More than ever today advertisers need to demonstrate understanding and relevance with the particular interests of their target consumers. In our previous article we introduced hyperlocal advertising as a powerful way to create a truly stand-out campaign. Through hyperlocal advertising advertisers can tailor their message to connect with very specific audience target groups based on their location. This location can be as specific as the neighborhood they live in, their street, or even their zip code.
To visualize this - let’s say that you have a real estate agency and you want to start a campaign in five big cities, targeting people by zip code in close proximity to offices of your agency. Each campaign will have a separate target group, different creative, different data segments etc.
All of this amounts to quite the number of different sub-campaigns to manage, not to mention the level of details with each of them baring its own unique parameters that need to be taken into account. For certain, such workload is not an easy task to keep track of and optimize.
So how do you make sure that your hyperlocal campaign is bringing the best results?
The most important thing is that you have to have ubiquitous and constant vision on the behavior of each element in your campaign and be quick to react instantly to the slightest change.
Recently, we had a direct experience with a hyperlocal advertising campaign and it is true that as exciting and beneficial it is for advertisers, the optimization could be a challenge for the people managing the campaign. The solution comes in the hands of science. Algorithms powered by Artificial Intelligence are the powerful partners that seamlessly support the management of the campaign and champion the optimization efforts.
They have this exact ubiquitous and yet detailed vision on the performance of the campaign and are able to calculate at any moment the most appropriate action. The way they do this is by “diving” in the intricacies of the campaign data.
There are 4 steps that the algorithms perform to make to derive actions from the data - 1) examine the data, 2) identify patterns to reveal similarities; 3) choose an action (in our case this could be adjust the bid for the ad space); 4) learn from the result of the action and improve further. This granular understanding of the characteristics and the performance of the campaign allows the algorithms stay relevant to the constant dynamics of the market and calculate where to focus their next action.
Back to our example - the optimization algorithms will be able to notice how the people of each specific geolocation are behaving throughout the different times of the day and allocate more budget where there is more traffic. Thus the efficiency and effectiveness of the hyperlocal campaign can reach its full potential.