Mobile Apps, Blind Spots, Tomatoes and IoT Sensors
Blind spots are defined as, “Areas where a person's view is obstructed.” Many business decisions today are still made based on conjecture (unsubstantiated assumptions), because the data needed to make a data-driven decision lies in an operational “blind spot.”
Smart companies when designing mobile applications consider how they can personalize the user experience. They ask themselves how they can utilize all the accumulated data they have collected on their customers or prospects, plus third-party data sources, to make the experience as beautiful and pleasurable as possible. To start, they can often access the following kinds of data from their own and/or purchased databases to personalize the experience:
- Demographic data
- Income estimate
- Credit history
- Education level
- Marital status
- Purchase history
- Locations of purchases
- Browsing/Shopping history
This data, however, is basic. It is merely a digital profile. It has many blind spots. It is often not based on real-time data. As competition stiffens, the above profile data will not be enough to deliver a competitive advantage. Companies will need to find ways to reduce blind spots in their data so they can increase the degree of personalization.
Sensors connected to the IoT (Internet of Things) will play an important role in reducing blind spots. Sensors, often cost only a few dollars, and can be set-up to detect or measure physical properties, and then wirelessly communicate the results to a designated server. Also as smartphones (aka sensor platforms) increase the number of sensors they include, and then make these sensors available to mobile application developers through APIs, the competitive playing field will shift to how these sensors can be used to increase the level of personalization.
Let’s imagine a garden supply company, GardenHelpers, developing a mobile application. The goal of the application is to provide competitive differentiation in the market. The GardenHelpers use the following smartphone sensors in their design to provide more personalized gardening advice:
- GPS sensor (location data)
- Cell Tower signal strength (location data)
- Magnetometer sensor (location of sun)
- Ambient light sensor (available sunlight)
- Barometer sensor (altitude)
GardenHelpers combine the sensor data with date and time, plus third-party information such as:
- GIS (geospatial information system on terrain, slopes, angles, watershed, etc.) data
- Historic weather information
- Government soil quality information
- Government crop data
GardenHelpers also encourages the user to capture the GPS coordinates, via their smartphone, on each corner of their garden to input the estimated garden size, and to capture the amount of sunlight at various times of the day through the ambient light sensor. This information is compared with area weather data and the amount of shade and sunlight on their garden is estimated.
GardenHelpers now understands a great deal about the gardener (mobile app user), the garden location, size, lay of the land and sunlight at various times. However, there remain “blind spots.” GardenHelpers doesn't know the exact temperature, wind speeds, humidity levels, or the amount of water in the soil of the garden. How do they remedy these blind spots? They offer to sell the gardeners a kit of wireless IoT sensors to measure these. These sensors then wirelessly update GardenHelpers servers so their garden analytic solutions can recommend the best and most personalized garden care.
With all of this information now the blind spots are now greatly reduced, but some remain. What about local pests, soil issues and advice? GardenHelpers adds a social and analytics element to their solution. This enables gardeners to share real-time advice with other local gardeners of similar garden sizes and crops.
GardenHelpers can now deliver a mobile app that is hyper-personalized for their customers and prospects. The products they offer and recommend are not selected randomly, but are based on precise smartphone and sensor data.
This is an example of how mobile apps combined with IoT sensors will become indispensable tools for companies wanting to personalize experiences for their customers and prospects.