top of page
  • Writer's pictureJoshua Gould

Navigating Online Engagement

Updated: Sep 27, 2018

The Information Age has connected humankind through its most decisive tool, the Internet. Today, more than half of the world’s population is online (World Internet Statistics, 2018). This global digital network is a product of revolutionary changes in communication technology. One of the most profound internet applications has been Social Media. These web-based platforms promote user-generated content, instantaneous participation and have fundamentally shifted the way we experience our daily lives, both on a personal and business level.

The depth of change from these major technological shifts has effected culture, society and even government. Today, nearly 70% of the American public uses some form of social media (Pew Research, 2018). Additionally, as more people adopt and use these platforms, the user base becomes more representative of the broader population. More specifically, two-thirds of adults in the United States are Facebook users today, and from those users roughly three-quarters are active on Facebook daily (Pew Research, 2018). However, the impact of social media and importance of web-based user activity extends far beyond Facebook alone.

Although YouTube is not a traditional social media platform, functioning primarily as a video-sharing site, it is used by almost 75% of adults and 94% of 18- to 24-year-olds in the United States (YouTube, 2018). YouTube also serves as an example of the incredible amount of data and information these platforms are producing in our digital lives. Over 400 hours of video are uploaded to YouTube every minute, which equates to an astonishing 576,000 hours of content per day (Tran, 2017). With each click, like and swipe our connected devices flood the Internet with data, creating 2.5 quintillion bytes of data every day (Winans et al, 2017). All of this data makes content navigation, moderation and user engagement online quite arduous.

Digital media offers users countless choices with an endless supply of sources. However, the supply of human attention is vastly limited, especially as these sources begin to compete for attention and visibility in data-saturated environment (Webster, 2014). As we discussed above, the world is now awash in digital media and these emerging patterns of Internet-based communication and the use of social media have profound economic and social implications. Therefore, it has become a priority for businesses, institutions and even governments to measure and understand media use and user engagement online. Although the idea of media capturing the public’s attention as a gateway for influence isn’t new to the Internet era, these digital networks have evolved our culture’s social structures, enabling complex interactions, and allow for new ways of targeting users, cultivating interests and shaping attention (Andrejevic, 2015).

Emergent technologies aren’t just creating mass amounts of content. Digitally driven innovations and social media tools are opening up incredible opportunities to capture data, evolving our insights and strategies, helping to employ smarter tactics that better connect and target users on these platforms (Qualman, 2010). Although there are serious implications regarding privacy (Zomorodi, 2017), access to user insights in conjunction with the use of predictive analytics and algorithms has proven to be astonishing tools for user engagement and targeting attention online. These algorithms, which are quickly increasing in accuracy and efficiency, are driven to simplify the limitless amount of unstructured data online (Ghaznavi, 2016), categorizing complex user behavior and content. For a better understanding of intuitive and programmatic targeting, we should look no further than our online marketplace, or e-commerce.

Contemporary developments in communication technology have created high-choice media landscapes allowing users to customize and access content whenever they wish (Knobloch-Westerwick, 2015) Therefore, user engagement has become a central objective of digital marketing. Using content specific analytics, marketers actively measure and conceptualize consumer experiences in today’s digital age. Marketers have identified 4 main stages within a consumer life cycle: Reach, Act, Convert, Engage (Chaffey, 2013). By sumarizing these key activities, and exploring how they are being managed, we can better learn how content strategy effects our online experiences.

The first goal for content strategists is reach. This involves building awareness of their brand, product or service through web presence and visibility. Because of their interactive and communicative capabilities, social networks have become pivotal tools for customer engagement and generating brand awareness (Barreda et al, 2015). On average, a user’s posts on a popular social media platform, like Facebook, is seen by an average 35% of their connections (Bernstein et al, 2013). Ultimately, raising visibility will encourage user interaction through different paid, owned and earned media touchpoints (Lieb & Owyang). Before moving on, we should define these terms and their application to digital channels.

Cultural and technological shifts have changed marketing and created an entirely new ecosystem of connections online. Paid media channels are display and broadcast advertisements that can be presented to users through context specific channels based on their user profile and behavior (Bruce et al, 2017). By selectively targeting specific users, companies hope to establish brand awareness and increase future recall, recognition and intention to recommend or visit through exposure (Mason & Nassivera, 2013). Owned media refers to assets a brand owns or wholly controls like their company website, social media profiles, blogs, etc. Lastly, earned media is the most coveted channel for technocratic marketers today due to the everlasting importance of reputation and word of mouth in regard to brand awareness and loyalty (Pfeffer et al, 2014). Earned media is user-generated and/or shared content, like consumer mentions or shares within their personal social channels, and therefore is the most valuable and elusive channel of the three.

Marketers and content strategists may be able to influence and target users, but prompting action and interactivity cannot be directly controlled (Voorveld et al, 2018). Most brands deploy a concert of media channels to target users in an attempt to encourage action, or interaction, the second goal in our content strategy life-cycle. Encouraging the participation of site visitors or prospective users requires effective strategy through relevant, compelling content. Increasing active engagement requires strategists and marketers to have a good understanding of their key demographic and audience (Conrad, 2015). Today, successful content strategists employ an eclectic mix of social listening (Stewart & Arnold, 2018), insightful surveys, and analytical data to create the information architecture of their potential audience.

The third goal for content strategists is conversion, or the act of a sale for marketers specifically. Trust is a huge factor for conducting business, and this is even more crucial for online conversion (Santosh & Mukherjee, 2017). The Internet is rife with security breaches, hacks and scammers, which also pose threats to businesses hoping to establish trust and secure conversion online. Technically, sites can overcome some of these factors by enabling protocols like Secure Sockets Layers (SSL). However, generating Social Proof can leverage authentic endorsement to boost credibility more effectively (Amblee & Bui, 2011). These proofs involve an authority figure, influencer, or community member endorsing or positively reviewing a brand, product or service.

Prolonged engagement, or developing long-term relationships, with users is the ultimate goal for content strategists online. A primary factor for repeat interactions is simplicity. Similar to trust factors, decision simplicity can help users gather, understand and navigate information and content presented to them, readily weighing their options (Spenner & Freeman, 2012). Streamlining the user experience is an easy way to build brand familiarity with potential users. However, content strategists are now recognizing the importance of influencer based marketing and its impact for user engagement (Munnukka et al, 2016).

Recently, influencer marketing has established itself as a highly effective strategy for brands to build and engage with audiences through social media. Many studies have shown that social media influencers, through their authentic content creation and community interaction, help to engage prospective users online, gaining their trust and directly impacting engagement and conversion (Xin et al, 2017). These influencers can be celebrities themselves, as companies hope to astride their brand to one’s established notoriety (Nam-Hyun, 2016), or they may have started as an average networking contributor whose reputation and expertise become a valuable tool (Mazella, 2016). Regardless of their variety, influencers all share one thing in common, their immense, or niche, social networks. Utilizing the networks of authentic and reliable influencers is a quick and effective approach to reaching a wide audience online.

In the age of the Internet and big data we are witnessing a quantitative revolution in human knowledge, catalyzed by accompanying tools for data mining and analytics. As social networks and new media become even more prevalent in the way we communicate and interact with each other, which have wide spread economic and societal impacts, discovering methods of effective engagement and content strategy also become significant. By simplifying the engagement process and creating goals (ie. Reach, Act, Convert, Engage) we can better measure and structure content strategy. As previously discussed, authenticity, especially when wielded by community and network influencers, can be a powerful tool for effective engagement online. Finally, as technology continues to evolve we will envision new strategies for forecasting, targeting and engaging audiences through a growing range of social platforms and tools.


  • Amblee, N., & Bui, T. (2011). Harnessing the Influence of Social Proof in Online Shopping: The Effect of Electronic Word of Mouth on Sales of Digital Microproducts. International Journal Of Electronic Commerce, 16(2), 91-114.

  • Andrejevic, M., Hearn, A., & Kennedy, H. (2015). “Cultural studies of data mining: Introduction”. European Journal Of Cultural Studies, 18(4/5), 379-394.

  • Barreda, A. A., Bilgihan, A., Nusair, K., & Okumus, F. (2015). Generating brand awareness in Online Social Networks. Computers In Human Behavior, 50600-609. 

  • Bernstein, M. S., Bakshy, E., Burke, M., & Karrer, B. (2013, April), Quantifying the invisible audience in social networks. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 21–30).

  • Bruce, N. I., Murthi, B. S., & Rao, R. C. (n.d). A Dynamic Model for Digital Advertising: The Effects of Creative Format, Message Content, and Targeting on Engagement.

  • Chaffey, D., Bosomworth, D. (2013). “Digital Marketing Strategy”. Smart Insights. Januarys 2013.

  • Conrad, S. (2015). Manage Your Online Reputation. Physical Therapy, 6-10.

  • Internet World Statistics. (2017). Internet Usage Statistics: World Internet Users and 2018 Population Stats. Miniwatts Marketing Group.

  • Knobloch-Westerwick, S., Westerwick, A., & Johnson, B. K. (2015). “Selective Exposure in the Communication Technology Context”. In S. S. Sundar (Ed.), The handbook of psychology of communication technology (pp. 407-424). Malden, MA: Wiley-Blackwell.

  • Lieb, R., Owyang, J. (2012). “The Converged media Imperative: How Brands Must Combine Paird, Owned & Earned Media”. Altimeter. July 18, 2012.

  • Mason, M. )., & Nassivera, F. ). (2013). A Conceptualization of the Relationships Between Quality, Satisfaction, Behavioral Intention, and Awareness of a Festival. Journal Of Hospitality Marketing And Management, 22(2), 162-182.

  • Mazella, V. (2016). “The Micro-Celebrity Economy: How Influencers Impact the Restaurant Industry”. Rutgers University. Spring 2016.

  • Munnukka, J., Uusitalo, O., & Toivonen, H. (2016). Credibility of a peer endorser and advertising effectiveness. Journal Of Consumer Marketing, 33(3), 182-192.

  • Nam-Hyun, U. (2016). Predictors Of The Effectiveness Of Celebrity Endorsement On Facebook. Social Behavior & Personality: An International Journal, 44(11), 1839-1850.

  • Pew Research Center. (2018). Social Media Fact Sheet. February 5, 2018.

  • Pfeffer, J., Zorbach, T., & Carley, K. M. (2014). Understanding online firestorms: Negative word-of-mouth dynamics in social media networks. Journal Of Marketing Communications, 20(1/2), 117-128.

  • Qualman, E. (2009). “Socialnomics: How social media transforms the way we live and do business”. Hoboken, N.J: Wiley.

  • Santosh C, K., & Mukherjee, A. (2017). Characterizing Product Lifecycle in Online Marketing: Sales, Trust, Revenue, and Competition Modeling.

  • Spenner, P., & Freeman, K. (2012). To Keep Your Customers, Keep It Simple. Harvard Business Review, 90(5), 108-114.

  • Stewart, M. C., & Arnold, C. L. (2018). Defining Social Listening: Recognizing an Emerging Dimension of Listening. International Journal Of Listening, 32(2), 85-100.

  • Tran, K. (2017). Viewers Find Objectionable Content on YouTube for Kids. Business Insider. November 7, 2017.

  • Voorveld, H. M., van Noort, G., Muntinga, D. G., & Bronner, F. (2018). Engagement with Social Media and Social Media Advertising: The Differentiating Role of Platform Type. Journal Of Advertising, 47(1), 38-54.

  • Webster, J. G. (2014). “The marketplace of attention: how audiences take shape in a digital age”. Cambridge, Massachusetts: The MIT Press, 2014.

  • Winans, M., Faupel, D., Armstrong, A., Henderson, J., Valentine, E., & McDonald, L. (2017). 10 Marketing Trends for 2017. IBM Marketing Cloud.

  • Xin Jean, L., Radzol, A. M., Jun-Hwa Cheah, (., & Mun Wai, W. (2017). The Impact of Social Media Influencers on Purchase Intention and the Mediation Effect of Customer Attitude. Asian Journal Of Business Research, 7(2), 19-36.

  • YouTube. (2018). YouTube for Press. Accessed on March 20, 2018.

  • Zomorodi, M. (2017, March 29). Do You Know How Much Private Information You Give Away Every Day? Time.

20 views0 comments


bottom of page