{"id":6304,"date":"2024-01-31T19:38:20","date_gmt":"2024-01-31T19:38:20","guid":{"rendered":"https:\/\/tup.kxe.temporary.site\/why-you-should-add-predictive-modeling-to-your-marketing-mix\/"},"modified":"2024-01-31T19:38:20","modified_gmt":"2024-01-31T19:38:20","slug":"why-you-should-add-predictive-modeling-to-your-marketing-mix","status":"publish","type":"post","link":"https:\/\/okdesign.ca\/en\/why-you-should-add-predictive-modeling-to-your-marketing-mix\/","title":{"rendered":"Why you should add predictive modeling to your marketing mix"},"content":{"rendered":"


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Predictive analytics provides a solid foundation for data-driven decision-making for marketing campaigns. But while most marketing leaders already use some form of predictive analytics, specifically predictive modeling, many still struggle to fully integrate it into their decisions. <\/p>\n

Concerns about data quality and using the right data hinder the wider adoption of predictive analytics. Likewise, some marketers think predictive analytics is too complex, assuming they must fully grasp data science or employ advanced AI tools to benefit from it. While advanced analytics and AI can enhance predictive modeling, you can still improve decisions using predictive modeling without these capabilities.<\/p>\n

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To boost your brand\u2019s sales in the upcoming year, let\u2019s explore how predictive modeling can enhance your marketing. Learn about its current use in the advertising industry, kickstart your approach in 2024 and discover real-world examples of its impactful results.<\/p>\n

Accurate audience segmentation<\/h3>\n

With data on customer demographics, online behavior, purchase history, etc., you can segment your audience and create customized campaigns for each target segment\u2019s distinct preferences and needs. <\/p>\n

Up to 71%<\/a> of customers expect personalization from brands, so segmentation is important to help meet this demand.\u00a0\u00a0<\/p>\n

Ideal placement and timing for advertising campaigns<\/h3>\n

Historical data analysis can produce predictive models that indicate which channels or platforms are likely to be most effective and when it\u2019s optimal to place ads. <\/p>\n

Advertisers can use this analysis to develop better media planning strategies, assuring the right audiences are served ads when they are most primed to engage with a brand or make a purchase.\u00a0\u00a0<\/p>\n

Maximize customer lifetime value estimates<\/h3>\n

Customer lifetime value<\/a> (LTV) is the predicted profit a brand can expect to make throughout the customer relationship. By using predictive modeling to project customer LTV, you can make data-driven investment decisions to retain existing high-value customers across media channels. <\/p>\n

You can also use this to identify prospective customers who will most likely be valuable to the brand over time.<\/p>\n

Dig deeper: The power of predictive analytics: Is the future now?<\/a><\/em><\/strong><\/p>\n

Predictive modeling aids in audience targeting, ad campaign optimization and prioritizing high-value customers. It also has various applications, such as trend analysis for responding to industry shifts and ROI projections for resource allocation.<\/p>\n

Many marketers are upgrading predictive modeling with generative AI and machine learning, reflecting the industry\u2019s trend. However, predictive modeling remains accessible even without investing in advanced technology.<\/p>\n

For example, predictive modeling based on statistical principles don\u2019t require machine learning. Use cases include:<\/p>\n