Short-term vs long-term pricing analysis

Published on by Joannes Vermorel.

At first glance the notion of price elasticity of demand looks fairly reasonable, tractable even. Elasticity gives the percentage change in quantity demanded in response to a one percent change in price.

Putting aside a couple of odd situations where an increase of price creates an increase of demand, in the vast majority of situations, the elasticity is negative: demand decreases as price increases.

Most of the pricing literature indicates that price elasticity is a very desirable metric, because, through its analysis, it becomes possible to calculate the optimal price, that is, the price that maximizes the margin.

However, as far commerce is concerned, we observe that price elasticity is a misleading metric, even when measured correctly - which is also a tough challenge.

Indeed, for about any commerce, most elasticity analysis tend to show that prices can be increased and it won't impact much the demand. Worse, if a small scale A/B test is carried out, the test will confirm the analytical insight provided by the elasticity analysis.

And yet, the conclusion is plainly (deadly) incorrect.

Pricing is a signal sent to the market, and the market is made of habits. Only the most price-sensitive buyers do the effort of systematically checking the price of the competition. Most buyers do it only once in a while.

If your commerce were to increase all its prices by 20%, what would happen for the next 2 weeks? For most commerce, not much. Yet, within a couple of months, market shares would decline rather abruptly - unless the pricing shift is part of complete rebranding in order to reach richer segments.

In the short-term, demand tends to be fairly inelastic because habits dominate. In the long-term, it's the opposite: it's almost impossible to maintain a higher price than the competition if the package (product + service) is the same.

From a pricing perspective, it's important not to be fooled by short-sighted quantitative analysis. Price elasticity is relevant, but by construction, it is short-signed because it ignores that commerce is a repeated game where the goal is not to maximize the margin of the next client purchase, but rather optimize the market shares that offer the best sustainable margins.

Need a tool that gives you the insights it takes to craft solid prices? Don't hesitate to have a look at Priceforge, our pricing webapp.

Categories: Tags: pricing insights No Comments

Ignore prices, only pricing strategies matter

Published on by Joannes Vermorel.

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.

Categories: Tags: pricing priceforge insights No Comments

We're hiring: Junior Account Manager

Published on by Joannes Vermorel.

Lokad is growing, and we need you. As a junior account manager, you will learn how to grow prospects into clients, and then, learn how to support them so that they make the most of our technology.


Your goal will be to establish and grow commercial relationships between Lokad and its clients. As Lokad is reaching a global market, you will be expected to converse by email in English with companies located pretty much anywhere on the globe. You will also be expected to handle phone calls, in English. Note that we don’t do much cold calling - so you won't spend your days being rejected after trying to call people who don’t know or don’t care about Lokad.

The position in our office Paris (13th arrondissement). This job is not eligible for remote work. Salary depends on the experience and subject to negotiation.


We are seeking an enthusiastic and communicative junior. Impeccable command of English is critical because most of our business happens outside France. For practical reasons, we also expect impeccable command of French.

We do not expect you to be knowledgeable about Big Data, but if you happen to be a bit savvy with the web, or software, or ecommerce, it will be appreciated.


  • Impeccable command of English and French.
  • Sharp analytical mind.

To apply, send us your resume to In your message, please explain shortly why you would be a good fit for this job.

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Control your service levels, don’t let them control you

Published on by Joannes Vermorel.

In retail, many companies don’t have much control over their service levels. In fact, many companies don’t monitor the service level where it matters the most: the physical store. Indeed, measuring in-store service level is a tedious exercise. Some companies - mostly panelists - specialize in doing this sort of measurements, but the cost is steep as there is no workaround for the extensive manpower involved in the process.

Taking a step back, why do we even need to measure the service level?

Wouldn’t it be more convenient if the service level was something obtained by design and defined through explicit settings within the inventory optimization software? This would certainly be a lot more practical. Service levels certainly don't need to be an afterthought of the inventory optimization process.

It turns out that, historically, the need to measure the service levels came from early inventory optimization methods such as the safety stock analysis that offer about no control on actual service levels. Indeed, the underlying models rely on the assumption that the demand is normally distributed and this assumption is so wrong in practice that most retailers gave up on this assumption in favor of ad-hoc safety stock coefficients.

Those ad-hoc safety stock coefficients are not bad per se: they are certainly better than relying on abusive assumptions about the future demand. However, the quantitative relationship between the safety stock and the service level is lost. Hence, retailers end-up measuring their service levels and tweaking coefficients until inventory stabilizes somehow. At the end, the situation is not satisfying because the inventory strategy is inflexible: safety stock coefficients can’t be changed without exposing the company to a myriad of problems, repeating the tedious empirical adjustments done originally.

However, with the advent of quantile forecasting, it’s now possible to produce forecasts that very accurately drive the service levels, even if the quantile forecasts themselves are not accurate. All it takes is unbiased forecasts, and not perfectly accurate forecasts.

Indeed, quantile forecasts directly and very natively address the problem of producing the reorder quantities it takes to cover target service levels. If a new and better quantile forecasting technology is found, then this technology might be capable of achieving the same service levels with less inventory, but both technologies deliver the service levels they promise by design.

This behavior is very unlike the case of classic forecasting allied to safety stock analysis where an improvement of the accuracy, while being desirable, leads to erratic results in practice. For example, for many low volume products, as observed in stores, shifting to a dumb forecasting model that always returns zero usually improves the accuracy defined as the absolute difference between the actual sales and the forecasted sales. Obviously, shifting toward zero forecasts for half of the products can only end with dismal business results. This example might appears as anecdotal but it is not. Zero forecasts are the most accurate classic forecasts in numerous situations.

Thus, in order to take control of your service levels, it takes an inventory optimization methodology where such a control is built-in.

Categories: Tags: quantiles inventory insights inventory optimization No Comments

Prestashop add-on for BotDefender

Published on by Joannes Vermorel.

BotDefender logo A few weeks ago, we released an add-on for Magento, and now it's the turn of Prestashop to get protected by BotDefender against the automated retrieval of all the prices by competitors. Check out our Prestashop add-on for BotDefender (also available from the Xtendify store).

Protecting your store has now its 1-click solution!

This add-on has been co-designed as a joint effort between Xtendify, our Prestashop specialist, and the Lokad team. As we did for Magento, we paid the same attention to the add-on performance.

Meantime, while add-ons are being released, the BotDefender infrastructure has also been upgraded for better performance. Now BotDefender auto-diagnostics the need for SSL in order to revert to plain HTTP whenever it's applicable.

Categories: Tags: prestashop botdefender scraping No Comments