{"id":32184,"date":"2018-11-13T16:06:26","date_gmt":"2018-11-13T21:06:26","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/fitbit-will-machine-learning-help-fitbit-change-consumer-health-and-regain-leadership-in-wearables\/"},"modified":"2018-11-13T18:43:59","modified_gmt":"2018-11-13T23:43:59","slug":"fitbit-will-machine-learning-help-fitbit-change-consumer-health-and-regain-leadership-in-tech-wearables","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/fitbit-will-machine-learning-help-fitbit-change-consumer-health-and-regain-leadership-in-tech-wearables\/","title":{"rendered":"Fitbit: Will Machine Learning Help Fitbit Change Consumer Health and Regain Leadership in Tech Wearables?"},"content":{"rendered":"

The fitness tracking and wearable technology industry is poised to radically change the healthcare landscape for consumers and insurers. Indicative of this, US consumers have increased the use of wearables for health purposes from 9% in 2014 to 33% in 20181<\/sup>. Additionally, 90% of consumers are now willing to share wearable device data with their doctors and 72% are willing to share with this information with their insurers1<\/sup>. This shift represents an opportunity for leading wearable companies, such as Fitbit, to leverage its vast data set and utilize machine learning to develop new products that will differentiate it from the competition.<\/p>\n

Fitbit has been facing recent profit declines since 2015 and was passed by Apple as the leading tech wearable company in Q2 2018. The value proposition of providing step and sleep tracking is no longer enough to excite and retain customers. Instead, Fitbit needs to continue to develop products that will integrate with the healthcare system, forging a long-term relationship with its customers by helping them prevent, identify, and manage illness and disease more effectively than ever before. Furthermore, Fitbit needs to continue to partner with healthcare insurance providers to produce incentives for consumers to lower costs by living a healthier lifestyle. Machine learning will play a critical role in the product development of services needed to achieve these goals. If handled properly, Fitbit will be able to utilize its large data set from its existing customer base to identify patterns and trends that will produce actionable insights. Weaving a link between its products and a consumer\u2019s healthcare journey will result in significantly higher customer lifetime valuation.<\/p>\n

Figure 1<\/strong>: Fitbit Global Shipments and Revenue5<\/sup><\/p>\n

\"\"<\/a><\/p>\n

Fitbit has already shown proof that its data set is well positioned to benefit from machine learning. Fitbit has been the partner of choice in healthcare research, used in twice as many research validation studies than any other fitness tracker between 2014 and 20172<\/sup>. An example of this came from a February 2018 Washington University study that applied machine learning to step, sleep, and heart rate data. The study showcased a 96% accuracy in predicting clinical deterioration in recently discharged heart failure patients2,3<\/sup>.<\/p>\n

Recently, Fitbit began shifting its strategy towards enhanced machine learning and healthcare solutions by offering two new product services: Fitbit Coach and Fitbit Care. Fitbit Coach, launched August 2017, \u00a0is a personalized training app that uses machine learning to create individualized workouts. The service leverages exercise data from Fitbit\u2019s large customer base coupled with individual feedback to develop highly customized training routines4<\/sup>. Fitbit Care, launched September 2018, is a component of Fitbit Health Solutions, the company\u2019s corporate health and wellness arm that has partnerships with over 1005<\/sup>. Fitbit Care acts as an engagement platform for the employees of companies that use Fitbit Health Solutions, offering services such as health coaching and data tracking6<\/sup>. While still a new product offering, the future potential for this service is enormous as Fitbit will be able to access third party data such as blood pressure levels5<\/sup>. As Fitbit expands its data set beyond what its own wearables can detect, the possibilities of new insights from machine learning increases drastically.<\/p>\n

Figure 2:<\/strong> Fitbit Coach Application4<\/sup><\/p>\n

\"\"<\/a><\/p>\n

Fitbit has also been focusing on partnerships that will help it win in the long term. In April 2018, Fitbit partnered with Google to gain access to the machine learning tools within the Google Cloud Platform in an effort to enhance the insights gained from its wearable data7<\/sup>. Fitbit has also been aiming to increase its insurer relationships via new partnerships with Blue Cross Blue Shield and Humana in August and September of 2018, respectively. The Blue Cross Blue Shield Partnership gives Fitbit access to 60 million members8<\/sup>; the Humana Partnership yields access to another 5 million members5<\/sup>. While there will not be an immediate diffusion with these members, they represent a large potential of future customers for Fitbit to pursue. Furthermore, the merging of Fitbit and insurers\u2019 data will allow for new and more powerful insights gained from machine learning.<\/p>\n

The future of wearables will be won by companies that can bring in as much relevant data as possible, allowing machine learning to continue to improve the product\u2019s predictions and insights. As Fitbit progresses into the Healthcare space, its management team needs to consider other ways it can improve its product device offerings by gaining access to even more data. Can Fitbit develop new products that collect additional metrics needed to produce superior insights? Additionally, Fitbit has recently been passed by Apple as the leading wearable product5<\/sup>. Will it be able to beat such a fierce competitor who is able to utilize third party apps to enhance its product?<\/p>\n

Wordcount: 778<\/p>\n

 <\/p>\n

Endnotes<\/strong><\/p>\n

1 <\/sup>Nicky Lineaweaver, \u201cTHE WEARABLES IN US HEALTHCARE REPORT: How insurers, providers, and employers can harness new market opportunities using wearables\u201d, Business Insider<\/em>, August 28, 2018 \u00a0https:\/\/intelligence.businessinsider.com\/post\/the-wearables-in-us-healthcare-report-how-insurers-providers-and-employers-can-harness-new-market-opportunities-using-wearables-2018-8<\/p>\n

2 <\/sup>Nicky Lineaweaver, \u201cHow healthcare providers can use wearables to reimagine the delivery of care\u201d, Business Insider<\/em>, August 13, 2018
\nhttps:\/\/intelligence.businessinsider.com\/post\/the-wearables-in-us-healthcare-report-how-insurers-providers-and-employers-can-harness-new-market-opportunities-using-wearables-2018-8<\/p>\n

3 <\/sup>\u201cPredicting Clinical Deterioration of Outpatients Using Multimodal Data Collected by Wearables\u201d, Washington University<\/em>, June 1, 2018
\nhttps:\/\/arxiv.org\/pdf\/1803.04456.pdf<\/p>\n

4 <\/sup>Fitbit Premium, https:\/\/www.fitbit.com\/fitbit-premium<\/p>\n

5 <\/sup>Nicky Lineaweaver, \u201dFitbit looks to steal Apple’s market share\u201d, Business Insider<\/em>, September 21, 2018
\nhttps:\/\/intelligence.businessinsider.com\/post\/fitbit-looks-to-steal-apples-market-share-retail-pharmacies-could-stymie-amazon-health-firms-lobby-congress-to-unlock-opioid-data-2018-9<\/p>\n

6 <\/sup>Fitbit Health Solutions, https:\/\/healthsolutions.fitbit.com\/wellness\/<\/p>\n

7 <\/sup>Laurie Beaver and Nicky Lineaweaver, \u201cFitbit unveils new health solutions\u201d, Business Insider<\/em>, May 7, 2018
\nhttps:\/\/intelligence.businessinsider.com\/post\/fitbit-unveils-new-health-solutions-allscripts-strengthens-patient-platform-garmin-ku-medical-explore-health-data-usecases-2018-5<\/p>\n

8 <\/sup>Nicky Lineaweaver, \u201cFitbit lands 60M member insurance deal\u201d, Business Insider<\/em>, August 10, 2018
\nhttps:\/\/intelligence.businessinsider.com\/post\/fitbit-unveils-new-health-solutions-allscripts-strengthens-patient-platform-garmin-ku-medical-explore-health-data-usecases-2018-5<\/p>\n

 <\/p>\n

 <\/p>\n

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 <\/p>\n","protected":false},"excerpt":{"rendered":"

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