So what does it all boil down to ? 3 questions
  1. How do we convert "Industrial Data Streams" to "Real Revenue streams”?
  2. Which of the IOT offering has practical value resonance with the industrial customers? (Industrial folks have a “no nonsense” mental model :). Our experience has been fluffy stuff won’t cut it )
  3. Is it a “Pain” or “Vitamin”? Does the customer have budgets already allocated or is there a need creation process to be gone thru
So we started on a journey to catalogue IOT models based on specific real life use cases where we impacted concrete outcomes and monetized machine data for our customers. That exercise resulted in 5 monetization models which we present below
a) IOT-Monetization-Model-1: RMAAS model (Remote Monitoring as a Service)
For example: Flutura was working on a connected buildings initiative where 11,000 strategic government buildings need to be risk optimised. Flutura created an industrial data product which processed billions of events per day from 96,000+ crucial industrial assets like boilers, chillers, pumps in real time with an intra-event interval of 3 seconds to the central command centre. The pricing model was price per asset per month for business models involving remote asset maintenance
b) IOT-Monetization-Model-2: PAAAS model (Predictive Action as a Service)
For example: Flutura worked on a predictive IOT data product for a global industrial giant which would proactively recommend which of its electrical assets needs to be recalibrated. (Most of these assets are installed with default settings and field conditions necessitate recalibration interventions). The pricing model here was a value added annuity service offering on a 3 year contractual basis which is a new revenue stream for asset manufacturers
c) IOT-Monetization-Model-3: Cross Sell VAS ( Value Added Services )
For example : Utility industry in the last mile is getting deregulated and REP( Retail Energy Providers) are coming with customer centric value added services like recommending energy audits based on guzzling habits of similar peer customers and offering asset refinancing is a new revenue stream
d) IOT-Monetization-Model-4 : EPS model ( Extreme Pricing Personalisation )
For example : Progressive insurance created an offering around bartering machine data (mileage, braking, turns, acceleration) from car which are a proxy for driving habits in return for discounted insurance prices.They would install a device in the car which would tap into the machine data generated by the car and infer driving habits from which a risk profile was created and a pricing model was decided which is unique to the individual as opposed to being a part of a generic segment
e) IOT-Monetization-Model-5 : MDAAS model ( Machine Data as a Service )
For exampleIn the consumer generated data world Nielsen, Experian and IRI created data exchanges where blind spots in consumer information where syndicated. What if we created Machine Data Exchanges and data blind spots in industrial organisations can be monetized based on benchmarking and other syndication constructs. The Pricing model could be pay per row or pay per analytical output. This is still at an infant stage and we expect this trend to rapidly accelerate in the coming years
Flutura does not claim to have an exhaustive catalogue of monetization model for the industrial world. We are sharing our humble experiences for the larger eco system to share and evolve. We are sure during the next time folks have a water cooler discussion on Industrial Internet monetization, Fluturas IOT monetization model taxonomy could come handy and they would depart as friends and have a glass of beer :). As the father of data monetization – Arthur C Nielsen said “The price of light is less than the cost of darkness”. We at Flutura passionately believe the price of machine data intelligence is less than the cost of “non digitization” in the industrial world.Happy to hear your views