Executive summary of "Recommendations as personalized marketing: insights from customer experiences

Citation

(2014), "Executive summary of "Recommendations as personalized marketing: insights from customer experiences", Journal of Services Marketing, Vol. 28 No. 5. https://doi.org/10.1108/JSM-06-2014-0222

Executive summary of "Recommendations as personalized marketing: insights from customer experiences"

Article Type: Executive summary and implications for managers and executives From: Journal of Services Marketing, Volume 28, Issue 5

This summary has been provided to allow managers and executives a rapid appreciation of the content of the article. Those with a particular interest in the topic covered may then read the article in toto to take advantage of the more comprehensive description of the research undertaken and its results to get the full benefit of the material present.

If you buy a book via a Web site – for example, the Conrad classic "Typhoon" – you might be pleased to get a message from the online shop recommending another Conrad adventure or maybe another seafaring tale from another author. But you might be less than impressed if the recommendation is for books about fighter jets or extreme weather.

Maybe that is being a little unfair to the complex technical wizardry – involving algorithms and filtering and all sorts of clever stuff – which has the ability to learn customer preferences and make appropriate recommendations. Perhaps Amazon is among the most widely known, and its customers who are helped in discovering an item that they might not otherwise have done are likely to be pleased. Ok, maybe the technology tools get it wrong sometimes, but you have to admit they are a remarkable piece of marketing kit. "How do they do that?" might be a difficult question for the non-technically minded to answer. But maybe the more pertinent question would be "How well do they do that?" Because, for all its potential to enhance customer satisfaction, the practice of making personalized recommendations has met with both successes and – well, challenges. In fact, in some cases, these recommendations might actually lead to customer dissatisfaction, even annoyance or irritation.

To online service firms embracing personalization as part of their core competitive strategy, (dis)satisfaction with agent recommendations is not without consequences. First, customer (dis)satisfaction may have an immediate impact on sales transactions in the current service episode. While satisfied customers are more likely to be engaged and to proceed to placing orders, dissatisfied customers may simply drift away from the current episode, or even to a competitor’s Web site (which is just a click away). Second, customer (dis)satisfaction may also affect their overall experiences with the service firm, which may, in turn, exhibit its impact on customer retention and long-term profitability. Furthermore, with customers of ever-increasing technology savvy, marketing of personalized products and services is becoming a business necessity. A solid understanding of sources of customer (dis)satisfaction with recommendation services may help the development of recommendation algorithms and may have broader implications for service firms hoping to be more adaptive in their individual marketing strategies.

In "Recommendations as personalized marketing: insights from customer experiences", Dr Shen takes the customer perspective in trying to understand sources of customer satisfaction or dissatisfaction and what are the underlying expectations driving this satisfaction or dissatisfaction. The study focuses on three service firms (Amazon, Netflix and Apple’s iTunes) whose proprietary recommender systems have been in operation for a relatively extended period.

From that customer perspective, providing explanations why the system recommends the suggested items (e.g. "We recommend this item because you like X") is an attempt to make the algorithm "transparent" and to increase customer satisfaction and acceptance. However, although explanations may make the recommendation logic more "transparent", the current research finds that they are not enough.

What is needed is a convincing connection between the recommended item and previously related preference. For example, recommending accessory items or items often purchased together is considered convincing. In contrast, unconvincing recommendations are often weird or difficult to understand, even if the recommendation algorithm is clearly transparent. For example, in this research, a customer was dissatisfied by the recommendation of "The A Team" by Ed Sheeran based on liking of Elliott Smith because, according to the customer, their music styles should not go along together.

The author’s research propositions are that:

• customers who have well-defined, stable preferences and good knowledge into their own preferences will expect accuracy benefit in recommendation outcomes;

• customers who do not have well-defined preferences and who clearly know their lack of well-defined preferences will expect discovery benefit in recommendation outcomes;

• customers who have the technology savvy about recommendation agents will expect algorithm benefit in recommendation process;

• customers who do not have the technology savvy about recommendation agents will expect convincing connection benefit in recommendation process;

• customers who use recommendation agents are committed to the learning relationship with the service firms; and

• customers who use recommendation agents are motivated by a subjective belief in their true preferences, regardless of whether preferences are overt to, or hidden from, themselves.

This research suggests that customer expectations should be inherently relational in that roles are being defined for relationship partners and norms are being articulated for the relationship. When a recommender system is deployed as a personalized marketing tool, its performance is not just quality of recommendations as a consumer decision support – something that can be left to the discretion of the algorithm team. Rather, its performance should be a marketing management concern and should be assessed more comprehensively in terms of how effective the recommender system is as a marketing tool.

To read the full article enter 10.1108/JSM-04-2013-0083 into your search engine.

(A précis of the article "Recommendations as personalized marketing: insights from customer experiences". Supplied by Marketing Consultants for Emerald.)