datalicious data driven media planning

> Media Planning < Media mix modelling and media a-ribu1on to boost media ROI

> Smart data driven marke3ng

Media A5ribu3on & Modeling

Op3mise channel mix, predict sales

Tes3ng & Op3misa3on Remove barriers, drive sales

Boos3ng ROMI

Targe3ng & Merchandising Increase relevance, reduce churn

“Using data to widen the funnel”

May 2013 © Datalicious Pty Ltd 2

> Wide range of data services

Data PlaHorms Data collec3on and processing Adobe, Google Analy3cs, etc Web and mobile analy3cs Tag-‐less online data capture Retail and call center analy3cs Big data & data warehousing Single customer view

Insights Analy3cs Data mining and modelling Tableau, Splunk, SPSS, R, etc Customised dashboards Media a5ribu3on analysis Marke3ng mix modelling Social media monitoring Customer segmenta3on

Ac3on Campaigns Data usage and applica3on SiteCore, ExactTarget, etc Targe3ng and merchandising Marke3ng automa3on CRM strategy and execu3on Data driven websites Tes3ng programs


> Best of breed technologies


> Clients across all industries


> Data driven media planning

§ Media mix modelling – Predic1ng future media performance – Macro insights to inform channel strategy

§ Media a-ribu1on – Analysis of historic media performance – Micro insights to inform channel op1misa1on


> Media mix modelling

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> Media mix modelling

§  Predict the future based on the past, i.e. predict sales based on media investment using a model that is based on historic data – Old school approach: Regression-‐based

§  Looks at correla1on between summary data, i.e. media investment and total sales per week

–  Recommended approach: Agent-‐based §  Simulates market condi1ons through a set of consumers (agents) and then exposes those agents to different media scenarios to test them (predict sales) before they are even implemented


> ThinkVine agent-‐based modelling


ThinkVine uses agent-‐based modeling (ABM) to simulate the ac1ons of consumers in the market based on a combina1on of internal client data as well as external market data. This enables adver1sers to predict and test the impact of different budget mix configura1ons before they have

been implemented thus improving overall media effec1veness.

> Data requirements and process


1.  Training the system: Datalicious analysts develop and calibrate a custom model based on agents that recreates past sales.

2.  Proving the system: Once the system is trained, we validate it by comparing predicted sales to actual sales.

3.  Using the system: Once the model has been calibrated & validated, it can be used to predict sales for different media scenarios thus tes1ng them before they are executed

> Modelling data requirements §  Sales data –  As granular as possible, i.e. by week by store

§  Media data –  Data on the media ac1vity that lead to the above sales

§  Customer profile data –  Any quan1ta1ve and qualita1ve insights on exis1ng customers that help configure the ThinkVine agents (i.e. simulated consumers)

§  Consumer profile data –  Any quan1ta1ve and qualita1ve insights on Australian consumers in general that help configure the agents (i.e. simulated consumers)


> Media mix modelling outputs


The so^ware enables fast, objec1ve comparisons of marke1ng plan alterna1ves, i.e. you can vary spending levels, 1ming, mix of tac1cs and consumer groups targeted.

The so^ware enables your team to easily run “what if?” scenarios, i.e. you can find the best investment levels to meet various strategic and tac1cal objec1ves by predic1ng poten1al sales for various different media budget scenarios.

> ThinkVine food services


In this case study, the company achieved greater media efficiencies and ROI and also quan1fied the halo effect of cross brand marke1ng. More specifically, more than $10 million of media spend was over-‐saturated due to a mismatch between the target consumers’ media consump1on habits and the deployed marke1ng tac1cs.

> Media a5ribu3on

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> The ideal media dashboard

Channel Investment ROMI Return

Brand equity Baseline ($100) n/a $40

Offline TV, print, outdoor, etc $7 330% $30

Direct Direct mail, email, etc $1 400% $5

Online Search, display, social, etc

$2 1150% $25


> ROMI as compe33ve advantage


74% of marketers do not engage in any form of media a-ribu1on aside from the last click leaving 26% of marketers with a serious compe11ve advantage as their media investment is likely to generate a much higher ROMI.

Closer

Paid search

Display ad views

TV/print responses

> Full purchase path tracking


Influencer Influencer $

Display ad clicks

Online sales

Affiliate clicks

Social referrals

Offline sales

Organic search

Social buzz

Retail visits

Life3me profit

Organic search

Emails, direct mail

Direct site visits

Introducer

> Custom models most effec3ve


56% of marketers consider a unique or custom (weighted) media a-ribu1on approach that does not use a standard out-‐of-‐the-‐box methodology as most effec1ve.

Touch point 1

> Analy3cs to pick the best model


Touch point 2

Touch point 3

Touch point N

Closer Influencer Influencer $ Introducer

Touch point 1

Touch point 2

Touch point 3

Touch point N

Touch point 1

Touch point 2

Touch point 3

Touch point N

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> A5ribu3on models compared


COST PER CONVERSION

Last click a-ribu1on

Custom (weighted) a-ribu1on

> Media a5ribu3on


Aussie purchase path tracking and media a-ribu1on modelling in close coopera1on with Amnesia designed to op1mise the overall Aussie budget mix across paid and earned media resul1ng in an overall project ROI of 910%.

> Media a5ribu3on


Suncorp purchase path tracking and media a-ribu1on modelling in order to op1mise the overall Suncorp insurance budget mix across paid and earned media resul1ng in an overall project ROI of 2,078%.


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Smart data driven marke3ng


datalicious data driven media planning

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