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Why your SLA Needs to Cover Methods for Confirming the Accuracy of your Data

Mihir Kittur, Co-founder and Chief Innovation Officer, Ugam,


Price Intelligence, pricing intelligence, Dynamic pricing,When signing up for a Pricing Intelligence or Dynamic Pricing system, it’s important that your service level agreement (SLA) covers how to verify the accuracy of your data.

What do we mean by this? When hundreds of thousands of SKUs are mapped and crawled each day, the opportunity for errors can be significant. Critical errors can creep into your data and then into your actionable insights. Your competitors’ ever-changing category pages and the complex structure of marketplace websites add to this challenge. Insights and critical pricing decisions based on faulty data could expose you to great risk.

So when researching a solution provider, it’s important to look into the strength of their Quality Assurance (QA) algorithms and processes for managing data. Often providers have a parallel process that only samples crawling and mapping accuracy, which may be grossly inadequate.

Mature providers offer comprehensive, rules-based data integrity check systems that conduct format, factual, timing, and logical checks on each data point. Make sure to thoroughly investigate the QA process and have your solution provider demo sample runs using their systems.

Don’t forget to ask vendors how they identify and map similar competing products, and ask them to explain their ongoing process for mapping new products. You need to know your coverage, which is the percentage of your products that match a competitor’s products. Unless your competitors stock a significant number of exclusive products or private label products, your coverage percentage generally should be very high.

There will be situations when a competing retailer carries the same products as you, but in different pack sizes. A sophisticated product matching system should be able to identify these cases and translate the prices per unit. Be aware that there are now several product matching systems on the market that cannot handle different pack sizes.

If you don’t see high coverage for exact product matches initially, you should determine your number of similar product matches.

For example, let’s say that your store carries the following fruit:



And your competitor carries these fruits:



On your first attempt to measure coverage, you would find only two exact matches: one red apple and one pineapple. However, if you redefine your matching rules to look for similar products, you would learn that both you and your competitor share a heavy focus on apples, as shown in the image below.



Bottom line: You cannot make smart data-driven decisions unless you are confident in the accuracy of your data, which is why it’s important to make sure that the system you are considering gives you the most accurate data possible.

To learn more tips for how to get started with a pricing intelligence technology solution, download a complimentary copy of our new eBook, “Pricing Intelligence 2.0: The Essential Guide to Price Intelligence and Dynamic Pricing,” for retailers and brands.

Download your copy today!



The Author:
Mihir Kittur is a Co-founder and Chief Innovation Officer at Ugam. He oversees sales, marketing and innovation and works with leading retailers and brands with insights and analytics solutions around their category decisions to improve business performance.



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