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Converting browsers into recurring customers: an analysis of the determinants of sponsored search success for monthly subscription services

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Abstract

Sponsored search is a multi-billion dollar market. Determining which characteristics of sponsored search campaigns are associated with campaign success has been an active research field, particularly in the consumer retail industry. We develop and examine a theoretical model that integrates and extends established theories of information content, persuasive content, emotion, and style of advertising in traditional media (print, television) to online advertising. We analyze the determinants of sponsored search success across multiple sub-industries in the long term subscription market, with data on four companies who sell long term subscription services with monthly recurring revenue. We use structured equation modeling to test a model that relates bid content features to the average position and number times an ad is displayed (i.e., impressions) in search engine results. In turn, the average position and number of impressions, together with the ad content features, influence the number of times that consumers will click on an ad. The results, based on the magnitude of path coefficients for all companies (i.e., brands), show that average position and total impressions (which are influenced by bid characteristics) have a far stronger effect on clicks than the impact of ad characteristics. The content analysis study undertaken here vividly illustrates that the effects of information content, persuasive content, emotion, and style on advertisement clicks are highly diverse across brands. This suggests the need for future research on brand, product, and consumer-specific factors that influence clicks, and further research on automated tools that provide tailored content optimization guidance to online campaign managers.

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Author information

Authors and Affiliations

  1. Department of Business Information Technology, Pamplin College of Business, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA, 24061, USA

    Alan S. Abrahams & Cliff T. Ragsdale

  2. Department of Accounting and Information Systems, Pamplin College of Business, Virginia Tech, 3007 Pamplin Hall, Blacksburg, VA, 24061, USA

    Reza Barkhi & Linda G. Wallace

  3. Department of Marketing, Pamplin College of Business, Virginia Tech, 2016 Pamplin Hall, Blacksburg, VA, 24061, USA

    Eloise Coupey

Authors
  1. Alan S. Abrahams
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  2. Reza Barkhi
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  3. Eloise Coupey
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  4. Cliff T. Ragsdale
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  5. Linda G. Wallace
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Corresponding author

Correspondence to Alan S. Abrahams.

Appendices

Appendix 1: description of custom taxonomy

Table 4 provides a mapping of the custom taxonomy for ad and bid words, used in this paper, to prior research. Numeric columns are as follows: Occurrences = The number of appearances of the word across all bids and ads (including ad title and ad text); Uniques = The number of unique words in this category, in the data set; Frequency = the number of times, on average, each unique word from this class was used across all bids and ads (Frequency = Occurrences ÷ Uniques). The table is sorted in descending order from categories with most word occurrences in the data set, to those with least. Categories omitted from our model are shown italicized in Table 4—these categories are omitted as they occur too infrequently to allow significant, generalizable conclusions to be drawn.

Table 4 Origin of classes in custom taxonomy
Full size table

“Information Content” categories from Resnik and Stern [44] and Abernethy and Franke [1] which do not appear in our taxonomy were:

  • Taste, Nutrition, and Packaging were omitted as these categories are appropriate to retail goods, but not to subscription services.

  • Warranties, Safety, Independent Research, Company Research, and New Ideas were omitted as there were no appearances of these categories in our data set; Resnik and Stern considered hardcopy advertisements that had greater word count than sponsored search advertisements (typically less than 12 words); sponsored search advertisements in our sample appear to omit these categories for lack of space.

Appendix 2: sentiment extraction

This appendix gives descriptions of General Inquirer, ANEW, AFFIN, and SentiStrength dictionaries used for sentiment extraction. The columns in Table 5 are as follows:

Table 5 Coverage of sentiment analysis methods for campaign data set
Full size table

  • Dictionary: the name of the metric

  • #: the number of lexical entries in the metric in the dictionary

  • Scale: the basis of scale the scale-minimum and maximum score for each word in the dictionary

  • Description: a brief description of the metric

  • Coverage: the percentage of bids/ads in the data set for which a non-neutral score is obtained for this metric. Low coverage indicates the metric is not helpful for describing a majority of the campaigns in the data set. We describe coverage, rather than simply ‘missing values’ or ‘non-zero values’, because SentiStrength measures neutral as +1 or −1. As +1 or −1 are both neutral values (but would not be counted as missing or non-zero), we specify coverage as non-neutral values.

Appendix 3: descriptive statistics and histograms

Tables 6 and 7 report descriptive statistics and histograms, respectively, for the variables in this study. Table 6 shows descriptive statistics for the portion of the data set that had no missing values that was used for PLS (15,658 records). The histograms in Table 7 show distributions for the original complete data set (25,582 records). In Table 6, data is omitted (marked as “Insufficient data”) for variables which had missing values for more than 99.5 % of bids, which had missing values for more than 99.5 % of ads, or which had insufficient occurrences (as per “Appendix 1”).

Table 6 Descriptive statistics for data set used for PLS
Full size table
Table 7 Descriptive histograms for full data set
Full size table

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Abrahams, A.S., Barkhi, R., Coupey, E. et al. Converting browsers into recurring customers: an analysis of the determinants of sponsored search success for monthly subscription services. Inf Technol Manag 15, 177–197 (2014). https://doi.org/10.1007/s10799-014-0186-0

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