The next thing is to define and parameterize the content lines, product range afghanistan cell service, geographies or any other variable that we want to evaluate. In this way we can establish audiences that combine the stereotypes that we have defined in the first phase with these new variables. Thus a matrix of combinations is constructed that is parameterized. Once we have built the ad-hoc model for each brand, the campaigns are executed, individually impacting each stereotype with each version of content. All interactions are extracted, analyzed and interpreted and painted on a dashboard. The marketing director knows in real time the affinity of his different buyer persona for each content and can now make decisions.
We have given him an anchor to pivot messages, content, tones, etc. and to be able to check on the timeline how the favorability towards his brand evolves. We can now take this learning based on social interactions to the rest of the channels: programmatic, off, etc.
We have gone from the old model to a real-time model, in which a branding campaign can evolve in a personalized way for each audience and multiply its effectiveness. We have gone from an incomplete full funnel model in performance where lists of hot audiences are created based on the success of watching 50% of a video to having lists of qualified audiences classified by affinity to the product or brand and being able to make adjustments in the second and third impact bid intelligently. Brand affinity was born as a successful KPI for marketing campaigns Gulf Phone Number List.