{"id":1034,"date":"2025-12-22T22:27:06","date_gmt":"2025-12-23T03:27:06","guid":{"rendered":"https:\/\/blog.data-principles.com\/?p=1034"},"modified":"2026-01-06T18:15:07","modified_gmt":"2026-01-06T23:15:07","slug":"qliks-extremely-powerful-and-easy-to-use-key-driver-analysis-capability","status":"publish","type":"post","link":"https:\/\/blog.data-principles.com\/index.php\/2025\/12\/22\/qliks-extremely-powerful-and-easy-to-use-key-driver-analysis-capability\/","title":{"rendered":"Qlik\u2019s Extremely Powerful and Easy-to-use Key Driver Analysis capability"},"content":{"rendered":"\n

By Pete Stiglich<\/em><\/p>\n\n\n\n

If you are a Qlik Sense Cloud customer (and if not, let me know and we can get you started!), the \u201cKey Driver\u201d analysis is something you should absolutely investigate and use. It will save you a ton of time to answer key business questions such as \u201cWhich factors influence sales the most?\u201d. \u201cWhich products, locations, customers, and employees are most associated with late invoice payments?\u201d<\/p>\n\n\n\n

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To do a Key Driver analysis, open \u201cAssets\u201d from a sheet in the app that has the applicable data and choose the \u201cKey drivers\u201d button. You must have a subscription plan that includes Qlik AutoML \/ Predict rights. You can then choose one measure and three or more features\/dimensions in order to see the influence these features\/dimensions have on the measure.<\/p>\n<\/div><\/div>\n\n\n\n

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In the example of Figure 2, I want to see factors (features\/dimensions) affect sales, and so I chose SUM(Sale Amount) as the measure and Employee, Gender (customer), Product Category, and Price to see how these affect sales.<\/p>\n\n\n\n

After it finishes running (may take a few minutes depending on the amount of data), you can show the results. As we can see below, Price and Product Category have the most influence on sales, while Employee has the least influence.<\/p>\n<\/div><\/div>\n\n\n\n

Influence is derived by \u201ccalculating the SHAP values for each feature data value in the subset of data you are analyzing\u201d<\/a>.  SHAP (Shapley Additive exPlanations) is a machine learning technique that Qlik uses for key driver analysis. Key Driver Analysis also provides some helpful textual narrative insights <\/p>\n\n\n

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On the right-hand side, we can see the breakdown of influence for the data values \u2013 in this case, we can see that $40 – $50 priced products have the least influence on sales, while products in the $120 – $130 range have the highest impact. Business leaders would probably want to investigate why the $40 – $50 priced products are performing so poorly \u2013 they might want to increase prices where possible to the $120 – $130 range to help increase sales. <\/p>\n\n\n\n

To produce the same type of analysis without Key Driver Analysis would require multiple visualizations and much more time for an analyst to interpret the data\/visualizations. <\/p>\n\n\n\n

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Conclusion<\/strong><\/p>\n\n\n\n

Key Driver Analysis capability as part of Qlik Predict in Qlik Sense Cloud is a very powerful and extremely easy-to-use tool to quickly identify which factors\/dimensions in your data affect key measures so that you can quickly identify potential business opportunities and\/or issues.<\/p>\n\n\n\n

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Pete Stiglich: Trusted Expert in Data Architecture & Modeling\ufeff<\/strong><\/p>\n\n\n\n

\ufeffPete Stiglich has over 30 years of data architecture, data management, and analytics experience, most of that time as a consultant in industries such as government, finance, healthcare, insurance, and more. He is an industry thought leader in data architecture and data modeling and has developed and taught many courses on these topics.  <\/p>\n\n\n\n

Pete enjoys helping clients solve complex data problems, leveraging proven approaches such as \u201cModeling the business before modeling the solution\u201d which provides a benefit to clients that many IT professionals miss.<\/p>\n<\/div><\/div>\n\n\n\n