- What metrics does the company track (or should track)? How do business models and stages of growth drive metrics this company tracks?
- In your opinion, what is the OMTM for this company? What should be their line in the sand?
- Why was Idarraga questioning the sustainability of Mate1's business model?
- What is your recommendation regarding the idea of acquiring an ad-sponsored website?
- How much is Mate1 worth? Should owners sell it?
IVEy Publishing 9B17E015 MATE1.COM: DIGITAL DATING DILEMMA Charles Wasserman, Dharsana Vijayaratnam, Thomas Yuk, Sharon Bir, and Deb Elkink wrote this case under the supervision of Professor Derrick Neufeld solely to provide material for class discussion. The authors do not intend to illustrate either effective or ineffective handling of a managerial situation. The authors may have disguised certain names and other identifying information to protect confidentiality. This publication may not be transmitted, photocopied, digitized, or otherwise reproduced in any form or by any means without the permission of the copyright holder. Reproduction of this material is not covered under authorization by any reproduction rights organization. To order copies or request permission to reproduce materials, contact Ivey Publishing, Ivey Business School, Western University, London, Ontario, Canada, N6G ON1; (t) 519.661.3208; (e) cases@ivey.ca; www.iveycases.com. Copyright @ 2017, Richard Ivey School of Business Foundation Version: 2017-11-24 Mauricio Idarraga had the perfect qualifications for his position as vice-president (VP) of operations for Matel.com (Matel), an online dating company headquartered in Montreal, Canada. His background in technology and finance had prepared him for a leadership position at the corporate level, while his South American roots, rich in family and community values, gave him a natural emotional intelligence that was essential for successfully managing interpersonal relationships-and at the moment, in the middle of Matel's weekly budget meeting, emotional intelligence was critical as emotions were running high. "We are losing money by the second!" barked Andrew Stern, VP of marketing. "We need the market spend reports. Now!" Idarraga nodded, instinctively aware of the rising frustration among his operations team members seated around the table. The imperative from marketing was indeed unambiguous: Attract new online dating subscribers to Matel's website (see Exhibit 1). The challenge lay in how to go about optimally allocating the precious marketing dollars, amounting to about CA$125,000 per week, and this depended on finely tuned, high quality, real-time business intelligence (BI) reports from the firm's data warehouse. Unfortunately, the Dino server that hosted the warehouse frequently crashed, leaving the data-driven company relying on guesswork for days at a time. Idarraga understood that timely marketing reports were a priority. Sub-optimal advertising that failed to drive customers to the website, day to day and minute to minute, directly affected revenue and profitability. But he wondered if this problem was a symptom of a larger malignancy. Was Matel pursuing the right strategy to achieve its big-picture objectives? THE ONLINE DATING INDUSTRY Idarraga had found his way naturally into the online dating industry when he joined the Matel team. The pace of life in North America was much busier than it was in his home country of Colombia and, he had All dollar amounts are in Canadian dollars.Page 2 9B17E015 realized, made "meeting online" a perfect answer for many people seeking a partner. Indeed, over the previous decade, online dating had become the number one method of connection for those who rejected the more traditional bar and nightclub scenes or the limited network of introductions by friends. Exchanging emails or instant messages let people engage in low-risk conversations to assess whether they had found a good fit before committing to a live date. This also opened up a much larger pool of singles, leveraging technology to make matches based on common interests, shared values, and attractiveness. By 2013, 30 to 40 million North Americans used online dating sites, and usage was increasing across different age groups (see Exhibit 2). Online daters could choose from a surplus of services across an array of niche service types. Two business models existed in the industry: fee-based subscriptions (which generated revenue by collecting credit card data from users) and advertisement (ad)-supported "free" accounts (which generated revenue via advertising and click-through and up-selling activities). The global online dating industry was a $3.7 billion market, with the United States accounting for 70 per cent of that total. Online daters appeared to be unaffected by the general price sensitivity seen in most other sectors; dating sites were able to charge premium subscriptions of up to $78 per month." The United States had the most mature market in the industry-and the most competitive. Through mergers and acquisitions, the largest conglomerate in this space was Match Group Inc., which owned many well-known brands including Match.com, Tinder, Plenty of Fish, and OKCupid, together representing 22 per cent of the global market and over 50 per cent of the North American market. Strategic acquisitions had allowed Match Group Inc. to dominate the market in a number of key dating niches, thereby gaining a greater share of the total online dating user base. Many companies attempted to differentiate their services by targeting a niche market. For example, Ashley Madison, a Canadian-based company, provided "discreet relationships" for people looking for a fling to escape the "mundane, boring, everyday activities" of marriage and family life. Others, such as eHarmony, focused on long-term relationships through "algorithm-based" matching and "science-based" compatibility spotting, with integrated Facebook analytics to understand a user's friend base and find prospective partners-an automated service mimicking a professional human matchmaker.' Online dating relied on state-of-the-art technology to reach prospective users, and the industry was one of the first to successfully utilize native mobile applications (apps) for commercial purposes. Robust technology was necessary to create and maintain extensive databases of member information as well as support real-time messaging and notifications. Cyber security was a major concern for site owners, who had invested significant sums of money to maintain and safeguard the anonymity of their clients' personal information. Linda Cotrina, "7 Best Mobile Apps for Dating," Canadian Living, July 23, 2014, accessed February 19, 2017, www.canadianliving.com/life-and-relationships/relationships/article/7-best-mobile-apps-for-dating. Interview with Elizabeth Wasserman, April 2016. " Street Authority, "This Online Dating Stock Is A 'Buy," Yahoo! Finance, May 24, 2016, accessed February 10, 2017, http://finance.yahoo.comews/online-dating-stock-buy-173000789.html. Interview with Elizabeth Wasserman, op. cit. Ashley Madison website, accessed February 16, 2017, www.ashleymadison.com. Katie Engelhart, "Online Dating and the Search for True Love-or Loves," January 30, 2013, accessed February 10, 2017, ty/life/true-loves/. A "native application" was designed and built for a specific platform. While a conventional web page could serve content to both desktop and mobile browsers, a native app often provided higher user performance and could take advantage of special features of a device (e.g., camera, accelerometer). However, to build one was resource-intensive.Page 3 9B17E015 THE MATE1 MODEL Brother-and-sister duo Charles and Elizabeth Wasserman founded Matel in 2004 after brainstorming ideas for a technology start-up with a proven business model. The Wassermans had been raised in an entrepreneurial family culture. Their grandfather had emigrated from Poland at the end of World War II, bringing with him experience in the needle trade from the old country. He and his son became magnates in Montreal's renowned textile industry, founding a garment manufacturing business and passing along their spirit of innovation to the junior Wassermans. Through family investment and mentorship, Matel grew to become one of the largest online dating companies in the world within two years of its launch. With a full-service offering related to mainstream/casual dating, and both desktop (web) and mobile (app) platforms, Matel operated throughout North America and in English-speaking countries around the world. By 2013, Matel had grown to 40 employees, and its mobile app had become one of the 15 highest- grossing social media apps in the United States (see Exhibit 3). Matel's success and competitive advantage was attributed to the Wassermans' credo that customers came first, and Idarraga, joining the company in 2015, fit right in with the company's philosophy. He believed that good businesses always took care of their tier-one problems-and for a client who was single and did not want to be, meeting someone was a tier-one problem. Idarraga made a number of physical changes at Matel's headquarters, such as replacing the traditional reception desk with a ping-pong table, which conveyed a positive and playful message, and spurred employees to challenge each other in weekly tournaments. The Matel brand was targeted to older consumers, 35 to 65 years of age, with more disposable income and the willingness to pay a subscription fee. Matel's monthly fee of $59.95 covered full access to its services. With over 35 million members and 8,000 new users registering daily, the service was available from both the desktop and mobile apps (see Exhibit 4). The mobile infrastructure was non-native, which meant that a common app codebase sat atop a framework that supported website, Android, and iOS experiences. Operating in an industry heavily influenced by new technology, Matel was continuously driven to adapt its technological capabilities, business strategies, and product innovations to maintain high customer growth. Emerging technology was having a significant impact. The advent and market adoption of smartphones, for example, had disrupted and transformed the industry: just three years previously, Matel had had no mobile products and only 5 per cent mobile usage, yet by the time Idarraga had joined the company, 90 per cent of new clients had signed up using their mobile phone Idarraga believed that the next generation of online dating was likely to incorporate new and emerging technologies, such as artificial intelligence and virtual reality, to provide increasingly deeper and more robust product experiences for customers. Technology Infrastructure Operations relied on a multi-tiered, service-oriented architecture that was largely built on open source software and that ran primarily on the Java virtual machine (JVM) platform. JVM was a popular computing platform for online businesses because it allowed a given piece of software-say, a program to display a data input form on the screen-to be written once and then run on multiple different platforms (e.g., in a desktop browser or inside a mobile tablet or phone app). As a result, it was less time consuming and much less expensive to deploy existing services when a new user technology platform popped up. (See Exhibit 5 for an illustration of overall data flow.)Page 4 9B17E015 The company's reporting platform was event driven, which meant that it was designed to respond to user actions in real time. The logging server captured every data event (e.g., mouse click, screen tap, key press) in Matel's distributed message queue, a detailed activity log. The site saw approximately 1,000 to 3,000 active user sessions at any moment. Operations managed two additional servers, named Dino and Raptor. Raptor was a small BI system used for quick and less-demanding calculations. Dino was the main BI machine that carried the biggest computational and data transformation workload; its main role was to receive most of the data and events that occurred within the entire Matel system in order to summarize, archive, and compute specific metrics and reports. Dino also served as an exploration tool used by the BI, product, and marketing teams to perform ad-hoc discoveries. With an increasing demand for data, transformations, and reports, the system was encountering a significant input-output bottleneck, leading to daily software panics, hardware crashes, and restarts. The estimated lost revenue because of these crashes was estimated to be about $5,000 per day. Subscription Business Model Subscription business models in the online dating industry typically suffered from high customer turnover in the range of 90 per cent annually, and Matel's turnover was in line with this industry average. However, leading large-scale players such as Match.com and eHarmony were adding pressure, as they worked to grab an ever-increasing share of the market. Ad-supported dating sites and apps such as Plenty of Fish and Tinder were also becoming more aggressive. Idarraga knew that if they were ignored, these competitors would eventually dominate the industry. Maintaining a positive churn of new subscribers to replace expiring ones was paramount to Matel's ongoing success. User data were employed in making decisions about products, as well as for digital marketing and ad publishing. With the advent of smartphones and tablets, the user interface and user experience had become central product differentiators. Digital firms like Matel carefully tracked their marketing effectiveness using a variety of key performance indicators such as unique log-ins, new registrations, and retention rates (see Exhibit 6). This data came from sources such as Google Analytics, Facebook Analytics, and a firm's own internal tracking systems. Ninety per cent of Matel's new customers signed up directly after clicking a paid ad. As a result of the turnover rate and its reliance on paid advertising, the company spent $15,000 per day on digital advertising to maintain sufficient churn. The profitability of these advertising campaigns hinged on accurate, up-to-date reports. On any given day, Idarraga saw over 200 media buys, 60,000 unique customers clicking on ads, 8,900 registrations, and hundreds of new subscriptions being generated. This data, along with client demographic information, funnelled into a single report that the company's media buyers used to make time-sensitive optimizations on their campaigns (see Exhibit 7). Decisions related to ad bidding, demographic targeting, segmentation, choice of ad copy, and landing page were made multiple times per day. The report utilized a predictive analytical model to project future revenues and to calculate a lifetime return on investment for each campaign. When the report failed to update, the media buyers lost the ability to make timely decisions on their campaigns, resulting in sub- optimal profitability, to the tune of thousands of dollars per day.Page 5 9B17E015 Digital Media Buying Marketing had changed drastically over the past few decades, with "market segmentation" no longer limited to the question of which geographical area to blanket with what sorts of static mass media advertising (television, radio, billboards, buses, flyers). Idarraga and his Matel team were dealing with prospective buyers who could now be segmented in much greater detail and approached with finely tuned messages, using media not confined to static ads but employing dynamic content. The long-standing marketing framework of product, place, promotion, and price had evolved dramatically as a result of the Internet and ubiquitous digitalization. The introduction to North America of the smartphone, starting with Apple's iPhone in 2007 and followed by mobile tablets, proved to be a turning point in online dating. Desktop marketing had matured, and the mobile industry was just beginning to take off when Stern, VP of marketing, joined Idarraga and the team in 2012 to oversee Matel's entry into this "wild, wild west" of mobile marketing innovation. (Stern had run some of his first pre-Matel ad campaigns for a phone-in chat line-related to online dating-but no marketing strategy yet existed in that arena.) Matel's customer base was shifting heavily to mobile usage, bringing with it an exponential rate of growth. By 2016, only 5 to 10 per cent of new acquisitions found their way to Matel through the desktop, and the mobile interface experience had become a central product differentiator. It seemed many people were using mobile apps to seek true love. Lacking the brand image and recognition of the big-name competitors, Matel developed its marketing approach by thinking outside the box. The company was constantly on the lookout for new ideas, new products, and new targeting techniques, using competitive intelligence to check out what other companies were doing. For example, under Stern's direction, Facebook became Matel's top venue for advertising. As a social network itself, Matel perceived itself as doing the same thing Facebook was doing- connecting people. In a natural segue, Facebook users naturally clicked on Matel ads as they popped up and piqued their interest. Ads were targeted and delivered directly to individual users based on the extensive information available to Facebook, one of the largest data owners in the world. (Facebook tracked data related to user age, gender, devices employed, phone carriers, content viewed, groups joined, location, and personal preferences a seemingly endless bank of information.) Facebook's segmenting, targeting, and deep data skills made it a perfect partner for Matel. A single user click on the Facebook connection button prominent on Matel's website brought Facebook a wealth of information for future targeting of ad campaigns. Collaboration abounded. For example, in 2016, Matel accepted Facebook's offer of a two-day product and marketing brainstorm session, to help reach women over the age of 35 (previously not a strong sector for the dating company). Facebook sweetened the pot with a promotional gift worth $26,000 towards Matel's ad spend. As a result of this collaboration, Matel became the first dating company to adapt Facebook's carousel ad, incorporating multiple images in a single ad unit; dating company competitors did not catch up until a full month later. Although 40 to 80 per cent of Matel's daily ad spend went to Facebook, social media was not the only aspect of its advertising strategy. One of the company's strengths was its ability to manage partnerships, such as those with Google, Twitter, Yahoo, Snapchat, and Bing, as well as a host of affiliate partners." While Matel received 90 per cent of its traffic from paid ads and 10 per cent from organic searches, many online dating companies invested far more heavily in the "art" of search engine optimization-a set of techniques designed to move a company's website into a top spot in search engine results. Some of the more highly recognized brands in the dating industry enjoyed close to a 50-50 split between paid and Mate1 ads appearing on affiliate search engines, websites, apps, and related services brought in fresh traffic.Page 6 9B17E015 organic marketing-a result of years of building equity through branded advertising to bring about a high level of brand awareness and word-of-mouth referrals. However, Matel made the strategic decision to avoid search engine optimization, except for specific apps towards known high-yielding results, and instead build its core value through paid ads. Stern attributed Matel's marketing success to a focus on its "back end"-analytics gathered through its internal tracking system. Matel's proprietary system allowed the company to directly measure the results of every ad campaign, every A/B test, and every click indicating a preference. The company kept track over time of which landing pages were most or least effective, which buttons users tended to press or avoid, which colours they preferred, and which they disliked. The most important aspect of Matel's ad strategy was its commitment to building out a broad, well-formed testing structure that consistently gathered real-time data-data that could be used to determine what was working and what was not. To determine the best digital media purchases, Matel did not follow the complicated, multi-tiered approach of most online dating companies, such as manipulating algorithms, focusing on brand identification, relying on investors, or spending millions on awareness. Instead, measures and numbers from real operations were preferred to selecting testing samples. Return on investment was calculated by looking at returns for every dollar actually spent by real customers, rather than modelling returns based on some arbitrary estimate. Performance was judged on actual credit card membership purchases, rather than typical metrics such as clicks per thousand impressions (CPM), cost per click (CPC), or even cost per action or acquisition (CPA). Every marketing choice was directly checked against profitability. This resulted in a somewhat flat marketing funnel and enabled the direct measurement of effectiveness. CURRENT SITUATION Back at his desk after the morning operations team meeting, Idarraga looked around at his office staff. All were within arms' reach, regardless of their position in the company, since he had ordered the cubicle dividers ripped out as a way to foster a collegial working culture. He looked at the handwritten testimonial from a grateful client, framed and hanging on the wall as a reminder to all that Matel cared about the individual over mass numbers: "I'm on my way," the once-lonely man had penned. "Eventually I'll know where I'm going, while a lady by my side will surely help immeasurably." Matel faced some serious challenges. Ninety percent of subscriptions were derived from paid marketing, with only 10 per cent coming from organic methods such as word of mouth, referrals, and delayed advertising effects. Advertising spend averaged $500,000 per month, which ate into the firm's profit margin. Meanwhile, a recent surge in membership owing to a series of successful ad campaigns had exceeded the capacity of the BI database and could quickly result in a potentially disastrous reporting lag. Idarraga thought about the company's client base and wondered, Could Matel maintain its position in the market with its existing customer acquisition strategy? Was it making optimal use of its resources? He needed to come up with a solution soon; Matel's owners, the Wasserman siblings, were eagerly expecting his recommendations for how to fix the media-buying issue and the performance issues. How would he meet the firm's aggressive two-year objective to achieve 50 per cent growth in revenue and 15 per cent growth in net operating margins? Amid the collegial conversations and clattering keyboards around him, Idarraga was lost in thought, with several possibilities and recent discussions running through his head.Page 7 9B17E015 Most immediately, addressing the Dino server performance problem was critical to "keeping the lights on" day to day. The estimated cost for an upgraded server was just $6,800, and this seemed like an easy and obvious solution. This would satisfy the VP of marketing and provide Matel with real-time data for media-buying decisions to gain more members. However, a more sobering challenge was that the current media-buying process did not seem very scalable. Every new customer was associated with a significant advertising cost, making the company exclusively reliant on costly digital advertising to acquire customers. How sustainable was this business model for the future? One idea that had been proposed was to acquire an ad-supported dating site that offered "free" subscriptions. Over the long term, Matel could leverage this site as a major source of new clients at a much lower advertising cost per subscriber, thereby increasing profit margins. Several second-tier, ad- supported dating sites had already been considered as possible acquisition targets. The growth outlook of such firms was likely to underperform industry growth; they therefore might be relatively inexpensive to purchase. High-growth companies in the online dating industry were sometimes valued at 1 x Revenue or 5 x EBITDA (earnings before interest, tax, depreciation, and amortization); however, underperforming companies would see lower valuations, and perhaps their owners would be motivated to sell. How much should Mate l consider investing in this kind of acquisition? Idarraga remembered yet another recent discussion he had had with Matel's owners. Given the firm's historical track record, the fact that it was debt-free, and the existence of heavy consolidation in the industry, was it time to explore divesting and selling the business to a leading competitor? Matel held a unique position because of its premium user base of older members as well as its state-of-the-art digital marketing strategy and team, and in fact it had recently been approached by a larger player. How much was this 12-year-old company really worth? Idarraga needed to develop his recommendations quickly. The company was losing revenue by the hour, and the owners wanted action. INSPEC Not For ReproPage 8 9B17E015 EXHIBIT 1: MATE1.COM WEBSITE matel.com G+ . Like 183K + Login Meet Singles in Toronto Connect with Facebook it's fast and we never post to Facebook - or am a: Man Seeking a: Woman Age: 18 : 10 35 Country: Canada Postal Code: M5P Search FREE > Already a member? Login here SECURED BY t thawte" 3 1 5 3 6 2 4 9 people are already here Source: Company website, www.mate1.com, accessed August 13 2017. tion EXHIBIT 2: ONLINE DATING BY AGE GROUP Use of Online Dating Sites or Mobile Apps 23 20 10 18-24 25-34 35-44 45-54 55-64 65+ created by authors using data from Aaron Smith and Monica Anderson, "5 Facts about Online Dating," Pew Research Center, February 29, 2016, accessed August 10, 2017, www.pewresearch.org/fact-tank/2016/02/29/5-facts-about- online-dating/.Page 9 9317E015 EXHIBIT 3: MATE1.COM COMPARATIVE STATEMENT (CA$OOOS) 2015 2016 Total Revenue 10,141 8,425 Cost of Goods Sold 6,817 5,793 Gross Margin 3,324 2,632 Operating Expense 2,619 2,226 Net Profit Before Tax 834 (364) 706 406 Income Taxes 224 (98) Net Income After Tax 610 (266) Source: Company records. Desktop Website (Launched 2003) Source: Company records. @Q obiIe-Android unched August 2013) Page 10 9B17E015 EXHIBIT 5: MATE1.COM DATA PIPELINE Apache Kafka* (distributed streaming server) Apache Cassandra* Apache Zookeeper* Kafka NOSQL DB for (sync service) consumer* client requests) (logging and Web servers* event data) Java web framework) App servers Apache Kafka Cassandra Other services Kafka pache Zookeeper consumer Play Cassandra Mobile Apache Kafka APN Gateway Apache Solr* Access Point Name fo (search and GSM, GPRS, 3G, 4g , . Ejabberd server* geo-location) (XMPP = Extensible Redis* Message and (NOSQL Presence Protocol) database) Source: Created by the case authors based on company records. INSPE Not For ReproductionPage 11 9B17E015 EXHIBIT 6: SUMMARY OF KEY PERFORMANCE INDICATORS (IN CA$) 2016-Q1 2016-Q2 2016-Q3 2016-04 Unique Logins x Platform -Web (i.e., desktop or mobile browser) 1,354,490 1,246,699 2,273,827 1,616,843 -iOS (mobile) 64,474 38,589 32,647 27,763 -Android (mobile) 164,350 132,210 168,536 87,445 Free Registrations x Platform -Web 617,657 568,656 632,739 629,373 -iOS 46,813 26,014 21,468 18,640 -Android 121,795 104,625 87,356 72,112 Paid Registrations x Platform -Web 361,954 304,209 307,994 217,575 iOS 46,813 26,0144 21, 468 18,640 -Android 121,795 104,625 87356 72,112 Paid Registrations x Country -The United States 597,402 525,356 543,074 569,939 -The United Kingdom 68,397 56,537 49,598 33,825 Canada 29 137 23,773 23,331 17,073 -Other 91,329 93,629 125,560 99,288 New vs Recurring Revenue x Platform -New Revenue --Web 197,169 411,549 316,740 --iOS 63,746 41,889 34,338 -Android 207,921 256,507 121,098 97,360 -Recurring Revenue --Web SRECT 1,190,762 1,137,391 1,038,046 965,624 --iOS 177,338 120,365 66,274 50,998 --Android 415,734 357,166 298,546 -Trial 29,639 25,657 23,510 17,776 -Re-bill 21,911 21,104 19,088 17,750 -Reactivation tunes New submission ot For 1,643 1,470 1,391 1,239 -Recovery 9,428 8,029 7,263 6,483 Subscription 1,231 1,037 382 841 2,214 1,689 1,529 1,382 -iTunes Re-bill 7,877 3,842 366 740 Revenue % x Channel -Affiliates 33.5% 36.0% 40.8% 42.6% -Facebook Direct 31.1% 30.6% 27.6% 25.3% Google Direct 9.4% 9.3% 9.6% 9.1% -Other 26.0% 24.1% 22.0% 23.1% Source: Created by the case authors based on company records.Page 12 9B17E015 EXHIBIT 7: MATE1.COM ADVERTISING CAMPAIGN REPORT SelfUpdating_GlobalMarketingReport_V2_dino (23) - Excel PIVOTTABLE TOOLS ? 3 - 6 FILE HOME INSERT PAGE LAYOUT FORMULAS DATA REVIEW V DEVELOPER ANALYZE DESIGN Charles Wasserman PivotTable Name: Active Field: Group Selection Insert Slicer Clear " 7 Fields, Items, & Sets . Field List PivotTable1 company_name Ungroup Insert Timeline En Select lifx OLAP Tools . "a +/- Buttons Options - Drill Drill -7 7) Group Field Refresh Change Data Field Settings Do Filter Connections Source - Move PivotTable D Relationships PivotTables Field Headers PivotTable Active Field Group Filter Data Actions Show G 23 Values 24 company_name SignupDate $Spend 1YR RO1% 1Y Profit Profiles #Credited d 24HR Transactions 24H_TransactionsCR 24Hr Cost/Trar 5 Facebook 6 263,377.85 102.30% $6,048.63 70,810 22,230 2,418 3.41% $ 26 ConversionSquared 209,205.57 124.54% $51,333.06 56,074 53,387 1,739 3.10% $ Cake 75,892.50 110.03% $7,612.49 45,350 28.536 785 1.73% $ 28 # Unattributed Mobile Installs 0.00 0.00% $71,127.77 17,766 781 4.40% $ 29 LifeStreet 28,954.50 88.58% ($3,306.36) 10,407 2,318 196 1.88% $ 30 NeverBlue2 17,211.25 132.53% $5,598.77 9,106 4,663 183 2.01% $ 31 Google US Mobile 21,330.64 132.49% $6,929.37 6,948 4,296 193 2.78% $ 32 : Takoomi 9,532.00 109.42% $898.16 4,673 2,468 39 1.90% $ 3 Google Search - UK 12,462.70 110.73% $1,336.71 4.055 3,575 85 2.12% $ 34 Google Adwords USA 18,718.02 66.34% ($6,299.55) 3,998 3,869 123 3.08% $ 35 Mingle2 9,404.50 172.99% $6,864.06 3,966 1,610 104 2.62% $ 36 B2 Direct 3,595.60 70.75% ($1,051.89) 2,076 1,449 2 2.02% $ 37 Google Adwords UK 7,807.03 73.91% ($2,036.58) 1,746 1 720 42) 2.41% $ 38 Google Search - Mate1 6,318.86 80.73% ($1,217.91) 1,740 1,634 3.56% $ 39 Wicher 5,772.00 139.39% $2,273.76 1,157 962 40 3.46% $ 40 * Google Search - US 4,068.11 134.03% $1,384.23 1,033 955 45 4.36% $ 41 Google - Image 3,120.03 86.08% ($434.46) 791 616 1.90% $ 42 Cappsool 2,441.80 66.75% ($811.88) 585 2.89% S Summary NewFormulas OldFormulas + : " READY Column definitions: Company Name: Company name of our traific supplier Signup Date: Date at which a user registers for free on the site luction Spend: Ad spend recorded on the date it was incurred 1YR ROI %: A projection of ROI one year after ad spend was incurred Profiles: A free registration (requiring username, password, date of birth, and email address) #Credited: Free registrations that fire the third-party tracking pixel 24HR Transactions: All transactions (trials, monthly, and multi-month purchases) occurring within a 24-hour period from signup 24H_TransactionsCR: Conversion rate of Profiles to 24HR Transactions Source: Company records. Not For
