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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

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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.

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• 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

Figure 1 Conversion Path View from Top Conversion Path Report

“LAST CLICK ATTRIBUTION IS STUPID” – Justin Cutroni, Google Analytics Evangelist at the Wharton Customer Analytics Initiative April, 2015

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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.

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.

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.

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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 1st and April 30th, 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 Description Direct Same as GA Default Organic GA Default augmented with 3rd tier search engines from referrals Email System definition plus webmail Google Brand CPC AdWords Campaigns featuring the site owner’s trademarked terms Affiliates Affiliate Marketing – coupons, etc. Bing Brand CPC Bing Ads Campaigns with owner’s trademark Google NTM CPC Non-trademark terms AdWords Campaigns Google PLA CPC Google Shopping Bing Non Brand CPC Non-trademark terms in Bing Ads Display Any type of display advertising Social Traffic from known social media sites, blogs, forums, etc. Self-Referrals Referrals from either subdomains of owner’s web site or from

company owned branded sites Other Ads Any other known marketing that does not fit in one of the definitions Referrals All remaining referrals CSEs - Non-Google or Bing Comparison Shopping Engines

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Payment Related Referrals Referrals caused by issues with alternative payment methods, such as PayPal.

Vendor/Suppliers Inbound links from suppliers, channel partners, etc.. Amazon referrals Referral traffic from Amazon whose marketing source is not

determinable Media Sites Online media sites, online sites of print publications, such as Cosmo Bing Product Ads / cse 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.

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.

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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.

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.

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.

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The average conversion path was 4.5 steps with a low of 2.4 in B2B and a high of 7.3 in Fashion.

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Figure 6 Single Step Conversions Share 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.

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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% $ 125 6.8 Organic 155,286 40,117 $17,925,019 31% $ 115 5.6 Email 116,670 44,853 $11,355,356 20% $ 97 8.7 Google Brand CPC 94,178 27,797 $10,859,029 19% $ 115 6.1 Affiliates 53,995 17,401 $4,744,113 8% $ 88 6.6 Bing Brand CPC 22,367 6,387 $3,501,182 6% $ 157 3.9 Google NTM CPC 34,678 12,947 $3,384,983 6% $ 98 6.3 Google PLA CPC 26,721 9,451 $2,042,689 4% $ 76 6.2 Bing Non Brand CPC 17,342 6,011 $1,180,444 2% $ 68 5.9 Display 9,287 6,244 $ 800,993 1% $ 86 9.4 Social 8,202 3,245 $ 622,256 1% $ 76 5.4 Self-Referrals 5,950 2,762 $ 519,315 1% $ 87 11.5 Other Ads 14,989 3,304 $ 511,960 1% $ 34 5.4

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Referrals 2,610 1,618 $ 314,067 1% $ 87 14.6 CSEs - Non-Google/Bing 2,245 951 $ 201,711 0% $ 90 5.0 Payment Related Referrals 2,174 865 $ 196,296 0% $ 90 7.1 Vendor/Suppliers 882 464 $ 120,973 0% $ 137 5.5 Amazon referrals 1,017 552 $48,470 0% $ 48 8.3 Media Sites 744 403 $44,362 0% $ 60 9.8 Inbound Calls 51 38 $16,160 0% $ 317 4.0 Bing Product Ads / cse 28 25 $ 1,905 0% $ 68 6.2

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:

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. Email 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.

Direct traffic was 82% of the total sessions in this study and participated

in 60% of the total revenue.

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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%

Email 4%

Google PLA CPC 3%

Top 5 Share 37%

Top 5 Share 33%

Health/Beauty Direct 12% Fashion Direct 12%

Email 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%

Email 4%

Email 4%

Google Brand CPC 4%

Top 5 Share 34% Top 5 Share

29%

Food Email 13% Gifts Direct 14%

Direct 13%

Organic Search 9%

Organic Search 8%

Google Brand CPC 6%

Google Brand CPC 7%

Email 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%

Email 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 Email as its #2 source of initial interactions. The presence of non-trademark campaigns in Home Furnishings and

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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 $ 132 30% 35% 6.07

Organic Search $ 115 22% 23% 3.12

Google Brand CPC $ 116 13% 14% 3.20

Email $ 93 14% 12% 6.55

Bing Brand CPC $ 155 3% 4% 2.38

Top 5 Share 82% 88%

Apparel Direct $ 104 29% 36% 11.96

Organic Search $ 83 24% 24% 3.42

Google Brand CPC $ 92 14% 15% 3.75

Google PLA CPC $ 62 10% 7% 3.64

Email $ 90 7% 7% 4.06

Top 5 Share 83% 89%

B2B Direct $ 217 41% 43% 2.34

Organic Search $ 201 23% 23% 2.35

Google Brand CPC $ 197 14% 13% 2.30

Bing Brand CPC $ 218 7% 7% 2.14

Email $ 173 8% 6% 3.27

Top 5 Share 91% 93%

Health & Beauty Direct $ 114 30% 29% 3.49

Email $ 131 21% 23% 3.32

Organic Search $ 120 20% 20% 2.83

Google Brand CPC $ 124 13% 13% 2.65

Bing Brand CPC $ 132 6% 6% 2.34

Top 5 Share 90% 91%

Home Furn Direct $ 165 20% 29% 4.68

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Organic Search $ 121 27% 28% 2.79

Email $ 108 12% 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 $ 100 28% 32% 8.06

Organic Search $ 90 19% 20% 3.37

Email $ 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 5th ranked first interaction channel vs that position in the Single Step Conversions being held by the Affiliate Channel. In Gifts, Email ranks 3rd for first clicks while in Single Step Conversions the Google Brand CPC channel takes that position slightly ahead of Email.

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Table 6 Top 5 Single Step Conversion Paths By Vertical

Sector Single Step Path AOV % of Total Conv

% of Total Conv Value

All Direct $ 125 13% 14% Organic Search $ 103 10% 9% Google Brand CPC $ 110 6% 5% Email $ 93 4% 3% Bing Brand CPC $ 145 2% 2% Top 5 Share 34% 35% Apparel Direct $ 104 10% 12% Organic Search $ 84 10% 10% Google Brand CPC $ 92 5% 6% Google PLA CPC $ 61 3% 3% Email $ 87 2% 2% Top 5 Share 30% 32% B2B Direct $ 192 22% 21% Organic Search $ 176 11% 10% Google Brand CPC $ 174 7% 6% Bing Brand CPC $ 197 4% 4% Email $ 148 3% 2% Top 5 Share 46% 42% Health & Beauty Direct $ 97 15% 12% Email $ 128 9% 10% Organic Search $ 112 10% 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% Email $ 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% Email $ 82 5% 5% Bing Brand CPC $ 115 2% 2% Top 5 Share 33% 35%

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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 Email > 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 Total

Bing Brand CPC 9

Google Brand CPC 9

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

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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 3rd 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.

Table 9 Top Paid Search Conversion Paths by Vertical All Sectors Google Brand CPC Bing Brand CPC Google NTM CPC Google Brand CPC > Direct Google Brand CPC > Google Brand CPC Apparel Google Brand CPC Google PLA CPC Google Brand CPC > Google Brand CPC Bing Brand CPC Google Brand CPC > Direct B2B Google Brand CPC Bing Brand CPC Google Brand CPC > Direct Google Brand CPC > Google Brand CPC Bing Brand CPC > Direct Health /Beauty Google Brand CPC Bing Brand CPC Google NTM CPC Google Brand CPC > Google Brand CPC Bing Non Brand CPC Home Furnishings Google Brand CPC Google NTM CPC Bing Brand CPC Google Brand CPC > Direct Google NTM CPC > Direct

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Gifts Google Brand CPC Bing Brand CPC Google NTM CPC Bing Non Brand CPC Google Brand CPC > Google Brand CPC Food Google Brand CPC Bing Brand CPC Google NTM CPC Bing Non Brand CPC Google Brand CPC > Google Brand CPC Hobby and Leisure Google Brand CPC Google NTM CPC Bing Non Brand CPC Bing Brand CPC Google Brand CPC > Google Brand CPC Fashion Google Brand CPC Google PLA CPC Google Brand CPC > Google Brand CPC Google Brand CPC > Direct Bing Brand CPC B2B/B2C Google Brand CPC Bing Brand CPC Google NTM CPC Google Brand CPC > Direct Google Brand CPC > Google Brand CPC Pure B2C 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.

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Table 10 Most Common Paths Including Organic Search

Top Organic Conversion Paths 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 Conv. Value Share

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 >

Direct $91,523 0%

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 > Email $22,591 0% Home Furnishings

Organic Search $ 655,265 9%

Organic Search > Direct $ 267,443 4% Organic Search > Direct > Direct $ 133,046 2% Organic Search > Direct > Direct >

Direct $78,250 1%

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%

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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 > Email $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 >

Direct $92,583 1%

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 >

Direct $ 110,110 1%

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 EMAIL INCLUDED (BASED ON CONVERSION VALUE)

Table 12 shows the top 5 conversion paths containing an Email step and their breakdown by vertical. All but one path involved either a combination of Email and Direct, or solely Email-driven visits. The one exception was the 5th ranked path involving Email for the Apparel sector, which starts with an organic search followed by Email.

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Table 12 Email Driven Top Conversion Paths Path Conversion $ Conv$ Share All Sectors Email $ 2,024,807 4% Email > Direct $ 487,499 1% Email > Email $ 477,277 1% Direct > Email $ 303,499 1% Email > Email > Email $ 195,787 0% Apparel Email $ 258,609 2% Email > Direct $ 59,723 1% Direct > Email $ 56,673 1% Email > Email $ 50,257 0% Organic Search > Email $ 21,651 0% B2B Email $ 371,504 2% Email > Direct $ 118,152 1% Direct > Email $ 116,268 1% Email > Email $ 83,892 0% Email > Direct > Direct $ 48,412 0% Health / Beauty Email $ 551,112 10%

Email > Email $ 122,053 2% Email > Direct $ 79,712 1% Email > Email > Email $ 44,730 1% Direct > Email $ 36,852 1%

Home Furnishings Email $ 270,602 4% Email > Direct $ 87,391 1% Email > Email $ 56,265 1% Email > Direct > Direct $ 27,264 0% Email > Email > Direct $ 23,338 0% Fashion Email $ 214,851 2% Email > Direct $ 53,008 1% Direct > Email $ 48,141 0% Email > Email $ 39,218 0% Direct > Direct > Email $ 18,036 0% Food Email $ 510,432 13% Email > Email $ 112,067 3% Email > Direct $ 69,192 2% Email > Email > Email $ 40,100 1% Direct > Email $ 32,669 1% Hobby and Leisure Email $ 642,711 4% Email > Email $ 182,183 1% Email > Direct $ 158,934 1% Email > Email > Email $ 91,717 1% Direct > Email $ 84,396 0% Gifts Email $ 1,165,154 5% Email > Email $ 302,005 1% Email > Direct $ 241,165 1% Email > Email > Email $ 142,263 1% Direct > Email $ 126,428 1%

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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 Email, the fact that there is little interaction with other non-Direct channels could lead one to the assumption that email 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 email-driven.

For the sake of this demonstration, let’s assume each Email-driven step represents a separate email marketing campaign so there are 55 email campaigns. 29 of the 40 paths are terminated by Email which means that 26 (55 minus 29) Email 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 Email Campaign steps and 11 non-Email steps get shortchanged in last click attribution. Those 29 conversion paths drove $5.8MM in revenue and in last click attribution each Email 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 Email 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 email marketing campaigns. Those email 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 email 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 email 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%

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Now let’s envision the people side of this example. Let’s assume in this organization different managers put together each of the email 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 0.15. 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

Sector

AOV Correl to Path 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 0.15

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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 1st 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.

Table 15 Channel Breakdown by Funnel Position Channel Single Path

% - All Sectors

First Click %

1st Interaction = Single Path + 1st Click %

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% Email 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%

Organic traffic is 4.35 more likely to be top of funnel than bottom.

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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 Email channels for the subsets of conversion paths that included those channels. You can see that Organic and Email 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, Email 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 non-

customer 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?

Figure 8 Conversion Path Position Breakdown for Direct, Email, Organic Search

0%

10%

20%

30%

40%

50%

60%

Single Path First Click Last Click MiddleSteps

first = last

Direct, Email, Organic Conversion Path Position as % of The Channel's Total

Direct

Organic

Email

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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 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 https://www.thinkwithgoogle.com/tools/customer-journey-to-online-purchase.html.

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 1/4th 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.

Branded Paid Search 1.8x more likely to be top of funnel

vs anywhere else

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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.

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.

0%5%

10%15%20%25%30%35%40%45%

Single Path First Click Last Click Middle Steps first = last

CPC Channels Conversion Path Position as % of Total

Google Brand CPC

Bing Brand CPC

Google NTM CPC

Google PLA CPC

Bing Non Brand CPC

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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 2nd 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 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 re-purchase more frequently and therefore enter the site URL directly.

For Apparel and Fashion, Google Shopping campaigns are better avenues

for attracting customers than non-branded terms

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Table 16 Single Path Conversion % By Channel By Sector

Direct, Organic and trademark campaigns in Google dominate the top 3 spots for all sectors except Health & Beauty and Food where Email is the 2nd highest driver of one touch conversions. This makes sense when you consider that both sectors sell products that are consumables, and therefore will need to be replaced frequently.

Apparel and Fashion perform above average for Google Shopping campaigns as single step conversions, most likely due to the visual nature of that shopping experience. When you look at these two similar sectors in Table 16 and Table 17, which contains a similar breakdown for the first click position, you see that Google Shopping campaigns are significantly better avenues for bringing customers into those sectors’ funnels than non-branded terms or even branded terms on Bing.

• Google Shopping single step conversions are 3% of Apparel’s conversion value vs 0% for both the Google NTM CPC and Bing Non Brand CPC channels and just 1% for the Bing Brand CPC Channel.

• When you look at the first click data in Table 17, the disparity is even larger with Google Shopping campaigns at the first click position participating in 5% of Apparels’ total value vs just 1% for Google NTM CPC and Bing Brand CPC and 0% for Bing Non Brand CPC.

FIRST CLICK INTERACTIONS BY CHANNEL Table 17 shows the breakdown for First Click Interactions in conversion paths with more than 1 step. The trends seen above in the single path conversions hold true here with a couple of notable exceptions. In Hobby & Leisure and Gifts, Email jumps into the #3 position for both sectors in driving initial clicks. Again, there is some logic to difference. Hobby & Leisure retailers sell products to highly engaged users who are predisposed to pay attention to emails from brands they use in their primary leisure pursuits. In addition, several of our clients in that sector sell to both businesses and consumers so some of that email traffic is likely coming from established business relationships.

Channel

SINGLE PATH % - ALL SECTORS APPAREL B2B

HEALTH & BEAUTY

HOME FURN FASHION FOOD

HOBBY, LEISURE

B2B/B2C GIFT

Direct 14% 12% 21% 12% 9% 12% 13% 11% 13% 14% Organic 9% 10% 10% 9% 9% 9% 8% 9% 10% 9% Google Brand CPC 6% 6% 6% 6% 4% 5% 7% 6% 5% 6% Email 4% 2% 2% 10% 4% 2% 13% 4% 3% 5% Bing Brand CPC 2% 1% 4% 3% 1% 1% 4% 1% 3% 2% Google NTM CPC 1% 0% 1% 1% 2% 0% 2% 2% 1% 1% Affiliates 1% 2% 2% 0% 3% 2% 1% 0% 0% 1% Google PLA CPC 1% 3% 0% 0% 0% 3% 0% 1% 0% 1% Bing Non Brand CPC 1% 0% 0% 1% 1% 0% 1% 1% 0% 1% Display 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

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Table 17 First Click Conversion Contribution By Channel By Sector

Channel

FIRST CLICK % APPAREL B2B

HEALTH & BEAUTY

HOME FURN FASHION FOOD

HOBBY, LEISURE

B2B/B2C GIFTS

Direct 21% 24% 23% 17% 20% 27% 12% 20% 20% 18% Organic 14% 14% 13% 11% 19% 15% 7% 14% 18% 11% Google Brand CPC 8% 9% 8% 7% 7% 9% 7% 9% 8% 8% Email 8% 4% 4% 13% 8% 4% 16% 13% 7% 13% Bing Brand CPC 2% 1% 4% 3% 2% 1% 2% 1% 3% 2% Google NTM CPC 2% 1% 2% 2% 4% 1% 2% 3% 3% 3% Affiliates 1% 3% 0% 1% 3% 3% 1% 1% 0% 1% Google PLA CPC 2% 5% 1% 0% 1% 5% 0% 1% 1% 1% Bing Non Brand CPC 1% 0% 0% 1% 1% 0% 2% 2% 1% 1% Display 0% 1% 0% 1% 0% 0% 1% 0% 0% 0%

As indicated above, the Google Shopping channel’s impact in Apparel and Fashion for First Click conversion paths is almost double that of Single Step paths.

SURPRISING BELOW AVERAGE SHOPPING CAMPAIGN PERFORMANCE The below average performance of Shopping campaigns in sectors like Food, Gifts and the Hobby and Leisure market is surprising given that those industries sell items in which appearance can be a key part of the selling equation. One potential reason for this shortfall is the challenge many small to mid-size retailers have in optimizing the content of their merchant feeds. Often times the source of data that gets put in those feeds is from internal systems that serve multiple masters and therefore tweaking elements like product names or descriptions is not an insignificant undertaking. For those of you who can easily modify your data feed content, one tactic we have deployed successfully with clients is adding product category terms to their product names so that their shopping ads have an opportunity to get triggered by broader minded search queries. We’ve seen huge jumps in impressions after deploying this tactic.

MIDDLE STEPS BREAKDOWN BY CHANNEL Table 18 breaks down the contribution share by channel by sector for the middle of the conversion paths. These conversions represent instances when these channels were in the conversion path at least once but were not the first or last interaction, so this is a true look at the middle of the funnel and is commonly referred to as assisted conversions.

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Table 18 Middle Step Conversion Path Contribution by Channel By Sector

Channel

MIDDLE STEP % - ALL SECTORS APPAREL B2B

HEALTH & BEAUTY

HOME FURN FASHION FOOD

HOBBY, LEISURE

B2B/B2C GIFTS

Direct 7% 7% 5% 7% 7% 7% 8% 8% 6% 8% Organic 5% 7% 3% 4% 5% 7% 3% 6% 4% 6% Email 4% 5% 2% 4% 5% 5% 4% 5% 4% 6% Google Brand CPC 3% 5% 2% 3% 3% 5% 2% 3% 3% 3% Affiliates 2% 5% 2% 1% 2% 5% 1% 1% 2% 2% Google NTM CPC 2% 1% 1% 0% 1% 1% 1% 4% 1% 3% Google PLA CPC 1% 1% 0% 0% 1% 1% 0% 1% 0% 1% Display 1% 3% 0% 3% 0% 3% 4% 0% 0% 1% Bing Brand CPC 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% Bing Non Brand CPC 0% 0% 0% 0% 0% 0% 1% 1% 0% 1%

The first thing you will notice is the above average performance in Apparel and Fashion for Organic, Email, Google Brand CPC and Affiliates channels. One reason for the latter’s performance is a plethora of web sites that combine affiliate marketing with the idea of social media like fashiononlineadvisor.com,

Right next to the Apparel column, you can see the B2B column has below average performance almost across the board. Compare that to industries where customers can be passionate about the brands they use like Fashion, Hobby, Leisure and Gifts where the performance of Direct, Organic and Email is generally above the average. For the B2B market, there are 2 questions that this data raises.

1. Is the poor performance in the middle a function of the industry, i.e. it’s not worth investing time or money or

2. Does this data indicate there is an opportunity to do a better job of engaging users in the middle of a purchase cycle?

DISPLAY SHOWS UP An opportunity to engage users in the middle of their purchase cycle that the data in Table 18 suggests is the Display channel. Essentially negligible in the other steps of the cycle, here we see contributions from Display in Apparel, Health & Beauty, Fashion, and Food. It’s an interesting curiosity that sectors with poor performance using the PLA channel such as Health & Beauty and Food have success with another visual medium, Display ads.

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MIDDLE MIGHT MEAN “TRUE LAST CLICK” Keep in mind when you look at middle path steps that you might be looking at the true last meaningful interaction that brought the user back to your site to complete his/her purchase. If Direct is the last click or touch, it’s possible that entering the URL directly was just the convenient way of returning to your site to complete a purchase that had already been decided upon. Also, that Direct session could be a user who left his browser open on his shopping cart page, went to lunch and clicked “checkout” upon his return starting up a new Direct attributed session. The traffic source you care about in that scenario is the one that drove the person to your site when he added the items to his/her cart. That source is now a middle of the path step because the session after lunch is now the last click session in this user’s conversion path. This behavior is one reason to consider weighting your middle steps in a multiple step conversion flow almost as much as your last click source.

LAST CLICK INTERACTION BREAKDOWN BY CHANNEL Last but not least is the Last Click breakdown shown in Table 19. This is one most readers will probably find familiar since it is very close to the attribution you see by default in your GA account. Surprises here include the below average performance of Direct in several sectors, especially Health & Beauty and Food. In fact, if you go back and look at Tables 16 through 18, you can see that Direct never performs above average for Health & Beauty and only exceeds the average for Food in the middle (Table 18). Clicking through from an email seems to be a more preferred route to visit those sites as it outperforms the average in all positions but one, where it had average performance. Perhaps these retailers have “trained” their customers to wait for offers in their email before they re-stock their supplies.

Table 19 Last Click Contribution by Channel by Sector

Channel

LAST CLICK % ALL SECTORS APPAREL B2B

HEALTH & BEAUTY

HOME FURN FASHION FOOD

HOBBY, LEISURE

B2B/B2C GIFTS

Direct 35% 34% 35% 28% 44% 38% 21% 36% 38% 32% Email 7% 5% 4% 12% 5% 5% 15% 9% 5% 10% Organic 5% 5% 5% 5% 5% 6% 4% 6% 6% 5% Google Brand CPC 5% 5% 4% 4% 4% 5% 4% 5% 4% 4% Affiliates 4% 8% 4% 2% 5% 8% 2% 2% 5% 2% Bing Brand CPC 1% 0% 2% 2% 1% 1% 2% 1% 2% 1% Google NTM CPC 1% 0% 1% 1% 1% 0% 1% 2% 1% 2% Google PLA CPC 1% 2% 0% 0% 0% 2% 0% 0% 0% 1% Bing Non Brand CPC 0% 0% 0% 0% 0% 0% 0% 1% 0% 1% Display 0% 1% 0% 1% 0% 1% 1% 0% 0% 0%

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HYPOTHESIS: DISPLAY CAMPAIGNS APPEAR LATER DUE TO RETARGETING

Figure 11 Other Marketing Channels Conversion % by Path Position

Figure 11, similar to Figures 8 and 10, breaks down the contribution by path position for the Display, Affiliates, Other Ads and CSEs channels. You’ll note that Display and the Other Ads channel kick in during the middle phases of the conversion path with both reaching 60% of their channel’s conversion value. The huge difference between the middle steps and the other positions for Display where last click is the 2nd largest contributor at 17%, a little more than 1/4th the level of the middle steps, appears to validate this hypothesis that retargeting shifts display visits later in the conversion path. One reason for this behavior is the fact that a number of our clients use the GDN strictly for retargeting purposes. By definition, retargeting is aimed at the middle to bottom of your conversion funnel since the primary goal is to entice a previous non-buying visitor back to your site to make a purchase.

Another interpretation of this data and the data in Table 18 for the Display channel is that these campaigns may be an opportunity to nurture prospects in the middle of their sales cycle. That said, it should be noted that the overall contribution for Display remains small. It was only tangible, in the low single digits, in the middle steps breakdown (Table 18).

There are 2 other notable values in this chart.

1. Notice that CSEs, although like Display with a small overall contribution, garnered most of that contribution through first click interactions indicating that these channels were a good way to fill the top of your conversion funnel.

2. Affiliates are 3.5x more likely to be last click (49%) vs first click (14%), another example of the impact of coupon sites.

0%

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40%

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60%

Single Path First Click Last Click MiddleSteps

first = last

Other Marketing Channels Conversion Path Position as % of Total

Affiliates

Display

Other Ads

CSEs - Non-Google or Bing

Display, a small overall contributor, is most impactful in

the middle of the funnel

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Social media sites, both the big names and smaller more industry specific sites,

participated in the conversion paths for a little more than 1% of the total revenue in this study.

HYPOTHESIS: SOCIAL MEDIA IS A TOP OF THE FUNNEL FACTOR The thought process behind this hypothesis is that a user sees something on social media, clicks through to the retailer’s site to check it out and eventually comes back to buy. There were a couple of surprises in the data when we went to assess this hypothesis.

• First and foremost was the total lack of conversion contribution. Total conversions containing social sources (which includes traffic from social advertising and social referrals) was only $622k (Table 3), a little more than 1% of the $58MM in our dataset. This finding is in stark contrast to published reports of social marketing spend growth and how important marketers say social is to their business. Here are a couple of recent examples.

o For example, businessinsider.com estimated that 2014 spend in the US on social ads grew 39% YOY to $8.5B.

o Figure 12 shows 2 charts taken from Salesforce’s 2015 State of Marketing report which can be viewed at https://www.salesforce.com/blog/2015/01/2015-state-of-marketing.html. Note in the top chart that Social Media related spending comprises the top 3 areas for increased spending in 2015. In the bottom chart, almost 2/3 of the surveyed marketers believe that social media is a critical enabler to their business which was an increase of 39% from the same question a year earlier. Almost 60% the respondents report social media marketing as producing ROI either directly or indirectly.

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Figure 12 Taken From Salesforce.com 2015 State of Marketing Report

• The second surprise was that social contributed more as Last Click and Middle Steps than they do as originally posited as initial interaction drivers. In Figure 13 below for the referral-related channels in our study, the Social channel is the leftmost, lighter orange colored columns. Note that the Last Click and Middle Steps contributions are significantly larger than Single Step or First Click conversions. Those 2 positions are 68% of Social’s $622k in revenue.

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Figure 13 Referral Based Channels Conversion Path Breakdown by Position

ATTRIBUTION IMPLICATIONS AND SUGGESTED ACTIONS Here is an action plan that you can pursue 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.

We will look at each step in more detail.

UNDERSTAND HOW YOUR BUSINESS BEHAVES FROM A CONVERSION PATH STANDPOINT. Here are a couple of questions to guide your thinking when it comes to understanding your own conversion path phenomena.

1. How is it similar or different from the data presented here? 2. What are the implications of those similarities or differences? 3. What does your data suggest might be an appropriate attribution model type to use?

Before you explore your top conversion path report, we strongly encourage you to follow the approach we did for this study and define your own set of custom channels to segment your traffic based upon the way you view your different marketing initiatives and would like to measure their performance. One goal to

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Single Path First Click Last Click Middle Steps first = last

Referral Channels Conversion Path Position as % of Total

Social

Self Referrals

Referrals

Payment RelatedReferralsVendor/Suppliers

Amazon referrals

Media Sites

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keep in mind is to minimize your non-attributable revenue from referrals. For example, in many cases in the dataset for this study we were able to reduce the non-attributable referral revenue to around 1% of total revenue.

Once you have your custom channel groups defined, study your Multi-Channel Funnel Reports in the Conversions section of Google Analytics to understand your conversion flow distribution and relative weighting. You’ll want to look at the Top Conversion Paths, Path Length and Time Lag reports.

DECIDE HOW YOU WILL ATTRIBUTE NON-ATTRIBUTABLE REVENUE, E.G. DIRECT AND MISCELLANEOUS REFERRALS If you are successful with your custom channel definition process, you will have at most 2 segments of traffic, the Direct channel and any remaining miscellaneous, un-categorized referrals which you couldn’t say were related or a result of a specific marketing initiative. You then need to decide how you want to deal with that bucket of revenue in your attribution modeling. Some options to consider:

1. Ignore this segment. Hopefully your custom channel groups will capture the majority of your revenue.

2. Spread this revenue across the other channels evenly. 3. Spread this revenue across the other channels in some kind of intelligent, weighted fashion.

a. For example, if you assume that much of the Direct traffic is a result of the session timeout factor, you can weigh the allocation for Direct’s revenue based on the last non-Direct touchpoint that precedes the Direct step, much like Google Analytic’s default attribution model.

b. Another rationale would be to assume that Direct traffic’s returning visitors revenue should be allocated more heavily to channels that drive retention or repeat business such as your email marketing. You could then allocate the new user portion of Direct in a manner like the one suggested above.

INCLUDE ALL MARKETING-RELATED EXPENSES IN YOUR ATTRIBUTION MODEL INCLUDING HEADCOUNT. The more relevant data you incorporate in your model, the more sophisticated the model can be. You can use Google Analytics custom data imports to bring in data from other sources for your attribution modeling. You can also execute the experimentation in Excel, which is how we conducted one of our experiments.

There are 2 non-online marketing costs we recommend you consider incorporating in your model.

1. Decide how to allocate any offline marketing expenditures. As an example, consider direct mail campaigns. If you don’t use unique or vanity URLs to track traffic from your direct mail pieces, such as catalog mailings, you can estimate the mailing’s impact on your traffic sources by studying the trends before and after the mailing and then building a cost allocation schema accordingly. For example, it is likely that a catalog mailing will end up in driving traffic to your Direct, Email, Organic and Paid Brand channels since those are the most likely methods for that person to respond online.

2. It makes sense to include your marketing team’s headcount expenses. It’s an easy to calculate number that you can allocate accordingly based on job functions, etc…

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EXPERIMENT WITH DIFFERENT ATTRIBUTION MODELS The goal is to find an attribution model that considers all of your channels’ participation and gives you the highest return for marketing $ spent. We conducted an experiment with one account to show you the sensitivity your data can have to the different attribution models available in Google Analytics including custom models.

First, here is some background on how we chose which models to try. The 2 charts below show the share of conversion value by path length and the share of transactions and conversion value based on the time lag between initial visit and conversion event in days. The Path Length report suggested to us that using a time decay model which puts most weight on the most recent visits would be one model to test. Similarly, the time lag data with its distinctive U shape suggested that a position-based model would be appropriate to test.

In our experiment, we ended up evaluating 9 different models. We used all of the standard models available in Google Analytics except the AdWords last click model as well as 3 custom models. The results are listed in Table 20.

Table 20 Attribution Model Comparison Results

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Here is a brief recap of the steps we took.

1. We used the same custom channel groups from this study to pull the different attribution model reports for a single month with the maximum 90 day lookback.

2. We assigned marketing spend to all channels that typically have an expenditure associated with them. For example, Email was set to $8,000 per month, the SEO program was set to $3,500 a month and Affiliates were estimated at $1,000 monthly service plus 7% average commission.

3. We chose to ignore the non-attributable revenue for simplicity sake. 4. For custom models, we experimented with the 3 multi-touch models: Time Decay, U Shaped

and Linear. a. The custom settings we used were:

i. Giving 0 credit to Direct steps and Bounced Visits. ii. Derating Brand CPC steps by 50%. iii. Increasing Non-Brand CPC steps by 50%. iv. For both of the U Shaped or Position models we used the default 40% - 20%- 40%

settings. 1. For each model, we summed up the conversion value for all the custom channels that had a

marketing expense allocated to it which in this case amounted to 8 channels. Those figures are shown in the column in Table 20 labeled MKTG PROG REV for marketing program revenue.

2. We then calculated the revenue per marketing spend and the CPA for each model.

Here are a few points to notice about our experiment.

1. Look at the revenue difference between the default Last Non-Direct Model and the true Last Interaction Model. The former is the most productive model while the latter is the least productive model and there is a 49% difference in the marketing program revenue attributed in those 2 models. This shows you the influence of Direct in that last click position as discussed earlier.

2. Note the impact of the simple customizations we applied to the 3 baseline model types. All 3 went from having around $1.2MM attributed to them to just over $1.6MM. Most of that change comes from the exclusion of Direct steps in the calculations.

3. Perhaps most importantly, notice how close the 3 custom models, highlighted in yellow come to the default last non-Direct model. Less than 1% difference across all 4 models.

So in our case, we went through a fairly elaborate process to build and compare some custom models that use the “smarter” multi-touch approach which doesn’t give any credit to Direct visits and visits where the end user didn’t engage. We also valued non-brand terms more heavily assuming they bring in more new prospects than the devalued brand terms, a logical adjustment for most retailers to consider. Still, at the end of the day, those models don’t do a better job of allocating revenue than the default single interaction model. It’s quite possible this could occur with your attempts to experiment. Options for moving forward from here are to:

• Give up and continue using a “stupid” single interaction model, the default model. • Pull data for a longer period and see if the results change. • Choose one of the 3 “smarter” models, which are all within 1% of each other, to use for the time

being until we decide to check our model for optimization opportunities next month, next quarter, etc.

• Keep experimenting with more customizations to see if we can create a “smarter” model that out-produces the default model in ROI and CPA.

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LIFETIME VALUE PLAYS A ROLE Another thing that will be useful is if you understand the lifetime value (LTV) of your customers. Knowing LTV is critical to setting CPA and ROAS targets for your marketing initiatives.

Go back and look at Figure 5, the histogram that shows the transaction dollars by number of conversion steps. More than ½ of those dollars come from conversion paths of 1 or 2 steps. The more your competitors figure this out, the more pressure you will feel on your media spend for top of the funnel, single step and 2 step conversion paths.

If you know your LTV, you’ll be able to build a model that will accurately give you a target for your CPA based on that LTV. Then if you use customized bid management, you can automate that CPA goal in your paid media.

If you can understand the conversion flow in your business as described here and know your customers’ LTV, you can begin to paint a picture of the most effective attribution model for your business by computing the weighted average revenue per marketing $ spent across all of your programs. It then becomes an experiment exercise to determine if your model actually works. You need to use your model to make budget decisions and then compare your actual results to what your model predicted. The goal is to find a model that appears to generate the most revenue per marketing $ spent.

HAPPY ANALYZING!

THE NETELIXIR ANALYTICS TEAM

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