Attribution Modelling with less guesswork

Panos Tsimpos – Data Analyst

MeasureFest Oct ‘14

13 years old40 digital specialists£25+ million billings7th ranked Drum Digital 1005th ranked digital media agency Econsultancy Top 100

Harvest by numbers

Position Based Time Decay Custom Attribution

Last Click First Click Linear

Which Attribution Model?

Custom Model rules?

The Analytics Process

Collect

Analyse

Transform

Apply

Attribution Study Toolset

• Web Analytics solutionGoogle Analytics / Omniture / Coremetrics, etc.

• Raw Clickstream data:GA MultiChannel Funnel API / Omniture Data Warehouse, etc.

• Data Transformation / Manipulation:Database or Statistics solution: R / SAS / SQL / even Excel

• Data VisualisationTableau / Visokio, etc.

• Attribution ModellingGA built-in, third-party DMP solution, own bespoke database

Offline Reach

Funnel Stage

Returning User

Demographics

Time Lag

CampaignTargeting

Engagement

Time &

Location

Click Position

Visit Duration

Conversion Rate

Marketing Channel

AttributionModel

The Attribution Landscape

Arriving at an Attribution Model

Examine Marketing Channels to identify their role in the user journey and the relationships between them.

Examine traffic signals to understand which activities should be receive greater rewards.

Examine conversion habits to find key influencers and inhibitors and weigh them accordingly.

• An astounding 69% of users have a single-visit journey, and of the remaining returning users 17% will visit on the same day.

• This only leaves a minority of 14% who have multiple visits over a length of time.

• With less than a third of users visiting in multiple sessions Last Click does not sound like a bad idea.

• What about after we examine traffic and conversion habits in more depth?

User Traffic Behaviour

Returning User Behaviour

• Users are not very likely to re-engage repeatedly with the site as time passes.

• If they make return visits they will do so in short intervals.

• Conversions and the CR% follow the same trend, so users who eventually come back are not necessarily more eager or loyal.

• The slow decline of the CR% over 5 days implies that the Day 0 peak is Immediate Intent rather than Direct Response to Advertising.

• Since the CR% is not higher for lagged users that click does not seem to have worked harder…

Days Since Last Session

• Certain channels however are effective at getting returning traffic, which could still convert.

• Out of the total Conversions 32% are made on return visits, but 24% actually return and apply on Day 0.

• The one dimensional channel behaviour points towards Last Click, but that would ignore the initial source of that 24%.

• First Click effectively covers 91% of conversions (Day 0), but does no justice to all the Reactivation channels.

Returning Visits by Channel

• Combine the two and use Position Based instead.

• We already know the First Interaction is the most crucial converter, but a 3rd of conversions are on a return visit, so the Last Interaction also deserves a good share of the credit.

• In-between interactions are not common but where they are present they should still receive a small amount of credit among them.

The first step

Spread of Returning Users

Immediate Visits :66% Converted11% Non Converted

Rapid successive Visits :7% Converted16% Non Converted

Short Term Visits :8% Converted4% Non Converted

Medium Term Visits :10% Converted20% Non Converted

Long Term Visits : 7% Converted21% Non Converted

• The majority of conversions happen within 14 days and quickly tail off between 15 and 30 days. Therefore a Lookback Window of 30 days will sufficiently cover the user base.

• The second biggest cluster is the Medium Term one, meaning that beyond the users that convert right away there is still an considerable Returning audience.

• Outliers not included in the main clusters make up 2-3% of Converted users and 28% of the NonConverted.That large percentage of users that have returned without converting could be targeted with a Reactivation campaign and improve customer acquisition.

• Visitors in the long-tail segments are much less likely to be converters, even though they do re-engage after a long time period.

User Visit analysis

• Are there any channels that often re-appear throughout the user journey?

• Are there any common sequence groups?

• What about a channel’s role in Assisted conversions?

Channel Paths

• 62% of conversions are Single-Path• 15.5% are Dual-Path • 22% are Multi-Path

• The vast majority of journeys are Single-Path and therefore irrelevant to MCF analysis.

• The remaining 37% do not show any patterns of magnitude.

Channel Assisted Last Click Assisted / Last Click

Conversions Conversions %

Paid Search 2,871 5,354 0.54 6804 45.54%

Direct 1,892 3,557 0.53 4268 28.57%

Organic Search 1,750 3,022 0.58 4272 28.59%

Affiliate 958 2,283 0.42 2952 19.76%

Referral 186 316 0.59 476 3.19%

Other 104 213 0.49 296 1.98%

Display 73 14 5.21 85 0.57%

Social Network 68 118 0.58 184 1.23%

Email 42 64 0.66 98 0.66%

Total 7,944 14,941 0.53 - -

Channel Paths: Assists

Channel AssistedLast

Click Assisted / Last Click

Conversions Conversions %Multi-Path

Contributions*Multi-Path

Contribution %

Paid Search 2,871 5,354 0.54 6804 45.54% 3226 21.59%

Direct 1,892 3,557 0.53 4268 28.57% 2955 19.78%

Organic Search 1,750 3,022 0.58 4272 28.59% 2170 14.52%

Affiliate 958 2,283 0.42 2952 19.76% 1155 7.73%

Referral 186 316 0.59 476 3.19% 276 1.85%

Other 104 213 0.49 296 1.98% 127 0.85%

Display 73 14 5.21 85 0.57% 78 0.52%

Social Network 68 118 0.58 184 1.23% 82 0.55%

Email 42 64 0.66 98 0.66% 59 0.39%

Total 7,944 14,941 0.53 - - - -

Channel Paths: Touchpoint contributions

* Multi-Path Contributions is the number of conversions for each multi-step user journey that the Channel has been present in.

Attribution Model Conclusions & Bonus Insights

Engagement

Base Model

Position based

Priorities: First Click >> Last Click > Middle Click

Attribution Window

No longer than 30 days necessary14 days already covers 90+ % of conversions

Time parameters

Down-weigh Day 0 conversions as channels only make minimal effort to win those

Add Time Lag modifiers to reward channels that quickly generate Return traffic

User Personae

As a more advanced option we could assign custom credit to each of the above Audience Segments based on their propensity to convert

AssistsAccount for Display, Email, Referral assist contributions by up-crediting the channels

Channel Sequences

Up-weigh Channel pairs that have consistently displayed mutually assistive gains

Retargeting

About ~7% of users returned after 2 weeks and within 3 months. This is a good percentage to plan a small, focused retargeting campaign around.

Segmentation

Can create multiple versions of a model by applying further filters/segments on results and adjusting weights

Or create custom reporting/target audiences

Additional credit or segments based on channels that result in above-average user engagement according to site metrics

Possible Signals

UserBehaviour

Campaign / Source

Conversions Marketing

• Pages / Session

• Session Duration

• Time of Visit

• Sequence of Visit

• Content Consumption

• Engagement

• Channel

• Organic vs Paid

• Prospecting / Remarketing

• Position of Visit

• Time Lag

• Macro & Micro Conversions

• Loyalty

• AOV

• Assists

• ROI

• Business Objectives

• Trends

• If you try to get to know your site, its channels and its users the data will make suggestions as to which type of Attribution Model is suitable.

This won’t be a straightforward answer, but combining that with overall marketing plans and targets will make the next steps much more clear.

• During this process of analysing user behaviour and dissecting traffic trends you will also uncover many other useful hints that can help fine-tune marketing activity:

Channel Role & Interplay, Engagement Thresholds & Time Window, User Clustering, Receptiveness and (inverse) Maturity Period.

• As with everything Web Analytics this is also a continuous, ongoing process. Even if the base of it remains the same, an Attribution Model should be re-examined and calibrated frequently to reflect the most recent changes in user and business behaviour.

• If precise enough, you could create a set of seasonal or occasion-specific models that are recycled every year with minor updates and modifications.

Methodology Suggestions

Thank you!

panos.tsimpos@harvestdigital.com

www.harvestdigital.com

DD: 020 7479 4058