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Here’s how you can Maximize Marketing Results in a Cookie Less World

This Blog is a part of a series that focuses on the importance of collection and utilization of First Party Data. The First Blog covers the need for advertisers to collect First Party Data and the amenities the data can unlock for them. This Blog focuses in detail on the specific solutions and their utilizations for brands and marketers. With marketing platforms such as Google and Meta increasingly utilizing AI and Data driven automated ads, brands and marketers are giving up the control towards the changes and customizations they can make. This makes them totally reliant on the optimizations made by automated ads. Although it can be helpful in the beginning to improve campaign KPIs, however since these automated ads are not personalized or custom tailored with respect to the brand, the campaigns will start stagnating. To combat with such limitations Google and Meta have started giving thought towards first party data solutions, however these solutions will still not solve the problem of personalization and brand specific customizations. Brands and advertisers can start taking control of their marketing strategies by using Data Analytics and Predictive modelling solutions. These solutions help generate actionable insights and can segment their target audience based on the behavioral trends of users.

Data Analytics & Insight Generation

One essential activity for brands is to understand what data is trying to convey about their users. Using that information brands can effectively formulate their campaign strategies and maximize results.

Upon acquisition of new users, brands often struggle to segregate their customers based on intent and end up retargeting every new user that comes on their platform. Such lack of insights can lead to an ineffective remarketing strategy and reduce user conversion rate. This can lead to an increase in spends and negatively affect KPI’s such as ROAS, CAC, CPT etc.

Another area where advertisers lack insights is the understanding of finding suitable channels according to their audiences. Each brand has their suitable target audience on a specific platform and brands often struggle to decide which channel and platform should they be giving importance to. Most brands look at the raw campaign KPI numbers to decide where they should be marketing and do not consider the quality of the customers acquired through those channels. Using attribution analysis brands can measure campaign KPIs while taking the quality factor into consideration to understand which channel and platform contributes the most towards the overall performance and revenue generation.

Understanding customer sentiment is one of the key factors that can help brands in elevating their performance. Since third party cookie phase out is right around the corner, marketers should investigate their own data to garner customer insights. Funnel Analytics is one such method that can help brands achieve this. Using effective analysis brands can identify problematic areas where majority drop-offs happen or can identify the types of products that most consumers are looking for.

Here is a sample structure of how an analysis is done to extract actionable insights and using those to optimise campaign objectives.


Data Analytics Life Cycle

Predictive Modelling & Efficient User Identification & Targeting (Whom and When to Target)  

The above-mentioned analysis can help marketers generate multiple insights about its users, which can further help in efficiently optimizing campaigns. In today's fast pacing world where data is ever changing specially with every new user acquired t1here is a growing need for more dynamic solutions that can adapt to changing user behavior instantly. In most cases i.e. 8/10 times brands and advertisers find it difficult to infer two things. Whom to Target? & When to Target? Although the above methods do provide us with that information what they lack is the real time interpretation. This can be achieved by utilizing Machine Learning and predictive modelling techniques. With predictive modelling techniques we can identify the different user behaviors that lead to conversion. In addition, it can also help identifying the different users on the brands platform and infer their intent towards converting. This approach works well for optimizing the middle of the funnel i.e. identifying different user intents and separating converters from non-converters. This helps in optimizing budgets for campaigns and improving various campaign KPI’s.

However, it is crucial to keep in mind that campaigns that are backed by predictive modelling needs to be gradually scaled up rather than being scaled up rapidly. This is necessary so that the campaigns and the predictive model deployed has time to adapt to the increased budgets. Here is a sample flow of events on how predictive modelling can help to achieve this.

Data Science Life Cycle

In conclusion, advertisers should take control their performance marketing strategies and not entirely rely on the automated ads. To prepare for a future without third party cookies, brands need to start collecting and understanding first party data using data analytics and predictive modelling techniques to gain in-depth information about their users and campaigns. With actionable insights, effective user identification and targeting strategies brands can work towards a better optimized performance marketing strategy.