Development in Pricing data
Many retailers use pricing data to gain insight into the price level of other players in the market. This is done by ‘offline’ players who want to know how their price level stands up to the online providers. Also this is done by online players to know what the bandwidth is of the price levels, and what their position is within this bandwidth. Naturally, this applies to an average of the total assortment, but especially at the (sub) category level and up to the individual product level.
The sources used to make this comparison vary in quality. The quality is reflected in various indicators: For example, there is the width and depth of the assortment that is taken into account. The width means the size of the assortment on product level: how many products are taken into account. The depth means how many shops are taken into account per product. Does the retailer only want to know what other (known) vendors are offering, or does the retailer also want to know about other shops selling this product (the unknown vendors).
This is often based on the fact that a shop itself has a good idea of the other providers. This is true to some extent. However, there is always turnover in the providers, either new providers are added or providers remove articles from their range. It is also important whether an item is available (within the foreseeable future) or completely ‘out-of-stock’ (also from suppliers). So combined is to say that the number of price points is important, but related to that also the frequency of updates of the price levels. This is to ensure that the own price level is possibly geared to the offer of actual providers and not on the basis of ‘ghost offer’, which is no longer applicable. Think of a discount week from a provider, or even a time slot to which a certain discount applies.
By using data from sources that themselves follow a reasonably time-consuming process from acquiring the data and processing it up to its export, you run the risk of using outdated price points and ‘ghost prices’. Since you also need some time to adjust the price level and put it live on your own website and various channels, this aspect is reinforced. Sometimes it is the case that short-term fluctuations are absorbed by allowing some rest (time) and are therefore no longer applicable. However, this is not the case with price level data. The longer it is delayed, the more parties have already been included in really important price updates and this has a direct effect on the turnover and margin realization of you as a retailer.
Did you know that channels like Google shopping have between 8-20% outdated prices? If your sources are based on this, then you really have a question with regard to which data you use to determine your own prices.
Dataedis has developed a technique with which the price points of new providers are updated very frequently. The prices of already known providers are of course also always up-to-date available. This offers a unique and ideal mix of market scan (who offers an article, at what price) and topicality (without delays due to processing and putting it live on various channels in addition to your own website). You can choose not to oversee the entire market, but only a selection of providers. Even then it is a great advantage to work with the correct data to prevent the ghost prices and to make the wrong decisions that will have a direct impact on turnover and margin.
Contact us about this unique service.