Is Big Data the Answer to Pricing Service Parts?

Recent articles addressing the Service Parts Pricing process have touted the utilization of “Big Data” to help companies achieve their potential. However, merely having access to “Big Data” does not inherently solve the unique and often complex problems that service organizations are facing in today’s competitive service parts marketplace.

Many pricing analytics solutions were built for addressing pricing across all industries, not just service parts pricing. But the nature of service parts pricing is unique within Service Lifecycle Management and requires special insights into information beyond the basic customer, geography and part analysis.

So, while product line, customer and volume analysis is important, digging deeper into the following types of questions often is the key to unlocking significant value:

  • Is a part re-manufactured?
  • Is a part under warranty?
  • Is a part a premier (best), second line (better) or base (good) part?
  • Is part considered to belong to a kit?
  • Are there pricing considerations based on the supplier?
  • Does the part have competitors?
  • Are the competitive parts “will fit” parts or merely similar?
  • What competitive pricing information exists?
  • Can competitive pricing information be extrapolated for use in other parts utilizing advanced regression analysis?
  • Does the part have unique qualities creating a captive non-competitive part situation?

Given these constraints, how/where can a service organization maximize profits?

Providing answers to these types of questions, which are unique to Service Lifecycle Management, require specific targeted solutions to managing business intelligence, not merely “Big Data”.

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