive? You will never know for sure. Actually, it’s not just you. Nobody and certainly not us at Lokad, will never ever know for sure.
Technically, we can argue that pricing is not eligible to backtesting.
Focusing on the prices themselves is a defective process in the sense that this process can’t be challenged. Prices can be changed, obviously, but, except for pathological situations where obvious pricing mistakes get corrected, your company won’t be able to decide if the new prices made the situation better or worse.
As the old saying goes, you can’t optimize what you can’t measure.
What can be challenged, however, is the pricing strategy. The pricing strategy is the logic, the set of rules, that processes the input data such as purchase costs, customer acquisition costs, inventory costs, prices of competitors ... and that produces the final public prices to be put on display.
Unlike raw prices, a pricing strategy can be challenged: given two pricing strategies, a strict experimentation protocol can be devised to decide which one of the two strategies is the most profitable one. Designing such a protocol is not a simple task, we will get back to this in a later post.
Intuitively, if you have 1000 items to be priced, you can assign the first 500 to the first strategy, and the last 500 to the second strategy. If the two pools of items are comparable, then it becomes possible to assess the performance of the respective strategies.
In the past, a few very large online merchants tried to display different prices to different customers just for the sake of gaining further market knowledge. In order to be fair, somehow, all customers were offered, at the end of the check-out, the lowest price. This approach stirred controversies, as far we know, it’s not used anymore, at least not at scale. Furthermore, in many countries, customer protection laws prevent retailers from tweaking their prices per customer.
Unfortunately, in most retail businesses, the pricing strategy does not exist anywhere but in the mind of the people in charge of setting the prices. Frequently, a myriad of spreadsheets also contains bits of pricing logic. However, as spreadsheets mix both data and logic, updating those spreadsheets with the latest data is error-prone and time-consuming.
With such a setup, pricing strategies remain implicit and unchallenged, and consequently the performance of the pricing remains stagnant. Worse, any market change that ends up reflected in the prices requires a lot of manpower just to re-enter the revised prices in the system somehow.
Thus, any pricing initiative in retail should start by transitioning toward an explicit pricing strategy that, given the proper data inputs, can be executed by a machine in order to produce the revised price values.
Some practitioners might argue that the machine is pretty dumb and that it will never know the market like they do. Well, this is absolutely true. Having a fully automated pricing logic just happens to be simplest way to make sure that the pricing logic is well-defined (non-ambiguous, conclusive, etc); however, this logic might be nothing more than the formal transcription of the pricing logic as understood by the practitioner herself. The machine is not expected to invent the pricing strategy, merely to execute it whenever refreshed prices are needed.
Priceforge, our pricing optimization webapp, has been designed precisely to let your company write its pricing strategies, because it’s the first step toward a situation where it becomes possible to actually improve the pricing.