Content Affiliates and Digital Advertising through Online Sales Channels

Transcription

1 Content Affiliates and Digital Advertising through Online Sales Channels Abstract. Digital advertisers can use affiliate related sales channels to drive more qualified and targeted user traffic to their websites. This study examines the nature of these online sales channels, their role in the customer journey and different attribution models that can be applied in an environment of incremental sale. We employ online transaction data provided by a large online sales firm to investigate these questions. We find that affiliates are most likely to act as a converter. We subsequently investigate what financial value an affiliate program brings to a company under the most commonly used the last click attribution model, and compare it to the Shapley value based attribution model. The analysis of these models allows conclusions to be drawn on whether affiliate revenue significantly differs between the models. As we find, average affiliate reward significantly decreases when changing from last-click to the Shapley Value based model. Our study of affiliates can be understood as an example of the ways online content creators can monetize their content that flows into commerce. Keywords: Affiliates; Customer journey; Attribution models; Last-click; Shapley Value 1

2 Introduction The new use of electronic media such as affiliate websites and social networking generate additional advertising space that provides advertisers with remarkable targeting opportunities. As argued by Goldfarb (2013), the fundamental economic difference between digital and offline advertising is that digital advertising reduces the cost of targeting substantially. The targeting issue arose in early research on online advertising from the insight that Internet technology increases the customization of marketing messages (Ansari and Mela, 2003; Murthi and Sarkar, 2003). Researchers then addressed a range of issues that explored areas such as the effect of an advertiser s increased ability to target loyal customers (Chen et al., 2001), targeted advertising by monopoly media outlets (Gal-Or and Gal-Or, 2005), and whether improved targeting in a competitive online market environment is relatively more valuable than targeted pricing (Iyer et al., 2005). Whereas these explorations showed in some detail how online advertising works, recent economics and marketing research examines the effects of different targeting strategies on how online markets evolve and what it means for different market players (Bergemann and Bonatti, 2011; Athey and Gans, 2010; Levin and Milgrom, 2010). For instance, Levin and Milgrom (2010) argue that too much targeting can hurt media revenues because markets could become so thin that only one advertiser wants to target it. Their solution to this problem is to combine markets in order to increase competition between advertisers, resulting in improved revenues. There is another strategy whereby media platforms combine as affiliate sales channels so as to reach a targeted audience. These sales channels (publishers) promote advertisers (merchants) by placing a variety of advertisements (banners, text ads, product links) on their websites. If a user makes a purchase through the coded link to the advertiser s website, the affiliate website will get a percentage of that sale. Affiliates can have a significant impact on an advertiser s conversion rate. Jupiter Research (2008) reports that online shopping through affiliate referrals are 43% more likely to convert than average. As merchants only pay for advertising when there is a sale, affiliate marketing is the lowest cost advertising platform. In terms of revenues gained from affiliate marketing, estimates vary widely with some expecting an affiliate to contribute up to 30% of a company s overall online sale (Jenner, 2012). In recent trends, the growth of mobile sales is driving affiliate marketing as it has been reported that retail is the fastest-growing category in mobile commerce, with approximately $25 billion projected sales by 2017 (Forrester Research, 2011). In an earlier study conducted by Forrester Research (2009), it was reported that the U.S. affiliate marketing spending would reach $4.1 billion in annual revenues by 2014, up from $1.9 billion in The big brand firms, such as Zappos, Best Buy, Amazon, and Nordstrom have all adopted affiliate channels as their core marketing tool. Moreover, companies such as VigLink and Amazon Associates manage corporate affiliate programs as independent organizations. VigLink works closely with Amazon, ebay, Walmart, Target, and 30,000 other companies to facilitate their affiliate operations. Affiliates are also likely to have a large number of direct partnerships with merchants who use an in-house affiliate program. In a recent study, it was found that 30% of US affiliates have more than five such relationships compared to 15% of UK affiliates. The growth of affiliate sites indicates the potential of online content creators to monetize their content by establishing collaborations with industry and commerce. 2

3 Targeting in Affiliate Sales Channels Online advertisers produce content that support their selling activities. An affiliate publisher promotes merchants by placing a variety of advertisements (banners, text ads, and product links) on its websites. They are targeted at persuading online users (e.g. page visitors) to review and buy a product or service from the merchant (Duffy, 2005, p162; Mariussen, Daniele and Bowie, 2010). When an affiliate performs such a role it must make a number of decisions about its activities, such as the products it wants to promote, strategies to get visitors to its websites, and measures that can convert the visitors to sales. Only a careful analysis of these decision variables will drive online user traffic as required. For example, Marks et al. (2010) argue that products sold through affiliates require higher product value for them to be attractive and beneficial to merchants. They also suggest that it is hard to engage customers using affiliate marketing when there is a low product value. Therefore, a publisher must identify the right kind of products that will potentially be of interest to a target audience, and then generate and publicize content to put these products in the spotlight. This may involve focusing on certain topics that highlight and create visibility for the selected products. The website itself must be packed with relevant information (e.g. news stories and blogs, product descriptions and auditing existing content) as well as regularly updated to attract that all-important returning users. These measures will likely improve website page content, and consequently create higher quality perception, ensuring that the rich content affiliates reach a wide variety of consumers (Auger, BarNir and Gallauger, 2001). There are a number of methods that can be utilized to reward an affiliate. A commonly practiced model is pay per sale or cost per sale, which bases commission on the number of purchases generated by affiliates (Mariussen, Daniele and Bowie, 2010, pp 1709). Under another approach, pay per lead, affiliates are rewarded for new sign-ups (Mariussen, Daniele and Bowie, 2010, pp 1709). Commonly known as pay per click method, it rewards the referrer site for every user that visits the target (or merchant) website through a hyperlink (Mariussen, Daniele and Bowie, 2010, pp 1709). Two examples of affiliate commission structures are presented at Appendix I. Although affiliate companies have seen considerable reward for their activities, there are concerns over whether, in a multitouch medial environment, commission is being accurately attributed (Chandler-Pepelnjak, 2010). It is generally believed that advertisers rushed into the simplistic last click model and are subsequently struggling to understand the true value of their online marketing tools (Lovett, 2009). Recognizing the limitations of the last click model, practitioners have begun to explore alternative methods to attribute commission and gain a better understanding of the role affiliates play (Clearsaleing, 2014; Lovett, 2009). Methodology We employ online transaction data provided by a large online sales firm to test where affiliates feature most prominently in the customer journey, and how to attribute conversion when affiliates are involved. In total, 996,708 transactions are included in the analysis, with total revenue of $158,519,417, at an average order value of $ In terms of the customer journey lengths; 65.95% are one step, 14.58% two step, 8.78% three step, 3.86% four step, and 6.84% five steps or more. Our conversion data span 104 weeks from January 2012 to February Currently, the firm attributes revenue generated through online transactions to 3

4 its various paid marketing tools on a last-click basis. Typically, it offers 3-5% commission, depending on the amount of transactions generated (Goldschmidt, Junghagen and Harris, 2003). Attribution models examine the issue of who receives the commission, and attribution metrics inform critical decisions on marketing expenditures while ensuring that affiliates get better incentives and rewards from merchants (Wiesel, Pauwels and Arts, 2011). We advance the hypothesis that affiliates generate higher average order values than other online marketing tools; and affiliates generate more revenue using the last click-model as opposed to the Shapley value-based attribution model. Both hypotheses shed light on the effective role of affiliates as a targeting mechanism for digital advertisers, which is also reflected in the lastclick model becoming a desirable attribution strategy in the current affiliate environment. The alternative we propose is the use of Shapley Value based estimation that also supports affiliate related online sales channel activity (Osborne and Rubinstein, 1994). In the following, we explore the last-click model and review Shapley value-based model that could be used. We outline the effects of these attribution models on affiliate revenue, and conclude with analysis of the proposed model against the current last-click model. To examine the differences between affiliates and the other online marketing tools, we assess whether the means of the two groups are statistically different from each other, with reference to the average order value. In addition, if there is any significant difference between the revenue attributed to the various models. Data were collected on the seven most common online marketing channels including the following: Display ad transaction consisted of any visitor that originated from display ads posted on any third-party non-search web domain; Organic search transaction consisted of any visitor that originated from an organic (non-paid) search on a web search engine; Paid search transaction consisted of any visitor that originated from a pay-per-click (PPC) advertisement on a web search engine; Price comparison transaction consisted of any visitor that originated from a price-comparison site; transaction consisted of any visitor that originated from ; Retargeting transaction consisted of any visitor that originated from retargeting; Social medial transaction consisted of any visitor that originated from a social media website; and Others; including transactions that consisted of any visitor that originated from a manual URL entry into a web browser. Furthermore, we define our conversion measures as follows: Purchase Conversions: Number of final sales transactions generated by the online sales channels ads; and Purchase Conversion Rate: Percentage of purchase conversions on ad impressions out of the total number of times that ads are clicked. Findings: The Role of Affiliates in the Customer Journey The first hypothesis we examine in this study suggests that affiliates are most likely to be the last site before the purchase is made (Chandler-Pepelnjak, 2010; IAB, 2011; Wiesel, Pauwels and Arts, 2011). Figure.1 shows the role of affiliates in customer journeys (all figures and tables are collected at the end of the paper). We find that the role an affiliate is most likely to play is that of a converter (39.08%), the last stage in a customer s purchase. An affiliate is least likely to take on the role of an introducer, with only 11.3% of affiliates being an introducer in a customer s journey. However, due to one step journeys being classified as direct, affiliates value as the first step in a customer s online journeys should not be ignored. Assists and direct comprise the rest of the journeys, with 20.57% of affiliates featuring as 4

5 assists and 29.05% affiliates directing customers straight to the website. Affiliates are responsible for 8.67% of all direct online transactions in our data, as Figure 2 shows. Affiliate introducers are slightly less significant, being accountable for 6.53% of introducer transactions. For the focal firm, affiliate sites undertook the assist role in 10.95% of all the assists involved in its online transactions. Finally, the area to which affiliates contribute the most is that of the converter role, with 22.59% of converting sites being affiliates. Affiliates Attribution: The Last-Click Model Current industry practice suggests that the majority of online sales are attributed on a last ad or last-click model that gives 100% credit on the conversion to the last screen viewed or clicked before the conversion (Lovett, 2009; Clearsaleing, 2014). Chandler-Pepelnjak (2010) argue that this attribution measure may undervalue the impact of other ad formats that may have helped a consumer down the conversion funnel. Despite these apprehensions, the last-click model is the most simplistic and elegant approach for the merchants as it encourages affiliate sites to vie for the activity that results in a conversion. This is the reason that Google (2012) encourages the use of a last-click model if ads and campaigns are designed to attract people at the moment of purchase. The contribution of affiliate marketing and the other online marketing tools to online revenue are presented in Table 1. It can be seen that using the current last click method, affiliates generate 18.42% of total online revenue. The highest revenue generating online marketing tool is that of organic search, bringing 63.45%. Social media contributes the least with the current model, at 0.02%. Table 1 shows variation between the revenue generated by affiliates and the orders made. However, the average (mean) order value for affiliates offers insight into this as it is higher than any other of the marketing tools at $ We conduct two-sample t-test comparing average order value of affiliates to the rest of online marketing tools. It examines if there is any significant difference between the means of the average order values for affiliates against the rest of the online marketing tools (Full results are presented at Appendix II). The T statistic of is greater than the two-tail critical value of 1.96 and therefore indicating (with a 95% confidence level) there is significant difference between the average order values. Consequently the null hypothesis that affiliates do not generate higher average order values than other online marketing tools can be rejected. Furthermore the p-value of 3.13E-98 supports this rejection as it is considerably lower than Shapley Value-based Attribution Model In digital advertising, multi-touch attribution is one of the most important problems, especially as a wide variety of media are involved. In recent years, researchers have made considerable efforts to develop a true data-driven methodology to account for the influence of each user interaction to the final user decision. Shao and Li (2011) have developed a probabilistic model based on a combination of first and second-order conditional probabilities. There are two steps involved in generating the probabilistic model: Step 1. First compute the empirical probability of the main factors, 5

6 P(y x i )= (1) and the pair-wise conditional probabilities P(y x i, x j )=, (2) for i j. A conversion event (purchase or sign-up) is denoted as y which is a binary outcome variable, and x i,i = 1,...,p, denote p different advertising channels. N positive (x i ) and N negative (x i ) denote the number of positive or negative users exposed to channel i, respectively, and N positive (x i, x j ) and N negative (x i, x j ) denote the number of positive or negative users ex- posed to both channels i and j. Step 2. The contribution of channel i is then computed at each positive user level as: C(x i ) = p(y x i ) + { ( ) } (3) where N j i denotes the total number of j s not equal to i. In this case it equals to N-1, or the total number of channels minus one (the channel i itself) for a particular user. An advantage of using the above estimation is that it includes the second-order interaction terms in the probability model. As there is significant overlap between the influences of different channel impressions due to the user s exposure to multiple media channels, the model can fully estimate the empirical probability with the second-order interactions. Another important characteristic is that the net effect of the second-order interaction goes evenly to each of the two factors involved. Dalessandro, et al. (2012) show that, after rescaling, this probability model is equivalent to their Shapely Value formulation under certain simplifying assumptions. The Shapley value methodology was developed in a cooperative game setting, and has been applied from measuring systemic risk in a macroeconomic environment to inequality indices (Osborne and Rubinstein, 1994). In a typical Shapley value cooperative game, a group of players generates a shared value (e.g. wealth, cost) for a group as a whole. The Shapley value of a player in a game is calculated as his expected marginal contribution over the set of all permutations on the set of players. The Shapley value of an advertising medium is its expected marginal contribution over all possible set of the interacting channels. We have noted these assumptions in the above formulation, and use it to calculate the percentage of value allocated to each given channel. Figure 3 shows the effects on revenue attribution for the online marketing tools using the probability based model. Our results show that organic search contributes 64.17%, 6

7 whereas affiliates represent 14.34% of the revenue generated, down on the 18.42%, revenue accumulated under the last-click model. Social media and record the largest change in value percentage, as reflected in increases of 2.14% and 2.58% respectively. There is a sizable increase in paid search, increasing from 10.92% to 12.85%. We conduct two-sample t-test comparing last-click with Shapley Value-based affiliate rewards. The T statistic of is greater than the two-tail critical value of and therefore indicating (with a 95% confidence level) there is significant difference between the average affiliate reward. It could therefore be concluded that the Shapley Value on average attributes lower revenue to affiliates. This is also supported by Table 2 that shows affiliates are allocated 24.86% lower revenue under the Shapley Value. Discussion and Conclusion An affiliate website is a new media platform that provides improved targeting capacity and increased exposure times. Our empirical results largely support this assertion. Our data show that affiliates feature most prominently as the converter. Based on this finding, digital advertisers can easily establish where to target affiliate campaigns in the customer online journey. Accordingly, more engagement and utilization of content affiliates may increase their presence at the final stages of a customer journey. We then determine the financial importance of affiliates to a merchant under the last-click attribution model. As affiliates are frequently acting as the converter (22.59%) and the last-click model attributes 100% reward to a converter, it was expected that this model would offer a favorable proportion of revenue to affiliates. As we find, affiliates generated 18.42% of all online sales under the last-click attribution model; therefore exceeding estimates of 8-9% asserted in previous research undertaken by IAB (2011). The findings indicate that affiliates currently generate a fairly significant percentage of the company s online sales. We tested the hypothesis that affiliates generate higher average order values than the other online marketing tools. The t- Test results show that the average order value for affiliate attributed transactions is significantly higher than transactions attributed to all other marketing tools. We subsequently compared the effects of moving to the Shapley Value attribution model against the current last-click model under the assumption that affiliates generate more revenue under the last-click model than the Shapley Value-based attribution model. As discussed, affiliates predominantly play the role of the converter. Therefore, any activity that takes credit away from the converter is likely to be to the detriment of affiliate revenue. The revenue attributed to affiliates under the last-click model is 29.73% of the total revenue; however, it declines by 24.86% when moving to the Shapley Value-based attribution model from the current last-click model. It is important to note that the Shapley Value approach takes into consideration the entire sales cycle as having relevance, indicating that the reduction in importance of the converter has a negative effect on affiliate revenue. The t-tests, when comparing the last-click model against the Shapley Value-based model, showed a significant difference in the average affiliate reward value, with the last-click model attributing significantly higher. Therefore, both the revenue and average affiliate reward value showed a significant difference between the current model and the alternative model used. 7

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11 The Role of Affiliates in Customer Journeys 39.08% 29.05% 11.30% Direct Introducer Assist Converter 20.57% Figure 1. The role of affiliates in customer journeys % The Role of Affiliates in Comparison to Total Transactions in Customer Journeys 80.00% 60.00% 40.00% Other Affiliate 20.00% 0.00% 8.67% 6.53% 10.95% 22.59% Direct Introducer Assist Converter Figure 2. The role of affiliates in comparison to total transactions in customer journeys. Table 1: Different online marketing tools and revenue generated under last-click method. Tool Revenue (%) Orders (%) Average Order Value (in dollars) Affiliates Organic Search Paid Search Price Comparison Retargeting Social Media Other

12 Table 2: Affiliate revenue from linear, position based and time decay attribution compared to last-click attribution. Tool Affiliate Last-click Increase/decrease Revenue (%) Difference (%) from Last-click (%) Last-click n/a n/a Shapley Value % 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Revenue Generated for Online Marketing Tool Using Shapley Value based Model 14.34% 64.17% 12.85% 1.54% 2.58% 1.29% 2.14% 1.09% Revenue Figure 3: Probability (Shapley Value) attribution modelling 12

13 Appendix 1: Affiliate Commission Structures Example 1: Shorthaul flight Longhaul flight Flight & hotel or flight & car booking Hotel booking Car rental Hotel & car booking 0.50% of revenue 1.25% of revenue 3% of revenue 5% of revenue 5% of revenue 5% of revenue If an affiliate joins it will earn commission on a qualifying transaction, which is a valid flight, holiday, car rental, or hotel booking made on the company s website. This must be within the cookie period and its link must be the last one used to direct the customer to make their booking on the company s website. Example 2: Highlights of the affiliate program are as follows: 15% commission on all sales $1 commission on leads 90 day cookie: Affiliate get credit for any orders within 90 days of the original click placed by the customer Exclusive monthly newsletter with special promotions, content, and codes that Affiliate can adapt for its own marketing efforts Commission rate for coupon sites is assessed on an individual basis 13

14 Appendix II: t-test results I: t-test: Two-Sample Assuming Unequal Variances Affiliates Rest of OMT Mean Variance Observations Hypothesized Mean Difference 0 Df t Stat P(T<=t) one-tail 1.56E-98 t Critical one-tail P(T<=t) two-tail 3.13E-98 t Critical two-tail II: t-test: Two-Sample Assuming Unequal Variances Last Click Probability Mean Variance Observations Hypothesized Mean Difference 0 Df t Stat P(T<=t) one-tail E-136 t Critical one-tail P(T<=t) two-tail 1.9E-139 t Critical two-tail