Design of multichannel attribution model using click-

stream data MeasureCamp Prague 2015

Lucie Šperková

Everything you need to know (about me)

I used to work in bank.The only language I can use is SQL.I have never worked directly with GA, just extract the data from it.

Somebody said: “Without data you are just a person with an opinion”I say in addition:“… but with data, which are messy nad shitty, you are a clear liar.”

Data overload?Lack of data -> incomplete decisionsToo much data -> overload and still lack of knowledge (What I should focus on?!)

Basement / garage problemI store big volume of data just for case, but will probably never use it. -> Ask yourself why you will need them (have a target)

Why?costs and revenues

expenses and benefitsincome and spending

profit and loss

customer loyalty/satisfaction

TargetCreate exponential model that takes into account all the inputs into

the conversion funnel.

With the use of AdForm metadata: for every cookie (user) on the particular trackingpoint calculate number of interactions for the

particular time period and assign weights to campaign channels.

What I do / will do with the data...- calculation of the weights and share of channels in conversions

- budgeting the total cost to the individual channels according their share

- visualize the shares of the channels

- drill down the channels - to medium, campaign,...

- slice according to refferer type, device type, customer segments …

- find the right campaign mixture (how to achieve particular number of conversions for the lowest price)

- prediction of the future development and setting the right campaign mixture

- observe the conversional / non-conversional rates (how many interactions didn’t lead to conversion)

- intregration of data from other sources (GA, sklik, CRM, budgets, etc.)

- revenues from conversions

- customers data

- ...

seen the banner 1

seen the banner 2click

PRclick PPC

click Organic

click banner1

Web - Conversion1point 1point2points 2points2points 3 points

Weights assigned according to:

basic division:

conversion click (triggered the trackingpoint)

last impression (triggered the trackingpoint)

direct entry

click

impression

refining the weights: ● by mouse overs, mouse over time, visibility time,

refferer type, medium etc.

● on the web there are many trackingpoints cookie has visited (not interested about the move through websites)

● focus on conversion points or points foregoing conversions (e.g. where customer left the action)

Trackingpoint A

Trackingpoint B

Trackingpoint C

Trackingpoint D

Trackingpoint Econversion

metadata

calculationsextract

extract

transform

Process of basic transformation

data cleaning- delete robotic transactions- transactions, which happened in less than 30 minutes from the last transaction (same cookie, same

trackingpoint, same session) - avoid refreshjoins

- for every cookie at the trackingpoint find all interactions which happened during the time between trigger of the last trackingpoint and today’s trackingoint (for more conversions of single cookie)

- every cookie can have interaction with different campaign: calculation for every campaign (avoid multipletimes counting of the same add - banners etc)

- the campaign of the conversion interaction is known (higher weight)weights calculation and refining

!

Predictionscosts

conversions (revenues)

more investments to this campaign mix won’t help

right campaign mix for acceptable price

100 300 330

Thanks. Let’s talk!

mail: lucie.sperkova@gmail.comlinkedin: https://cz.linkedin.com/in/luciesperkovatwitter: https://twitter.com/pihatka