Post by Diego Usai

Recently, I carried out a 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐌𝐚𝐫𝐓𝐞𝐜𝐡 𝐀𝐮𝐝𝐢𝐭 for a prospective client of mine to evaluate their marketing and customer data assets, and how they’re currently captured, stored, integrated and governed. This is a stand alone, obligation-free service I offer to assess existing tools and data infrastructure to determine the readiness for implementation of advanced analytical methods such as 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐌𝐢𝐱 𝐌𝐨𝐝𝐞𝐥𝐥𝐢𝐧𝐠 and 𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥𝐢𝐭𝐲 𝐓𝐞𝐬𝐭𝐢𝐧𝐠. My final recommendations provided the client with a practical roadmap based on the findings, which included 𝐬𝐡𝐨𝐫𝐭-𝐭𝐞𝐫𝐦 𝐟𝐢𝐱𝐞𝐬 (tracking adjustments, taxonomy clean-up, etc.), 𝐦𝐞𝐝𝐢𝐮𝐦-𝐭𝐞𝐫𝐦 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭𝐬 (e.g. better alignment of owned and paid data streams), as well as 𝐦𝐨𝐫𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 around warehouse consolidation and platforms integration. As to the modelling approach, I suggested that they started with Meta's 𝐑𝐨𝐛𝐲𝐧 as a way to build confidence in the approach and lay the foundations for future optimisation. Over time, I said, I could help them explore different approaches like 𝐁𝐚𝐲𝐞𝐬𝐢𝐚𝐧, perhaps using something like Google's 𝐌𝐞𝐫𝐢𝐝𝐢𝐚𝐧 once they had the right data infrastructure and internal capability in place. Their response was adamant: if it’s not a 𝐁𝐚𝐲𝐞𝐬𝐢𝐚𝐧 solution based on 𝐌𝐞𝐫𝐢𝐝𝐢𝐚𝐧 from the start, it’s not good enough. So it's 𝐌𝐞𝐫𝐢𝐝𝐢𝐚𝐧 𝐨𝐫 𝐧𝐨𝐭𝐡𝐢𝐧𝐠! That exchange really stuck with me and got me wondering whether 𝐌𝐞𝐫𝐢𝐝𝐢𝐚𝐧 is somewhat perceived as being superior to 𝐑𝐨𝐛𝐲𝐧. And if so, where is that belief coming from? What is the "lay of the land" on this topic? So I did a little research and the results were quite interesting. Read all about it in my article. outbloom Incubeta Forrester Aryma Labs Cassandra Linea Analytics Meta Google David Walsh Warren Jolly Jim Gianoglio