A case study and digital advertising suggestion on different ads channels, modelling, format and suggestions.

1. Robin Goel

2. Advertising Channels and Reach

3. Advertising Framework
 Search Network ( Google, Yahoo and Bing)
 Lead generation/ online sales
 Use of text ads
 Display Network ( Management Placements, DFA and Ad Networks)
 Branding and Awareness
 Lead generation/ online sales
 Social Media ( Facebook and Instagram)
 Branding and Engagement
 Lead generation/ online sales
 Video Network ( Trueview and GDN)
 Branding and Engagement
 In-stream and click to play videos
 Mobile and Tabs
 Text search ads
 Banner display ads

4. Search & Display Recommendation
Leverage Ad formats
• Ad customizers ( real time ads)
• Ad extensions ( reviews & direction)
• Extensive rich media ad formats
• Video ads on display networks
Remarketing and Segmented Lists
• Optimized ads for remarketing lists
• Test affinity groups and similar audiences
• Video ads for remarketing
Keywords and Bid Optimization
• Keyword matching with search query report
• Keyword themes and ad groups restructuring
• Bidding at ECPC, CPA, TCPA, ROAS for conversions
Landing page optimization
• Check for the landing page grade
• Specific landing pages for different ad groups

5. Social Media-Recommendation
 Facebook, Instagram, Twitter and YouTube
 Objective based campaigns
 Conversion pixels
 Video ads limited to Wi-Fi networks only
 Advanced advertising with power editor
 Leverage remarketing and custom audience
 Location Targeting
 Detailed targeting
 Advanced ads on Twitter
 Display ads on YouTube for conversions
 Skippable video ads for conversions
 Non skippable video ad for branding
 Sponsored cards

6. Mobile-Recommendation
Mobile optimized content
Mobile ad extensions
Effective ad placement
Limit auto play video ads to Wi-Fi network
Use of Multi-image carousel
Mobile Bid Modifiers

7. Attribution Models
20% 20% 20% 20% 20%
1 2 3 4 5
Linear
0% 0% 0% 0%
100%
First/Last
2% 8% 20%
30% 40%
Time Decay
40%
8%
4%
8%
40%
Position
Touchpoints

8. Arriving at an Attribution Model
 Key questions:
 What period click window should be used?
 What period impression window should be used?
 What influence does an impression have against a click?
 What influence does each individual click in the click path have?

9. Arriving at an Attribution Model
There are a number of elements that can be used to drive a
variance in the attribution calculation.
• Taking into consideration the order / relationship and/or volume of touchpoints
Sequence /
Location
• Taking into consideration the time between touchpoints
Recency /
Frequency
• Individual channels impact on the propensity to purchase. Also breaking the
analysis past the channel level to keywords / creative etc.
Channel / Channel
Specific
• Understanding the impact an impression has on all future visits
Impression
Relationship
• Understanding the influence customer knowledge has on future sales (MGM)Customer Insight

10. Suggested Model
Engagement
Base Model
Position based
Priorities:
First Click >> Last Click > Middle Click
Attribution
Window
No longer than 45 days necessary
30 days already covers most %age 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
Assists
Account for Display, Videos, Referral assist
contributions by up-crediting the channels
Channel
Sequences
Up-weigh Channel pairs that have consistently
displayed mutually assistive gains
Retargeting
If atleast 15-20% of users returns within 2
weeks .
This could be 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

11. Methodology & Suggestions
 During the process of analyzing user behavior and traffic trends, we may use other useful hints
that can help fine-tune marketing activity:
 Channel Role & Interplay, Engagement Thresholds & Time Window, User Clustering, Receptiveness and (inverse) Maturity Period.
 This should be the continuous, ongoing process. Even if the campaign remains the same, an
Attribution Model should be re-examined and calibrated frequently to reflect the most recent
changes in user and business behavior. We may test data driven modelling to see how it’s
affecting ROI.
 If precise enough, we may create a set of seasonal or occasion-specific models that are recycled
each quarterly with minor updates and modifications.
 Since by default, Google attributes conversions to the last clicks, branded keywords end up
getting tons of undue credit for sales. As a result branded keywords campaigns seem to perform
well in PPC.
 To be most effective, we also need to invest in keywords which initiate or assist in sales along
with tweaking (add, pause, delete, change bids) last click keywords to remain under CPA target.