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Chris Haleua - Sr. Product Marketing Manager
Advanced Bidding
3x3 = 9 Segments
CPA Conversions
155.48 1
270.22 1
CV CE CP
NV NE NP
IV IE IP
Cost Conversions
250 0
190 0
Impressions Position
1,100 8.9
870 7.6
3
1 | LAWS - Bidding Dynamics
2 | PATTERNS - Deadend Quadrants
3 | APPLICATION - Optimization Methods
4 | BALANCE - Dimensional Modeling
5 | ADVANTAGE Secret Tools
Summary
Bidding Giants
George Michie Brad Geddes Sid Shah
Wijnand MeierHal VarianBen Vigneron
tiny.cc/wijnandtiny.cc/halvariantiny.cc/vigneron
tiny.cc/michie tiny.cc/sidshahtiny.cc/geddes
Marie Curie
no fear of perfection;
you'll never reach it.
Nothing in life is to be feared;
it is only to be understood
Rene Descartes
solved
became a rule,
which serve afterwards
to
Vilfredo Pareto
many events,
roughly 80% of the effects
come from 20% of the
Isaac Newton
I have seen further
than others,
it is by standing
upon the shoulders
of giants
Newtonian Laws
AERODYNAMICS THERMODYNAMICS
1. Inertia
2. Acceleration
3. Equilibrium
Transfer
Entropy
Absolute Zero
F=ma
A=0m/s2
FA=-FB
-W
dS
459.67° F
Newtonian Application
MOTION ENERGY
1. Inertia:Things in motion keep going
2. Acceleration: Smaller things speed up faster
3. Equilibrium: Equal & opposite reaction
Transfer: Energy becomes heat & work
Entropy: Nothing is 100% efficient
Absolute 0: No entropy for the coldest
Search Laws
OPTIMIZATION ANALYSIS
1. Bidding Inertia
2. Learning Acceleration
3. Modifier Equilibrium
Focus Transfer
Volume Entropy
Absolute Deadweight
Search Application
OPTIMIZATION ANALYSIS
1. Inertia no contradiction in exclusive segments
2. Acceleration assist & micro > clustering
3. Equilibrium balance volume & efficiency
Transfer - filter zero, sort next best
Entropy averages lie
Deadweight - archive not hoard
13
Overcome bid inertia by reducing mass
14
Extra focus on the critical few
2 Dimensional Cartesian Plane
1 | LAWS - Bidding Dynamics
2 | PATTERNS - Deadend Quadrants
3 | APPLICATION - Optimization Methods
4 | BALANCE - Dimensional Modeling
5 | ADVANTAGE Secret Tools
3x3 Intersection
17
Conversion
Success
Non-Converting
Spend
Ignored
Impressions
Volume
Leaders
Efficiency
Threats
Visibility
Struggles
Conversions > 0
Conversions = 0
Cost > 0
Cost = 0
Impressions > 0
FILTERS
Rev.
(Conv.)
Cost
Imp.
ROAS
(CPA)
Pos.
CTR Pos.
Q Score Pos.
SORTS
CV CE CP
NV NE NP
IV IE IP
How are we defending our top terms?
How are we avoiding waste?
How are we capturing interest?
How are we supporting growth?
How are we balancing returns?
How are we maintaining visibility?
How are we qualifying prospects?
How are we engaging visitors?
How are we demonstrating relevance?
How should we invest this extra budget?
How should we project the potential?
How did we grow 25% YOY in non-brand?
Dead-end
Quadrants
FILTERS
Volume
Leaders
Efficiency
Threats
SORTS
Conversion
Success
Non-Converting
Spend
Conversions > 0
Conversions = 0
Cost > 0
Revenue ROAS
CPCCost
Dead-end Quadrants
18
FILTERS
FILTERS
Volume
Leaders
Efficiency
Threats
SORTS
Non-Converting
Spend
Ignored
Impressions
Conversions = 0
Cost > 0
Cost = 0
Impressions > 0
CPCCost
Impressions Quality Score
Dead-end Quadrants
19
FILTERS
Pick up where we left off
Filter zero, then sort the next best volume
to identify the critical few leaders
resulting in dead-end bottlenecks
Dead-ends
 You might know your total conversions
 ...but do you know the minority of kw driving the majority of $?
 You might know your efficiency threats...
 ...but do you know your deadends in the step before?
 You might know your loss leaders...
 ...but do you know the segments & pivots that caused them?
Keep ratios in context
where their volume building blocks
are the closest available signal
to the bottom line
Avg. Pos CTR CPC CVR ROAS ROI
23
1 | LAWS - Bidding Dynamics
2 | PATTERNS - Deadend Quadrants
3 | APPLICATION - Optimization Methods
4 | BALANCE - Dimensional Modeling
5 | ADVANTAGE Secret Tools
Keyword Segments
Conversion
Success
Non-Converting
Spend
Ignored
Impressions
25
3 reports hidden in every keyword report
Segment by Sorting & Filtering
1. Sort by conversions
 Identify the first conversion zero
 Filter out blank conversion data
2. Sort the rest by cost
 Identify the first cost zero
 Filter out blank traffic data
3. Sort the rest by
Filter zero then sort by
the next best metric
Conversion
Success
Non-Converting
Spend
Ignored
Impressions
3x3 Segments in Rules
ROAS or CPA
Cost
Impressions
Filter zero then sort by
the next best metric
Segment Metric 3x3 Att Micro Weight Path
3 Dimensional Cartesian Coordinates
Conversions
(Last)
Conversions
(First)
Conversions
(Assists)
3x3 to 4x3 Keyword Segments
ROAS (last) / CPA (last)
Cost
Impressions
Converting
Non-Converting
Ignored
ROAS (first) / CPA (first)
Cost Per Assist
Attribution Perspectives
Is this how your funnel feels?
Prioritize Micro-Conversions According to the Customer Path
Traffic
Engagement
Attribution
Conversions
Revenue
Profit
Weight micro-conversions to refine portfolio objectives
Traffic
Engagement
Attribution
Conversions
Revenue
Profit
Filter zero then sort by
the next best metric
Brian Shindurling
Use the Best Tool Available
Until the Fuel is gone
Blake Haleua
In the absence of your preferred KPI,
choose the metric closest to the bottom-line
Cameron Cowan
When a more precise metric is missing,
fall back to the best derivative
Chris Haleua
Do what you can,
with what you have,
where you are.
Theodore Roosevelt
Beyond the 3x3
36
Conversion
Success
Non-Converting
Spend
Ignored
Impressions
Volume
Leaders
Efficiency
Threats
Visibility
Struggles
CV CE CP
NV NE NP
IV IE IP
Above: CPA  COGS  Profit  Total On/Offline
Between: Attribution Assists & Micro-Conversions
Below: Long tail learning or Dead Weight Cleanup
Conversion Distribution
$0
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
$80,000
1
11
21
31
41
51
61
71
81
91
101
111
121
131
141
151
161
171
181
191
201
211
221
231
241
251
261
271
281
291
301
311
321
331
341
351
361
371
381
391
401
411
421
431
441
451
461
471
481
491
501
511
521
531
541
551
561
571
581
591
601
611
621
631
641
651
661
671
681
691
701
711
721
731
741
751
761
771
781
791
Revenue Pre Revenue Post
Forecast Adjustments in Predictive Modeling
38
 Massive scale
 Sparse data
 Time delays
 Conversion paths
 Complex objectives
 Micro-conversion weighting
𝑀𝑎𝑥 ෍
𝑖
𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑏𝑖
𝑠. 𝑡. ෍
𝑖
𝐶𝑜𝑠𝑡 𝑏𝑖 ≤ 𝐵
+ 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝑐𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠
Portfolio Modelling Summary
Apply portfolio theory to make bid tradeoffs for maximum marginal return
Maximize return for any budget through media mix modeling
Build and show granular bid unit model tables for traffic & conversions
Combine forecasts with dimensional algorithms for accurate simulations
Build trust in forecasts through model accuracy trends by metric & day
Simulation Yield Curve
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
Average Daily Spend
Average Daily Conversions
17%
59%
-20
-15
-10
-5
0
5
10
15
Average Cost accuracy
Average of cost_accuracy_no_dow Average of cost_accuracy_w_dow
Tough Questions to Ask 3rd Party Tools
Method
Optimization types available
Most Popular optimization types
Intraday frequency
Advantage
Unique differentiators
Competitor threats
Innovation pace
Budgeting
Manual overrides
Spend strategies
Pacing visualizations
Modifiers
Report segments for traffic / conversions
Geo, device, time, audience manually
Fully automated dimensional modeling
Attribution
Intra-channel
Cross-channel
Cross-device
Objectives
Position
Efficiency
Multiple weights
Assignment
Cross-engine
Cross-account
Execution granularity
Learning
Clustering
Engagement / Micro-conversions
Prediction accuracy
Method What types of bid strategies are available?
Method What is the most commonly-used bid management feature in your platform, and why?
Method Can you set automated bidding rules for different segments of keywords?
Method Does the system leverage any type of portfolio optimizations?
Method For the automated bid changes, is there capability to receive forecasted results from changes prior to implementing?
Method Would you share methodology around automated bidding services?
Advantage What would you consider your competitive advantage to be over your competitive set?
Advantage What are your plans to maintain your competitive advantage and stay at the forefront of innovation?
Advantage How often do you innovate and refine your algorithms?
Budgeting What budget options exist in the system? (Monthly/Daily Caps)
Budgeting What budget pacing tools exist? (can I pull a pacing report, set a monthly campaign cap, and let the system adjust budgets?
Budgeting Are there any budget pacing alerts capabilities in the UI?
Modifiers Can you assign time/days of week to apply bid multipliers through the system?
Modifiers How can device, geo, time, and audience modifiers be automated?
Modifiers Does the system have ability to implement any type of day-parting or geo optimization? (ex. increase bids by X% during specific hours)
Attribution Please describe how attribution is done across channels (ex. tiered system (if so, is this customized), custom weighting, etc.).
Attribution What insight can the platform give into a client's customers path to conversion?
Attribution Please describe any attribution settings within search (ex. last click/first click, even distribution across all in path, custom distribution, etc.).
Attribution
Do you offer custom attribution models (first click, "prefer last," dynamic, etc.)? If yes, describe how you help each team to begin to find the
best fit.
Manual
What tools are in place to more efficiently change bids in bulk (ex. campaign or group setting to up all keyword level bids within group by
X%.)
Manual
Can you change bids, by keyword, across accounts via one bulk sheet? If not, please detail the process needed for cross account
optimizations.
Manual How do manual bidding & automated bidding co-exist?
Assignment Can you assign automated bidding for predefined segments/groupings cross account?
Assignment Do you have cross engine bidding strategy? One massive portfolio or engine first?
Assignment At what granularity is bid optimization executed? (at the group level, keyword level or both)?
Objectives What options exist for bidding to position (ability to target a specific position and let the system adjust bids accordingly)
Objectives What options exist for conversion efficiency thresholds? (CPA/ROI goal targeting by a custom formula or set of metrics)
Objectives Can you specify specific actions or weighting to count toward the goal? Can I state my goals to match how my business really works?
1 | LAWS - Bidding Dynamics
2 | PATTERNS - Deadend Quadrants
3 | APPLICATION - Optimization Methods
4 | BALANCE - Dimensional Modeling
5 | ADVANTAGE Secret Tools
Bid Modifiers
Programmatic
Display
Traditional
SEM
Enhanced
Campaigns
Audience
Keyword
Match Type
[exact]
+broad
Bidding
Geo
Device
Time Category
Frequency
Difficult Decisions
50
First PPC
Platforms
Enhanced
Campaigns
AdWords
Facebook Ads
Real-Time
Bidding
Shopping
Campaigns
Complexity
DIMENSIONAL
PORTFOLIORULES
Growing Complexity
KW1 KW2
Desktop
Mobile
SaturdayFridayThursdayWednesdayTuesdayMondaySunday
SaturdayFridayThursdayWednesdayTuesdayMondaySunday
Desktop
Mobile
SaturdayFridayThursdayWednesdayTuesdayMondaySunday
SaturdayFridayThursdayWednesdayTuesdayMondaySunday
KW1 KW2 KW1 KW2
PORTFOLIO
Device
Geo
54
1 | LAWS - Bidding Dynamics
2 | PATTERNS - Deadend Quadrants
3 | APPLICATION - Optimization Methods
4 | BALANCE - Dimensional Modeling
5 | ADVANTAGE Secret Tools
FREE STUFF!
 Bid Rule Playbook: tiny.cc/rule
 Bid Rule Library: tiny.cc/templaterule
 3x3 Template: tiny.cc/3x3template
 3x3 Macro: tiny.cc/3x3macro
I hear and I forget.
I see and I remember.
I do and I understand.
Time
Targeting
Analysis
Testing
BiddingCoverage
Structure
Life
IDEAL COMMON
Why
57
What
58
How
59
Key Takeaways
60
Performance
Segments
Matched
Metrics
3x3
Pattern
Micro-
Conversions
Attribution
Perspectives
Weighted
Objectives
Conversion
Paths
Excel Template Filters Saved Views
Sorts
Multi-Purpose Rules If Zero, Sort the Next
Best Metric
Look Beyond Last for
Influencers
Refine Portfolio
Objectives
Multi-Metric
Simulations
Certified Knowledge Audit Series on Bidding
tiny.cc/wijnand
QUESTIONS?

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