As an ingredient brand, Intel doesn’t sell its products directly to IT decision makers. This meant that Intel was lacking audience insights with no database of those decision makers. Before this, the brand could only serve 'standard creatives' to prospects with diversified media behaviour.
To influence purchase intent in more ways, Intel required a better way to learn what messaging really worked and in what context, in order to eliminate wasted impressions. This highlighted the need to gather audience behavioural data for a holistic understanding of Intel’s prospects so that they could be driven to Intel’s landing pages and converted using dynamic messaging.
This approach will shorten the long conversion path, as getting IT decision-makers to choose Intel over other ingredient brands isn’t an easy task. Complex decision-making processes are made more complicated as product information and assessments vary drastically at different times and in different contexts.
The bigger challenge was that third-party ad tracking platforms in China were isolated from one another. There was no established model for integrated data analysis to aid campaign implementation. To truly understand Intel prospects, Intel needed data from various sources to be integrated throughout the purchase journey.
Data from multiple sources including contextual cookie-level data, dynamic advertising data, onsite data and third-party research data were collected. The vast pool of data then enabled behavioural segmentation and allowed Intel to accurately capture and engage qualified prospects in real time.
Firstly, a testing stage to analyse the user path and define prospect connection rules with Google DoubleClick Rich Media, DoubleClick Campaign Manager and Adwords strategically serving ads to more than 150 websites on weekdays when IT solutions were actively sought.
Intel also controlled the frequency of its ad exposure to minimise information overload. 5,000+ cookie journeys from ads to landing pages were successfully collected, weighted and conducted with attribution analysis to uncover what makes better content for qualified prospects.
Secondly, Intel distributed dynamic creative content to the qualified prospects at scale and in real time based on the defined connection rules. More than 100 articles were tactically served at stages of the consideration cycle to deliver best-matched content and drive step-by-step engagement. Using real-time analysis, Intel also identified the optimal balance for ad relevancy, volume, performance and engagement.