CONTENTS EXECUTIVE SUMMARY 3 INTRODUCTION 4 HYPOTHESES 6 METHODOLOGY 6 FINDINGS 8 ATTRIBUTION IMPLICATIONS AND SUGGESTED ACTIONS 36

Transcription

1 1

2 CONTENTS EXECUTIVE SUMMARY 3 INTRODUCTION 4 ATTRIBUTION, ATTRIBUTION NOT AN IF, BUT A WHEN 5 THE ATTRIBUTION SPECTRUM 5 HYPOTHESES 6 METHODOLOGY 6 CUSTOM CHANNEL DEFINITIONS 6 FINDINGS 8 HYPOTHESIS: MOST REVENUE COMES FROM MULTI-STEP CONVERSION PATHS. 8 HYPOTHESIS: CONVERSION PATHS VARY BY SECTOR 10 HYPOTHESIS: CONVERSION PATH LENGTHS ARE POSITIVELY CORRELATED TO AOV. 23 HYPOTHESIS: ORGANIC SEARCH DRIVES TOP OF THE FUNNEL TRAFFIC WHILE PAID SEARCH DRIVES THE BOTTOM. 24 CONVERSION PATH POSITION CONTRIBUTION BY CHANNEL BY SECTOR 28 LAST CLICK INTERACTION BREAKDOWN BY CHANNEL 32 HYPOTHESIS: DISPLAY CAMPAIGNS APPEAR LATER DUE TO RETARGETING 33 HYPOTHESIS: SOCIAL MEDIA IS A TOP OF THE FUNNEL FACTOR 34 ATTRIBUTION IMPLICATIONS AND SUGGESTED ACTIONS 36 UNDERSTAND HOW YOUR BUSINESS BEHAVES FROM A CONVERSION PATH STANDPOINT. 36 DECIDE HOW YOU WILL ATTRIBUTE NON-ATTRIBUTABLE REVENUE, E.G. DIRECT AND MISCELLANEOUS REFERRALS 37 INCLUDE ALL MARKETING-RELATED EXPENSES IN YOUR ATTRIBUTION MODEL INCLUDING HEADCOUNT. 37 EXPERIMENT WITH DIFFERENT ATTRIBUTION MODELS 38 LIFETIME VALUE PLAYS A ROLE 40 2

3 EXECUTIVE SUMMARY Market forces will eventually force retailers who use a single interaction attribution model to migrate to multi-touch attribution. It is not an if question, it is a when question. Here at NetElixir University, we decided to study conversion paths and multi-touch attribution models to discern the best approaches to take towards evolving your attribution process. We started with the following hypotheses: 1. Most revenue comes from multi-step conversion paths. 2. Conversion paths vary by length by vertical. 3. Conversion path length is positively correlated with AOV. 4. Organic Search drives the top of conversion funnels. Paid Search, especially trademark terms, drives the bottom. 5. Using display campaigns for retargeting causes it to appear later in the conversion path. 6. Social media is a top of the funnel phenomena. Using a set of custom channel definitions, we built a dataset with over 89,000 different conversion paths that ranged from single step paths to paths with over 300 steps. This dataset included over 500,000 transactions and $58MM across 8 industry sectors. We found the first 3 hypotheses, half of #4, and #5 to be true. Here are some of the other interesting findings from this study: None of our 8 industry sectors had more than 50% of their conversions from single step conversion paths. The average number of site visits per conversion is 4.5. The range by sector goes from low of 2.4 for B2B sites to a high of 7.3 for fashion. Social media sites, both the big names and smaller more industry specific sites, participated in the conversion paths for just a little more than 1% of the total revenue in this study. Direct traffic was 82% of the total sessions in this study and had its hand in over 60% of the $58MM. Organic traffic is 4.35x more likely to be the top of funnel than the bottom. Paid Search is 1.67x more likely to be the top of the funnel vs anywhere else. If your business mirrors this pattern, there are significant implications to consider for your SEO efforts. Branded Paid Search is 1.8x more likely to be the top of funnel vs anywhere else. A key takeaway for retailers from this finding is to think twice about cutting back on your trademark search campaigns. In the Apparel and Fashion sectors, Google Shopping campaigns are significantly better avenues for bringing customers into the conversion funnel than non-branded terms and branded terms on Bing. Display s impact is small and has most of its impact in the middle of the funnel. CSEs, another small overall contributor and perhaps a dying channel, garnered most of that contribution through first click interactions. These channels are a good way to fill the top of your conversion funnel. Affiliates are 3.5x more likely to be last click vs first click, an example of the impact of coupon sites. This channel outperformed non-trademark or non-brand campaigns combined in Google and Bing. When you are ready to start creating your optimal attribution model, here are a few key points to consider. You need to understand the contribution of as many marketing channels as you can measure. Attribution models are not something to set it and forget it. You will need to periodically test your model to make sure it remains optimal. 3

4 Consider an attribution method that incorporates a weighted model based on the channel s strength (% of total contribution value) and position in the conversion path. Here is an action plan outlined in this study you can use to take the first step along the Attribution Spectrum to multi-touch attribution for your digital marketing efforts. 1. Understand how your business behaves from a conversion path standpoint. 2. Decide how you will attribute non-attributable revenue, e.g. Direct and miscellaneous referrals. 3. Include all marketing-related expenses in your attribution model 4. Experiment with different attribution models including custom models. 5. Know your average customer LTV. 6. Revisit your attribution models periodically to ensure they remain optimal. INTRODUCTION LAST CLICK ATTRIBUTION IS STUPID Figure 1 Conversion Path View from Top Conversion Path Report Justin Cutroni, Google Analytics Evangelist at the Wharton Customer Analytics Initiative April,

5 ATTRIBUTION, ATTRIBUTION NOT AN IF, BUT A WHEN Figure 1 is an actual conversion path we found in a Google Analytics Top Conversion Path report after applying the custom channel definitions created for this study. Perhaps it is conversion paths like this one that have retailers throwing their arms up in the air saying I know last click attribution is stupid, but what else am I supposed to do when I have a conversion path with over 500 steps in it? We decided to conduct this study to see if we could arm retailers like you with ammunition for your request to get resources, either human or financial, to help you tackle the attribution question. Like we said for mobile-friendly sites a year ago, it s not an if question, but a when question. If you are not thinking about your attribution methodology now, competition will eventually force you to do so. THE ATTRIBUTION SPECTRUM Like your other marketing processes, your marketing attribution methodology will evolve along The Attribution Spectrum, depicted in Figure 2. As you move along the spectrum, your methods will become more complex, incorporate more touchpoints and become increasingly more sophisticated as you improve the ROI you generate from each marketing dollar you invest. The far right of the spectrum represents the Holy Grail of Attribution where you are able to assign a cost and a dollar value to every interaction touchpoint, online and offline, by individual person, aggregate and segment that data by such values as existing customer vs prospect, and build predictive models to help you identify which marketing/touchpoint initiatives to apply to those individuals. Attribution Spectrum 1 touch interaction - e.g. last click digital attribution GA default model All digital interactions considered not just first or last click. GA multi-channel funnel reports GA data imports All digital interactions considered categorized by known individuals and unknown. UA's Unique User ID enables individual tracking. predictive analytics are used to model marketing mixes for known vs unknown. Only in Premium GA Holy Grail of Attribution Integrates all touchpoints that cost $$ by individual including offline AND online touches. Applies math to figure revenue and margin value of each individual touchpoint, sums those by touchpoint across all customers and non-customers and weighs each area accordingly. Predictive analytics are used at the individual customer level. Figure 2 Attribution Spectrum Google Analytics uses a modified single interaction attribution model as the default condition which puts it at the beginning of the Attribution Spectrum. That said, the use of its multi-channel funnel reports, such as the Top Conversion Path report used extensively in this study, and its multi-step attribution models are first steps you can take down the Attribution Spectrum. Additionally, Google Analytics data import features allow you to integrate other data sources, such as offline marketing program data, so that you can include more marketing intelligence in your analytics account and in your attribution efforts. 5

6 HYPOTHESES Here at NetElixir University, we decided to study conversion paths to see what we can discern about the best approaches to take towards attribution. To start our study, we first formulated a set of hypotheses that were based on common perceptions among our clients, as well as anecdotal evidence. Here are those hypotheses. 1. Most revenue comes from multi-step conversion paths. 2. Conversion paths vary by length by vertical. 3. Conversion path length is positively correlated with AOV. 4. Organic Search drives the top of conversion funnels. Paid Search, especially trademark terms, drives the bottom. 5. Using display campaigns for retargeting causes it to appear later in the conversion path. 6. Social media is a top of the funnel phenomena. METHODOLOGY CUSTOM CHANNEL DEFINITIONS In order to start creating your optimal attribution model, you will need to understand the contribution of as many marketing channels as you can you measure. That is why we took great pains to define a set of over 20 custom channels in Google Analytics in undertaking this study. We attempted to segment traffic and define segments in ways that would identify all traffic that was driven by some kind of marketing investment or effort. For example, one of our clients is involved in providing college scholarships. That program delivers tangible traffic to their site from their education partners, which we categorized in the Other Advertising & Marketing channel. Through an iterative process, we defined a set of custom channels and applied them to a substantial sample of our clients accounts. We then studied conversions that occurred between January 1 st and April 30 th, 2015 with a 90 day look back period. The definitions that we ended up with are in Table 1 below. Table 1 Custom Channel Definitions Channel Group Direct Organic Google Brand CPC Affiliates Bing Brand CPC Google NTM CPC Google PLA CPC Bing Non Brand CPC Display Social Self-Referrals Other Ads Referrals CSEs - Non-Google or Bing Description Same as GA Default GA Default augmented with 3 rd tier search engines from referrals System definition plus webmail AdWords Campaigns featuring the site owner s trademarked terms Affiliate Marketing coupons, etc. Bing Ads Campaigns with owner s trademark Non-trademark terms AdWords Campaigns Google Shopping Non-trademark terms in Bing Ads Any type of display advertising Traffic from known social media sites, blogs, forums, etc. Referrals from either subdomains of owner s web site or from company owned branded sites Any other known marketing that does not fit in one of the definitions All remaining referrals Comparison Shopping Engines 6

7 Payment Related Referrals Vendor/Suppliers Amazon referrals Media Sites Bing Product Ads / cse Referrals caused by issues with alternative payment methods, such as PayPal. Inbound links from suppliers, channel partners, etc.. Referral traffic from Amazon whose marketing source is not determinable Online media sites, online sites of print publications, such as Cosmo Bing Product Listing Ads The dataset we ended up with had over 89,000 different conversion paths that range from having a single step to conversion paths with over 300 steps. Figure 3 below shows the frequency distribution of conversion paths by number of steps. As you can see, there are almost 30,000 conversion paths with up to 10 steps. # PATHS 30,000 25,000 20,000 15,000 # PATHS 10,000 5, Figure 3 Frequency Distribution of Conversion Paths by Path Length Figure 4, shown below, charts the conversion count by # of path steps or path length. This dataset includes over 500,000 transactions across 8 industry sectors. 250,000 # CONVERSIONS BY PATH STEPS 200, , ,000 50,

8 Figure 4 Conversion Path Count by Path Length In Figure 5, we chart the total conversion value by path length. The original dataset we pulled had over $60MM. After removing outlying data, we ended up with a working dataset for the study of about $58MM. $25,000,000 CONVERSION VALUE BY # STEPS $20,000,000 $15,000,000 $10,000,000 $5,000,000 $ Figure 5 Conversion Value by Path Length FINDINGS HYPOTHESIS: MOST REVENUE COMES FROM MULTI-STEP CONVERSION PATHS. In our study, we found that 61% of the $58MM in conversions resulted from conversion paths with more than a single step, as well as 59% of all transactions. This mix between single step path and multiple paths is consistent with other published research on this topic. The average conversion path was 4.5 steps with a low of 2.4 in B2B and a high of 7.3 in Fashion. We also found that not one of our 8 industry sectors had more than 50% of their conversions come from single step conversions. The implication here is that a single interaction attribution model like GA s default last non-direct click is treating most of your sales unfairly and that decisions made from that data are misguided. Figure 6 depicts the single step, i.e. path length = 1, conversions share of total conversions (transactions) and total conversion value (ecommerce revenue) for our entire dataset and for the industry sectors included in that dataset. 8

9 Single Step Conversions Share 50% 45% 40% 35% 30% 25% % of conversions % of value Figure 6 Single Step Conversions Share by Sector Avg Touches Per Conversion By Sector Figure 7 Average Path Length Per Conversion by Sector As you can see in Figure 7, the average number of site visits per conversion after adjusting for outliers was 4.5. That number is a little deceiving, as the range by sector goes from a low of 2.4 for B2B sites to a high of 7.3 for fashion products. The low number of visits per conversion for B2B sites is a bit misleading and shows one of the limitations of Google Analytics multi-path conversion tracking. With a lookback period limit of just 90 days, it is possible that some touchpoints are being missed for highly considered B2B purchases that have long sales cycles that exceed the 90 day lookback period from the time of conversion. Purchase frequency is another possible factor. For example, one of our B2B clients is in a business where many of their customers make only a single purchase each year. 9

10 HYPOTHESIS: CONVERSION PATHS VARY BY SECTOR The variation in average touches per conversion (Figure 7) and conversion share for single step conversions by sector (Figure 6) clearly shows that conversion paths vary by sector, if not by retailer. The implication here is that you need to build your own attribution system and experiment to find the optimal model for your business. Additionally, attribution models are not something to set it and forget it. You will need to periodically test your model to make sure it remains optimal. To demonstrate this point, we took one client s May top conversion path data and compared it to the January through April data that is part of our dataset. Table 2 summarizes the findings. 82% of May s conversion paths were unique. That is, the number and order of the channel touchpoints in those paths did not match any of the more than 13k conversion paths we had in our dataset for this client. More importantly, look at the share of revenue and orders those unique paths contributed in May. 40% of orders and 41% of revenue in the month of May came from conversion path sequences we did not see in the previous 4 months. In theory, an attribution model that was built and optimized on the January April dataset may not be the optimal model to apply to the May results. Table 2 Sample Comparison of Conversion Path Overlap May to Jan - Apr # Paths $ Orders May paths overlap with Jan - Apr 667 $1,288,434 12,063 May unique paths 3,074 $901,246 8,083 May Total 3,741 $2,189,680 20,146 May unique % of Total 82% 41% 40% CHANNEL GROUP CONVERSION PATH DATA SUMMARY The below table shows the participation in conversion paths by the custom channels we defined. Table 3 Channel Group Conversion Path Data Summary Channel Group # of Conversions (Transactions) # of Conv. Paths Conversion Value % of Total Conv. Value AOV Avg. Path Len (# Steps) Direct 283,668 74,426 $ 35,525,363 61% $ Organic 155,286 40,117 $17,925,019 31% $ ,670 44,853 $11,355,356 20% $ Google Brand CPC 94,178 27,797 $10,859,029 19% $ Affiliates 53,995 17,401 $4,744,113 8% $ Bing Brand CPC 22,367 6,387 $3,501,182 6% $ Google NTM CPC 34,678 12,947 $3,384,983 6% $ Google PLA CPC 26,721 9,451 $2,042,689 4% $ Bing Non Brand CPC 17,342 6,011 $1,180,444 2% $ Display 9,287 6,244 $ 800,993 1% $ Social 8,202 3,245 $ 622,256 1% $ Self-Referrals 5,950 2,762 $ 519,315 1% $ Other Ads 14,989 3,304 $ 511,960 1% $

11 Referrals 2,610 1,618 $ 314,067 1% $ CSEs - Non-Google/Bing 2, $ 201,711 0% $ Payment Related Referrals 2, $ 196,296 0% $ Vendor/Suppliers $ 120,973 0% $ Amazon referrals 1, $48,470 0% $ Media Sites $44,362 0% $ Inbound Calls $16,160 0% $ Bing Product Ads / cse $ 1,905 0% $ The conversion value you see in Table 3 represents the total revenue for all of the conversion paths within which the particular channel participated. Given today s multi-tabbed browsing environment and GA s session timeout behavior, it is not surprising that Direct traffic had their hand in over 60% of the conversion value. Yet, there were a few surprises in this data: 1. The lack of social s contribution is surprise, contributing to little more than 1%. 2. Affiliates slightly outperforming NTM combined in Google and Bing. Other noteworthy findings: Direct traffic was 82% of the total sessions in this study and participated in 60% of the total revenue. 1. Display channel s low contribution. Like social, this might be a function of the dataset of clients in this study which skews from small to midsized retailers. Because we use GA to collect data, all of our clients using enterprise class analytics are excluded. 2. The death of CSEs as a channel. Google s shifting from GoogleBase to a paid medium back in 2012 sounded the death toll for CSEs and this evidence is clearly proof of that impact. Speaking anecdotally, this data seems to indicate that Google Shopping has taken that share from CSEs for when I ran an ecommerce channel for a mid-sized specialty retailer around the time of Google s shift, CSEs were contributing 5-6% of my last click revenue. At that time, my CSE channel did not include GoogleBase. ATTRIBUTION MODEL CONSIDERATIONS Although we identified 20 different channels to categorize traffic sources based on marketing initiatives, you can see in Table 3 that a few channels dominate the conversion paths in our dataset. Another point to take note of is the range in average path lengths by channel which goes from less than 4 to over 14. Given these findings, considering an attribution method that incorporates a weighted model based on the channel s strength (% of total contribution value) and position in the conversion path makes sense. TOP 5 CONVERSION PATHS BY VERTICAL Table 4 shows the top 5 conversion paths for each of 9 verticals based on their share of total conversion value. The 45 paths are comprised of just 7 unique paths. All verticals top 5 paths included single step conversions in the Direct, Organic Search and Google Brand CPC channels, with some rank order differences by vertical. 8 of the 9 verticals have the single step Direct conversion as the top producer, with the single step Organic Search conversion path a close second in 7 of those 8 sectors. appears in 6 of the 9 sectors. The Bing Brand CPC channel appears in the Health & Beauty, Food and B2B sectors while Google Shopping campaigns cracked the top 5 for the Apparel and Fashion sectors. 11

12 You ll also notice that 7 of the 8 verticals have a step in the top 5 that is a 2 step conversion path. A 2 step Direct to Direct conversion path appears in 7 sectors top 5. The fact that this 2 step path of Direct sessions is the only multi-step conversion path in any of these sectors top 5 and the fact that the Direct channel was 82% of the total conversion path steps in the entire dataset makes us believe that a significant portion of the Direct sessions in this study are the result of a previous session timing out due to inactivity. The top 5 paths share of total conversion value for the sector ranges from 29% in Home Furnishings to almost ½ in the B2B sector. Table 4 Top 5 Conversion Paths by Vertical Vertical Path Conv $ % Path Conv $ % All Sectors Direct 14% Apparel Direct 12% Organic Search 9% Organic Search 10% Google Brand CPC 6% Google Brand CPC 6% Direct > Direct 5% Direct > Direct 3% 4% Google PLA CPC 3% Top 5 Share 37% Top 5 Share 33% Health/Beauty Direct 12% Fashion Direct 12% 10% Organic Search 9% Organic Search 9% Google Brand CPC 5% Google Brand CPC 6% Direct > Direct 4% Bing Brand CPC 3% Google PLA CPC 3% Top 5 Share 41% Top 5 Share 32% Hobby Direct 11% Home Furn. Direct 9% Organic Search 9% Organic Search 9% Google Brand CPC 6% Direct > Direct 4% Direct > Direct 4% 4% 4% Google Brand CPC 4% Top 5 Share 34% Top 5 Share 29% Food 13% Gifts Direct 14% Direct 13% Organic Search 9% Organic Search 8% Google Brand CPC 6% Google Brand CPC 7% 5% Bing Brand CPC 4% Direct > Direct 4% Top 5 Share 44% Top 5 Share 37% B2B Direct 21% B2C Direct 11% Organic Search 10% Organic Search 9% Direct > Direct 7% Google Brand CPC 6% Google Brand CPC 6% 4% Bing Brand CPC 4% Direct > Direct 3% Top 5 Share 47% Top 5 Share 33% To give you a greater sense for how conversion paths vary by sector, Table 5 shows the top 5 sources of the first interactions, or steps, in the conversion paths in our dataset for a few of the sectors. Although Direct is the top source of initiating the conversion path journey for these sectors, we will see later that Direct plays a stronger role in the last interaction. 4 of the 5 have Direct followed by Organic Search traffic, with Health & Beauty as the exception which has as its #2 source of initial interactions. The presence of non-trademark campaigns in Home Furnishings and 12

13 Gifts seems logical as those categories include infrequent purchase patterns, along with the fact that they tend to be more highly considered or planned purchases. Table 5 Top 5 1st Conversion Path Steps by Vertical Sector 1st step AOV % of Total Conv. % of Total Conv. Value Avg. Path Length All Direct $ % 35% 6.07 Organic Search $ % 23% 3.12 Google Brand CPC $ % 14% $ 93 14% 12% 6.55 Bing Brand CPC $ 155 3% 4% 2.38 Top 5 Share 82% 88% Apparel Direct $ % 36% Organic Search $ 83 24% 24% 3.42 Google Brand CPC $ 92 14% 15% 3.75 Google PLA CPC $ 62 10% 7% $ 90 7% 7% 4.06 Top 5 Share 83% 89% B2B Direct $ % 43% 2.34 Organic Search $ % 23% 2.35 Google Brand CPC $ % 13% 2.30 Bing Brand CPC $ 218 7% 7% $ 173 8% 6% 3.27 Top 5 Share 91% 93% Health & Beauty Direct $ % 29% $ % 23% 3.32 Organic Search $ % 20% 2.83 Google Brand CPC $ % 13% 2.65 Bing Brand CPC $ 132 6% 6% 2.34 Top 5 Share 90% 91% Home Furn Direct $ % 29%

14 Organic Search $ % 28% $ % 12% 3.84 Google Brand CPC $ 150 8% 11% 2.98 Google NTM CPC $ 140 5% 6% 2.63 Top 5 Share 72% 86% Gifts Direct $ % 32% 8.06 Organic Search $ 90 19% 20% $ 79 20% 18% 7.71 Google Brand CPC $ 93 13% 14% 3.44 Google NTM CPC $ 74 5% 4% 3.64 Top 5 Share 84% 87% Table 6 lists the top 5 single step conversion paths by sector. The conversion paths that make up Table 6 are a subset of those that make up Table 5 since a single step conversion path will be counted as both a first and last interaction. Therefore, as you saw in Table 5, 4 of the 5 have Direct and Organic Search as the top 2 contributors. The contribution of those 2 channels by sector varies from 20% to 33% as a single step conversion whereas in Table 5 the contribution as the first click varies from 52% to 66%. There are some differences when you compare Table 6 to Table 5. For example, in Home Furnishings, non-trademark campaigns in Google is the 5 th ranked first interaction channel vs that position in the Single Step Conversions being held by the Affiliate Channel. In Gifts, ranks 3 rd for first clicks while in Single Step Conversions the Google Brand CPC channel takes that position slightly ahead of . 14

15 Table 6 Top 5 Single Step Conversion Paths By Vertical Sector Single Step Path AOV % of Total Conv % of Total Conv Value All Direct $ % 14% Organic Search $ % 9% Google Brand CPC $ 110 6% 5% $ 93 4% 3% Bing Brand CPC $ 145 2% 2% Top 5 Share 34% 35% Apparel Direct $ % 12% Organic Search $ 84 10% 10% Google Brand CPC $ 92 5% 6% Google PLA CPC $ 61 3% 3% $ 87 2% 2% Top 5 Share 30% 32% B2B Direct $ % 21% Organic Search $ % 10% Google Brand CPC $ 174 7% 6% Bing Brand CPC $ 197 4% 4% $ 148 3% 2% Top 5 Share 46% 42% Health & Beauty Direct $ 97 15% 12% $ 128 9% 10% Organic Search $ % 9% Google Brand CPC $ 121 6% 6% Bing Brand CPC $ 127 3% 3% Top 5 Share 43% 41% Home Furn Direct $ 127 8% 9% Organic Search $ 81 13% 9% $ 93 4% 4% Google Brand CPC $ 130 3% 4% Affiliates $ 31 9% 3% Top 5 Share 38% 28% Gifts Direct $ 98 12% 14% Organic Search $ 86 9% 9% Google Brand CPC $ 90 6% 6% $ 82 5% 5% Bing Brand CPC $ 115 2% 2% Top 5 Share 33% 35% 15

16 After seeing patterns emerge in the first click and single step data, we were curious to know if we would find similar behavior when we increased the path length of our view of the data. We studied conversion paths with just 2 interactions by sector and did indeed find points of commonality. Table 7 lists the 2 step conversion paths that appeared in every vertical s top 10 conversion paths for that length. Based on what we see in Table 4, there is no surprise that the 2 step Direct > Direct path shows up at the top. 3 of the 6 appear to be the behavior of repeat visitors, already familiar with your site, who use the same approach each time to get to your site. Table 7 Top 2 Step Conversion Paths Top 2 Step Conversion Paths Appearances In Sector Top 10 Direct > Direct 9 > Direct 9 Google Brand CPC > Direct 9 Google Brand CPC > Google Brand CPC 9 Organic Search > Direct 9 Organic Search > Organic Search 9 PAID SEARCH MOST COMMON PATHS BASED ON CONVERSION VALUE Table 8 lists the most common conversion paths for Paid Search-related campaigns across our entire dataset. Similar to Table 7, the Total column below indicates how many times that particular path shows up in the top 5 Paid Search related conversion paths for the 9 sectors in our study. Given that we are using conversion value to rank the conversion paths, it is not surprising that the list is dominated by campaigns featuring the site owners trademark terms. Given the amount of overlap seen in Tables 4 through 7, it is somewhat surprising to see just 2 paths, the single step conversions from branded (trademark) campaigns, appear in all 9 sectors. Table 8 Top Paid Search Conversion Paths Top 5 CPC Conversion Paths Bing Brand CPC 9 Google Brand CPC 9 Total Google Brand CPC > Google Brand CPC 8 Google NTM CPC 7 Bing Non Brand CPC 4 Google Brand CPC > Direct 4 Google PLA CPC 2 Bing Brand CPC > Direct 1 16

17 Table 9 lists the top 5 Paid Search related conversion paths by segment. As most would expect, position #1 is held by single path trademark campaigns in Google across all sectors. That said, there are some notable differences by sector in the other positions. In Home Furnishings & Hobby & Leisure, single step non-trademark AdWords campaigns are #2 Single step Google Shopping campaign driven conversions were #2 in the Apparel and Fashion sectors. Shopping campaigns success in Apparel and Fashion makes sense, given the visual medium and the nature of those products. In Home Furnishings, the success of non-trademark campaigns can be attributed to 2 factors. In this study, campaigns containing keywords with the names of 3 rd party brands that the retailer sells are included in the non-trademark segment. For example, queries looking for a particular brand of window covering or area rug would be in the non-trademark segment. Home Furnishings is an industry where search queries tend to be more broad and product category-related, such as vertical blinds or flatware sets, versus the Fashion and Apparel industry where queries tend to be more specific, such as leather thigh high boots or black size 8 tunics and therefore more likely to trigger a Shopping campaign ad. All Sectors Apparel B2B Health /Beauty Home Furnishings Table 9 Top Paid Search Conversion Paths by Vertical Google Brand CPC Bing Brand CPC Google NTM CPC Google Brand CPC > Direct Google Brand CPC > Google Brand CPC Google Brand CPC Google PLA CPC Google Brand CPC > Google Brand CPC Bing Brand CPC Google Brand CPC > Direct Google Brand CPC Bing Brand CPC Google Brand CPC > Direct Google Brand CPC > Google Brand CPC Bing Brand CPC > Direct Google Brand CPC Bing Brand CPC Google NTM CPC Google Brand CPC > Google Brand CPC Bing Non Brand CPC Google Brand CPC Google NTM CPC Bing Brand CPC Google Brand CPC > Direct Google NTM CPC > Direct 17

18 Gifts Food Hobby and Leisure Fashion B2B/B2C Pure B2C Google Brand CPC Bing Brand CPC Google NTM CPC Bing Non Brand CPC Google Brand CPC > Google Brand CPC Google Brand CPC Bing Brand CPC Google NTM CPC Bing Non Brand CPC Google Brand CPC > Google Brand CPC Google Brand CPC Google NTM CPC Bing Non Brand CPC Bing Brand CPC Google Brand CPC > Google Brand CPC Google Brand CPC Google PLA CPC Google Brand CPC > Google Brand CPC Google Brand CPC > Direct Bing Brand CPC Google Brand CPC Bing Brand CPC Google NTM CPC Google Brand CPC > Direct Google Brand CPC > Google Brand CPC Google Brand CPC Bing Brand CPC Google NTM CPC Google PLA CPC Google Brand CPC > Google Brand CPC ORGANIC SEARCH MOST COMMON PATHS BASED ON CONVERSION VALUE It is a commonly held belief among many retailers that organic search tends to be a top of the funnel channel. Our research echoes this belief. Below are the top 5 conversion paths that include at least one Organic Search step. All 5 start with an organic search. 18

19 Top Organic Conversion Paths Table 10 Most Common Paths Including Organic Search Conversion Value Conv. Value Share Organic Search $ 5,326,544 9% Organic Search > Direct $ 1,442,353 2% Organic Search > Direct > Direct $ 589,481 1% Organic Search > Organic Search $ 526,823 1% Organic Search > Direct > Direct > Direct $ 290,959 1% Table 11 is the breakdown by sector. Demonstrating its dominant role as a top of the funnel driver, Organic Search kicked off the conversion path in 49 of the 50 paths. Table 11 Top Organic Search Conversion Paths By Vertical Sector Top Organic Conversion Paths Conversion Value Apparel Organic Search $ 1,020,537 10% Organic Search > Direct $ 227,518 2% Organic Search > Affiliates $ 106,254 1% Organic Search > Organic Search $89,474 1% Organic Search > Direct > Direct $79,176 1% B2B Organic Search $ 1,861,942 10% Organic Search > Direct $ 556,415 3% Organic Search > Direct > Direct $ 228,143 1% Organic Search > Organic Search $ 188,589 1% Organic Search > Direct > Direct > $91,523 0% Direct Health & Beauty Organic Search $ 519,555 9% Organic Search > Direct $ 101,355 2% Organic Search > Organic Search $49,017 1% Organic Search > Direct > Direct $29,764 1% Organic Search > $22,591 0% Home Organic Search $ 655,265 9% Furnishings Organic Search > Direct $ 267,443 4% Organic Search > Direct > Direct $ 133,046 2% Organic Search > Direct > Direct > $78,250 1% Direct Organic Search > Organic Search $69,863 1% Fashion Organic Search $ 929,303 9% Organic Search > Direct $ 217,379 2% Organic Search > Affiliates $ 105,603 1% Organic Search > Organic Search $99,788 1% Conv. Value Share 19

20 Organic Search > Direct > Direct $74,511 1% Food Organic Search $ 305,414 8% Organic Search > Direct $47,822 1% Organic Search > Organic Search $20,372 1% Organic Search > $15,911 0% Organic Search > Direct > Direct $14,319 0% Hobby and Leisure Organic Search $ 1,667,746 9% Organic Search > Direct $ 412,246 2% Organic Search > Direct > Direct $ 172,997 1% Organic Search > Organic Search $ 168,443 1% Organic Search > Direct > Direct > $92,583 1% Direct B2B/B2C Organic Search $ 1,565,474 10% Organic Search > Direct $ 528,152 3% Organic Search > Direct > Direct $ 223,839 1% Organic Search > Organic Search $ 173,278 1% Organic Search > Direct > Direct > $ 110,110 1% Direct Gifts Organic Search $ 2,023,008 9% Organic Search > Direct $ 438,248 2% Organic Search > Organic Search $ 185,700 1% Organic Search > Direct > Direct $ 169,103 1% Google Brand CPC > Organic Search $90,534 0% TOP CONVERSION PATHS WITH INCLUDED (BASED ON CONVERSION VALUE) Table 12 shows the top 5 conversion paths containing an step and their breakdown by vertical. All but one path involved either a combination of and Direct, or solely -driven visits. The one exception was the 5 th ranked path involving for the Apparel sector, which starts with an organic search followed by . 20

21 Table 12 Driven Top Conversion Paths Path Conversion $ Conv$ Share All Sectors $ 2,024,807 4% > Direct $ 487,499 1% > $ 477,277 1% Direct > $ 303,499 1% > > $ 195,787 0% Apparel $ 258,609 2% > Direct $ 59,723 1% Direct > $ 56,673 1% > $ 50,257 0% Organic Search > $ 21,651 0% B2B $ 371,504 2% > Direct $ 118,152 1% Direct > $ 116,268 1% > $ 83,892 0% > Direct > Direct $ 48,412 0% Health / Beauty $ 551,112 10% > $ 122,053 2% > Direct $ 79,712 1% > > $ 44,730 1% Direct > $ 36,852 1% Home Furnishings $ 270,602 4% > Direct $ 87,391 1% > $ 56,265 1% > Direct > Direct $ 27,264 0% > > Direct $ 23,338 0% Fashion $ 214,851 2% > Direct $ 53,008 1% Direct > $ 48,141 0% > $ 39,218 0% Direct > Direct > $ 18,036 0% Food $ 510,432 13% > $ 112,067 3% > Direct $ 69,192 2% > > $ 40,100 1% Direct > $ 32,669 1% Hobby and Leisure $ 642,711 4% > $ 182,183 1% > Direct $ 158,934 1% > > $ 91,717 1% Direct > $ 84,396 0% Gifts $ 1,165,154 5% > $ 302,005 1% > Direct $ 241,165 1% > > $ 142,263 1% Direct > $ 126,428 1% 21

22 One well-worn lesson in marketing is the idea of integrated communications and speaking with a consistent voice across all of your marketing channels. Looking at these top paths for , the fact that there is little interaction with other non-direct channels could lead one to the assumption that can be treated separately. GREAT EXAMPLE OF WHY SINGLE CLICK ATTRIBUTION IS FLAWED This breakdown is a great example to point out one of the flaws in a pure last click interaction attribution model, or even in GA s last non-direct click model. The majority of the 40 paths listed across the verticals have more than 1 step. In fact, there are 11 paths with more than 2 steps. Across the 8 verticals, the 40 paths total 80 steps. Therefore, a single click interaction attribution model would ignore ½ of those steps and gives 100% credit to whichever click you choose, usually first or last, thereby giving them more credit than they deserve. Of the 80 steps, 55 are -driven. For the sake of this demonstration, let s assume each -driven step represents a separate marketing campaign so there are 55 campaigns. 29 of the 40 paths are terminated by which means that 26 (55 minus 29) campaigns would receive no revenue credit in a last click attribution model. As the numbers work out, those 29 conversion paths contain a total of 55 steps so 26 steps, 15 Campaign steps and 11 non- steps get shortchanged in last click attribution. Those 29 conversion paths drove $5.8MM in revenue and in last click attribution each campaign in the last click position would have averaged $199k in attributed revenue. In order to demonstrate the point about multi-touch attribution and to keep the math simple, we will use a linear model where each step gets equal weighting or credit for the revenue that the path generated. In that model, the 29 last click campaigns would have only received $166k each, so in total those last steps would receive ~$1MM less in revenue credit. That $1MM gets credited to the other 26 steps which included 15 marketing campaigns. Those campaigns go from $0 in revenue credit using last click to about $40k each using a linear model. Now let s put some costs against those 55 campaigns and we ll assume each campaign costs $10k to prepare and distribute. Table 13 shows how the math breaks down with respect to sales credit and ROI. In the linear model, all campaigns show a positive return. In the last click model, the 29 campaigns in the last position generate a great 1900% ROI while the other 15 campaigns ROI is less than 0. Table 13 COMPARING LAST CLICK TO LINEAR ATTRIBUTION EXAMPLE # Campaigns Costs Last Click Sales Last Click ROI Linear Click Sales Linear Click ROI 29 $290k $5,8MM 1900% $4.8MM 1555% 15 $150k $0 0% $1MM 667% 22

23 Now let s envision the people side of this example. Let s assume in this organization different managers put together each of the campaigns. In a last click attribution, the people who developed the 29 last click campaigns receive revenue credit that is essentially 20% greater than it would be in a linear model. The other managers sit around trying to figure out why their campaign didn t work and update their resumes. Some people get promoted, some people get fired. Hopefully you will agree with Justin Cutroni -- Last click is indeed stupid. HYPOTHESIS: CONVERSION PATH LENGTHS ARE POSITIVELY CORRELATED TO AOV. The theory with this hypothesis is that higher ticket purchases are more considered purchases with longer sale cycles and therefore would have longer conversion paths. Average conversion value (AOV) is positively correlated to conversion path length when viewing the entire dataset of 89k conversion paths, but it is a weak positive at When you break it down by sector, you get different results. Interestingly, there were 3 sectors (B2B, Health & Beauty and Hobby and Leisure) where the correlation factor was actually negative, (that is, as AOV drops, conversion path lengthens). Table 14 Correlation of Path Length to AOV by Vertical AOV Correl to Path Sector Length All 0.15 Apparel 0.1 B2B (0.32) Health & Beauty (0.1) Home Furn 0.1 Hobby, Leisure (0.3) Food (0.0) Fashion 0.11 Gifts 0.15 B2B/B2C 0.02 Pure B2C

24 HYPOTHESIS: ORGANIC SEARCH DRIVES TOP OF THE FUNNEL TRAFFIC WHILE PAID SEARCH DRIVES THE BOTTOM. Many advertisers believe that their paid search campaigns, especially the campaigns featuring terms including their trademarks, drive the bottom of their conversion funnel. Table 15 breaks down each channel s position in conversion paths based on their conversions share of total conversion value for that path position. In order to assess this hypothesis, we identified whether the channel was: The driver of a single path conversion, i.e. the user converted on their initial visit. The first touchpoint or a click in a multi-step conversion path. The last interaction in a multi-step conversion path. One of the intermediate or middle steps in the conversion path. Both the first interaction point and the last interaction point in a multi-step conversion path. For example, the Direct channel was the first click in multi-step conversion paths that contributed 21% of the ~$58MM in total conversion value in our dataset. Similarly, single path conversions that were attributed to the Direct channel were 14% of the $58MM. When Direct showed up somewhere in the middle, those paths contributed 7% while those paths with Direct as the last click were 35%. In the column labeled 1 st Interaction, we add the Single Path % to the First Click % to give you a better perspective on each channel s total contribution for attracting customers to the top of your conversion funnel. Organic Search has a 23% first interaction contribution share vs 10% combined for the middle and last click, so that part of our hypothesis appears to be true. Channel Table 15 Channel Breakdown by Funnel Position Single Path First Click 1st Interaction = % - All % Single Path + Sectors 1st Click % Organic traffic is 4.35 more likely to be top of funnel than bottom. Middle Steps % Last Click % First & Last Direct 14% 21% 35% 7% 35% 16% Organic 9% 14% 23% 5% 5% 2% Google Brand CPC 6% 8% 14% 3% 5% 2% 4% 8% 12% 4% 7% 3% Bing Brand CPC 2% 2% 4% 1% 1% 0% Google NTM CPC 1% 2% 4% 2% 1% 0% Affiliates 1% 1% 2% 2% 4% 0% Google PLA CPC 1% 2% 2% 1% 1% 0% Bing Non Brand CPC 1% 1% 1% 0% 0% 0% Display 0% 0% 0% 1% 0% 0% Self-Referrals 0% 0% 0% 0% 0% 0% CSEs - Non-Google or Bing 0% 0% 0% 0% 0% 0% Referrals 0% 0% 0% 0% 0% 0% Vendor/Suppliers 0% 0% 0% 0% 0% 0% Other Ads 0% 0% 0% 1% 0% 0% Payment Related Referrals 0% 0% 0% 0% 0% 0% Amazon referrals 0% 0% 0% 0% 0% 0% Media Sites 0% 0% 0% 0% 0% 0% Social 0% 0% 0% 0% 0% 0% 24

25 When you look specifically at the subset of conversions that Organic Search is involved in, this propensity for the beginning of the conversion funnel becomes very clear. Figure 8 below shows the breakdown by funnel position for the Organic, Direct and channels for the subsets of conversion paths that included those channels. You can see that Organic and tend to have more impact as the first interaction (Single Path + First Click). Organic Search as a first interaction participates in 74% of its total conversion value ($17.9MM, see Table 3) vs just 17% for last click. Said differently, Organic Search is 4.35x more likely to be seen in the top of a conversion path funnel than the bottom. Similarly, as first interaction participates in 61% of its total conversion value vs 34% when it was the last click. That is 1.8x more likely to be top of funnel vs bottom. WHERE ARE YOUR SEO EFFORTS FOCUSED? If your business resembles this type of a bias towards the top of the funnel for organic search, there are significant implications for your SEO efforts in these numbers. Are your site-side SEO efforts aimed at the top of the funnel terms? Have you done everything you can to optimize your broad category pages, the ones that are likely to appear in early information gathering queries? Are you authoring authentic content that your customers (and more importantly your noncustomer prospects) will find useful? Product reviews are great for attracting users who are 2/3 of the way through their buying cycle. What do you have on your site or in your blog that shoppers further up the funnel might find useful? Direct, , Organic Conversion Path Position as % of The Channel's Total 60% 50% 40% 30% 20% 10% Direct Organic 0% Single Path First Click Last Click Middle Steps first = last Figure 8 Conversion Path Position Breakdown for Direct, , Organic Search 25

26 LAST CLICK IS DIRECT CHANNEL S DOMINANT FUNNEL POSITION Earlier in this paper we mentioned that the Direct channel has a dominant role as a last click interaction. You see this in Table 15 and Figure 8. In Table 15, you can see Direct as the last click occurred for 35% of the $58MM vs 21% for the first click. When you filter the $58MM to look in Figure 8 at the $35MM that the Direct channel participates in at any position in the conversion path, Direct as the last click occurs in 57% of that $35MM. We posit that multi-tabbed browsing and Google Analytics session timeout parameter are causing some of this dominance for Direct in the last click position. This trend is undoubtedly what led Google to switch the default attribution in Google Analytics to last non-direct click a few years ago. DON T TOUCH THOSE TRADEMARK CAMPAIGNS Branded Paid Search 1.8x more likely to be top of funnel vs anywhere else With respect to paid search, some readers might be surprised to see in Table 15 and in Figure 10 that trademark term campaigns actually contribute more at the top of the funnel. In Table 15, we see that the 2 trademark campaign channels (Google Brand CPC, Bing Brand CPC) combined have a first interaction contribution of 18% vs just 10% for the middle and last click positions. Therefore, trademark campaigns are 1.8x more likely to start the conversion path. This top of the funnel dominance is consistent with results we have seen for multiple verticals in which our clients are members in Google s interactive customer journey tool. Figure 9 is a view of this tool for the Hobbies & Leisure sector showing that Brand Paid Search occurs in the top of the funnel 31% of the time vs 24% at the bottom. You can see this tool at Figure 9 Google Customer Journey Tool Example This top of the funnel dominant position doesn t just apply to trademark campaigns. From Table 15, if we aggregate the first interaction position for the 5 paid search channels it totals 1/4 th of the $58MM vs a combined 15% for the middle and last click positions. In other words, Paid Search is 1.67x more likely to start a conversion path than to be anywhere else in the path. 26

27 The first interaction dominance also holds to each of the 5 channels when you look at them individually, which we do in Figure 10. Similar to Figure 8, this chart breaks down the 5 CPC channels by position contribution for the conversions those channels participated in. CPC Channels Conversion Path Position as % of Total 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Single Path First Click Last Click Middle Steps first = last Google Brand CPC Bing Brand CPC Google NTM CPC Google PLA CPC Bing Non Brand CPC Figure 10 Path Position Breakdown for Paid Search Conversion Paths For example, Google and Bing trademark campaigns (the 2 leftmost columns in each category) are significantly more likely to contribute to a conversion as a first click than a last click. Google Brand CPC was the first click in 43% of its total conversion value ($10.9MM, see Table 3) vs just 24% for last click. Bing Brand CPC was the first click in 37% of its conversion value ($3.5MM, see Table 3) vs 21% for last click. THINK TWICE BEFORE CUTTING TRADEMARK BUDGETS One key takeaway for retailers from this finding is to think twice about cutting back on your trademark search campaigns. Many of you may share the belief that your trademark campaigns are bringing you users who are already familiar with your company and that this spend is essentially wasted. We suggest: Take a look at your New User % for your Trademark campaigns. Is it greater than or less than the % for your other channels? Look at your top conversion path report data like we did in this study to understand the role these campaigns play by funnel position for your specific business. Look at the impact of cutting this spend on your total ad spend. In most cases, trademark campaigns tend to be the lowest portion of your spend and therefore cost cutting here won t have a huge impact. One exception to the rule about trademark spend significance is those of you whose trademark contains a commonly used word or phrase that causes your CPCs to be higher. One tactic we have deployed for some clients looking to squeeze their ad spend wherever possible is to create Remarketing Search Ad lists for previous site visitors with a duration that covers your typical sale cycle. We then exclude those audiences from your trademark campaigns. The theory is that if someone has been to your site recently and is entering one of your trademark terms into Google, this is their way of returning to your site and there is no need to spend on an ad when they will most likely click through organically. With this strategy, you still get to promote your brand to users who are entering your trademark into Google because they heard of you through word-of-mouth, saw one of your YouTube videos or were exposed to your brand in some other way. 27

28 COUPON SITE REALITY In Table 15 you can see the impact of coupon sites on ecommerce conversion paths. Those sites dominate the Affiliate channel whose contribution is heavily weighted towards the middle to bottom of the conversion funnel with a 2% first interaction contribution and a 3x larger 6% contribution from the middle and last click. The impact of coupon sites is very likely understated in today s multi-tab browsing environment. A consumer who decides to make a purchase online and opens up a 2 nd tab to go to his or her favorite coupon site looking for a deal in many cases doesn t have to click through to the retailer s site from the affiliate s site to use a promo code they found there. These types of coupon site visits don t get captured in their conversion path and don t change the attribution of their visit to the retailer s site in Google Analytics, so the site owner has no visibility to this behavior. CONVERSION PATH POSITION CONTRIBUTION BY CHANNEL BY SECTOR Given the hypotheses we had regarding the position of several channels in the purchase conversion cycle, we decided to break down that cycle by channel and by sector. SINGLE STEP CONVERSION PATHS SHARE OF TOTAL CONVERSIONS BY SECTOR Table 16 shows the single path conversion share by channel by sector. The first column is the baseline for all sectors for each channel and matches the first column in Table 15. The green shading in the individual sector columns indicates at least a 10% positive difference from the All Sectors baseline while red indicates a similar negative difference. For example, the B2B sector had 21% of its total conversion value from single step For Apparel and Fashion, Google Shopping campaigns are better avenues for attracting customers than non-branded terms Direct visits. It is shaded green because it is 50% above the baseline of 14%. One theory to explain this large variance for B2B is that B2B companies are more likely to have repeat customers who repurchase more frequently and therefore enter the site URL directly. 28