What can Big Data Scientists learn from Tesla + Apple + Nest ?

All the 3 companies added more soul into their products

We are at an inflection point in the evolution of data products.The value perceived by information consumers from data products is shifting from intelligence embedded to building emotional resonance.

What exactly is 'Emotional resonance' and why does it matter now ?

Emotional resonance occurs when a product is aligned to the mental model of the consumer .Lets decompose ... When a consumer interacts with a product the 5 things occur for a product with emotional resonance

1. The interaction with the product feels intuitive & natural.
2. The interaction with the product is meaningful
3. The interaction with the product is engaging
4. The interaction with the product evokes an emotion.
5. The interaction with the product results in a tangible outcome.

In short these 5 small touches create memorability and product begins to have a personality. This results in the product becoming STICKY ! Why does Stickiness matter more now ?

• Stickiness results in repeat usage.
• As a result of repeat usage, the end user forms a habit .
• As a result of a habit being formed, the products create a big barrier to entry to competitive products available in the market place.

Increasingly we at Flutura feel that this dominant dimension is going to be crucial for the success of a Data Product.

This is a natural trajectory and consumer product companies like Tesla, Apple and Nest have shown the path for disrupting the market place where creating desirability is a crucial dimension. Lets understand this with specific real life examples

• Example-1 : Apples "desirability" signature : In 1998 a South Korean company created the first mp3 player. It was very fragmented market till Apple came along and innovated on 2 key dimensions which changed the game - the ecosystem model around mp3 music store combined it with a frictionless experience for the consumer. Apples "desirability" signature in its product interaction framework was subconsciously perceived by the user and set an all new aspirational standard.

• Example-2 : Teslas "desirability" signature : Electric cars are not new, but Elon Musk decided to disrupt the market using innovation on 2 key dimensions - the battery + the driver interaction using a 17 inch screen and SOTA - Software over the air framework

• Example-3 : Nest "desirability" signature in Consumer IOT space. In the Consumer IOT space , NEST reimagined thermostats. Yes, the difficult to program device which controls room temperatures. Thermostats were always available from vendors like Honeywell, but Nest took it to the next level by building “desirability” into a mundane device. How did they do it ? They primarily innovated on 2 dimensions - Pattern sensing ( For example it knows your preferred settings, schedule and senses when you enter/leave the house in addition to giving you feedback on energy usage) and Aspirational design ( The boring thermostat suddenly became a show piece to guests in the house).

In all the above examples , 3 things were common

1. "Doing vs Thinking" : The Product interaction was designed for high frequency 'doing' instead of thinking ( Repeat users getting jobs done went through action workflows in a natural flow path without much thought ). The flow became a habit !
2. "Embrace Minimalism" : The design based communication was very minimalistic ( resisting the temptation to show too much in a cluttered fashion )
3. "It felt like a friend" : By evoking a nuanced conversationalist tone, the interaction/communication was sensitive & humane ( It feels like talking to an understanding friend - Deeply personalized , offering them ample choice and respecting their privacy )

So how does this apply to the world of big data products ?

It’s not surprising that the big data product evolution is following the exact trajectory taken by successful consumer products where innovations in interaction is a crucial dimension for determining success in the marketplace. Again, best understood thru a couple of examples

"Desirable" Data Product -1 : Linkedin alerts are a manifestation of a data product which drives repeat usage by overlaying ultra specific numbers on red icons ( number of mails, number of new connection requests, engaging network events) inviting/engaging users in a compelling way to taken an action . Repetition of this action forms a behavior and over time becomes a ritual. Consumers of these products emotionally respond to small cues ( icon + red colored numbers ) from the product at a subliminal level. The small touch of overlaying ultra specific numbers has big effects on users response and drive repeat high frequency behavior

"Desirable" Data Product -2 : Opower consumer bill. Opower helped utilities reimagine the monthly energy bill as a data product which offers a chance to engage with consumer and change their energy habits.

How did they do it ? They used 2 constructs to engage the consumer - Firstly they used very visual ways to present their data . Secondly they used social proof - benchmarked neighbourhood energy consumption habits as a mechanism to trigger a micro action and thereby impact a larger business outcome of reducing peak stress on power grid .

So how does one get started to reimagine data product design

Fluturas data scientists curate data product for Energy/IOT industry and the data product consumers in this traditional industry have some of the toughest mindsets where product resonance is crucial for adoption. In order to bring in this sensitivity into every energy data product we curate, we fell thru the following framework of 12 questions to guide the data product design process. I

1. Does the data product have a Personality ?

2. Does the data product have a bias towards doing than thinking ?

3. Does the data product form a Habit ?

4. Do we piggy back behind an existent ritual or do we create a new ritual ?

5. Overlaying the emotional fabric - How can we wrap/enclose an existing data in an 'experiential container' ?  How does the data product consumer feel ?

6. What specific signals do we emit to the data product consumer to communicate that we ''know" him/her and learnt from past usage ?

7. What is the tone of the messages which the data product sends to the info consumer ? Is it informal ? Is it emotionless ? Is it crisp ?

8. Can the data product consumer get the job done in 3 clicks ? Why not ? What steps should we eliminate ?

9. Brains are wired for instant gratification - Is there some form of instant gratification we can deliver for using the data product ? ("Congrats, you saved $ 312 on the asset by doing xyz ")

10. How can we increase the level of "humanness" exhibited by the data product ?

11. Can we articulate the Emotional Value Proposition in addition to the Functional Value proposition of the data product ?

12. What are 3 things we can do to reduce cognitive friction involved in using our data products ?

Closing thoughts

The movement has begun ... Data products are undergoing a tectonic transition

• From data products designed for utilitarian value to experiential value
• From data product being robotic to having a personality

We hope the above experiences and  questions  have sparked a stimulating conversation within your data science teams to add more "soul" into the data products which exist in a human context. As always happy to hear your thoughts ...