In this discussion, we explore the feasibility of applying e-marketing web analytics and concepts to online social marketing interventions and e-Research initiatives. E-marketers frequently use web data, analytics packages and financial measures to optimize websites for customer engagement and sales. Although many online social marketers already use web analytics tools, they do not seem to conduct online social marketing with the same costing and behavioural frameworks used on e-commerce websites.

Social marketing is an approach to social change that applies commercial marketing principles to behavioural change interventions such as encouraging environmental protection, public health, safety or community development (Kotler et al., 2002). Frequently drawing upon the 4Ps of marketing—product, price, place, promotion—social marketers frequently use this model as a framework for designing behaviour change interventions (Kotler and Roberto, 1989). Social marketers ‘sell’ behaviours by enhancing the benefits of behavioural ‘products’ and reducing ‘prices’. For example, consider a campaign that aims to encourage composting among home gardeners. The campaign may frame composting (the behaviour) as a good way to enhance ones’ garden (the product’s benefits), and then make composters readily available (reducing time and hassle prices).

Online social marketing is the conduct of social marketing over the Internet. Given the conceptual similarity between marketing and social marketing (Lefebvre, 2000), there is a natural link between e-marketing and online social marketing. E-marketers sell products over the Internet and online social marketers sell behaviours over the Internet.

The term web analytics is frequently used by private sector e-marketers and website designers. It has been defined as the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing web usage (Web Analytics Association, 2008). Website optimization is a process that ensures that a website fulfils an organization’s purposes and customers’ expectations (Weischedel and Huizingh, 2006). Frequently using advanced analytic tools, either drawing data from server-side log files or client-side JavaScript tags, research indicates that Google Analytics may be the most prevalent client side web analytics package in the market (Krishnamurthy and Wills, 2006).

Return on Investment (ROI) could be the most widely used performance indicator: it measures business profits per expenditures. As a key business indicator, ROI is statistically correlated with corporate stock returns, which also serves as a primary business performance indicator (Jacobson, 1987). Within organizations, ROI can be improved when marketers achieve greater efficiency and effectiveness (Kotler, 2003). Online, ROI has been deemed a central measure of e-marketing (Peterson, 2005). ROI is frequently discussed within web analytics literature; and within web analytics software, a number of packages display ROI or easily export the data required to calculate it.

When conducting web experiments, there are a number of dependent variables to choose from; however, from a private sector perspective, a single long-term variable is ideal (Kohavi et al., 2007). When used as a dependent variable for fixed experiments or as a guide within web analytics packages, ROI serves as the ultimate arbitrator for measuring the performance of e-marketing approaches.

In terms of social marketing, the way problems are framed impacts on the way they are perceived by the public and policy makers (Andreasen, 2006). This may explain why there is substantial research dedicated to quantifying the social cost savings resulting from marketing preventative interventions. These savings are called Social Return on Investment (SROI). In the UK, where the government is investing heavily to promote social marketing, substantial work has been conducted to quantify the financial savings from advocating preventative health and responsible behaviours, such as exercise, diet or reducing binge drinking. For example, every £1 spent on preventative health promotion can save the state from £34 to more than £200 of health spending. Building on numerous social cost accounting methods, this particular UK initiative is advocating the development of international standard SROI measures (Lister et al., 2007).

As e-marketers focus on optimizing online campaigns to maximize ROI, social marketers can also optimize their online campaigns to maximize SROI. Given a tangible dollar value placed on social and health behaviours, this will enable social marketers to optimize online interventions according to the same metrics that guide e-commerce. For example, instead of applying behavioural goal funnels that lead towards online sales, the same tools can track users’ progress through persuasive behavioural chains (Fogg and Eckles, 2007) that guide audiences through a series of steps toward particular behavioural goals. Likewise, campaigns based on the transtheoretical stages of change approach (Prochaska and Norcross, 2001, Prochaska et al., 1995), which guides citizens through a series of phases, toward a target behaviour, SROI can serve as the ultimate arbitrator of campaign effectiveness. The fusion of e-marketing with online social marketing, through the use of SROI and web analytics, could result in campaign optimizations that offer interesting insights into online psychology and behaviour.

Although feasible, such a vision faces serious practical hurdles. For example, e-marketers can calculate ROI by simply comparing online sales to online marketing expenditures; while SROI is much harder to measure because of the difficulty in measuring complex attitudinal and behavioural change impacts. Also, SROI estimates are not yet standard, and given that the majority of SROI estimates address public health expenditures, there are limited works to help quantify the social costs of environmental, safety and community development behaviours.

This discussion is based on a conference proposal by  Brian Cugelman, Mike Thelwall, and Phil Dawes from the University of Wolverhampton’s Statistical Cybermetrics Research Group and the Wolverhampton Business School. Due to shifting priorities, this research track was not explored, but these initial thoughts have been posted for anyone who may be interested in exploring this issue.

Proposed citation
Cugelman, B. Thelwall, M. Dawes, P. (2008) “Web analytics, behavioural change and Social Return on Investment (SROI)” Retrieved from:


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