In a world without cookies, conversion modeling will be crucial for Google advertising to reach people wherever they are with timely and relevant messages.
When we run ads on Google platform, we always expect to gain the maximum result out of it. This would only happen when the ads reach people with relevant communication at the proper time. To make this possible measuring its efficiency is highly important. However, with phasing out of third-party cookies, the accurate measurement of performance and efficiency could be a concern as it’s going to impact the measurement framework of the campaigns.
So, is there an efficient way of handling this? Yes, there is! Through – Conversion Modeling! Conversion modeling refers to the use of machine learning to quantify the impact of marketing efforts when a subset of conversions can’t be observed. For example, when measuring conversions across browsers, there may not be cookies available to link these browsers. In such cases, attribution of some of the conversions to the corresponding customers who interacted with an ad could not be done. If we do not use modeling techniques, this attribution problem will leave holes in the customer journey, prohibiting us from fully understanding your customers’ paths to conversion. But with a modeling foundation in place, observable data can feed algorithms that also make use of historical trends to confidently validate and inform measurement. In simple words, it enables accurate measurement of aggregated and anonymous data thereby keeping the privacy of the user or the customer intact.
Moreover, it also helps to understand – if the campaigns are performing well relative to one another and collectively, the advertising meeting the target revenue goals, and more without a complete view of performance and a strong measurement infrastructure.
Well then, it’s high time we work on Conversion Modeling and use the power of machine learning to bridge the measurement gaps of your marketing efforts. Stay tuned, as we are gonna come back with more on this.