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Advertisements efficiency in lead generation and subsequent conversion can be increased by AI adoption
The Client presumed AI could help create a more predictable and scalable business model and wanted to prove this before going all in with a company-wide adoption.
Client is a leading firm dealing with advertisements and marketing in Europe. Core product. They approached us with the task to validate whether AI can improve the overall increase in lead generation and subsequent conversion rate by targeting prospective and potential audiences based on their behaviour and interaction with different platforms. They wanted to find out if the available data can be analyzed deeper to draw conclusions and used more efficiently to help brands close more deals.
During the AI sprint, to prove whether a business case can take help of available data and come to a conclusion, the data scientists in our team took help from a set of models and ran several trials.
1. Low planned schedule of ads
The Client did not have a fixed or planned schedule of putting up advertisements on selected platforms and hence the chances of customers engaging on the ads were very low.
2. No single data funnel in place
There was no single data funnel in place and analyzing the customer behavior and planning customized ads accordingly was not possible.
3. A need for more efficient capture and use of data
After running the ads, a significant amount of data gets lost if not captured and filtered. To achieve this, an AI based system was necessary.
4. A lot of Manual Innervation
As the existing process of lead management involved a lot of manual intervention, this resulted in increase in work time and more possibilityof error. The manual process is also not very cost effective. Thus, the need for more efficient and cost effective AI automation was inevitable.
1.Performing exploratory data analysis
Based on the data obtained from the selected platforms about the customer behavior and browsing pattern, we performed exploratory data analysis on optimizing our model that predicted the ads that better suited the customer requirement and engagement pattern. Triggers were identified and fed into the model through
2.Creating the first version of the predictive model
During the AI Sprint, our data scientists created the first model with the data the Client supplied us with. The model would help validate the hypothesis that AI can be used to overcome the Client’s business challenge.
3. Conducting the AI Sprint with the test sample
The created model was then tested with a set of customers selected randomly and data from their engagement was used to test the model and the end result was that the percentage of prospective leads and subsequent conversion increased by over 80% from the existing model. Thus the hypothesis got validated and AI adoption could be accepted to be successful for larger sample size as well.
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