DIGITAL MEDIA ATTRIBUTION - Smart Insights · Media attribution 6 1 ONE UNDERSTAND DIGITAL MEDIA...

DIGITAL MEDIA ATTRIBUTIONHow to setup and use Google Analytics to assess the

effectiveness of your media spend and reallocate budget

Author: Hugh Gage, Edited by Dave Chaffey

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© Smart Insights (Marketing Intelligence) Limited. Please go to www.smartinsights.com to feedback or access our other guides.

Media attribution

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Media attribution

The Holy Grail of acquisition marketing

Contents

Introduction .............................................................................................4

ONE UNDERSTAND DIGITAL MEDIA ATTRIBUTION ...........................6Glossary ................................................................................................................................8

TWO WHY IS ATTRIBUTION MODELLING IMPORTANT? .................10Allocation of budget to the media mix .................................................................................10Wastage ..............................................................................................................................10Margin .................................................................................................................................10Conversion ..........................................................................................................................10

THREE WHICH MODELS TO USE AND HOW TO VISUALISE DATA .13Visualising the data .............................................................................................................15

FOUR MISTAKES TO AVOID ................................................................19Analysis ...............................................................................................................................19&RQ¿JXUDWLRQ.......................................................................................................................20

FIVE WHAT ARE THE LIMITATIONS OF ATTRIBUTION .....................22Cookie deletion ...................................................................................................................22Cross-device browsing .......................................................................................................22Blocking Javascript ............................................................................................................22'LUHFW�WUDI¿F .........................................................................................................................22Search keywords ................................................................................................................23

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SIX USING IT IN PRACTICE .................................................................24Reality bites ........................................................................................................................24A framework ........................................................................................................................24

SEVEN RESOURCES............................................................................265HVRXUFHV�WR�¿QG�RXW�PRUH�DERXW�DWWULEXWLRQ ......................................................................26

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IntroductionEver since online advertising and other digital media arrived on the marketing scene back in WKH�PLG��V��RQH�RI�LWV�NH\�EHQH¿WV�ZDV�FOHDU��LW�SURPLVHG�D�PRUH�DFFRXQWDEOH�DGYHUWLVLQJ�medium. In reality, that accountability didn’t arrive until nearly 10 years later with attribution DQG�LQ�WKH��RIÀLQH�ZRUOG�E\�WKDW�WLPH�PHGLD�PRGHOOLQJ�ZLWK�WKH�JURZLQJ�XVH��DQG�DEXVH��RI�HFRQRPHWULFLDQV�WR�GHOLYHU�D�PRUH�VFLHQWL¿F�ORRNLQJ�DSSURDFK�WR�WKH�DOORFDWLRQ�RI�DGYHUWLVLQJ�media spend.

With digital media spend in many companies now into double digit percentages, its vital that businesses understand whether they are getting ‘bang for their digital media buck’ and this is where media attribution can help.

As we explain in more detail in Step 1, digital media attribution analysis reviews the combination of media touchpoints that are responsible for generating a lead or sale on a website or in a mobile app.

What is it? Digital media attribution analysisDigital media attribution is a method of evaluating the combination of online media WRXFKSRLQWV�ZKLFK�LQÀXHQFH�RU�DVVLVW�FRQYHUVLRQ�WR�VDOH�

It will be often be the case that a lead or sale is not prompted by a single visit to a website, but instead depends on several visits prompted by digital media. Either a direct clickthrough or a ‘viewthrough’

What is it? A viewthroughA web user sees an ad from a brand on a media placement. They don’t clickthrough directly, but are prompted to visit the brand site at a later point-in-time, either through searching on the brand or typing in the URL.

Take this example from Dave Chaffey’s Digital Marketing: Strategy, Implementation and Practice book. Here a searcher for a rental car performs a series of searches and responds WR�DG�SODFHPHQWV�RQ�GLIIHUHQW�VLWHV�DQG�DI¿OLDWHV�EHIRUH�FRPSOHWLQJ�WKH�UHQWDO�ERRNLQJ�

The obvious question is, ‘which of these channels is responsible for booking’? Clearly it LV�QRW�VROHO\�WKH�ODVW�FOLFN�RU�¿QDO�UHIHUUDO�IURP�$I¿OLDWH��7KLV�LV�ZK\�PRYLQJ�WR�DWWULEXWLRQ�is so important, the default method by which most companies use is the ‘Last click wins’ method. In reality there is often a series of other ‘media assists’ where paid investments KDYH�LQÀXHQFHG�VDOH��

If we’re serious about working out which media have contributed to sale we have to review the relative contribution of each of these and ask which are most important, are

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WKH�¿UVW�WRXFKSRLQWV�PRUH�LPSRUWDQW�WKDQ�WKH�ODWHU�WRXFK�SRLQWV�IRU�H[DPSOH��7KLV�DQDO\VLV�will help us determine whether we should be spending more on certain media types, for example are we under-investing on display or social media advertising which tends to prompt early interactions between a consumer and a brand.

What is it? ‘Last clicks wins’The media type or channel responsible for a consumers’ last clickthrough to a website before a lead or sale is transacted is considered to be solely responsible. Previous media interactions are ignored. This is the standard or default method of analysis in Acquisition or WUDI¿F�UHSRUWV�LQ�*RRJOH�$QDO\WLFV�

The natural choice of digital petri dish in which to nurture the systematic analysis of media attribution was web analytics and in particular, tag management solutions. But it has really been Google Analytics that ultimately put attribution modeling to the fore. 7KLV�ZDV�QRW�EHFDXVH�*RRJOH�ZDV�WKH�¿UVW�WR�GHOLYHU�RQ�LW��RU�EHFDXVH�WKHLU�VROXWLRQ�ZDV�the best but because, their version came free of charge and was in an instant readily available to everybody who used the almost ubiquitous free version of Google Analytics.

6LQFH�*RRJOH�$QDO\WLFV�RI¿FLDOO\�UHOHDVHG�LW¶V�¿UVW�IRUD\�LQWR�DWWULEXWLRQ�DQDO\VLV�LQ�$XJXVW�2011 with Multi Channel Funnels there has been debate over the wider issue of how advanced the industry has become and with not a few voices questioning the utility of what we currently have in this area.

This guide will help you understand the complex discipline of media attribution and how you can use it to improve the effectiveness of your media spend. The examples will focus on Google Analytics since this is the most common used tool for assessing attribution. The sections we cover are:

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; What is attribution modelling

; An overview of the models

; A walk through the terminology used in this area of measurement.

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; Why you should care about attribution modelling

; Why it is worth spending some of your time getting to know it.

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; Which models should you use when running your own attribution modelling analysis

; How to present your data

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; What to watch out for when running your own attribution modelling analysis.

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; The known unknowns.

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; The reality of attribution modelling in the context of your day to day job.

; A framework for insight

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ONEUNDERSTAND DIGITAL MEDIA ATTRIBUTIONMedia attribution or attribution modelling is the analytical process by which the credit for an RQ�VLWH�FRQYHUVLRQ�LV�DOORFDWHG�WR�WKH�DSSURSULDWH�DFTXLVLWLRQ�FKDQQHO�V��ZLWK�WKH�DSSURSULDWH�amount of credit assigned.

Let’s start with a simple scenario. A runner buys a pair of running shoes from sportsshoes.com and in the process of doing so visits the site four times. He does so because he can’t make a decision right away and needs to ensure he’s done all his research around product and prices including visiting other sites.

7KH�µWUDGLWLRQDO¶�DWWULEXWLRQ�PRGHO�DSSOLHG�WR�WKH�¿QDO�VDOH�ZRXOG�EH�WKDW�RI�ODVW��QRQ�GLUHFW��click. In this instance the email channel would be given 100% of the credit for generating the sale because it was the last non-direct referring source or touch point that the customer interacted with before he made his purchase.

NB: The standard reports in Google Analytics such as the Acquisition reports use what is known as the last non-direct click model. This refers to the last interaction with a source that was not a direct visit i.e via bookmark or typing the URL into the address bar of the browser.

Attribution can be allocated in different ways – this is known as an attribution model.

What is it? Attribution model examplesIf the ¿UVW�FOLFN model were applied to the scenario above all the credit would be allocated to organic search, if the ODVW�FOLFN�model were applied all the credit would be allocated to the direct channel, if a linear model were applied the credit would be equally shared between the four and if a time decay model were applied credit would be divided up and DOORFDWHG�DFURVV�WKH�IRXU�FKDQQHOV�DFFRUGLQJ�WR�WKH�OHQJWK�RI�WLPH�HODSVHG�EHIRUH�WKH�¿QDO�sale in which the customer had interacted with them - the closer to the sale the more credit is allocated.

Because, in this example, the buyer’s decision-making process was protracted and involved WZR�YLVLWV�WR�WKH�VLWH�SULRU�WR�WKH�HPDLO�YLVLW��DQG�RQH�DIWHU��LW�LV�QRZ�LQFUHDVLQJO\�FRQVLGHUHG�that the credit needs to be shared between the other referring sources in order to understand the real impact of an integrated marketing approach. It is the difference between a team effort in which the spoils of victory are shared and a team effort in which only one person takes all the glory.

The process of attribution modelling is one in which various models are compared in order to ¿QG�WKH�RSWLPDO�FRPELQDWLRQ�RI�DFTXLVLWLRQ�FKDQQHOV�EDVHG�RQ�WKH�SUH�GH¿QHG�PDUNHWLQJ�objectives.

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What is it? Attribution modellingAttribution modelling is the process of applying various different models to an acquisition VWUDWHJ\�LQ�RUGHU�WR�RSWLPLVH�LW�IRU�WKH�PRVW�FRVW�HI¿FLHQW�PHDQV�RI�DFTXLULQJ�D�SRVLWLYH�outcome / sale.

TagMan��QRZ�SDUW�RI�HQVLJKWHQ��VKDUHG�WKLV�QLFH�H[DPSOH�ZLWK�XV�ZKHQ�WKH\�ZHUH�MXVW�launching their service. We think it’s a nice visual showing the need for attribution in a slightly more detailed way. It also shows how you need to consider different types of keywords and YLVLWV�ZKLFK�LQÀXHQFH�VDOH��)LJXUH��VKRZV�WKH�³SUREOHP´�L�H��PXOWLSOH�FKDQQHOV�DUH�XVHG�DQG�several outcomes occur when the visitor arrives on the site.

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Figure 2 illustrate how attribution modelling would deal with the problem if it was looked at using only single click attribution models.

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http://www.tagman.com/

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Figure 3 shows how the attribution modelling would deal with the problem if different forms of multi-click attribution models were used.

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GlossaryTerm 'H¿QLWLRQMedia source 8VHG�WR�UHIHU�WR�WKH�VRXUFH�RI�WUDI¿F�H�J��SDLG�

VHDUFK��RUJDQLF�VHDUFK��GLUHFW��DI¿OLDWH�HWFChannel $OVR�XVHG�WR�UHIHU�WR�WKH�VRXUFH�RI�WUDI¿F�

but may also be applied to a collection of VRXUFHV�RI�WUDI¿F�WKDW�KDYH�EHHQ�JURXSHG�together.

Touch-point A broader term that can apply to both channels and media sources but which refers to the act of interacting with these sources or channels, e.g. clicking on a paid search ad on a Google search results page.

�0HGLD��$WWULEXWLRQ Used to refer to the process by which credit LV�VKDUHG�EHWZHHQ�UHIHUULQJ�VRXUFHV�RI�WUDI¿F�or channels in the customer acquisition process.

Multi-channel funnel / customer journey 8VHG�WR�UHIHU�WR�WKH�FROOHFWLRQ�RI�WUDI¿F�sources, channels or ‘touch-points’ that a customer interacts with prior to making a purchase or completing a goal.

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Term 'H¿QLWLRQAssist Used to refer to channels or sources that

are said to have helped with the conversion but were not the primary driving force in the context of the particular model being used. e.g. in the last click model an assisting source will be any source that was interacted with prior to the last click. NB: In a multi WRXFKSRLQW�IXQQHO�WKH�¿UVW�FOLFN�FDQ�DOVR�EH�considered an assisting channel.

Last click 7KH�¿QDO�VRXUFH�RU�FKDQQHO�WKDW�ZDV�interacted with prior to the sale / goal completion

Last non-direct click 7KH�¿QDO�QRQ�GLUHFW�VRXUFH�RU�FKDQQHO�WKDW�was interacted with prior to the sale / goal completion. In this case the direct source of WUDI¿F�LV�FRQVLGHUHG�WR�EH�LUUHOHYDQW��

Time decay / Decay half life Used to refer to the number of days set as the half life in a time decay model. Any click occurring within the stated number of days will be awarded half the credit. Clicks occurring either side will be awarded more or less credit accordingly.

Linear Refers to the Linear model in which all VRXUFHV�RI�WUDI¿F�DUH�DZDUGHG�DQ�HTXDO�credit regardless of where they sit in the funnel.

Position based 8VHG�WR�UHIHU�WR�D�VSHFL¿F�SRVLWLRQ�ZLWKLQ�D�model. In this instance the referring source occupying this position garners the majority of credit.

Look back window 7KH�XVHU�GH¿QHG�SHULRG�RI�WLPH�SULRU�WR�WKH�conversion from which a model draws it’s dataset.

Credit Allocated to the source or collection of referring sources in a multi-channel funnel. Must sum to 100%.

Also, custom credit can be set at each touch point in a model e.g. a channel that sits at the start of the journey can be awarded 2.5x the credit of channels that occur at different touch points - this may be desirable if the objectives are more geared towards new business acquisition. This is like an importance weighting.

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TWOWHY IS ATTRIBUTION MODELLING IMPORTANT?The answer to this question should be fairly obvious based on the previous examples. It essentially boils down to four budget-related options:

; Allocation of budget to media mix

; Wastage

; Margin

; Conversion

Allocation of budget to the media mixYou can see from the examples that we have looked at that it may the case that too much FUHGLW�LV�JLYHQ�WR�FHUWDLQ�WUDI¿F�GULYHUV�ZLWK�WKH�ODVW�FOLFN�PRGHO��7KHVH�GULYHUV�FRXOG�EH�channels, media placements or keywords. Some typical examples of reallocation of budget that may be indicated by attribution reviews are:

; Increase display budget. Banner ads typically have more of an awareness-driving role, so their value be underestimated by last-click.

; Increase generic search term spend. Consumers are more likely to search on general NH\ZRUGV�ZKLFK�VHDUFKHUV�PD\�W\SH�³WUDLQHUV´�RU�³UXQQLQJ�VKRHV´�ZKHQ�H[SORULQJ�D�category such as in our sport shoes examples.

; Increase social media investment. Paid ads or organic clicks from social media XSGDWHV��OLNH�GLVSOD\�DGV�DUH�OHVV�OLNHO\�WR�GLUHFWO\�UHVXOW�LQ�D�OHDG�RU�VDOH�RQ�D�¿UVW�YLVLW��EXW��PD\�EH�WKH�¿UVW�SRLQW�RI�FRQWDFW�RI�D�YLVLWRU�ZLWK�D�EUDQG�

So, a study of attribution can show that investment in a particular media channel or tactic is actually more worthwhile than the last click method would suggest, so budget can be UHDVVLJQHG�WR�LW��SDUWLFXODUO\�LI�ZDVWDJH�FDQ�EH�LGHQWL¿HG�LQ�RWKHU�FKDQQHOV��

Wastage1R��GLJLWDO��PDUNHWLQJ�PDQDJHU�ZDQW¶V�WR�EH�ZDVWLQJ�EXGJHW��,I�IRU�WKH�VDNH�RI�DUJXPHQW��D�sale can be acquired in fewer steps and with fewer ad dollars spent, then budget can be VDYHG�DQG�XVHG�HOVHZKHUH��)RU�H[DPSOH��D�UHWDLOHU�ZRQW�ZDQW�WR�SD\�DI¿OLDWHV�IRU�ODVW�FOLFN�UHODWHG�VDOHV�WKDW�DUHQ¶W�LQÀXHQFHG�E\�WKH�DI¿OLDWH��EXW�HDUOLHU�WRXFKSRLQWV��

MarginThis is related to the second reason in that saved budget may also contribute to improved PDUJLQV�DQG�EHWWHU�SUR¿WV�GHSHQGLQJ�RQ�KRZ�D�EXVLQHVV�FKRRVHV�WR�DFFRXQW�IRU�WKHVH�WZR�items.

The third reason is more complicated...

ConversionFor a long time conversion has been one of the single metrics by which pretty much all efforts in digital marketing are measured - for better or worse.

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On the face of it attribution modelling is all about delivering a greater volume of good outcomes and revenue for less budget however, conversion rate is not a metric that is commonly included in attribution modelling and yet if a marketeer is spending £$€xxxxx each month on an integrated marketing acquisition program with an overall conversion rate WR�VDOH�RI��WKHQ�WKH�SURFHVV�RI�LQFUHDVLQJ�WKDW�RYHUDOO�FRQYHUVLRQ�UDWH�E\�UHFRQ¿JXULQJ�the digital media mix could potentially yield more revenue for less spend depending on the individual media costs associated with the channels involved.

A further consideration is that if a single channel in the mix is shown to act comparatively independently i.e. it neither assists nor receives much assistance from other channels and therefore the sales it delivers are unlikely to be harvested from any of the other sources then an argument might be made to increase investment in this channel on the basis that it will deliver a corresponding increase in sales and revenue which could not have been harvested from anywhere else. However this would be to miss two fundamental points:

1. If the cost of using this channel is greater than the others then an increase in investment here will push up the overall cost of acquisition.

2. If the conversion rate for this channel is lower than the others then this too will drive up the overall cost of acquisition and potentially bring down the site average conversion rate.

5LJKWO\�RU�ZURQJO\��FRQYHUVLRQ�UDWH�LV�D�ÀDJVKLS�PHWULF�IRU�GLJLWDO�PDUNHWLQJ�DQG�VR�PDQ\�digital marketing activities are often, and sometimes wrongly, evaluated using this metric.

There are some obvious problems at this point:

� How do you calculate conversion when you are no longer using a single attribution PRGHO"�%\�GH¿QLWLRQ�PRVW�PXOWL�FKDQQHO�DWWULEXWLRQ�PRGHOV�ZLOO�UHVXOW�LQ�VRPH�GHJUHH�RI�duplication in the awarding of credit and this will vary from channel to channel meaning that establishing a benchmark for comparing conversion would be impossible.

� When thinking about acquisition source, should you even be thinking about conversion rate or should the primary marketing metric now become cost of sale / cost per newly acquired customer / cost per retained customer? The issue here is that not all costs are VR�HDVLO\�TXDQWL¿DEOH�H�J��RUJDQLF�VHDUFK�FRVWV�FDQ�EH�YHU\�YDULDEOH�DQG�GR�QRW�HDVLO\�OHQG�WKHPVHOYHV�WR�DQDO\VLV�LQ�*RRJOH��8QLYHUVDO��$QDO\WLFV�

� Should conversion ratio now only be viewed on context of site design and usability.

� What about lifetime customer value in the context of ROI?

Because of all this it’s important to realise that optimising your acquisition strategy using attribution modeling might not necessarily result in improved conversion even if it does yield JUHDWHU�FRVW�HI¿FLHQFLHV�

It is important to understand that attribution modelling is not a panacea but it does have the potential to upend even the most basic assumptions on how we’ve been measuring acquisition performance by channel until now and even in circumstances where the site’s overall conversion rate is in decline, attribution modelling may help to more adequately assess the impact of an acquisition strategy as well as improve it.

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Example : Monarch AirlinesThis example shows how agency Redeye used attribution modelling to advise their client how WR�UHDOORFDWH�WKHLU�EXGJHW��7KLV�FKDUW�VKRZV�VLJQL¿FDQW�GLIIHUHQFHV�EHWZHHQ�ODVW�FOLFN�DQG�D�weighted average across all interactions.

A more detailed study of assists enabled a deeper understanding of customer interactions leading to sale:

Actions taken including re-allocating most of the budget from expensive paid search GHVWLQDWLRQ�WHUPV��H�J��µÀLJKWV�WR�0DODJD¶��WR�LQFUHDVH�WKH�ZRUN�GRQH�RQ�LPSURYLQJ�QDWXUDO�VHDUFK��LQFUHDVH�VOLJKWO\�WKH�LQYHVWPHQW�LQ�SDLG�VHDUFK�EUDQG�WHUPV��IRU�H[DPSOH�µ0RQDUFK�ÀLJKWV¶��DV�WKH\�ZRUNHG�EHVW�ZLWK�QDWXUDO�VHDUFK�DQG�GRXEOH�WKH�DYDLODEOH�EXGJHW�IRU�comparison sites as evidence had shown that there was a strong possibility of doubling sales from this source.

,QLWLDOO\��D��UHGXFWLRQ�LQ�FRVW�SHU�SDVVHQJHU�IURP�VHDUFK��FRPELQLQJ�33&�DQG�6(2��DV�a channel overall based on a better allocation of the spend. A more detailed analysis of the search results showed that a 14% increase in SEO investment led to a 55% increase in revenue and 45% increase in bookings. The 100% increase in investment into comparison ZHEVLWH��&3&��PHGLD��OHG�WR�D�GRXEOLQJ�LQ�UHYHQXH�DQG�SDVVHQJHUV�IURP�WKLV�DFWLYLW\��SURYLQJ�its scalability and effectiveness as a stand-alone channel.

http://www.redeye.com/clients/monarch/

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THREEWHICH MODELS TO USE AND HOW TO VISUALISE THE DATAGoogle Analytics employs seven default attribution models. These are:

; Last interaction

; Last non-direct interaction

; Last AdWords Click

; First Interaction

; Linear

; Time Decay

; Position Based

It’s worth remembering that all the standard reports in GA use last non-direct interaction as the default model.

,Q�RUGHU�WR�HYDOXDWH�ZKLFK�PRGHO�LV�EHVW�WR�XVH�LW�KHOSV�WR�GH¿QH�ZKDW�HDFK�LV��,Q�GRLQJ�VR�ZH�will refer to the individual steps through which a customer passes as ‘touch-points’. Keep in mind though that a single referring source might occur in more than once and as such count as more than one touch-point.

Last interaction

7KLV�PRGHO�DWWULEXWHV��RI�WKH�FUHGLW�WR�WKH�¿QDO�WRXFK�SRLQW�LQ�WKH�FXVWRPHU¶V�acquisition journey.

It is generally considered to be an intellectually defunct model but Google cite it as being useful in situations where the buying process for your product or service requires OLWWOH�RU�QR�FRQVLGHUDWLRQ�L�H��ZKHUH�LW�LV�OLNHO\�WR�EH�D��ORZ�FRVW��LPSXOVH�SXUFKDVH�

Last non-direct interaction

In this model the direct referring channel is effectively considered to be transparent or, from a credit attribution perspective, irrelevant. This model attributes 100% of credit to the referring channel that was the last touch-point but if that touch-point was followed by a direct visit then the credit would still go the touch-point immediately prior to the direct visit.

e.g. PPC > Organic > Direct. In this scenario all the credit would go to the organic source.

This is also considered to be an intellectually defunct model but again Google cite it as being useful in situations where the business might consider that direct visitors mainly represent customers that have already been won via another channel and as such should not be considered as part of the model dataset. This scenario may match for businesses where repeat purchases occur with a high degree of frequency such as online supermarkets.

https://support.google.com/analytics/answer/1665189

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Last AdWords click

This model gives 100% of the credit to the most recent click on a Google AdWords ad SULRU�WR�¿QDO�FRQYHUVLRQ�

First Interaction

7KLV�PRGHO�DWWULEXWHV��RI�WKH�FUHGLW�WR�WKH�¿UVW�WRXFK�SRLQW�LQ�WKH�FXVWRPHU¶V�acquisition journey.

This attribution model is also considered to be of little practical use, perhaps unfairly so VLQFH�ZLWKRXW�WKH�LQLWLDO�LQWURGXFWLRQ�WKH�FRQYHUVLRQ�PD\�QHYHU�KDYH��HYHQWXDOO\��WDNHQ�place - everything has to have a genesis.

This model could bear more relevance if the marketing mix is carrying a great deal of branding and awareness activity for example with a new product launch.

Linear

This is the most democratic of all the attribution models for the simple reason that it accords each touch point an exactly equal share of the credit regardless of where they occurred in the acquisition funnel. It’s also clearly understood.

This model may be more relevant in circumstances where the marketing effort and objectives are less sophisticated and more consistent over time.

Time decay

In this model the touch points that are closest in time to the point at which the conversion occurred are awarded the most credit. The analyst needs to set a half life which establishes the point at which exactly half the credit is awarded, by default the half life is set to 7 days in the Google Analytics attribution modelling tool. For example, where the half life is set to 7 days, a touchpoint that occurs 7 days prior to the conversion will receive exactly half the credit of a touch point that occurred on the day of conversion. A touchpoint that occurred 14 days prior to the conversion will receive only ¼ of the credit.

This model is considered to be the best of the bunch and in particular it could be helpful in situations where the marketing activity includes limited time promotions where touchpoints that occur before the start of the promotion are of greatly reduced value.

Position based

,Q�WKLV�PRGHO�WKH�DQDO\VW�FDQ�GH¿QH�ZKDW�SHUFHQWDJH�RI�FUHGLW�LV�DWWULEXWHG�WR�WKH�¿UVW�DQG�ODVW�FOLFN�WRXFK�SRLQWV�DV�ZHOO�DV�DOO�WRXFKSRLQWV�WKDW�VXEVHTXHQWO\�RFFXU�LQ�between. In the Google Analytics attribution modelling tool these percentages are set by default to 40% / 20% / 40%. Whatever they are set to they must of course always add up to 100%.

This model might prove most effective in situations where the product or service has D�ORQJ��PXOWL�VWDJH��SXUFKDVH�FRQVLGHUDWLRQ�F\FOH�DQG�\RX�ZDQW�WR�NQRZ�LQ�SDUWLFXODU�which channels performed best in delivering new leads and which performed best in closing them.

For more detailed discussion on these alternative models see this post by Avinash Kaushik of Google who recommends Time Decay as a starting point.

http://www.kaushik.net/avinash/multi-channel-attribution-modeling-good-bad-ugly-models/

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Visualising the dataIn some respects attribution modelling is like a giant multivariate test for acquisition in which WKH�WHVWHU�LV�WU\LQJ�WR�¿JXUH�RXW�ZKLFK�FRPELQDWLRQ�RI�UHIHUULQJ�VRXUFHV�GHOLYHU�WKH�RSWLPXP�return. The issue with attribution modelling is that the tester not only has to look for the ideal PL[�EXW�V�KH�DOVR�KDV�WR�¿JXUH�RXW�ZKLFK�PRGHO�LV�JRLQJ�WR�GHOLYHU�WKDW�LQVLJKW�

7KH�¿UVW�WKLQJ�WKH�WHVWHU�QHHGV�WR�GR�LV�XQGHUVWDQG�KRZ�PXFK�RI�DQ�LVVXH�LV�DW�VWDNH�

Google Analytics does a nice job of this in a simple visual way by using a venn diagram in ZKLFK�\RX�FDQ�¿QG�LQ�WKH�0XOWL�&KDQQHO�)XQQHOV�RYHUYLHZ�UHSRUW��XQGHU�WKH�&RQYHUVLRQV�PHQX�LQ�WKH�OHIW�VLGHEDU��

The less the circles overlap, the less of an ‘attribution issue’ exists. The presence or DEVHQFH�RI�DQ�DWWULEXWLRQ�LVVXH�ZLOO�EH�HLWKHU�JRRG�RU�EDG�DFFRUGLQJ�WR�\RXU�VSHFL¿F�VHW�of circumstances in relation to the business, the purchasing consideration cycle and your marketing objectives.

Once you’ve established the presence of an attribution overlap you can gather more insight by looking at the number of interactions it takes before a visitor makes a transaction or completes a desired outcome on your site.

Best Practice Tip 1 Use Path link reports to determine how important attribution is,I�D�EXVLQHVV�¿QGV�WKDW�WKH�YDVW�PDMRULW\�RI�FRQYHUVLRQV�RFFXU�RQ�D�VLQJOH�YLVLW�WKHQ�DWWULEXWLRQ�ZLOO�EH�OHVV�LPSRUWDQW�WKDQ�IRU�FRQVLGHUHG�SXUFKDVHV�VXFK�DV�LQ�WUDYHO�RU�¿QDQFLDO�VHUYLFHV�where multiple visits occur before conversion.

The path length report below shows that, in this case, most of the conversions happen on the ¿UVW�LQWHUDFWLRQ�DQG�WKLV�UHÀHFWV�WKH�IDFW�WKDW�DWWULEXWLRQ�RYHUODS�LV�QRW�YHU\�VHYHUH�

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<RX�FDQ�¿QG�WKLV�UHSRUW�LQ�\RXU�*RRJOH�$QDO\WLFV�UHSRUWLQJ�LQWHUIDFH�E\�JRLQJ�WR�� Conversions > Multi-Channel Funnels > Path Length

Best Practice Tip 2 Use the Attribution ratio to understand channel ‘assist’ importance,I�\RX�¿QG�WKDW�\RX�GR�KDYH�DQ�DWWULEXWLRQ�RYHUODS�LVVXH��D�VLPSOH�ZD\�WR�WKHQ�JHW�DQ�LGHD�RI�ZKHWKHU�\RXU�WUDI¿F�VRXUFHV�WHQG�WRZDUGV�ODVW�FOLFN�RU�DVVLVW�LV�WR�ORRN�DW�WKH�DWWULEXWLRQ�UDWLR.

,Q�*RRJOH�$QDO\WLFV�WKH�DWWULEXWLRQ�UDWLR�LV�PHDVXUHG�DURXQG�D�EDVH�¿JXUH�RI��DQ\WKLQJ�OHVV�WKDQ��DQG�WKH�VRXUFH�LV�PRUH�OLNHO\�WR�WHQG�WRZDUGV�ODVW�FOLFN��DQ\WKLQJ�PRUH�WKDQ�RQH��H�J��VRFLDO�PHGLD�LQ�WKLV�FDVH��DQG�LW�LV�PRUH�OLNHO\�WR�WHQG�WRZDUGV�DQ�DVVLVW�UROH��+HUH�\RX�FDQ�see that social media has more assisted conversions than last click conversions, but are still relatively unimportant overall.

By looking at this report and this metric in particular, it will be easier to establish a starting point from which to begin your attribution modeling analysis.

However, you will still need to expand the context of your channel performance and one way to do this using data from Google Analytics is to chart the data output from the Assisted &RQYHUVLRQ�UHSRUW�ZKLFK�\RX�ZLOO�¿QG�LQ�WKH�0XOWL�&KDQQHO�)XQQHO�VXE�PHQX��

NB: If you’re extracting this data to excel be very careful. The exported data simply sums the rows of data in the assisted columns which will not be the same as the totals shown for the assisted columns in the GA UI. There will inevitably be duplication across the assisted channels so it is likely that the summed totals

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in the exported data will be incorrect while the summed totals in the GA UI will be correct.

In this example chart, organic search delivers a greater percentage of transactions in the context of both assisted and last interaction touch points, but the bubble size indicates that it is very similar to paid search regarding its attribution ratio.

In fact we have to look at the raw data to know that both these two channels are slightly more likely to act in an assisting capacity but we can clearly see that ‘other advertising’ and ‘Direct’ are both more likely to operate in a last click capacity while email is more likely to operate in an assist capacity.

Best Practice Tip 3 3ORW�DVVLVWV�DJDLQVW�¿UVW�LQWHUDFWLRQ�WUDQVDFWLRQV�Presenting the data like this shows it in a way which makes it easier to compare the individual channels against key delivery metrics and this helps with context and perspective.

While looking at data like this it is also very important to remember what each of these channels costs to maintain as this will help in forming a hypothesis about how to reallocate budget. In this example, if we assume organic search improvements are carried out internally �DV�RSSRVHG�WR�E\�D�WKLUG�SDUW\��DJHQF\��WKHQ�WKH�FRVW�LQ�WLPH�LV�OLNHO\�WR�EH�ORZHU�WKDQ�the variable costs of the paid search advertising. A more granular analysis of paid search campaigns using the same charting technique might show that some campaigns perform better than others. If that is the case then a hypothesis could be put forward to transfer some budget from PPC campaigns that perform less well and put it into SEO where it should, in this case, deliver better returns. Alternatively another hypothesis might suggest that budget should be moved around within the various PPC campaigns in order to maximise returns based on the marketing objectives, e.g. drive new business or reduce costs.

2QFH�WKLV��RU�DQ\��K\SRWKHVLV�KDV�EHHQ�HVWDEOLVKHG�LW�FDQ�EH�WHVWHG�DQG�YHUL¿HG�XVLQJ�WKH�attribution modelling comparison tool in Google Analytics. Here the analyst will have to apply WKH�PRGHO�WKDW�WKH\�WKLQN�EHVW�¿WV�WKHLU�UHTXLUHPHQWV�DQG�SRVVLEO\�HYHQ�FXVWRP�FRQ¿JXUH�D�model and it’s credit allocation.

These models can then be used as a comparison against a base model, which must in itself DOVR�UHÀHFW�HLWKHU�WKH�VWDWXV�TXR��ODVW�QRQ�GLUHFW�FOLFN��RU�D�NH\�HOHPHQW�RI�WKH�K\SRWKHVLV�

If the Google Analytics model comparison tool is being used, the results will then be shown in the context of an uplift or downshift in performance against the metrics selected, these

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results are shown in the columns to the far right of the data table:

At this point, the models can be tweaked based on the new insight and this can be done until WKH�K\SRWKHVLV�KDV�HQRXJK�VXSSRUWLQJ�GDWD�DQG�LQVLJKW�WR�FDUU\�LW�IRUZDUG��RU�QRW��

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FOURMISTAKES TO AVOIDMistakes can generally be grouped into two categories:

; analysis

; FRQ¿JXUDWLRQ

We will look at each group in turn.

Analysis

Make sure you know what you want to testAlthough it may sound silly, you will need to ensure that you have a clear understanding of your business and product purchasing cycle and process.

You will need to apply your understanding of that process so that you can come up with a hypotheses to investigate and test using your attribution modelling.

You cannot expect to approach the attribution modelling analytical process by randomly applying and comparing models, if you do you may eventually unearth some insight but it will be more by luck than judgement and you may lose days in the process.

Multi Channel Funnels or attribution models? What to use & when?If you are using Google Analytics you will already know that you have two groups of reports that you can draw on for your attribution analysis, these are Multi Channel Funnels �&RQYHUVLRQ�!�0XOWL�&KDQQHO�)XQQHOV��DQG�WKH�DWWULEXWLRQ�PRGHOV�WKHPVHOYHV��&RQYHUVLRQ�!�$WWULEXWLRQ�!�0RGHO�&RPSDULVRQ��

Generally speaking the Multi Channel Funnels group of reports are better used for primary analysis and the Model Comparison function is better used for secondary analysis. In other words you would use the MCF reports to help generate your hypothesis and your model comparison reports to test your hypothesis. That is not to say you can’t use these reports the other way around, just that you might save a great deal of time otherwise.

Assist5HPHPEHU�WKDW�WKH�GH¿QLWLRQ�RI�³DVVLVW´�LQFOXGHV�¿UVW�FOLFN�EXW�ZKHQ�\RX�DSSO\�FUHGLW�ZLWKLQ�D�PRGHO�\RX�FDQ�VSOLW�RXW�PLGGOH�DQG�¿UVW�FOLFN�DWWULEXWLRQ�SRLQWV��7KDW�PHDQV�\RX�PD\�¿QG�PLGGOH�DQG�¿UVW�FOLFN��ERWK�RI�ZKLFK�FDQ�EH�FRQVLGHUHG�LQ�DQ�DVVLVWLQJ�UROH��PD\�SHUIRUP�TXLWH�GLIIHUHQWO\�

Comparing data from attribution models to standard GA reportsThis is an understandable thing to do especially if most analysis until this point has been done using the standard GA reports.

Remember that in Google Analytics the standard reports are based on the last non-direct model, that means they automatically exclude the ‘None’ or ‘Direct’ channel unless it is the only channel in the funnel. As a result if you or somebody else are comparing the transaction or revenue data output from one of the standard reports to one of the attribution models it will of course be different to a greater or lesser degree.

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6HWWLQJ�\RXU�RZQ�H[SHFWDWLRQV��WKLV�RQH�LV�LPSRUWDQW�Attribution modelling is an analytical process. It will not, in its current format as provided by the free version of Google Analytics, deliver the answers to your questions about which is the optimum combination of acquisition sources to use in the pursuit of maximum ROI, to do that you must be prepared to research your hypothesis using attribution modelling and then test them in the wild.

Don’t get bogged down in the minutiae of the top conversion path report Like all reports, the ones that look the prettiest are often the ones that garner the most attention but aren’t necessarily of the greatest use.

In GA’s Multi Channel Funnel group of reports the Top Conversion Paths report is just one of those. It shows conversions and conversion value ranked by total volume for every combination of acquisition sources used in a conversion path over the given time period. This LV�QLFH�EXW�HIIHFWLYHO\�D�WLPH�VLQN��,W¶V�OLNH�WKH�WRS�SDWK�UHSRUW�IRU�\RXU�VLWH¶V�ZHE�SDJHV��*$�GRHVQ¶W�KDYH�RQH�RI�WKHVH�EXW�+%;�XVHG�WR�DQG�RWKHU�HQWHUSULVH�WRROV�VWLOO�GR��

To get a sense of the futility of this report consider that it will show you the top 10 conversion paths but if you then look down to the bottom right of the report you will see how many lines RI�GDWD�WKHUH�DUH��GR�QRW�EH�VXUSULVHG�LI�WKH�QXPEHU�UXQV�WR�WKH�WHQV�RI�WKRXVDQGV��,Q�RWKHU�words a lot of noise.

&RQ¿JXUDWLRQ

Conversion trackingSince Attribution is a fairly advanced topic, in this guide we have assumed that conversion tracking has already been setup in Google Analytics. If not, read our 7 Steps guide to using Google Analytics to setup goals or see this introductory post.

Make sure you are tracking your campaigns properlyThis seems obvious but without it the whole concept of attribution modelling becomes VRPHZKDW�ÀDZHG��*RRJOH�$QDO\WLFV�PXVW�EH�DEOH�WR�LGHQWLI\�HDFK�LQGLYLGXDO�UHIHUULQJ�VRXUFH�in order to be able to correctly allocate credit for a conversion / sale.

Of course GA and AdWords work very well in this regard but for other non Google sources of paid for online advertising GA uses a series of UTM tracking parameters which are applied WKH�GHVWLQDWLRQ�85/��7KHVH�HQDEOH�*$�WR�UHFRJQLVH�SDLG�IRU�VRXUFHV�RI�WUDI¿F��7R�OHDUQ�more about campaign tracking for Google Analytics read our 7 Steps guide to using Google Analytics or click here.

Setting a half lifeWhen setting up a time decay model remember to set the half life for the model.

As mentioned above, this defaults to 7 days but you should look at your own dataset to get an idea of what kind of half life you should apply.

,Q�*RRJOH�$QDO\WLFV�D�JRRG�ZD\�WR�GR�WKLV�E\�XVLQJ�WKH�7LPH�/DJ�UHSRUW��WR�¿QG�WKLV�JR��

Conversions > Multi Channel Funnels > Time lag

%XW�EH�FDUHIXO�KHUH��ZKHQ�\RX�¿UVW�FRPH�WR�ORRN�DW�WKLV�UHSRUW�LW�ZLOO�E\�GHIDXOW�EH�VKRZLQJ�you the time lag against all conversions that you have set up, both goal and e-commerce transactions. Depending one what you have set up for your goals this could be very

http://www.smartinsights.com/google-analytics/google-analytics-setup/getting-to-grips-with-goals-in-google-analytics/

http://www.smartinsights.com/google-analytics/google-analytics-campaign-tracking/marketing-campaigns-tracking/

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misleading if you are really only interested in the time lag to a sale.

To resolve this look up to the top left of the screen and locate the ‘Conversion Type’ drop down, then ensure that you have only the e-commerce transaction selected.

You will need to use you own judgement in assessing the data but a fair starting point would be to select the average Time Lag for anything over 50% of all transactions within the given period.

Setting the lookback windowFollowing on from the point above, you will also need to think about your lookback window. $OPRVW�E\�GH¿QLWLRQ�WKLV�LV�FULWLFDO��

The maximum lookback window in Google Analytics is 90 days but the default setting is 30 GD\V��WKLV�UHÀHFWV�WKH�FRRNLH�H[SLUDWLRQ�SHULRG�RI�PDQ\�SDLG�IRU�DGYHUWLVLQJ�QHWZRUNV��7KDW�said, your requirement may be different depending on your objectives.

For example the further out you push your lookback window the more chance you will create for a media source to be included in your model and this can be good as well as bad.

,I�\RX�DUH�QHJRWLDWLQJ�ZLWK�DQ�DI¿OLDWH�QHWZRUN�WKH\�ZLOO�TXLWH�SRVVLEO\�EH�XVLQJ�D��GD\�lookback window on a last click basis, obviously the last thing you will want to do is to extend the lookback window, on the other hand if you are selling a product of service that has a very ORQJ�SXUFKDVLQJ�F\FOH�\RX�PD\�¿QG�LW�KDUG�WR�HVWDEOLVK�WKH�ZRUWK�RI�DQ\�VRXUFH�LQFOXGLQJ�DI¿OLDWH�ZLWKRXW�SXVKLQJ�WKH�ORRNEDFN�ZLQGRZ�RXW�D�ELW�IXUWKHU�

Setting the credit weightingFor a few attribution models such as linear, position based and time decay it may be desirable to weight the credit according to the hypothesis. It’s actually only a mistake to forget or adjust the credit weighting if the hypothesis requires it.

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FIVEWHAT ARE THE LIMITATIONS OF ATTRIBUTION

&RRNLH�GHOHWLRQ%\�GH¿QLWLRQ��DWWULEXWLRQ�PRGHOOLQJ�UHTXLUHV�WKH�XVH�RI�SHUVLVWHQW�FRRNLHV�WR�ZRUN�HIIHFWLYHO\��If browsers can’t be tracked from session to session then the data relating to cross session activity will become meaningless.

It follows then that if users delete cookies from their browsers, or visit sites using the private mode on their browser or choose not to accept cookies when given the option as has EHFRPH�FRPPRQSODFH�RQ�PDQ\�KLJK�DQG�HYHQ�QRW�VR�KLJK�SUR¿OH�VLWHV��WKHQ�DWWULEXWLRQ�modeling will become a casualty along with many other web tracking services.

Cross-device browsing$�PRUH�FXUUHQW�DQG�PRUH�VLJQL¿FDQW�WKUHDW�WR�HIIHFWLYH�DWWULEXWLRQ�PRGHOOLQJ�LV�WKH�LQFUHDVLQJ�prevalence of cross device browsing.

Precisely because of the splintering of device types that are now being used to access the web and in particular the growth of mobile platforms people can now access sites at times that most suit them and this has brought about a change in motivation. For example, initial research may be done from a desktop at work, followed by more casual browsing on a tablet in the evening followed by a last minute check on competitor prices before entering a high street retail outlet on the weekend to try on a pair of running shoes, followed by the purchase which might be made using a tablet again that evening.

Unless you are one of the few who have updated your privacy policy and have implemented FURVV�GHYLFH�WUDFNLQJ�XVLQJ�ORJLQ�GHWDLOV��IRU�ZKLFK�\RX�ZRXOG�KDYH�WR�JDWKHU�WKH�YLVLWRUV¶V�H[SOLFLW�FRQVHQW��LW�ZLOO�EH�YHU\�KDUG�WR�WUDFN�DQG�PRGHO�DWWULEXWLRQ�DFURVV�WKLV�NLQG�RI�browsing behaviour.

As a side note: If you have conquered cross device tracking then your attribution modelling possibilities extent beyond the channel in into the device.

%ORFNLQJ�-DYDVFULSW�If browsers are blocking Javascript then this too will stymie attribution for very similar reasons that relate to cookie deletion or prevention. Google Analytics tracks click stream data using a javascript tracking code, if this doesn’t load then tracking will not occur.

'LUHFW�WUDI¿F7KH�µ'LUHFW¶�RU�µ1RQH¶�FKDQQHO�LQ�*RRJOH�$QDO\WLFV�RI¿FLDOO\�UHSUHVHQWV�YLVLWRUV�WKDW�KDYH�come to the site via a bookmark or from the URL being typed into the browser address bar. +RZHYHU��ZKHUH�D�VRXUFH�RI�WUDI¿F�FDQQRW�EH�LGHQWL¿HG�E\�*$��LW�LV�RIWHQ�OXPSHG�LQ�ZLWK�WKLV�channel.

$Q�H[DPSOH�RI�WKLV�LV�RIWHQ�IRXQG�LQ�YLVLWV�IURP�$I¿OLDWH�VLWHV�WKDW�FRPH�YLD�DQ�DI¿OLDWH�QHWZRUN��%HFDXVH�DI¿OLDWH�QHWZRUNV�KDYH�WR�WUDFN�WKH�UHIHUUDOV�XVLQJ�LWV�RZQ�WHFKQRORJ\��WKLV�can often strip out Google Analytics tracking parameters and the resultant referrer is hard IRU�*$�WR�UHFRJQLVH��$V�D�UHVXOW�XQOHVV�WUDFNLQJ�IRU�DI¿OLDWH�QHWZRUN�DFWLYLW\�LV�QRW�DGHTXDWHO\�WUDFNHG��WKLV�ZKROH�FKDQQHO��ZKLFK�LV�RIWHQ�YHU\�OXFUDWLYH��FDQ�JR�XQUHFRJQLVHG�DOWRJHWKHU�

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6HDUFK�NH\ZRUGVOne of the mainstays of search analysis, either organic or paid, has always been to compare brand vs non brand terms. Unfortunately Google no longer supply keyword data in meaningful quantities for either. This is a nuisance because it makes life slightly harder for anybody wanting to look at attribution related data.

Unfortunately the analyst will have to adopt other techniques to deal with this such as cross UHIHUHQFLQJ�FKDQQHO�ZLWK�ODQGLQJ�SDJH��IRU�6(2��RU�E\�FDPSDLJQ��DG�JURXS�IRU�SDLG�VHDUFK��this is possible by creating a custom channel. It’s is a slightly blunter approach but it’s better than nothing.

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SIXUSING IT IN PRACTICE

Reality bitesAttribution modelling is not an answer in and of itself, taken as a whole it is more akin to what LV�RIWHQ�UHIHUUHG�WR�DV�&52��&RQYHUVLRQ�5DWH�2SWLPLVDWLRQ��

-XVW�DV�ZLWK�&52��DWWULEXWLRQ�PRGHOOLQJ�DQG�LW¶V�¿QDO�RXWSXW�DUH�FKDUDFWHULVHG�E\�WKUHH�fundamental elements:

1. Analysis

2. Hypothesis

3. Experimentation

It would be wrong to think that running some attribution analysis will provide an answer that ZLOO�EH�JXDUDQWHHG�WR�UHVXOW�LQ�D�SRVLWLYH�RXWFRPH��$WWULEXWLRQ�PRGHOOLQJ�LV�WKH�¿OOLQJ�LQ�WKH�sandwich between the business / marketing situation and the testing process.

As has been mentioned already, it is very important that there is a clear understanding of the product or service purchasing cycle as well as the business and marketing requirements. Once you know what these are you will then be in a position to select a model that you WKLQN�ZLOO�EHVW�UHSUHVHQW�\RXU�VFHQDULR��,I�\RX�GRQ¶W�JR�WKURXJK�WKDW�SURFHVV�¿UVW�\RX�ZLOO�¿QG�\RXUVHOI�VLPSO\�WHVWLQJ�D�YDULHW\�RI�PRGHOV�DQG�PRGHO�FRQ¿JXUDWLRQV��RI�ZKLFK�WKHUH�LV�DQ�DOPRVW�OLPLWOHVV�QXPEHUV�RI�YDULDWLRQV��DQG�\RX�FRXOG�HQG�XS�ZDVWLQJ�D�ORW�RI�WLPH�

The model selection process pairs with the business and communication scenario, the analysis follows that and the testing follows the analysis.

$�IUDPHZRUN

Step 1: Map out your customer purchasing cycle / buying process

As mentioned more than once in this white paper, you must have a clear understanding of your product and it’s purchasing consideration cycle, is it:

; Long and requiring multiple visits

; Short and impulsive

; Is the product or service simple or complicated for the customer / consumer to grasp?

Step 2: Map out your existing acquisition strategy.

; What channels are you currently using?

; Which are free and which are paid for?

; How much are you spending on each paid for channel?

; :KDW�LV�\RXU�FXUUHQW�PHDVXUHPHQW�PRGHO"��PRVW�OLNHO\�WR�EH�ODVW�QRQ�GLUHFW�FOLFN�

; $UH�\RX�UXQQLQJ�DQ\�WDFWLFDO�DFWLYLW\"��SURPRWLRQV�HWF��EUDQGLQJ«�

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Step 3: Develop your hypothesis or hypotheses by running some primary analysis.

; Use the Path Length and or Time Lag reports in Google Analytics to gather insight about your customer’s journey and the length of time is take to make a purchase.

; Use the Assisted Conversions report in Google Analytics to get an idea of which channels RSHUDWH�LQ�DQ�DVVLVW�RU�ODVW�FOLFN��GLUHFW�FDSDFLW\��XVLQJ�WKH�DWWULEXWLRQ�UDWLRQ�PHWULF��

; Map your data in a way which makes it easy for you to make a comparative analysis.

Step 4: Review the attribution models at your disposal and select the one or ones that most closely UHÀHFW�\RXU�SURGXFW��VHUYLFH�SXUFKDVH�FRQVLGHUDWLRQ�F\FOH�DQG�\RXU�FXUUHQW�DQG�LQWHQGHG�marketing acquisition intentions.

Step 5: Establish the most relevant attribution models to run comparative analysis against.

; Base model will be last non-direct click as that is what they’ve been working to until now

; Run comparative model analysis

Step 6: Experiment with the acquisition strategy.

1 What Is M

edia A

ttribution6 U

sing It In P

ractice5 W

hat Are Its

Limitations

4 Mistakes

To Avoid3 W

hich Models

To Use

2 Why Is A

ttribution M

odelling Important?


Media attribution

�26

7

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5HVRXUFHV�WR�¿QG�RXW�PRUH�DERXW�DWWULEXWLRQ ; Google support pages on Multichannel Funnels

; Extensive post by Google’s Avinash Kaushik reviewing Google’s Attribution models

; Post from Dave Chaffey on alternative methods to visualise channel contribution

; $�FDVH�VWXG\�VKRZLQJ�KRZ�6N\�XVH�DWWULEXWLRQ�ZLWKLQ�DI¿OLDWH�PDUNHWLQJ

https://support.google.com/analytics/answer/1191180

http://www.kaushik.net/avinash/multi-channel-attribution-modeling-good-bad-ugly-models/

http://www.smartinsights.com/traffic-building-strategy/media-attribution/media-conversion-attribution/

http://www.smartinsights.com/affiliate-marketing/affiliate-marketing-analytics/big-data-how-sky-have-used-attribution-to-understand-customer-journeys/

DIGITAL MEDIA ATTRIBUTION - Smart Insights · Media attribution 6 1 ONE UNDERSTAND DIGITAL MEDIA...

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Transcript of DIGITAL MEDIA ATTRIBUTION - Smart Insights · Media attribution 6 1 ONE UNDERSTAND DIGITAL MEDIA...