{"id":7037242,"date":"2026-08-11T00:17:26","date_gmt":"2026-08-11T00:17:26","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/kevin-hillstrom-minethatdata-top-12-analysis-impact-of-pricing\/"},"modified":"2026-08-11T00:17:26","modified_gmt":"2026-08-11T00:17:26","slug":"kevin-hillstrom-minethatdata-top-12-analysis-impact-of-pricing","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=7037242","title":{"rendered":"Kevin Hillstrom: MineThatData: Top 12 Analysis: Impact of Pricing"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"post-body-4666884171087737651\" itemprop=\"description articleBody\">\n<p style=\"text-align: justify;\"><span style=\"font-family: arial;\">One of the analyses I run in a pricing project is measurement of customer response by price point. If inexpensive price point customers are willing to buy expensive price point items in the future, you&#8217;re in good shape? If not? You need to maintain price integrity.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: arial;\">I&#8217;ll run logistic regression models (#oldschool) of next year&#8217;s response within price point bands &#8230; always a fun and informative analysis!<\/span><\/p>\n<div class=\"separator\" style=\"clear: both; text-align: center;\"><span style=\"font-family: arial;\"><a href=\"https:\/\/blogger.googleusercontent.com\/img\/a\/AVvXsEjIbGK1jgnQvZA81wXGmy2tkL0Rx2xASeY7gqWuVhC8xFQeODP9lNoyYFJ5nGP3Q2JK7pNWYA2N3qQRA7j_0PrEtDPfIERCxvs4bNFz5Vjchv_idm8bdOdPJhHrrz69hjqZlub7oif7IYvSksvJaoxp16gaGGnD2d4ILTcu_NHVAGyWUzS-YeOy6A\" style=\"margin-left: 1em; margin-right: 1em;\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" data-original-height=\"209\" data-original-width=\"447\" height=\"188\" src=\"https:\/\/blogger.googleusercontent.com\/img\/a\/AVvXsEjIbGK1jgnQvZA81wXGmy2tkL0Rx2xASeY7gqWuVhC8xFQeODP9lNoyYFJ5nGP3Q2JK7pNWYA2N3qQRA7j_0PrEtDPfIERCxvs4bNFz5Vjchv_idm8bdOdPJhHrrz69hjqZlub7oif7IYvSksvJaoxp16gaGGnD2d4ILTcu_NHVAGyWUzS-YeOy6A=w400-h188\" width=\"400\"\/><\/a><\/span><\/div>\n<p><span style=\"font-family: arial;\"\/><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: arial;\">The table shows the increase in rebuy rates &#8230; for instance, if a customer buys from the low price point band, each item purchased there increases your probability of buying in the future regardless of price point band &#8230; but adds the most in low prices and average prices.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: arial;\">Interestingly (in this case) if the customer buys from the highest price point band, the customer is most likely to keep buying in the highest price point band next year, though the purchase does help increase odds of buying in all price point bands.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: arial;\">There are companies I analyze that have all sorts of odd outcomes &#8230; low price point customers that refuse to move up, high price point customers who buy from everything, average price point customers who default back to low price point bands. Regardless, it&#8217;s important information you need to learn for your brand.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: arial;\">It&#8217;s one of the Top 12 Analyses you respond to when we work together on a project! You have a few days left to take me up on my Top 12 offer. Contact me now (kevinh@minethatdata.com).<\/span><\/p>\n<div class=\"separator\" style=\"clear: both; text-align: center;\"><span style=\"font-family: arial;\"><a href=\"https:\/\/blogger.googleusercontent.com\/img\/a\/AVvXsEgkNrwoGAGBsJm79Rbv0izlN4VJlAB2323JnXmDUmuAqrGSO0DhIv3sIpIDeTvpPNvNDiX0v5pHXKcTTUS_9F9MOCRnnF5JYZ8D2UxTGSIab6oXcAhqQKgNpsOX9Ye21XmWIfQOjRAlzZYfQPMycN8JdsucQCHH_juHk7AC98KMjqKfF0rbfaFItA\" style=\"margin-left: 1em; margin-right: 1em;\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" data-original-height=\"750\" data-original-width=\"1336\" height=\"225\" src=\"https:\/\/blogger.googleusercontent.com\/img\/a\/AVvXsEgkNrwoGAGBsJm79Rbv0izlN4VJlAB2323JnXmDUmuAqrGSO0DhIv3sIpIDeTvpPNvNDiX0v5pHXKcTTUS_9F9MOCRnnF5JYZ8D2UxTGSIab6oXcAhqQKgNpsOX9Ye21XmWIfQOjRAlzZYfQPMycN8JdsucQCHH_juHk7AC98KMjqKfF0rbfaFItA=w400-h225\" width=\"400\"\/><\/a><\/span><\/div>\n<p><span style=\"font-family: arial;\"\/><\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>One of the analyses I run in a pricing project is measurement of customer response by price point. If inexpensive price point customers are willing to buy expensive price point items in the future, you&#8217;re in good shape? If not? You need to maintain price integrity. I&#8217;ll run logistic regression models (#oldschool) of next year&#8217;s [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":7037243,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[10990,13773,4197,13772,13774,14980,213],"dealstore":[],"offerexpiration":[],"class_list":["post-7037242","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-analysis","tag-hillstrom","tag-impact","tag-kevin","tag-minethatdata","tag-pricing","tag-top"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Kevin Hillstrom: MineThatData: Top 12 Analysis: Impact of Pricing - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=7037242\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Kevin Hillstrom: MineThatData: Top 12 Analysis: Impact of Pricing - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"One of the analyses I run in a pricing project is measurement of customer response by price point. 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