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I am Joannes Vermorel, founder at Lokad. I am also an engineer from the Corps des Mines who initially graduated from the ENS.

I have been passionate about computer science, software matters and data mining for almost two decades. (RSS - ATOM)

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Monday
Mar192012

Bizarre pricing, does it matter? (B2B)

My company has just released quantile forecasts upgrade. It's no less than a small revolution for us, however, unless you've got some inventory to manage, it's probably not too relevant to your business.

Another salient aspect is our new pricing for quantiles (the old pricing for classic forecasts remains untouched). Lokad is selling a monthly subscription, and if $q_i$ represents one of the actual quantile values retrieved by the client during the month, then the monthly cost $C$ is given by:

$$C = 0.15 \times \left(\sum_{i=0}^n q_i^{2/3} \right)^{2/3}$$

We hesitated to round 0.15 as $\frac{\pi}{2}$ because formula look better with Greek letters. Obviously, it's not simple, and most people would go as far as saying it's downright obscure, but it is really a good pricing, or just plain insanity?

To understand a bit where Lokad is coming from, let's start with the fact that we are a B2B software company. About 95% of competitors don't have any kind of public pricing: you can only ask for a quote, and then a talented sales guy will contact you to figure out your maximum budget, only to get back to you with a quote at 120% of the figure you gave him.

However, I strongly favor public pricing, not because it's more transparent, honest, fair, whatever, but because it's a massive time saver. At Lokad, we don't enter into time-consuming pricing negotiations except for the largest clients, where it does make sense to spend time negotiating.

The cardinal rule of software pricing is that it should capture the willingness to pay of the client, which, in B2B, is typically related to the economic gains generated by the usage of the product. In the case of demand forecasting, benefits can be accurately computed. However, turning this forecasting benefits formula into a pricing formula is insaly complex in the general case.

Hence, we decided to settle for heuristics that somehow mimic this theoretical willingness to pay, ran many simulations over our existing customer base, and finally figured out the formula. I do not claim that this pricing formula is optimal in any way: it is not. However, it does bring a very reasonable pricing for clients ranging from 1-man companies to 100,000+ employees companies.

Pros:

• (As far we can judge) It's aligned with the value Lokad creates for clients.
• It's still simple enough to be memorized in 20s.
• It does not put incentive to game the pricing by excluding slow movers (i.e. products with low sales) from the forecasting process.
• There is no threshold effect, where the pricing jumps to a much larger number just because the company has 1 more product than what the license would support.

Cons:

• It certainly falls into the category of bizarre pricing.
• The only way to know for sure the real monthly cost is to give a try (1).
• Some prospects try the pricing formula on their own, and get it wrong (2).

(1) This statement applies to most metered SaaS, even if the pricing is linear. For example, at Lokad we had very little clue about our exact bandwidth consumption until we migrated toward the cloud (with dedicated servers, bandwidth was part of the package).

(2) I believe this partly explains why 95% of our competitors don't put any public price on display. That, and the fact that a very expensive pricing is likely to scare away prospects, before getting the chance of cornering them into the sales process.

I would be interested to see if other B2B niches have designed their own bizarre pricing formulas. Don't hesitate to submit them in comments.

are you sure about the formula? The analogy with the norm lp space would make the formula look like
$$C = 0.15 \times \left(\sum_{i=0}^n q_i^{3/2} \right)^{2/3}$$
besides it is more homogeneous.

March 21, 2012 | Benoit Patra

Yes, I am pretty sure of the formula :-) It's not intended to be homogeneous, but to reflect a discount for large clients.

March 23, 2012 | Joannes Vermorel