If I knew that my customers favourite colour was red I could paint all the walls red and sell more. That's how most businesses think about the need for data. But what if you could predict the future. If you knew when something was likely to occur and how it is to happen, you could plan to proactively and economically avoid it (service of machinery, overly tired workers, jumpers not selling on a rainy day) and adjust your business strategies accordingly.
Being intune with your consumers is key and to do that you have to be speaking the same language. Mobile phone manufacturers are currently fighting for first place in the "I've got the best camera" league. One campaign targeting digital camera enthusists talked about the best camera to take pictures while any camera guru will tell you that they dont "take pictures", they "shoot images". Apple's poster campaign cleverly used deliberately different language "Shot with iPhone 6".
Are you getting it really wrong without even knowing it? Slight nuances in language can make all the difference when you are trying to sell a product. The trick is understanding the important trigger points in language.
Body Language & Facial Expression
Language & Facial
How does a doctor interact with a patient. What are the small nuances that makes them feel comfortable or not. How do subtle the differences in facial expression and body language result in one sales colleague being successful while another seeming to do and say all the right things isn't. What if you knew and you could change that.
Build a commercial value proposition
Complete needs analysis
Select the right technology partners
Create a pricing model
Sell your data on - using a SaaS model
Create a global (versus silo) corporate data strategy
Put your data to work making and saving you money. After all...isn't that the whole point?!
Think ahead. Everything from the data you collect to the way it is used is constantly evolving. Establish a process to keep up coupled with a dynamic vision for the future
Retail & Grocery
Age, race, ethnicity. Who are the people who buy from you but more importantly who are the people that don't. She walked into your store, picked up a product, looked at it but did not buy? Why?
What is the best way to discretely capture the data?
Which direction did they come from? How long did they dewell there?
We can now discover location-based patterns and relationships from data that may exist in many disparate places. This reveal patterns and trends that would previously have remained hidden.
Bringing together maps and multiple data layers let you see the story behind your data.
Retailers can see where promotions are most effective and where the competition is. The commercial value proposition for energy saving can be improved by studying the collelation between demographic, solar positioning and roof pitch to identify housing that is suitable for specific energy-saving measures.
Therefore what? You need to get answers in real time that allow you to rapidly respond. Gondola with new dresses on. After two hours no-one walking has stopped to look at it yet its selling well online. new window display. 30% less people coming from the right enter the store. Lunch time congestion and unexpected long queues. Put the data to work by getting management tools that make the data easy to read allowing you to automate triggers and alerts.