"It’s a sobering stat: Seven out of 10 executives whose companies had made investments in artificial intelligence (AI) said they had seen minimal or no impact from them, according to the 2019 MIT SMR-BCG Artificial Intelligence Global Executive Study and Research Report."
Why? Well, there are 8 reasons but the second most important reason is the absence of training data. But where is the training data? In many cases, valuable data sits at a location - to collect it, somebody needs to be dispatched to a certain location. And when a person needs to go somewhere to collect data the value of which is yet to be proven, the cost/value ratio is not in the favour of innovation.
In previous post we showed how high data collection costs can be tackled with Bitskout campaign, in this example we will go a bit further and show how a local operator can built a competition map. Here is how it can work but a small disclaimer before going forward:
GDPR and Privacy note: In Bitskout we are very strict on privacy and compliance. In this scenario we are showing a campaign as an example of how a data collection job can be scaled. Additionally, in this campaign, the participants are not collecting any personal information but publicly broadcast data which does not carry ‘a reasonable expectation of privacy’ for the data transmitted.
Every day field reps or technicians visit clients. In above picture let's assume that the technician needs to visit four clients at this location. She needs to be physically there to do some repairs or installations. While at that location, using Bitskout we can activate extra tasks for her to do once the main tasks are finished. Therefore, we prepare a campaign that provide an extra work with reward for technicians and field reps and allow us to achieve our objective - get new data at a very low cost.
Once the campaign is published, field reps need to be very efficient and they will have extra time to help their company and earn extra money or points by collecting WiFi screenshots from their phones.
By collecting screenshots of available WiFi networks we can find out a more or less accurate list of competition (from WiFi names).
From above screenshot you can see that there are 4 Movistar networks nearby, and some unknown ones. Therefore, simple division that there are six houses around her, we can assume that the prevalent operator here is Movistar. Of course, this is not 100% accurate but it is a massive step forward from no data at all to some data. And later in subsequent visits we can complete the picture:
Then we can build a map of the competition in this area. Using this information the local operator can tailor solutions, offers, packages by analysing the competition.
Because the collected data contains important parameters like timestamp and location you can map the competition across geo area and time.
By programming tasks using Bitskout platform enterprises can scale volume tasks extremely easy and collect data, motivate their people and get new insights, solutions and move their business to the next level. See how it works live:
If you are interested and want to have more details about volume tasks you have, we can easily run a virtual workshop (this is usually how we start) and figure out the best use cases to boost your productivity and motivation. Let's talk!