{"id":56052,"date":"2023-11-08T18:23:35","date_gmt":"2023-11-08T17:23:35","guid":{"rendered":"https:\/\/www.makingscience.com\/?p=56052"},"modified":"2023-11-08T18:23:35","modified_gmt":"2023-11-08T17:23:35","slug":"the-power-and-problems-of-value-based-bidding","status":"publish","type":"post","link":"https:\/\/www.makingscience.com\/en\/blog\/the-power-and-problems-of-value-based-bidding\/","title":{"rendered":"The power [and problems] of value-based bidding"},"content":{"rendered":"

In an increasingly competitive and complex digital marketing landscape, it\u2019s now more important than ever to ensure optimisation of both data and media to achieve marketing ROI. The best decisions are the ones informed by data and AI.<\/span><\/p>\n

Across the two performance marketing behemoths, there are over 4 billion monthly active users worldwide on Google, and over 3 billion on Meta. Of course, not all users are created equal, far from it. Value-based bidding (VBB) gives advertisers the ability to adjust their marketing bids<\/strong> for their most and least valuable users. It combines the power of an advertising platform\u2019s rich user data and AI smart bidding algorithms<\/strong> with a brand\u2019s first-party conversion value data. In short, VBB drives stronger efficiencies and increased revenue for businesses by aligning the bidding goal with business outcomes.<\/span><\/p>\n

It\u2019s a powerful tool; Google\u2019s study on its internal data in 2021 showed a 14% uplift in conversion value moving from tCPA to tROAS, Meta has quoted up to 35% improvement in ROI from their value bidding strategy and Making Science\u2019s proprietary predictive VBB solution – Gauss Smart Advertising – drives 10-30% uplift in lifetime value ROI.<\/strong><\/span><\/p>\n

So why would an advertiser not harness the power of VBB? Well, it\u2019s not always easy.<\/span><\/p>\n

To start, advertisers need to understand clearly what value means<\/strong> to them – revenue, lead quality, customer lifetime value, new vs returning customer ratio, conversion propensity (and countless more) are all applicable value metrics, requiring distinct optimisation strategies.<\/span><\/p>\n

Below the surface, advertisers need robust measurement solutions<\/strong> in place to track customer value in online and offline environments. This data needs to be accurate and accessible. Bidding experts are required to steer VBB algorithms through a volume, efficiency and efficacy balancing act. The necessity for predictive modelling arises when advertisers want to make quick decisions on bidding and investment.<\/span><\/p>\n

All of this is to say that effective VBB is far from a flick of a switch. Durable VBB is best viewed as a journey to maturity, on which accuracy, speed and actionability are the guiding stars. <\/span><\/p>\n

If you\u2019re interested in understanding how better bidding can deliver you more ROI, please reach out.<\/span><\/p><\/blockquote>\n

 <\/p>\n

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In an increasingly competitive and complex digital marketing landscape, it\u2019s now more important than ever to ensure optimisation of both data and media to achieve marketing ROI. The best decisions are the ones informed by data and AI. Across the two performance marketing behemoths, there are over 4 billion monthly active users worldwide on Google, […]<\/p>\n","protected":false},"author":23,"featured_media":56069,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[806,837,882,913],"tags":[62,429,956,655,954,955],"class_list":["post-56052","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-advertising-en","category-digital-transformation-en","category-performance-en","category-technology-ai-en","tag-gauss-ai","tag-google","tag-ltv-en","tag-meta-en","tag-value-based-bidding","tag-vbb"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/posts\/56052","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/users\/23"}],"replies":[{"embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/comments?post=56052"}],"version-history":[{"count":0,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/posts\/56052\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/media\/56069"}],"wp:attachment":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/media?parent=56052"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/categories?post=56052"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/tags?post=56052"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}