THE ADVERTISING GENOME

applying  big-­‐data  science  to  ignite  growth

DATA DRIVEN ATTRIBUTION

“It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.” Sherlock Holmes in “A Scandal in Bohemia”

ATTRIBUTION STARTS with data

visi6ng  a  webpage

clicking  on  a  banner  ad

searching  on  the  internet

making  an  inquiry

pos6ng  on  social  media

placing  an  item  into  a  shopping  cart

making  a  purchase

watching  a  TV  show

redeeming  a  coupon

booking  a  trip

using  an  app

paying  a  bill

Data is the byproduct of consumers doing th ings !

CURRENT ATTRIBUTION APPROACHES FIRST TOUCH

Assigns all of the revenue credit to the first media channel to touch the customer.

LAST TOUCH

Assigns all of the revenue credit to the last media channel to touch the customer

LINEAR

Assigns equal revenue credit to each media channel to touch the customer.

TIME DECAY

Assigns increasingly more revenue credit to the most recent channel to touch the customer.

MOST MODELS TOO SIMPLISTIC MINIMAL COMPUTATION OF OFFLINE TO ONLINE EFFECTS NO ACCOUNTING FOR EXTERNAL FACTORS COMPUTES CUSTOMER INFLUENCE BUT NOT ROI

TRANSFORM ADVERTISING genome

data  driven  model

environmental  data

social  sen6ment  data

compe66ve  media  spend  data

What  is  the  true  value  of  TV  adver6sing?

How  do  offline  ads  drive  online  ac6vity?

What  is  my  cost  per  lead?    Per  acquisi6on?

What  is  the  best  media  mix?

What  will  a  par6cular  marke6ng  budget  generate  in  terms  of  inquiries  or  sales?

What  media  budget  is  required  to  generate  my  sales  targets?

ADVERTISING genome COMPUTATION ENGINE

YIELD CURVES Computation model that determines the point at which incremental input does not produce corresponding incremental output.

Saturation rate for various forms of outbound advertising such as TV, radio or display.

COVARIANCE MATRIX Computation model that determines interrelationships between what may otherwise appear to be random values and variables. Effect or intermediate influence of one media channel on another.

Based on tools and models used by Wall Street to manage portfolios that have multiple types of securities such as bonds, stocks, cash, etc.

OUR genome data engine

bought  data

3rd  party  web  data

Client  Business  Data

public  data

census  data

labor  stats

weather industry  data

social  sen6ment

interac6ve  surveys

DATA  LAKE INGEST  -­‐  TRANSFORM

GENOME

DASHBOARDS

MARKETING  AUTOMATION

MEDIA TYPES •  Television •  Website •  Print •  Radio •  Paid Search

•  Retargeted Display •  Social •  Aggregator •  In-game •  Pre-role •  Other (e.g. yellow pages)

GEOGRAPHY •  By zip code •  By DMA •  By state •  By urban area

WE MAKE ATTRIBUTION ACCESSIBLE

1. Sophistication = Complexity The Advertising Genome is highly sophisticated but hides complexity from users via a use-case driven graphical application.

2. Expensive, Lack of ROI The Advertising Genome generates meaningful ROI by optimizing media spend to generate more measurable results.

3. Lack of Data The Advertising Genome comes integrated with our Data Lake, which is over 20 terabytes of 3rd party data necessary to map the intricate relationships between customers, products, markets and media channels.

44% of interactive marketers do not have a measurement

process in place, not even post-click

30% of marketers still use first or last touch metrics, even though most know it is too

simplistic to be effective

Only 11% of marketers use any form of algorithmic attribution

tools

addressing issues that have kept most companies f rom using at t r ibut ion !

The ADVERTISING genome IN ACTION The  predic6ve  customer  

acquisi6on  model The  actual  customer  acquisi6on  

analy6cal  dashboard

The ADVERTISING genome

Use-case based apps

External data integration

Intuitive user interface

Dynamic slicers

Built-in reporting & analytics

Custom UI ATTRIBUTION genome

Advanced graphic user interfaces

Integration of social intelligence data

Cross-channel intermediate influence

Real-time updates

Custom screens based on user roles

External  data  essen6al  to  adjust  for  changes  in  customer  sen6ment,  global/na6on/regional  economic  ac6vity,  compe6tor  ac6vity  and  scores  of  other  environmental  factors.

Formulaic  computa6on  provides  greater  than  95%  accuracy  of  predicted  cost  per  ac6on  versus  actuals.

User  adop6on  increases  exponen6ally  when  “a]ribu6on”  is  integrated  into  day-­‐to-­‐day  applica6ons  such  as  media  budge6ng  planning,  repor6ng  and  ROI  analysis.

DATA DRIVEN ATTRIBUTION

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