Noosphere #13. Hiper-local targeted advertisement

aaeaaqaaaaaaaakxaaaajgm5odlmogniltayywitndu1ms04zmyzltu2zdu3odzmmjkznw1Part 13. Local business advertisement

Ad integration

I should remind you in this section that the concept of Story provided the ability to integrate advertising messages into the audio data stream. Since the flow is meaningfully fragmented, such advertising messages do not interrupt the main information flow for tourists unexpectedly.

Ad message personalization

A system should be developed that allows advertisers to target messages by gender, age group, language, time. And of course, by location. System is so simple after the setup that, for example, a bakery owner can pronounce a phrase into his phone: «Right now selling hot buns!», and specify the time 1 hour, so the phrase is automatically translated within an hour to all who pass by the bakery. I expect that the restaurant owners will be able to create a tourist-oriented messages, so that, for example, the head of the family, passing by the restaurant at mid-day will get the message «Every second mug of home-brewed beer in our restaurant is free upon provision of QR-code from the noosphere application«; his wife finds out that «In our restaurant, large selection of salads, and children’s play room«; and the child hears «Come in the evening with parents and we’ll give you the secret of ice cream.» Of course, all this may sound in the native language of the listener.

In some cities (in Italy — in many cities) outdoor advertisement of restaurants is banned. The noosphere is not yet, so I think it’s a great solution.

Ad bidding

We should think about collision resolution procedure in the advertising system for competitors wishing to post messages in the same spot. From the one hand, the system should be similar to the auction Google Adwords, but has yet to give preference to owners of retail outlets, depending on the distance to them. Payment will be taken on the basis of CPL (Listen) or CPA (Action).

Adaptive ad based on client behavior

We can build advanced recommendation functions for various segments using data about each user, and collected data on all users (big data). In particular, we can identify the user’s interests based on the places he visited, and to broadcast the ad which is potentially more relevant for him. Based on QR-codes readable in shops, we can determine the advertising campaigns success in terms of ROI.

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