In times of web 2.0 consumer generated content tends to have an even stronger influence on potential customers than marketing activities from the business side. The electronic world vastly accelerated the proliferation of information. Especially younger people often collect independent information about a product online, before actually puchasing it. Classic advertisement is predominantly considered to be biased. Due to this potential of autonomous information, it is crucial for companies to find effective ways to track, measure and interpret electronic Word-of-Mouth (e-WOM).
This paper presents ways to measure web based contents quantitatively and introduces appropriate indicators to provide the company with a full-spectrum-view of the consumer generated media. Moreover the interrelation between e-WOM and sales is shown. As a conclusion, suitable metrics are adapted to a practically usable dashboard for the management.
Table of Contents
1 Introduction
2 The Usefulness of a Dashboard
3 Word of Mouth
4 WOM Dimensions
4.1 WOM Volume
4.2 WOM Valence
5 Adapted Metrics for e-WOM
5.1 WOM Volume-Related Metrics
5.1.1 Return on Marketing Investment (ROMI) / Cost per Click
5.1.2 Homepage Views and Page Impressions per Visit
5.1.3 Google Hits
5.1.4 Online-Forum Activity
5.1.5 Adapted PageRank (APR)
5.2 WOM Valence-Related Metrics
5.2.1 Retention Rate
5.2.2 Willingness to Pay
5.2.3 Conversion Rate
5.2.4 Willingness to Recommend (Net Promoter Score)
5.2.5 Review Star Quantity
5.2.6 Forum Valence
6 Development of an e-WOM Dashboard
6.1 The e-WOM Dashboard
6.2 Utilization of the “right” Metrics
7 Implications for Practical Intervention
7.1 Matrix-Based Strategies
7.2 APR-Based Strategies
8 Summary
Table of Figures
Figure 1: Return on Marketing Investment Formula
Figure 2: Online Forum Activity Formula
Figure 3: Adapted PageRank Formula
Figure 4: Retention Rate Formula
Figure 5: Conversion Rate Formula
Figure 6: Net Promotor Score Formula
Figure 7: Review Star Quantity Formula
Figure 9: Forum Valence Formula
Figure 10: The e-WOM Dashboard – Company Level
Figure 11: The e-WOM Dashboard – Product Level
Figure 12: Relative WOM Volume Formula
Figure 13: WOM Valence Formula
1 Introduction
“No WOM is the worst. It means you and your organization are destined to die a miserable meaningless death never having impacted anyone's day enough to generate a comment” (Safrit 2006).
This sentence of Zane Safrit might sound a bit harsh but nevertheless, in times of web 2.0 consumer generated content tends to have an even stronger influence on potential customers than marketing activities from the business side. The electronic world vastly accelerated the proliferation of information. Especially younger people often collect independent information about a product online, before actually puchasing it. Classic advertisement is predominantly considered to be biased.
Due to this potential of autonomous information, it is crucial for companies to find effective ways to track, measure and interpret electronic Word-of-Mouth (e-WOM).
This paper presents ways to measure web based contents quantitatively and introduces appropriate indicators to provide the company with a full-spectrum-view of the consumer generated media. Moreover the interrelation between e-WOM and sales is shown. As a conclusion, suitable metrics are adapted to a practically usable dashboard for the management.
2 The Usefulness of a Dashboard
Legendary football coach Vince Lombardi once said “It is hard to be aggressive when you do not know who to hit” (Craig 2008). A difficulty in marketing is that one is facing unstructured and complex problems, which have to be broken down into smaller and less complex problems. The restructuring process leads to a large and confusing amount of numbers and metrics that are often summarized and explained in long reports (Craig 2008).
Because time is crucial in business environments, management needs a tool that describes the actual situation of the company in a comprehensive and understandable way. The solution to this need is the dashboard. It is a tool which delivers “at-a-glance summaries presented in a highly visual and intuitive format[…]” (Dover 2004, p. 44).
A dashboard summarizes the most important metrics, the so-called key performance indicators. The nature of a key performance indicator (KPI) is the strategic importance of this metric (Craig 2008). The shown KPIs have to provide the decision making person with a full-spectrum view of the problem.
The main difference between a scorecard and a dashboard is the visualization. While on a scorecard the KPIs of a company are presented in a numerical way, on the dashboard the KPIs are pictured as gauges, stoplights or graphs. Thus, the dashboard shows the evaluated performance of the shown KPI. The results of the comparison between the planned progress towards an established goal and the real progress are shown in an intuitive way which allows a wider audience to understand the performance of the company (Craig 2008).
The visualization of KPIs for a wider audience has a lot of advantages as it provides a framework on which everyone can rely on. Therefore no one has to interpret or to justify numbers to someone else who might use different numbers. Thus, it is a timesaving tool which provides a consistent view on the key performance areas. This consistency enables executives to align resources where they are needed the most. Saving time and aligning resources gives the company the possibility of proactive responses for endangered areas (Dover 2004).
Providing a wider audience with easy-to-understand performance measures, dashboards drive a culture of transparency and accountability in which employees can check their performance at all time (Rust et. al. 2004).
The crucial part in every dashboard development is the selection of the KPIs. On the quality of these KPIs will depend the validity of the entire dashboard. Even though often the most time is spent on the availability of data, it is highly important to have chosen up front the right way to evaluate this data (Craig 2008).
Wrong performance indicators will mislead the company and wrong interpretation might even hurt more because the visualization of the numbers complicates the controllability of the numbers.
3 Word of Mouth
About two thirds of all sales in the consumer goods industry are based on Word-of-Mouth (Taylor 2003). The strong impact is to explain, as personal sources are in general viewed to be more trustworthy (Buttle 1998). As researchers found out, e-WOM is considered today as an endogenous factor that is mutually interrelated with the sales volume. Not only sales are affected by e-WOM but also increased sales cause a higher amount of interpersonal information exchange (Duan, Gu, Winston 2008). The internet made significant changes to the concept of WOM by heavily increasing the diffusion rate of information and making it less personal.
The main difference of e-WOM compared to traditional mass communication measures is the bi-directionality. The internet enables message recipients to publicly respond to messages and to publish their personal opinions and thoughts (Dellarocas 2003). But not only goods and services are reviewed on the internet today, the reviewers themselves have distinctive reputations and exposure values. Research found out, that the readers of e-WOM not only pay attention to the content of a message but also to the quality of the source of information (Hu, Liu, Zhang 2008; Forman, Ghose, Wiesenfeld 2008; Duan, Gu, Winston 2008).
Furthermore, a certain spillover effect has been discovered, showing that the sales of products can also be affected by WOM about other related products (Sicilia, Ruiz, Johar 2008).
To approach the e-WOM problem in a more structured way, this paper focuses on the two main dimensions, volume and valence and how they can be reasonably represented by appropriate metrics.
4 WOM Dimensions
The two most important dimensions of WOM in terms of its impact on sales are the volume and the valence.
WOM volume represents the quantity of WOM messages about a certain topic without considering the content.
Valence instead signifies the attitude of the reviewer towards a topic, considering the message either as negative Word-of-Mouth (NWOM) or positive Word-of-Mouth (PWOM).
4.1 WOM Volume
As marketing research discovered, the mere quantity of WOM is strongly intertwined with sales volume (Duan, Gu, Winston 2008; Liu 2006). Examplarily in the movie industry it was found out, that albeit the content of the review, people’s interest in a film rise with the amount of posts in the internet.
Hence, the WOM volume alone can be a major driver of revenues, regardless of the content of the consumer generated content.
It is assumed to be more important to generate high amounts of WOM instead of focusing on the personal attitude of the disseminators (Liu 2006).
e-WOM do not only emerge as a customer reaction to a new product or service, it can already be found well before the market entry. This so-called speculative e-WOM is a good precursor about how the product / service will be adopted by the customers.
Thus, WOM can also be used for sales forecasting, helping companies to adjust their capacities in advance (Liu 2006; Samson 2006).
The quantity of WOM is considered to be a highly relevant index for a company. Albeit differences in the importance of WOM volume among different industries are supposed, a correlation with sales is assumed in this paper. Methods for the measurement and interpretation of WOM volume are shown in chapter 5.1.
4.2 WOM Valence
Compared to the volume, the content of messages has a more indirect effect on sales. Strong valence is assumed to cause more WOM volume, which then again enhances the sales (Duan, Gu, Winston 2008).
As mentioned before, valence captures the nature of the message. By positive or negative ratings, review stars or the content itself, the sender expresses his attitude towards a certain product or service. PWOM and NWOM are supposed to have distinct consequences on the market success of a company.
Research found out, that sales are predominantly depressed by negative reviews rather than they are boosted by positive posts (Chevalier, Mayzlin 2006; Samson 2006). Because any company executive has the opportunity to publish positive reviews about a certain product in order to improve the customers attitude, PWOM is assumed to be less credible than NWOM.
Considering the effect of WOM valence on the brand choice instead of sales, NWOM and PWOM are supposed to distinct effect (East, Hammond, Lomax 2008). While PWOM is not considered as an important driver for product / service purchases, it has an important impact on the customers brand choice in the first place.
In terms of the market structure NWOM is more important in high commitment environments with little choice. However, PWOM is presumed to have a stronger impact in low commitment markets which are offering a vast variety of choices (Samson 2006). As mentioned above, the effect of the valence strongly depends on the reputation of the person that posts a respective message. The higher the reputation of the disseminator, the more he is accepted as an opinion leader.
Recapitulatory the effect of WOM valence on sales seems to be rather oblique. However, the valence represents the general attitude of the web users towards a product or service. In chapter 5.2 metrics for the measurement of the WOM valence are presented.
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- Christian Hackel (Autor:in), 2009, Dashboard for Consumer Generated Media, München, GRIN Verlag, https://www.grin.com/document/126264
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