• Marc in Madison

Big Data and the Coworking Industry

Updated: Mar 31, 2021

This was written for CAS 839: Media Analytics Communication in the Michigan State University Strategic Communications MA program.



Serendipity Labs (SL) is a national coworking company that provides virtual office, coworking spaces, and private offices. In large cities throughout the United States (and soon in Oxford, England), SL operates on a hospitality model; thus, members have a concierge/receptionist on staff to help and to greet guests. Upscale coffee, snacks, and amenities offer SL members (who pay monthly membership fees in a contract) a five-star worksp experience for their businesses.

Serendipity Labs & COVID-19

Before the COVID-19 pandemic, experts predicted nearly five million people would work from coworking spaces by 2024—but the pandemic thwarted this growth trend (Coworking Resources, 2020). As one might predict, the success and growth in this industry ties strongly to COVID-19. Some people are anxious to venture out of the home, save for necessities, and view renting a private office as unsafe.

Others, who find cabin fever and distractions unbearable, want a private workspace.

Big data considerations for SL include national, state, and county COVID-19 statistics (positive cases, deaths, new strains, mask efficacy, outbreaks, and vaccination rates). Data from online communities can come from social media groups (Facebook groups local to each SL office), streaming data (WI-FI usage from members who are connected to in-house network to obtain snapshots of occupancy and usage), and public government sources (Centers for Disease Control, Coronavirus,gov, or even the Small Business Administration).

Using data from these areas, SL leadership can task managers at the local level to monitor the virus in their areas and continuously cross-reference conditions with lab occupancy, sales leads, new contracts, administrative costs, and Salesforce metrics (opportunities won or lost).

Serendipity Labs & Predictive Intent

Coworking is the hub for many industries: private contractors, consultants, and freelancers (who want a private, quiet space to work); small business teams (who enjoy the amenities of a Class A building without overhead and fixed costs), and people who sign up merely for the business address (who do not come into the space).

Intent is crucial in the coworking model because it defines and drives service, membership tiers, and value-adds; thus, SL can learn from behavioral patterns when creating targeted campaigns (Medal, 2017). Why are they considering a coworking space? What is the appeal to them? How do they plan to use this space?

The SL sales team prefills prospective-member Salesforce with data that can predict behavioral intent through many categories: estimated budget; probability percentage of closed sale; budget; office size; team size; contract value; and contract duration. Based on these predictive metrics, SL corporate assigns marketing-qualified leads to the local managers. The SL process is similar to the Salesforce analytics process Quantzig (2020) describes as enhancing operational efficiency, identifying bottlenecks, analyzing purchase patterns, and driving profits.

Salesforce trend reports can help SL leadership create sponsored, “programmatic advertising” at the local level as well (Livingstone, 2017, p. 12). SL creates in-house big data that is structured and numeric, and a mix of historical and new data helps one develop predictive models (Beaty, 2021; Livingstone, 2017).

Serendipity Labs & Consumer Attitudes

Results derived from local-lab Survey Monkey blasts keep SL managers current with overall consumer insights, attitudes, perspectives, preferences, comments, and concerns. Metrics like open, click-through, conversion, and bounce rates enable managers to determine what is working, what needs improvement, and what is considered added value. Comparing this data with competitors’ pricing and amenities, SL creates campaigns that align with the demographic, geographic, and psychographic criteria unique to each city’s market. Thus, SL leadership can connect data points to new and existing customer relationships—reaching consumers strategically and intuitively by studying motivations and behavior (Beaty, 2021; Medal, 2017).

Through these big-data strategies, SL truly lives up to its reputation of "inspiration at work."


Beaty, J. (2021, January 23). Big Data Analytics. Retrieved from the Michigan State University D2L site.

Coworking Resources. (2020, July 3). Global coworking growth study 2020. https://www.coworkingresources.org/blog/key-figures-coworking-growth

Livingstone, R. (2017, March 13). The future of online advertising is big data and algorithms. The Conversation. https://theconversation.com/the-future-of-online-advertising-is-big-data-and-algorithms-69297

Medal, A. (2017, May 16). How big data analytics is solving big advertiser problems. Entrepreneur. https://www.entrepreneur.com/article/293678#

Quantzig. (2020, May 8). Big data solutions help a chemical company to improve operational efficiency. Business Wire. https://www.businesswire.com/news/home/20200507006243/en/Big-Data-Solutions-Chemical-Company-Improve-Operational

#MediaAnalytics #SocialMedia #StratCom #BigData #Coworking

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