Determine the True Value of Each Marketing Channel

Transcription

1 Determine the True Value of Each Marketing Channel Research by Digital Relevance using client data from Jan -Dec 2011

2 Please feel free to share digital copies of this ebook with your colleagues. You may also use data and figures from this ebook in presentations and papers using standard citations. Thank you for downloading this ebook, and we hope you find value in the information provided.

3 introduction A wise executive once said, I have unlimited budget for marketing that works. Although, it is the finding out what works part that is the key to running a competitive digital marketing campaign. With all the different types of online marketing campaigns and areas of overlap, how can marketers know for sure which channel is generating returns? A much clearer view of the impact on sales from each digital marketing channel can be easily generated using multi-touch attribution modeling. Each attribution model represents specific marketing goals. This allows marketers to not only calculate the true impact on sales from each digital marketing channel, but also do it in a way that is relevant to the marketing goals of the campaign. This information allows marketers to find out what works and further optimize the top-performing formula of digital marketing channels. Digital marketing has become a game of inches. Attribution modeling will enable digital marketers to gain the inches that produce the miles of separation between their organization and the competition. Attributing sales or revenue to online marketing channels is available through free software, yet it is still a practice that many organizations don t have the time or expertise to do in-house. At Digital Relevance, we do everything that we can to make our clients the most educated teams in online marketing. Sometimes this means getting jobs done like online sales and conversion attribution. This ebook was created from a compilation of research data and findings over the calendar year of 2011 after analyzing 30 client domains and over 23 million online conversions. This ebook de-mystifies multi-touch attribution and the benefits of using this model, allowing marketers like you to jumpstart attribution modeling at your organization. what's inside: overview» An overview of the multi-touch attribution and the benefits of using common single-touch and multi-touch models. our study» Our Multi-Touch Attribution Study, which analyzed 30 client domains and over 23 million unique online conversions with over 150 million touch points. campaign model assessment» A quick assessment to provide insights into which model(s) are most relevant to your needs and goals. Google Analytics guide» A step-by-step guide on how you can implement multi-touch tracking on your website using the free online software, Google Analytics. p3

4 why marketers use attribution modeling Attribution modeling not only measures marketing channel effectiveness, but also provides valuable information necessary to justify marketing budgets, optimize digital spends, and increase ROI. Here are a few of the most important reasons that marketers use attribution modeling. Justify Digital Marketing Budgets Marketing channels that reliably produce ROI are the easiest budgets to justify. Attribution modeling provides the opportunity to hone in on each marketing campaign and measure individual channel effectiveness with more accuracy than ever before. Optimize Digital Budget Allocation Marketers want to know the most optimal mix of digital marketing channels that provide the highest ROI. Attribution modeling allows marketers to find this optimal mix for each marketing campaign. When this information is known, budgets can be adjusted accordingly to strengthen their best plays in the book. Calculate More Accurate CPA (cost-per-acquisition) Figures Multi-touch modeling assigns credit to marketing channels more fairly with respect to the campaign goals. This system calculates individual channel CPA figures that are much closer to reality. Plan Campaigns Accordingly Attribution modeling provides deeper insights into sales cycle length and the sales funnel. Better understanding of the sales funnel will drive smarter marketing campaigns that generate a higher ROI. Optimizing Affiliate Payments Flying blind in affiliate marketing is a dangerous game. Information generated through attribution modeling can empower marketers to optimize when, where, and how much they bid on affiliate marketing leads to produce the highest ROI. p4

5 Attribution Models What is a model? A model is a compromise that falls somewhere on the spectrum between simplicity and reality. simplicity Simplistic models such as last-touch attribution are typically the default attribution system on web analytics software. It is too simple to credit the last referring channel with 100% of the conversion value. The reality is that users will interact with a brand a number of times, often through different marketing channels, along the way to a final conversion. Assigning zero credit to marketing channel touch points that assist with a purchase along the customer journey vastly undervalues the impact of these channels. Using a simplistic attribution model typically results in inaccurate CPA calculations for each individual digital marketing channel. Marketers may find that downgrading or cutting marketing spends based upon inaccurate cost per acquisition calculations result in negative downstream effects on total sales due to the lack of the assist from the channel that was cut. Conversely, wanton increases in spend across marketing channels would most likely decrease the overall ROI due to the less valuable channels getting more budget than they can return. Due to these limitations, last-touch attribution is an unreliable method for making accurate strategic marketing decisions. Reality In the real world, different users across multiple backgrounds, personalities, and situations make purchases in ways that are impossible to predict on an individual basis. Therefore, is too complicated to develop an attribution model for each and every path to purchase. Compromise A multi-touch attribution model serves as the compromise between simplicity and reality. These models can be customized to the specific goals of any digital marketing campaign and provide marketers deeper insights into the value generated from individual marketing channels. Understanding the true value of each marketing channel allows marketers to optimize marketing spends, establish a higher level of accountability, and ultimately increase ROI. p5

6 Fundamental Single-Touch Attribution Models Before we jump into multi-touch attribution, let s review the more traditional approach of Last-Touch or First-Touch attribution models. Last-Touch Attribution 100% By default, Google Analytics credits each conversion to the last referring touch point. Last-Touch attribution assigns all credit towards the final interaction leading to the conversion, which represents Bottom Funnel marketing channels. 0% 0% 0% Assigns all credit towards the last interaction before conversion. This model assumes that the final touch point was the only influence on the user and assigns no value to all previous touch points in the customer journey. In a recent econsultancy 1 survey, 13% of respondents judged Last-Touch as a Very ineffective attribution model. The limitations of Last-Touch attribution has lead many marketers to abandon this method as a way of valuing all channels within a digital marketing campaign. However, Last-Touch attribution is useful for determining which channels best lead users to the final conversion, or buying decision. last-touch model The visit gets credit for this conversion since it was the last referring campaign. 1 "Marketing Attribution: Valuing the Customer Journey", Econsultancy.com, Ltd p6

7 Fundamental Single-Touch Attribution Models 100% First-Touch Attribution First-Touch attribution assigns all credit towards Top Funnel marketing channels, as the First-Touch interaction is the brand discovery. 0% 0% 0% 0% Assigns all credit to the first interaction. This model assumes the first interaction was the main influence on the user and attributes no value to all interactions that follow. However, nurturing potential customers and closing the sale is just as important as grabbing their attention in the first place. It may take an assisting interaction like a social network or a coupon offered through an to influence a user and close the purchase. First-Touch attribution is most useful for determining which channels best lead users to brand discovery. first-touch model Organic search gets credit for this conversion since it was the first interaction. p7

8 Fundamental MULTI-Touch Attribution Models Many organizations use their own custom models built upon experience, expertise, or even algorithms. However, you will find one of the following three fundamental multi-touch models at the root of any custom built model. These fundamental models are known as linear, position-based, and time-decay. Each of the three fundamental models assign a value to each touch point within the customer journey based upon specific campaign goals. Choose the appropriate model(s) based upon the campaign goals, product offering, timing, and marketing & sales strategies. linear 25% 25% 25% 25% In a linear multi-touch attribution model, the value of each conversion is divided equally among each channel in the path. Equal credit is given to each marketing channel linear model This model assumes that each interaction in the customer journey has equal influence on the user s buying decision, regardless of where the touch point occurs in the journey. In reality, each interaction may not have equal influence. Many organizations acknowledge that each touch point has some influence over the user, yet the intrinsic value of each touch point cannot be easily quantified. Many of these organizations find that it is most useful to assume that each touch point contributes equal value to the conversion. If your campaign is comfortable with this assumption, then you will find that the simplicity of the linear multi-touch model has significant advantages over other models. p8

9 Fundamental MULTI-Touch Attribution Models Position-based 30% 10% 10% 50% Typically gives a greater weight to the first and last interactions and is adjusted based on the remaining positions The Position-Based model attributes value to specific parts of the customer journey. Conversions with PR goals may find that assigning a higher value to the first interaction is more goal-oriented since they require lots of leads into the funnel and are focused on creating awareness. Conversely, conversions with a short sales cycle may want to assign a higher value to the channel that closed the sale at the end of the customer journey rather than earlier touch points. Most algorithm-based attribution models use machine learning to optimize each position within the customer journey. A popular version of the Position-Based model uses a Pareto distribution (the 80/20 rule), which credits 80% of the conversion value to the first and last touch points while the remaining 20% is distributed across all the nurturing touch points in the middle of the customer journey. position-based model p9

10 Fundamental MULTI-Touch Attribution Models Time-Decay 5% 15% 30% Progressively assigns the most credit to the last interactions 50% A Time-Decay model adjusts credit that progressively increases in value across customer journey from initial discovery to final conversion. A Time-Decay model can be useful for campaigns dealing with short-lived deals or promotional offers. Since these campaigns need marketing channels that close the deal as quickly as possible, the value should be progressively weighted higher for channels nearest to the last touch. time-decay model p10

11 campaign model assessment

12 campaign model assessment Your business needs and marketing goals should serve as your guide when choosing an attribution model. This brief questionnaire will provide insights into which models are most relevant to your needs and goals. Apply this questionnaire to each of your marketing campaigns individually, as different campaign goals should be measured using different models. Read each question and enter a score using the system below. Then calculate your answers on the final answer sheet. The models with highest scores are the most relevant to your campaign. scoring system Valuing Digital Marketing Channels Strongly Agree Somewhat Agree Undecided Somewhat Disagree Strongly Disagree 1. Customer awareness, lead nurturing, and closing the deal are all equally important 2. This campaign relies heavily on a short-lived promotional offer 3. Customer awareness is most important 4. Channels that 'close the deal' are the most important 5. The lead nurturing channels in the middle of the sales funnel are more important 6. Consumers are generally not aware of most components or offers of this campaign 7. This campaign involves a very short sales cycle 8. Customer awareness as well as 'closing the deal' are both the most important 9. My offer, brand, or products/services are well known in my market so the final conversion is most important 10. My sales funnel is well defined and contains steps that greatly vary in value across the customer journey 11. I am new to attribution modeling and I just need a general model to get started 12. Lead nurturing is less important than getting the customer in and closing the deal (position) 13. This is a brief offer that we run every periodically (such as a seasonal offer) 14. This campaign needs to attract visitors through non-branded search keywords 15. This campaign typically shows high ROI with optimized affiliate advertising or CPC 16. The touch point type (click, impression, visit, etc.) or source (campaign, keyword, etc.) is more important than the funnel sequence 17. Attracting repeat customers is most important to this campaign 18. Visitors are generally unaware of the brand and also have trouble converting once they reach the offer p13

13 the results The model(s) with the highest scores are the most relevant valuation framework for your campaign. However, the most insightful information will come from a comparison of multiple attribution models. So don t worry if one model does not rise to the top after completing this questionnaire. Use a comparison of the top scoring models in this questionnaire to confirm your findings and unlock new insights through experimentation. linear Score from 1. Score from 11. Score from 17. First Score from 3. Score from 6. Score from 14. last Score from 4. Score from 9. Score from 15. time decay Score from 2. Score from 7. Score from 13. customized Score from 5. Score from 10. Score from 16. position Score from 8. Score from 12. Score from 18. p14

14 what is the true value of online marketing channels

15 executive summary The competition for online sales is more cutthroat than ever. This competition has driven game-changing increases in technologies and methodologies related to tracking and optimizing the digital marketing channels that drive online sales. Marketing teams are finding it more important than ever to measure and optimize marketing spends to drive maximum returns on marketing investments. Web analytics software has been at the front line in measurement and optimization efforts. There are many ways to attribute sales credit to digital marketing efforts, but the default setting for most web analytics softwares assigns all credit from each sale to the last marketing touch point prior to the conversion. Even though most marketers understand that the customer journey is complex and contains multiple touch points, many continue to attribute all sales credit to the last-touch in the sales funnel. According to a recent survey conducted by econsultancy 1 marketers ranked this mode of attribution as the most ineffective attribution method. These marketers on the front lines understand that a last-touch model is not providing the true value of each individual marketing channel, which typically results in uninformed decision-making when allocating digital marketing spend. The unreliability of the last touch model is due to the fact that most, if not all, touch points provide value throughout the customer journey. Analysis using multi-touch models can provide insights into the value generated by individual marketing channels. Using multi-touch modeling, our experts address three questions in this research study - What is the true value of each online marketing channel when comparing basic linear multi-touch attribution with last-touch attribution; where in the customer journey is each marketing channel most likely to appear; and what are the most common paths to conversion. To address these questions, we use data from 30 Slingshot SEO client domains during the calendar year of This data contained 23.6 million conversions with over 150 million touch points across seven industry verticals: Business & Industrial, Computers & Consumer Electronics, Finance & Insurance, Health & Wellness, Home & Garden, Occasions & Gifts, and Retailers & General Merchandise. +23M 23.6 million unique online conversions with over 150 million touch points. 1 "Marketing Attribution: Valuing the Customer Journey", Econsultancy.com, Ltd p17

16 linear vs last-touch value analysis When comparing a linear attribution model with the last touch attribution model [see figure 1.1], our results found that Organic Search was undervalued on every domain in the analysis, in some cases by as much as 77%. In other words, Organic Search received more value attributed in a multi-touch model than a last-touch model, by an average of 18.3% across all domains. Conversely, Direct Traffic was overvalued on every domain in the analysis, with an average 15.8% overvalue across all domains. Paid search was undervalued on some domains and overvalued on others, contributing to an average undervaluing of 21.3% across all domains. Referral Traffic was nearly split, being overvalued in some cases and undervalued on others, which contributed to an average undervaluing of 4.0% across all domains. The following channels are shown in Table 1.1: " search", "non-branded search", " visits", "" (PPC), and "referral visits". Figure 1.1 valuation by channel»» paid» referral» -60% -40% -20% 0% +20% +40% +60% +80% +100% +120% % Under (above 0%) or over (below 0%) value mean at 95% confidence range p18

17 Conversion Path Position Occurrence The following figure (fig 1.2) tabulated every touch point in the analysis according to each channel and the position it occurred in the customer journey using the scale explained in the Methodology section of this study. Figure 1.2 channel touch point occurrence in the customer journey 9,000 8,000 referral paid 7,000 number of conversions 6,000 5,000 4,000 3,000 2,000 1, Point in time on the conversion path Organic traffic had the highest occurrence by volume in the early stages of the conversion funnel than all other channels. Direct Traffic had very few touch points in the first touch and early stages of the conversion funnel, but showed a high occurrence as a deal closer in the latter stages and last touch point of the funnel. Paid Traffic was nearly a flat line showing very few touch points throughout the funnel. Referral traffic had a higher occurrence of touch points in the first touch and early stages of the funnel, but also showed the steepest drop of touch points toward the latter stages and last touch point of the conversion funnel. p19

18 Most Common Paths to Conversion Finally, we discovered the top twenty most common paths to conversion that contained 2 or more touch points (see figure 1.3). The percent number next to each path notes what percentage of the 23.6 million conversions were represented by each of the top paths. Figure %» 5.5%» 4.0%» top 20 paths to conversion 3.2%» 2.4%» 2.1%» 1.6%» 1.3%» referral referral 1.2%» 1.2%» 1.0%» 0.9%» 0.7%» 0.7%» 0.7%» referral referral referral 0.6%» 0.6%» 0.5%» 0.5%» 0.5%» p20

19 our methodology

20 Purpose The purpose of this research study was to address three questions - What is the true value of each online marketing channel when comparing linear multi-touch attribution with last-touch attribution; where in the customer journey is each marketing channel most likely to appear; and what are the most common paths to conversion. Data & Analysis Software Used Data was collected from 30 current Digital Relevance clients. The data criteria were as follows:» Each domain must have had Web Analytics tracking set up for the entire calendar year 2011» Conversion goals must have been set up for the entire calendar year 2011» Each conversion must have reflected a valuable insight that adds increased value to the business» Data was collected from the four most common online marketing channels used by the domains included in the study: Organic Search, Paid Search, Referral Traffic, and Direct Traffic. a. Organic Search touch points consisted of any visitor that originated from an (non-paid) search on a web search engine b. Paid Search touch points consisted of any visitor that originated from a PPC advertisement on a web search engine c. Referral Traffic consisted of any visitor who originated from any third-party non-search web domain, social media website, or d. Direct Traffic consisted of any visitors that originated from a manual URL entry into a web browser» All conversion & touch point data were generated by Google Analytics web tracking software» All conversion data occurred between January 1 December 31, 2011 Microsoft Excel was used for all data calculations, analysis, and graph generation. p23

21 Measurements & Calculations Undervalue / Overvalue Comparison The percentage by which each channel was undervalued or overvalued was calculated by using the following formula: [Linear Attributed Conversion Value / Last-Touch Conversion Value] 1 Figure % +10% 0% -10% -20% -30% -40% -50% paid referral last-touch first-touch linear time-decay The calculated value for each channel was then converted to a percentage. A positive percentage indicates that the channel was overvalued. A negative percentage indicates that the channel was undervalued. Taking the overall conversion value and dividing it equally among all channel touch points within the conversion calculated the Linear Attributed Conversion Value. For example, if the total conversion value was 100 and contained five channel touch points, then each channel touch point would receive a Channel Linear Attributed Conversion Value of 20. The Last-Touch Conversion Value was calculated by attributing the full conversion value to the last channel touch point within the conversion. For example, if the conversion contained five touch points then the full conversion value would be credited to the last channel touch point and the four other channel touch points would receive a credit of zero. The Time-Decay Model Conversion Value was calculated by progressively attributing more credit to the touches closer to conversion. For example, if a conversion had four touches, the first would receive 10% of the credit, the second 20%, third 30% and fourth 40%. In this model each touch gets at least some attribution for a conversion. p24

22 Measurements & Calculations Conversion Path Position Occurrence Data collected for this study contained over 23.6 million conversions with over 150 million touch points. To analyze where in the conversion path each marketing channel appears, we assigned each touch point within each conversion to it s relative sequence-based position on a scale. Position 1 is the first touch position and position 100 is the last-touch position. Positions 2-99 are attributed to conversions that appear on that point in the sequence. For example, if a conversion contained four touch points then positions 1-25 (out of 100) would represent that first touch, positions the second, the third and the last touch. 1 st touch 2 nd touch 3 rd touch last touch attributed position The purpose of this was to normalize conversion paths of different lengths so the data could be compared in aggregate. In addition, we removed instances where a conversion happened from only one channel as the purpose of this section was to determine change in channels over the conversion path. Figure 1.2 contains a graphical representation of this data. The x-axis represents the relative position of a touch in a standardized conversion path, the y-axis represents the relative volume of conversions attributed to the given channel. Each line on the graph represents one of four marketing channels analyzed within the study, which were Organic Search, Paid Search, Referral Traffic, and Direct Traffic. Top-Ten Most Common Paths to Conversion All conversions within the study were grouped according to the exact sequence of touch points inside each conversion. Each unique sequence is referred to as a path to conversion. After grouping all conversions according to their touch point sequences and calculating the total number of occurrences, the top ten most common conversion paths containing two or more touch points were chosen for display (see Fig 1.3). Limitations The data in this report omits all website visitor data that did not result in a conversion. The data also allows for different definitions of a conversion from client to client. p25

23 our findings

24 findings Using multi-touch modeling, our experts address three questions in this research study:» What is the true value of each online marketing channel when comparing linear multi-touch attribution with last-touch attribution?» Where in the customer journey is each marketing channel most likely to appear?» What are the most common paths to conversion. To address these questions, we use data from 30 Digital Relevance client domains during the calendar year of In order for a domain to qualify for the study, each domain must have had Google Analytics and meaningful conversion goals set up for the entire calendar year of A meaningful conversion goal is an actionable insight that adds increased value to a business. The data contained over 23 million conversions with over 150 million touch points across seven industry verticals: Business & Industrial, Computers & Consumer Electronics, Finance & Insurance, Health & Wellness, Home & Garden, Occasions & Gifts, and Retailers & General Merchandise. p27

25 Comparing Linear Multi-touch Value with Last-Touch We compared values generated for each digital marketing channel using a linear attribution model as well as a last touch attribution model [see figure 1.1]. Figure 1.1 valuation by channel mean at 95% confidence range»» paid» referral» -60% -40% -20% 0% +20% +40% +60% +80% +100% +120% % Under (above 0%) or over (below 0%) value Organic Search was undervalued on every domain in the analysis, by an average of 18.3% across all domains. In some cases Organic Search was undervalued by as much as 77%. Direct Traffic was overvalued on every domain in the analysis, by an average 15.8% overvalue across all domains. Paid search was undervalued on some domains and overvalued on others, contributing to an average undervaluing of 21.3% across all domains. Referral Traffic was nearly split, being overvalued in some cases and undervalued on others, which contributed to an average undervaluing of 4.0% across all domains. Conversion Path Position Occurrence The conversion data contained every touch point within the customer journey for over 23 million conversions. Our experts wanted to know if a trend existed between a marketing channel and it s occurrence in particular areas of the conversion funnel. Every touch point in the analysis was tabulated according to the specific channel and the position it occurred in the customer journey. We used a scale to note where the touch point occurred as a percentage of the total customer journey. The scale is further detailed in the methodology section of this research study. p28

26 Figure 1.2 channel touch point occurrence in the customer journey number of conversions 8,000 7,000 6,000 5,000 4,000 3,000 referral paid 2,000 1, Point in time on the conversion path Organic Search had the highest occurrence by volume in the early stages of the conversion funnel, and showed only a slight drop across the funnel to the final conversion touch point. Organic traffic represented 42% of the total first touch-points, representing a total opportunity cost of nearly 10 million conversions if these clients had not achieved top search rankings. Direct Traffic had very few touch points in the first touch and early stages of the conversion funnel, but showed a high occurrence as a deal closer in the latter stages and last touch point of the funnel. Paid Search was nearly a flat line showing very few touch-points throughout the funnel. Additionally, 64.2% of the conversions that started with a Paid Search also ended with a Paid Search, which required constant PPC investment across the entire customer journey. Paid Search did create brand awareness by leading 24.1% of the users who came in through Paid Search to convert using Direct Traffic. Referral Traffic had a much higher occurrence of touch points in the first touch and early stages of the funnel, but also showed the steepest drop of touch points in the latter stages of the customer journey. Conversions that began with Referral Traffic generated the largest brand awareness since 30.3% of the visitors converted through Direct Traffic on the last-touch. A modest 9.5% of these visitors chose to convert using Organic Search on the last-touch. p29

27 Figure 1.5 each channel's last-touch probability paid 1.8% Starting with referral 2.5% 12.2% 83.4% Paid 2.7% Starting with referral 5.7% 7.6% 83.9% paid 64.2% Starting with paid referral 5.4% 24.1% 6.2% paid 4.5% Starting with referral referral 9.5% 30.3% 55.7% p30

28 Our experts wanted to know what the last-touch probability was when starting with each channel in the analysis. Organic Search Organic Search also closed the deal on the last touch in over 83% of the conversions that started with an Organic Search, which makes it crucial for the domains to maintain consistent top search rankings over time to convert these users. Direct Traffic converted the majority of the remaining users that did not use Organic Search as a final touch point, which shows that Organic Search does generate brand awareness with a significant portion of users. Organic Search is also the second most frequent final touch, behind Direct Traffic, for conversions that started with any other channel. This infers a high level of trust of Organic Search as a third-party referral for brand awareness and deal closure. Direct Traffic Over 83% of users who entered through Direct Traffic also converted through Direct Traffic on the final touch point. Organic Search converted the majority of the remaining users that did not use Direct Traffic as a final touch point. Referral Traffic and Paid Search followed in third and fourth place, respectively. Direct Traffic is also the most frequent final touch point for conversions that started with any other channel. This infers a high level of trust of Direct Traffic for brand awareness and deal closure. Paid Search Over 64% of users who entered through Paid Search also converted through Paid Search on the final touch point. This data shows that the majority of users visiting through Paid Search are loyal to this channel throughout the buying process. However, this loyalty does result in a continued investment in PPC across the customer journey. However, over 24% of the users converted using Direct Traffic. This finding shows that Paid Search did generate brand awareness for these users that memorized the domain URL and converted using Direct Traffic at no additional cost to the domain. Referral Traffic Conversions that started with Referral Traffic showed the highest variation on the final touch point. With 55.7%, most users were loyal to Referral Traffic on the final touch point prior to conversion. However, over 30% of users converted through Direct Traffic, which shows that Referral Traffic generates a high level of brand awareness. Over 9% of the users chose to convert with Organic Traffic, also showing the user trust of Organic Traffic as a deal closer. Paid Traffic also generated 4.5% of the final touch points for these conversions. p31

29 Most Common Paths to Conversion Finally, we discovered the top twenty most common paths to conversion that contained 2 or more touch points (see figure 1.3). Figure %» 5.5%» 4.0%» top 20 paths to conversion 3.2%» 2.4%» 2.1%» 1.6%» 1.3%» referral referral 1.2%» 1.2%» 1.0%» 0.9%» 0.7%» 0.7%» 0.7%» referral referral referral 0.6%» 0.6%» 0.5%» 0.5%» 0.5%» p32

30 Discussion / Additional Remarks For these 30 businesses in 2011, " visits" were getting more credit for conversions under a last-touch model, as they were often the last interaction before a conversion. As a result, other channels like " search", "", and "referrals" were typically undervalued. Why would search be undervalued? Organic searches drive sales/conversions from the top of the funnel as users are doing initial research. This affects the channel as a whole, but also conversion rates for non-branded keywords. On average, users in this study took 2.79 interactions before converting. The most common touch point prior to a conversion was a Direct Visit or an Organic Search. The Multi-Touch Attribution model should be used to value digital marketing channels, determine the most influential paths, and prioritize digital marketing spend. Some marketers may prefer a model that weights the first and last conversions more than the assisting interactions. Regardless of which model is chosen, valuing conversions using multi-touch attribution should be part of ROI-focused discussions with clients and a key focus for all marketers. Remark: For all 30 websites, "branded search" and "non-branded search" were being undervalued in Multi-touch attribution showed that " search" should have been worth as much as 77.25% more than previously thought, and "non-branded " should have been worth as much as 81.59% more. Also, for all 30 websites, " visits" were being overvalued in "Direct visits" should have been worth as much as 35.74% less than previously thought. Remark: The most undervalued channel was typically "non-branded searches", while the most overvalued channel was typically " visits". Remark: Figure 1.3 was included because it demonstrates the quick-and-easy way of looking at conversions, but it does not necessarily reveal the user behavior patterns behind the scenes concerning which channels are the most influential. The table is helpful because it outlines which overall paths are most likely to be taken by a user without having to apply a multi-touch model to break out the values between each individual channel. Users take a number of different paths before converting, and as a result, there are many unique paths that would be overlooked since they are not in the top 20 most common paths. Just looking at this table would not reveal that " visits" are being overvalued or that " search" is being undervalued. This speaks to the need for the quantitative analysis from a multi-touch model to value digital marketing channels more effectively. p33

31 How To Implement multi-touch attribution

32 How To Implement a Multi-Touch attribution Google Analytics (GA) provides the ability to track how visitors interact with a site before a conversion in its new feature, Multi-Channel Funnels. In order to apply the flat multi-touch model to conversion data, make sure the data currently being tracking is valuable and accurate. Define conversions that reflect what you want a visitor to ultimately achieve on your website. It should be an actionable insight that adds increased value to your business. Examples of possible goals to set up in order to track conversions:» ecommerce transactions/purchases» Newsletter subscriptions» Free trial subscriptions» list subscriptions» Upgrades» Quote requests» Downloads» Watching a video about a service/product» Viewing 5+ pages on the website» Viewing 10+ pages on the website» Facebook Likes / Google +1s» Job application forms» Contact forms When ecommerce data is linked to GA, the customer transaction revenue (conversion values) will be assigned to each transaction. It would be helpful to assign conversion values to non-ecommerce goals as well. Each of these goals will be more valuable to some websites than others, so it is important to determine which add the most value to your business. Once you set up meaningful goals and have a time frame with enough data, you can analyze conversion paths through Multi-Channel Funnels. Complete the Campaign Model Assessment if you are unaware about what multi-touch model(s) to use. p35

33 multi-channel funnels Multi-Channel funnels in Google Analytics can be located under Standard Reporting > Conversions > Multi-Channel Funnels > Top Conversion Paths. From here, apply conversion segments to find the number of conversions for last-touch, first-touch, or any-touch across all channels. For additional help with Google Analytics, please see our guide SlingshotSEO.com/Resources/Guides/How-to-Use-Google-Analytics/ The goal is to compare conversions (last-touch) with attributed conversions (flat multi-touch) for a specific channel. Basic channel groupings from Google Analytics include Organic, Paid Advertising, Referral, Social Network, , and Direct. This comparison will help determine whether or not that particular channel is undervalued or overvalued. Below is a brief overview on how to find the value of each channel. For this walk-through, determine whether or not Organic Visits are undervalued or overvalued. Finding Conversions (last-touch) 1. 1a. Select Conversion. Create a New Conversion Segment (on the right) specifying Include Last Interaction, from: Medium containing. 1b. Then select a time range and apply the segment. 1c. Now record the total Conversion Value for this Last-Touch Organic segment. If there are no conversion values assigned, then record the number of total conversions. Finding Attributed Conversions (flat multi-touch) 2. 2a. This is the tricky part. First, create a conversion segment specifying Include Any Interaction, from: Medium containing p36

34 3. export a csv and open in excel 3a. Click Export at the top and export the CSV. There are three columns: Basic Channel Grouping Path, Conversions, and Conversion Values. 3b. For each path, the task is to divide each conversion value and attribute it to each channel, depending on how many times it appears in that path. One way would be to delimit the first column, which breaks out each interaction into a single column. This can be used to apply =COUNTIF() functions to determine the number of Organic interactions in that path. 3c. Determine the value of those Attributed Conversions that belong to Organic in each path and sum those values. This is the total amount Attributed Conversions for Organic Search. These steps are reiterated below: Download the CSV to get three columns of data. Basic Channel Grouping conversions conversion value Organic Search > Direct 100 $200,000 Organic Search > Organic Search >Direct 50 $9,000 In Excel, select chosen columns, go to data ribbon and select Text to Columns. When the dialogue box prompts, select Delimit (next), select Other and type in a > sign (next) and click finish. Basic Channel Grouping conversions conversion value Organic Search > Direct 100 $200,000 Organic Search Direct Organic Search > Organic Search >Direct 50 $9,000 Organic Search Organic Search Direct p37

35 For each row, determine the number of interactions in each path using COUNTIF statements. Next, determine how many times Organic Search appeared in each path. Determine what each interaction is worth for each path. Basic Channel Grouping conversions conversion value # of search # of total interactions each interaction value attributed conversions to Organic Search > Direct 100 $200, $100,000 $100,000 Organic Search > Organic Search > Direct 50 $9, $3,000 $6,000 For the first path, each interaction is worth $100,000, so Organic Search gets credit for $100,000 because it appears once in the path. For the second path, each interaction is worth $3,000, so Organic Search gets credit for $6,000 because it appears twice in the path. Sum the entire column for Attributed Conversions to Organic to yield $106,000. This is the amount of Attributed Conversions used to compare with Last-Touch Conversions. Divide (Attributed Conversions) by (Last-Touch Conversions) to determine how overvalued or undervalued that channel is. If Organic Search had 1,500 Attributed Conversions and 1,000 Conversions (Last-Touch), then Organic Search is undervalued and should be worth 50% more than its current value. This is useful because once attributed conversions have been determined for every available channel, a marketer can prioritize these events based on all interactions, not just the last-touch. p38

36 Turning Knowledge into Success The exponential increase digital marketing channels over the last twenty years has driven a much more detailed understanding of buyer behavior than ever before. With each new radical change in technology comes increased complexity. However, as new technologies and models to understand this complexity become available it is the first adopters that gain the edge and turn inches into miles that separate these marketers from their competition. Attribution models can be powerful, but always keep in mind that limitations exist with any model-based analysis. No model will ever give you the complete answer. It is up to you to continue to measure, experiment, and iterate to discover the most lucrative formula of digital marketing channels. After having shared in so many successes with our clients, we know that using the information in this ebook delivers results. Use this information to better understand the impact of your digital marketing channels and drive higher returns from marketing at your organization. Study Contributors Study written and conducted by Casey Szulc, Statistician Mark Rees, Statistician Allison Steele, Senior Graphic Designer Aaron Aders, Market Research Director & Co-Founder Questions & comments can be sent to Research@DigitalRelevance.com p39

37 Interested in more? Contact Aaron Aders, Market Research Director & Co-founder at Digital Relevance to discover how your company can dominate search engine rankings. Office» Mobile» Keystone Crossing, Suite 100 Indianapolis, IN toll-free local fax relevance.com 2013 Digital Relevance. All rights reserved. v.1, January 2013