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FANOMICS: More than Controlling Relationship Quality
Abstract
FANOMICS is designed to turn customers into fans and thus enable companies to achieve sustainable success. However, the possible applications go far beyond this, as we will explain to you in more detail in this chapter: (1) The assignment of customers in the Fan Portfolio creates the basis for customer value-based segmentation. It enables companies to address and serve their customers effectively and efficiently according to customer value. One important facet of this is fan marketing, whose task is to turn fans into even more profitable customers. (2) Potential customers can also be fans of a company. Identifying them and understanding why they have so far been non-customers creates the basis for efficient concepts for acquiring new customers. (3) With the Net Promotor Score (hereafter NPS), a new control parameter of customer relationship quality has become established in companies. Combining the NPS with the basic ideas of FANOMICS results in a control system that not only measures but also uses the logic of the Fan Principle for improvements.
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Net Promoter Score (NPS) has been widely adopted by managers as a measure of customer mindset and predictor of sales growth. Over time, practitioners have evolved the use of NPS from its original purpose as a transaction-based customer loyalty metric, towards a metric for tracking overall brand health which includes responses from non-customers. Despite enduring managerial popularity, academics remain skeptical of NPS, citing methodological issues and ongoing concerns with NPS measurement. This study re-visits the use of NPS as a predictor of sales growth by analyzing data from seven brands operating in the U.S. sportswear industry, measured over five years. Our results confirm—within the context of our study—that while the original premise of NPS is reasonable, the methodological concerns raised by academics are valid, and only the more recently developed brand health measure of NPS (using an all potential customer sample) is effective at predicting future sales growth.
So far, the systematic scaling of the acquisition of new customers via programmatic advertising has turned out to be associated with considerable difficulties. The main reason is a shortfall in terms of quantity and quality of available data and its limited predictive power regarding actual performance. In addition, accessing and using data in most cases have proven to be rather complex.
Aside from these difficulties, large players such as Google, Facebook, Amazon, Apple, and Alibaba have developed into platforms combining (a) large reach (b) high login frequency, and (c) great depth of data per user across different devices. Due to these factors, these “high/deep reach login platforms” continue to gain importance and lead to an increasingly transparent advertising landscape. Initially, this may appear to be a positive development for advertisers, since all relevant users can be reached via just a few platforms with great targeting precision. Medium to long-term, however, this can impair their position, since it becomes increasingly difficult for advertisers to differentiate themselves from others and maintain a competitive advantage based on the sophistication of their advertising activity. The problems arising for publishers and especially media agencies regarding this development are even more severe. But that will be covered only as a side issue in this article.
Using proprietary customer relationship management (CRM) data of the advertiser in conjunction with programmatic advertising appears to be one approach to mitigate the shift of power away from advertisers to the “high/deep reach login platforms”. And there is still a lot of unused potential to be systematically exploited in this area. In addition, the impact of this approach can be expanded and made more scalable by using CRM data to reach potential “upper funnel” customers via “lookalike” techniques.
To make systematic use of programmatic CRM in a coordinated approach of addressing customers, the underlying (a) logic/concept and (b) technical infrastructure have to be well designed so that existing customers and potential customers can be addressed in a structured way and across multiple channels/platforms. In order to implement integrated multi-channel/platform marketing, models based on individual user behavior are required to determine – among other factors – (a) what message (b) when (c) how/in which format (d) at which frequency (e) on which platform/channel, and (f) at what price should be placed in order to achieve optimal results. Furthermore, data collection, aggregation, and activation/usage need to be integrated into one comprehensive infrastructure.
Companies spend lots of time and money on complex tools to assess customer satisfaction. But they're measuring the wrong thing. The best predictor of top-line growth can usually be captured in a single survey question: Would you recommend this company to a friend? This finding is based on two years of research in which a variety of survey questions were tested by linking the responses with actual customer behavior--purchasing patterns and referrals--and ultimately with company growth. Surprisingly, the most effective question wasn't about customer satisfaction or even loyalty per se. In most of the industries studied, the percentage of customers enthusiastic enough about a company to refer it to a friend or colleague directly correlated with growth rates among competitors. Willingness to talk up a company or product to friends, family, and colleagues is one of the best indicators of loyalty because of the customer's sacrifice in making the recommendation. When customers act as references, they do more than indicate they've received good economic value from a company; they put their own reputations on the line. And they will risk their reputations only if they feel intense loyalty. The findings point to a new, simpler approach to customer research, one directly linked to a company's results. By substituting a single question--blunt tool though it may appear to be--for the complex black box of the customer satisfaction survey, companies can actually put consumer survey results to use and focus employees on the task of stimulating growth.
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