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Arthur D. Little

Arthur D. Little has been at the forefront of innovation since 1886. We are an acknowledged thought leader in linking strategy, innovation and transformation in technology-intensive and converging industries. We enable our clients to build innovation capabilities and transform their organizations. ADL is present in the most important business centers around the world. We are proud to serve most of the Fortune 1000 companies, in addition to other leading firms and public sector organizations. For further information, please visit


Finnovating is a Matching as a service platform that enables X-Techs from all around the word to connect , collaborate and get the funding they need to grow from investors and corporations globally. The Finnovating platform is a space were the key players of the Tech industry can connect together easily and boost global Tech innovations.


Sønr is the world’s most comprehensive source of innovation intelligence. It is a subscription platform used by some of the best known insurance companies globally.

It tracks millions of companies around the world and provides insight on the latest market trends, the startups and scaleups reshaping the industry, and intelligence on how other big insurers are innovating.

Sønr includes a suite of tools designed for teams to better collaborate and connect. From recording meetings to capturing and sharing Notes, to being able to track and share activity across the company using Watchlists and CRM boards.

The platform is backed up by a team of consultants, researchers and analysts who support clients in discovering and creating new business opportunities.

Leveraging Customer Analytics for Business Success – Decision Point

Article Synopsis :

In the era of Big Data, businesses must be smart about how they deploy analytics tools in pursuit of customer insights.

 The Digital Insurer reviews WNS’s Report on Leveraging Customer Analytics for Business Success - Decision Point (WNS) Interview

Insurers have mountains of data, the problem is not much of it is customer data

“Leveraging Customer Analytics for Business Success” from Knowledge @ Wharton is an interview transcript with Wharton professor Peter Fader and WNS executives Raj SivaKumar and Mike Nemeth.

Key takeaways from the interview include:

1. Analytics are of three types:

  1. Descriptive Analytics: simple data collection and visualization.
  2. Predictive Analytics: trend identification and building stories.
  3. Prescriptive Analytics: future actions based on collected insights, useful in planning resources.

2. Data sources include demographics, media habits, Net Promoter Score (NPS), social media and wearable technology.

3. Understand that not all data collected and stored will be useful. First, get to know the data. Then ask how the data can be used for your purpose.

4. The data the insurance industry has been collecting is not customer data. Rather, it’s more risk data – and these aren’t the same thing.  Insurers need to understand the importance and value of a customer-centric data gathering approach.

5. The future of customer analytics lies in the quality of data collected. Focus on data usability first, and then the science of using it to your advantage.

6. One of the keys to success is adding domain expertise to analytics project teams. Analysts, tools, and data aren’t enough. Domain expertise is required to inform the entire process.

Link to Full Article:: click here

Digital Insurer's Comments

Insurers across regions are striving to enhance customer experience via customer insights mined from the vast amounts of data they possess. The problem is most of the data isn’t customer data—it’s more about the risk, i.e., the house, the boat, the car. Collecting information specific to the customer is a relatively new thing in the insurance industry.

Carriers are learning how to leverage new sources of information and synthesize it with existing data sources for good analytics results. In our view success is not just a matter of collecting more data, but changing the type of data you collect and the questions you ask of it in a very transformational way.

Link to Source:: click here


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