How to Build an Attribution Solution in 1 Day

How to build an attribution solution in a day- well maybe a couple of days ;)Dr. Phillip Law

• By the end of this Talk I want to give you the tools and methods to be able to go away and build your own solution.

• I started this on Monday and got something useable in a day, then it took me a couple of days to polish this up and iron out any bugs.

• It’s not perfect, but give you an attribution solution with contextual data and importantly the power to slice and dice.

Overview• Blurb About Me and the Company I work For.• Quick Overview of Rules Based attribution.• Discuss the tools I used and why, this is effectively an ETL process using

Python and Tableau.• How I extracted the data.• What Transformations I did using Python.• What Transformations I did in Tableau.• Run through the models in Tableau.• Limitations (Scalability)• Next Steps (Improve the models, Allocation of Credit, Bayesian attribution)

About me: Dr. Phillip Law

© 2016 YOUR FAVOURITE STORY ALL RIGHTS RESERVED

.

To craft powerful digital experiences to help our clientsgrow

•Our Mission

CLIENTS LOGOS

1. Improving Marketing Performance

Improving conversion throughout the

customer journey and reducing inefficiency.

In a world where customer experience is the brand experience.

Digital has great power.

2. Brand Building 3. Future Proofing

It is particularly important to work to future web standards

and consumer patterns to create lasting solutions.

Reporting & implementation(Adobe and GA)

SEO & PPC

Data Science (modelling)

Data Visualisation (D3, Tableau)

Growth Audits

Optimisation (A/B Testing, Videos)

Analytics

Attribution

Attribution Model Types (Sort of)

RULES BASE BAYESIAN

Attribution Model Types?

RULES BASE WEIGHTING

2. USE THE SHAPLEY VALUE (GAME THEORY)

BAYESIAN MODELS

Types of Models1.First Touch2.Last Touch3.Linear/Evens4.Starter Player Closer5.Temporal6.Spatial

(Raw Data Feed)

(Credit to Marketing Channels)

Right Tools for the Job

Transformation

Visualise

Raw Data Feed

Raw Data Feed

Because of file size you’ll probably need to get it delivered to an FTP

You can ask for the full data feed, this file is delivered hourly and contains all data, this file is Huge, you can get this delivered to D3 on the amazon cloud, which is nice

Raw Data Feed

Because of file size you’ll probably need to get it delivered to an FTP

You can ask for the full data feed, this file is delivered hourly and contains all data, this file is Huge, you can get this delivered to D3 on the amazon cloud, which is nice

Process this Data File in Python (4 Steps)(Did this whole thing in 140 lines of code)

Step 1: Clean file (remove all page views where page views equals zero), flag fist touch point in visit, count page views in

visits, create sort key.

Step 2: Group by tracking ID, and sort by time (need to sort by the sort key), flag conversion event (Only one conversion Event

per Visitor)

Step 3: Read in file backwards, create attribution window, count touch points from conversion, write conversion time to the same

row as the conversion touchpoint.

Step 4: Re-order file and step three reversed the process.

Next Step to Open in Tableau

Next Steps

Fair Allocation of

Credit

Traditional rules for assigning credit are arbitrary not and do not reflect the true weighing of a touch point.

Weightings are skewed towards channels that retarget

There is a method from game theory that has been mathematically proven to allocate credit in a fair way.

Shapley Value

PPC Social

Email

$100

PPC Social $230

Email

PPC Social

$230

$50

PPC $90

Social $85

Email

$100

PPC

Social Email

PPC Social Email

$55

$150

$160

No Touchpoint

PPC Social Email

PPC Email Social

Social PPC Email

Social Email PPC

Email PPC Social

Email Social PPC

PPC Social Email

40 10 130

40 60 80

15 35 130

70 35 75

95 80 5

70 105 5

55 54 71

Average

Only Considers People

Who Convert

BAYESIAN

𝑝 (𝑃𝑃𝐶)

𝑝 (𝑃𝑃𝐶)

𝑝 (𝑐𝑜𝑛𝑣𝑒𝑟𝑡 )

𝑝 (𝑐𝑜𝑛𝑣𝑒𝑟𝑡 )

𝑝 (𝑐𝑜𝑛𝑣𝑒𝑟𝑡 )

𝑝 (𝑐𝑜𝑛𝑣𝑒𝑟𝑡 )

𝑝 (𝑃𝑃𝐶∩𝑐𝑜𝑛𝑣𝑒𝑟𝑡 )

𝑝 (𝑃𝑃𝐶∩𝑐𝑜𝑛𝑣𝑒𝑟𝑡 )

𝑝 (𝑃𝑃𝐶∩𝑐𝑜𝑛𝑣𝑒𝑟𝑡 )

𝑝 (𝑃𝑃𝐶∩𝑐𝑜𝑛𝑣𝑒𝑟𝑡 )

𝑝 (𝑐𝑜𝑛𝑣𝑒𝑟𝑡 |𝑃𝑃𝐶 ) 𝑝 (𝑐𝑜𝑛𝑣𝑒𝑟𝑡   |𝑃𝑃𝐶 )Probability they would have

converted anyway

Difference in probabilities is the impact that channel has on

conversion

∆ 𝑝

Probability someone converts given that they have seen a PPC

ad

Advantages

• Using the Shapley value provides a more “true” allocation of the influence of channels.

• Bayesian model takes into account user journey that don’t convert. Understand unconverted users that are the best prospect of conversion.

£5000

£750

31st October

Any QuestionsPhillip.law@yourfavouritestory.com

https://uk.linkedin.com/in/piplaw