Value drivers ensure the validation of your research results and can be set individually in the PRICEGUIDE.
Over the last years, we have developed a comprehensive set of so-called value drivers, which we use to evaluate and validate your research results to the highest degree. The interaction of over ten unique value drivers at the individual spare parts level ensures that you make validated pricing decisions at any time. In this article, we explain our value drivers so that you can efficiently calibrate them based on your individual revenue and company targets.
Result search method Sonar
This value driver evaluates whether we find results with our sonar research method. In general, we differentiate between underpriced parts, overpriced parts, exclusive parts, or whether the part is already priced in line with the market.
Result search method DeepDive
This value driver evaluates whether we find results with our DeepDive research method. For every part researched with our DeepDive method, we conducted a comprehensive offline search in addition to the Sonar online search. Any result collected through DeepDive holds higher value because, in addition to the Sonar search method, we incorporated this intensive offline research method.
Sales quantity champion
The sales volume provides an indication of how often a part has been sold relative to other parts. We assume that if a part is sold very frequently, this indicates potential for price increase and a monetary risk in case of a price reduction. To assess this value driver, we compare the sales volumes of all of your examined parts relative to each other. In our standard settings, each part within the top 20% of best-selling parts is rewarded with a confidence level of 20 points.
Markup in %
The mark-up provides information on whether the market prices determined and proposed as the new sales price are realistic. If previous sales prices differ greatly from the proposed new sales prices, this indicates potential issues with either the data provided for a specific part or with the newly researched market price. It may be, for example, that the wrong unit of measurement has been researched or that the data provided specifies the wrong unit, such as millimeters instead of meters. If the deviation is extreme, this is a clear sign that the parts should be validated and checked again. This value driver evaluates this deviation. Our default setting for extreme deviation is < -70% or > 400%.
Price range
Monitoring the price range limits the risk of setting new sales prices for parts with too large price ranges on the market. This value driver assesses both the price range and the market density in combination. The price spread is the difference between the lowest and highest price on the market for a single part. For example, if the lowest price of a part is $100 and the highest price is $200, the price range is 100%. The market density is the number of price points found for a part. In the default settings, this value driver is set for a price range > 50% and a market density of <5 price points.
High win-rate / Low win-rate
The win rate reflects the ratio between offers and orders for a single part. For example, if a part is offered 10 times and sold 7 times, the win rate is 70%. We call a part with a low win rate a "sleeper," indicating that end customers are taking advantage of better offers on the market. This suggests that a price reduction may be sensible, as there is potential to capture a larger market share. Conversely, a part with a high win rate is termed a "runner," suggesting that end customers do not perceive better offers on the market. In this case, a price increase makes sense, as there is potential for higher profits. By default, we have set a low win rate to below 30% and a high win rate to over 70%. However, our customers have the flexibility to choose their own settings based on their actual average win rate for all parts.
Market density: Underpriced parts / overprice parts
Market density considers the number of available price points per part. A higher value indicates greater market transparency and better quality of market price information for that part. Our customers frequently customize their settings, particularly for undervalued parts, based on the average price points per part and their individual security levels. Typically, a lower minimum market density is accepted for identifying price increase potential compared to identifying market share potential.
Consistency
Consistency provides information on whether a manufacturer maintains a consistent stance regarding the potential group (price increase, market share, exclusive potential). This indicates whether a manufacturer is following a specific sales strategy, which is particularly common with small manufacturers. For example, if 10 parts from a manufacturer were researched and no price point could be found for any of these parts, we have identified the same potential 10 times, namely an exclusive potential. This is a clear indicator that there is indeed an exclusive situation for all parts from this manufacturer.
Exclusivity agreement
Exclusivity agreement occurs when during our research, we receive feedback from the parts manufacturer indicating that the part may not be offered due to an agreement, and that we should contact the machine manufacturer directly. This strongly confirms the assumption of exclusive potential.
Average lead time competitors
The information on delivery times we have researched is not always reliable, as it is not always precisely maintained on websites/stores. However, we assume that if all stores on the market list a part as immediately available, there is a very high probability that this part is indeed available. This value driver assesses this situation. A price change made in this scenario could motivate end customers to switch to a competitor due to the delivery time.
Manufacturer has online store
We have built an unmatched database containing information about manufacturers. This value driver assesses whether a manufacturer has an online store or not. This information is particularly crucial if the research has identified exclusive potential. Given contradictory information with our database, the risk of implementing a price increase based on an assumption of exclusivity is very high. This value driver evaluates this scenario.
Manufacture sells parts
We have built an unmatched database containing information about manufacturers. This value driver examines whether a manufacturer sells parts in quantities of 1. This information is particularly relevant if the current research has identified exclusive potential. Given the contradictory information within our database, the risk of implementing a price increase due to an assumption of exclusivity is very high. This value driver evaluates this situation.
Manufactures sells known parts
We have built an unmatched database where information about manufacturers is stored. This value driver assesses whether we have previously identified price points for a specific manufacturer in previous searches. This information is especially crucial if the current search has revealed exclusive potential. Given the presence of contradictory information, the risk of implementing a price increase based on an assumption of exclusivity is very high. This value driver evaluates this scenario.