Social Networking: The Corporate Value Proposition
Almost two years ago, in an article in the July-August issue of the Ivey Business Journal, “Social Networking: The View from the C-Suite,” we wrote that, “Many managers today are uncertain about what social networking really means, how it fits their business strategy, and most importantly how they can define its practical value to the business.” How little the world changes! Despite two years of increasing corporate social media activity, our research is telling us that the C-Suite is still finding it extremely hard to define their organization’s value proposition for social media.
eMarketer, a U.S.-based firm that provides research and analysis of digital media, recently reported that 175 chief marketing officers were asked to identify social media activities with the highest Return on Investment. Most did not know the return (“Dramatic Difference in Approach to Social Media Metrics”, Feb 8, 2011). Even ‘Facebook’ and ‘ratings and reviews’, the two features rated as having the greatest ROI, were only so rated by about 15 percent of respondents. Other researchers have recently told similar stories. We agree with eMarketer, that, “The ROI question is still not answered”.
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This article takes a further look at social media’s value to C-Suite decision makers. How can executives quantify the benefits of fostering customer engagement and brand? How can they impute value to transforming influence? How should real-time, collaborative dialogue between the company and customers and vice versa best be expressed as a value proposition?
1. Rethinking how marketing views social media
Given marketing’s prominence as an expense category, the C-Suite has long wrestled with the question, “What is our return on marketing?”.
To test the question, we asked a number of practitioners how they measure the value of social media and what sort of results they were seeing from its use. We found the answer can all too easily default to marketing goals rather than specific metrics and results. Certainly, goals are a valid conceptual starting point, especially for social-media measurement beginners. Indeed, failure to identify goals before selecting metrics frequently leads to underperformance.
However, goals can only take us so far in defining and assessing the value of social media, and they will likely be insufficient when we have to make operational marketing decisions. If executives are to deliver on brand promises, they need a deep understanding of customers, one that can be gained from evaluating customer behavioural data at a granular level.
Finding actionable metrics
Analysis of customer data and other metrics has been evolving through a hierarchy of increasing sophistication (see the accompanying table):
LEVEL 1 – Volume oriented
Traditionally, marketers wanting to address operational matters have taken a more quantifiable approach, using metrics that have tended to be volume-oriented. Typical examples are: number of followers, traffic driven to the website, community traffic hit rates, page openings, click-throughs, time spent on-line, responds vs. non-responds, postings and comments, conversions, and units sold. Volume-oriented metrics are undoubtedly useful, but relied on by themselves they can foster a ‘more is better’ mindset. They also tend to provide only a partial answer – flagging increases or decreases in customer activity without actually telling us what to do. Our view is that volume-oriented practices limit value for decision makers in the C-suite.
LEVEL 2 – Customer attitudes and needs
Limitations of volume metrics have led behavioural marketers to examine customer attitudes and needs more closely. Metrics include: customer satisfaction, cost-of-acquisition, brand awareness, brand competitiveness, and brand likeability. The ‘net promoter score’ is an indicator of customers’ attitudes derived from measuring the customer’s likelihood to recommend the firm or product to others. These metrics bring a more qualitative view of customers but they still can reinforce volume oriented thinking and thus inadequate as proxies for quantitative insights. Naturally, the more longitudinal the data become over time, the more relevant they will be to those who really want to know ‘what happened’. Our view is that a more holistic view of the customer, one provided by a social media microscope, offers considerable promise. But a lack of consistent data, historical bases, sharing standards, and transparency will keep it off many C-suite dashboards.
LEVEL 3 – Qualitative measures
Some marketers in this ‘advanced’ category optimize their operational practices and brand- execution value proposition the same way that they work to optimize their ad spend. This has led to use of qualitative measures to support operational questions like: What are our customers’ individual needs? How good are our insights into the way our customers regard and connect with our brand? How and when can we best engage our customers and enlist them as collaborators? How innovative, differentiated, and resilient will our brand continue to be in these commoditized and competitive times? Our 2009 article gave some examples of social media qualitative metrics that bear repeating: customer share of wallet, reasons for changes in composition of customer lifetime value and satisfaction, channel effectiveness related to customer needs, and effect of time to market, pricing power, and brand equity. The aim is to get dynamic insights into brand engagement, audience captivation, level of interest, and content curation – why people buy, what triggers a stretch-purchase, who the key influencers are, and what strengthens relationships? Until these questions can be answered adequately, these qualitative measures will inform a C-suite member’s decision but not direct it.
LEVEL 4 – Modeling
There is an emerging fourth level in the hierarchy, modeling planning-related data. This is so far a relatively underdeveloped (and under-automated) aspect of marketing practice. As discussed in the rest of this article, it involves creating social media analytical models that synthesize the complexities of both volume and qualitative data – with value projections, iterative ‘what if’ calculations, decision criteria, and prioritization of activities. The challenge for modellers is to eliminate the bias inherent in the mathematics underlying the business-as-usual mix optimization models that have been in use for over 30 years now. A message to econometricians: it is no longer business as usual so stop running those forecasts. More than any other decision makers, marketing planners tend to get this.
Empirical results from brand lifecycle activities example: Mark Daprato, VP, Marketing at Swiss Chalet, measures “the social media cost of acquiring a fan, the incremental benefit of unpaid content compared with paid clicks, and soft benefits like fan responses to an on-line customer complaint posting” that together provide social media value added. He adds that brand lifecycle only delivers a return when you reach the affinity stage with customers.
Level 4 (modeling) in our view is the most robust — and is essentially the platform on which the rest of this article is based.
Actual measurement practices in supporting specific brand planning can often be somewhat experimental. Examples related mainly to the first three levels are illustrated in the boxes containing comments from three of the leading marketers we spoke to. Their comments reinforce the understanding that a combination of quantitative and qualitative marketing measures helps the marketer improve interaction tone, quality, and benefit to the customer – not just in social media but across the full spectrum of the business.
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