Prosper 2023 Unlocking value from a treasure of information Amazon shares with a sound data strategy
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Prosper 2023 Unlocking value from a treasure of information Amazon shares with a sound data strategy
Agenda
• Expanding Amazon data sources
• Types of Amazon data
• Amazon Marketing Cloud (AMC), Amazon Marketing Stream (AMS)
Deep Dive
• Current challenges with Amazon data
• Importance of data strategy
• Key elements of a successful data strategy
• The latest on AI, A deep-dive into a specific AI application
• A framework for data strategy implementation
2
The Rapidly Evolving Amazon Data Sources
Selling Partner API
Amazon Sponsored
Amazon Attribution (Seller Central,
Ads
Vendor Central)
Amazon Marketing Amazon Marketing
Amazon DSP Stream Cloud
(AMS) (AMC)
3
The Two Broad Types Of Amazon Data
Shopper Centric
Brand Centric Data
Signals
These are signals and data points This is data Amazon shares with you
that shoppers see when shopping across various functional areas such as
for your products on Amazon Advertising, Inventory Management,
Promotions, Catalog etc.
Shopper Centric Signals
Ads
Content Quality
Delivery Dates
Pricing
Badging Organic Ranking
Reviews
Brand Centric Data
Sponsored Sales Inventory
DSP
Ads
Advertising Amazon Retail
Amazon Marketing Catalog Pricing/Pro
Attribution Stream motions
(Hourly)
Amazon
Marketing Other
Cloud
How are the two data types different?
High
Shopper
Centric Signals
Volume
Brand Centric
Data
Low High
Precision
Amazon Marketing Cloud Overview
What is Amazon Marketing Cloud (AMC)?
DSP
● AMC is a secure, privacy-safe clean room
● AMC has event level ad-attributed data from
across Amazon owned/operated properties Search Sismek
Ads
● It is the “center-piece” of all Amazon ads
measurement going forward Amazon
Marketing
● AMC can be accessed via a web-based Cloud
interface or an API
● AMC is currently available for DSP advertisers only.
Retail Custom
Data Uploads
AMC Data — What Makes It Unique?
EXAMPLE Paths Spend Revenue ROAS
Cohort 1 DSP Sponsored Ads
Cohort 2 DSP Only
Cohort 3 Sponsored Ads Only
Access to data at an individual shopper and event level, opens up a world of
possibilities that did not exist before.AMC – Categories Of Use Cases
Multi-touch
Analytics
Audience Campaign
Discovery Optimization
AMC
Use Cases
Customer
Incrementality Insights
11Sample AMC Use Case: New-To-Brand Metrics For Sponsored Products
Sample AMC Use Case: Multi-touch: Path To Purchase By Campaign Type
Amazon Marketing Stream Overview
What is Amazon Marketing Stream (AMS)?
● AMS is an API that publishes hourly advertising
data for traffic, conversion and events
● Traffic and conversion metrics are published at
the target and placement level
● Current events include campaign level budget
consumption by the hour
● AMS allows for designing and measuring the
efficacy of advertising tactics executed at the
hourly grainAMS: The Available Data Types
1. Reporting
1. Sponsored Products => Traffic by the hour Conversion Rate ,
2. Sponsored Products => Conversions by the ACOS by the hour
hour
2. Messaging
• Budget usage by the hourAMS: Sample Observation
Challenges With Amazon Data
Data collection, ownership APIs and data sources
is manual and time- change constantly.
consuming
Available data is
Required skills are scarce
fragmented. Data Quality a
constant challengeThe Data Thesis Advertising
Retail
Shopper
Centric Brand Centric
Signals Data
Competing effectively on
Amazon requires the ability to
collect, organize and leverage
these disparate datasetsGoals Of A Data Strategy • Comprehensiveness: Understanding the full picture • Reliability: Maintaining data accuracy • Speed: Shorten time-to-insight • Accessibility: Operationalizing insights rapidly
Path To Data Success
Process
Tools People/SkillsProcess
Process Collect & Own Connect & Enrich Visualize Analyze & Act
Collect & Own • Amazon API connections need consistent TLC. Ask if this is your core competence. • Data quality issues are a given. Plan for them. • Data storage costs are cheap. Make a choice and collect everything. • Avoid the data-trap with your current service providers.
Connect & Enrich
• Source data is fragmented.
Connecting it will unlock new
insights
• Inventory + Advertising
• Sponsored Ads + DSP + Total Sales
• Item level profitability
• Enriching data allows for deeper
segmentation and analysis
• Normalizing currency across
geographies
• Adding custom groupings of your
productsVisualize
• Build automated views that
describe your past business
performance comprehensively
(Descriptive Analytics)
• Identify your top time-consumers
and high-impact use cases to
prioritize
• Dashboards get stale and it is ok. It
implies you are asking new
questions
• Envision before you invest time
buildingAnalyze & Act
• There are four broad ways to analyze
your Amazon data
• Descriptive - What happened? (What are
total sales and ad spend last month?)
• Diagnostics – Why things happened? (Why
is my ACOS up last week?)
• Predictive – What could happen? (When
will I run out of stock?)
• Prescriptive – What should you do? (What
should I set my bids to?)
• Lean into asking new, hard questions
constantly. That is how you differentiate
from competition
• Automate actions where possible
• Alerts
• Recommendations
• Automated updatesTools
Tech Stack Choices
Cloud Environments Databases Visualization Tools Analytics Tools
Amazon Redshift Quicksight Excel
Google Big Query Looker SASS
Microsoft SQL Server Tableau R
Oracle Oracle Power BI More….
IBM Snow Flake Excel
More… More… More….
The choice matters less. Commit to one and stick with it.People
Ownership, Skills • Assign a data steward who can collaborate between business users and IT • Build SQL skills on the team. This will dramatically increase your data agility • You don’t need to be an AI expert. However, you have to understand the basics to be able to ask the right questions
About AI
AI Is At Work All Around Us
Key Shifts In AI • Historically, AI was largely applied to structure, largely numeric data • The latest developments in AI are around unstructured data that includes text, images and video. • The likes of ChatGPT are making AI increasingly accessible to all of us
Application of AI. A Deep Dive With A Specific Use Case: Amazon Ads Bid Management
DEFINITIONS: Rules Vs AI Driving Bidding
Scenario: An advertiser wants to achieve a target ACOS of 10% ( ROAS = 10 )
RULES ML / AI Input: History Data
Objective = 10% ACOS
1. If ACOS >= 30%, bid down keywords by
25%
2. If ACOS between 15% and 30%, bid
down keywords by 15%
AI Model
3. If ACOS less than 10%, bid up by 15%
Recommended bidsHow An AI Model Works
1. Inputs Used 2. Relationships Established 3. Outputs Predicted
• Impressions
• Clicks • Model establishes • Model recommends bids for
• Conversion Rate relationships between all a desired Target ACOS
• …. these input variables
• ….
• Bid
• ACOS
AI ModelA Typical AI Model Development Process
1. Collect and choose
input data
4. Validate model against 2. Create training and test
test dataset AI Model data sets
3. Choose algorithm(s),
build model for the
training datasetRules Vs AI : Example
SCENARIO
Ad Spend $100
Ad Revenue $500
Current Bid $1.00
Current ACOS 20%
Target ACOS 10%
RULES ML / AI
If ACOS between 15% and 30%,
ACOS
Bid Up
bid down keywords by 15%
20%
Bid Down 10%
BID
$1.00 $1.20Rules Vs AI: The Differences
Keyword 1 Keyword 2 Keyword n
DIFFERENCES
• Exploiting Non-linear relationships
• Leveraging influence of multiple factors on the outcome
• Detecting local optimumsThe Sparse Data Problem
“Running Shoes”
Volume Rules not as effective as
ML when data is sparse
“Running Shoes with no laces size 13 ”
Head Keywords Long Tail Keywords
DIFFERENCES
• AI algorithms give us options to tackle this problem. Rules don’t.
41AI Models Are Not Perfect
• Models only as good as the inputs
• Human supervision essential at this stage
• Models require continuous learning and
investment
42Rules Vs AI: Summary of Differences
RULES ML / AI
• Model determines the
• Pre-defined logic / relationships
relationships • Explainability of every change a
challenge
• High on explainability • Can detect non-linear
• Expanding inputs a challenge relationships
• Can accommodate an expanding
• Relatively in-expensive list of factors
• Provides mechanisms to address
sparse data scenarios
43AI - Summary
• AI models for unstructured data (Text, Images, Videos) will
continue to get better
• AI will become more and more accessible
• Essential ingredients for any AI model are:
• Large volumes of data
• High quality data
• Connectable data
• Design your data strategy so that you are well positioned to
leverage future AI advancements
44Path To Data Success
Process
Tools People/Skills
45Data Strategy – Action Plan
1. Assign a data steward(s)
2. Commit to learning and development of team members
3. Make tool choices, in particular a datastore and a visualization
tool
4. Execute the process
46Process Framework
Connect &
Collect Visualize Analyze & Act
Enrich
1
Brand
Centric Data
Shopper 2
Centric
Signals
47Process Framework – Sample
Connect &
Collect Visualize Analyze & Act
Enrich
• Sponsored • Add custom • Build a • Account audits
ads and DSP product performance • Performance
• Overall Sales groupings dashboard diagnostics
• Include that includes
Brand currency advertising
Centric Data conversions and sales
Shopper
Centric
Signals
48Conclusion
• Amazon is exposing and more and more data by the day
• Unlocking value from this data requires a commitment to data
strategy
• A sound data strategy entails a focus on People, Process and Tools
• Don’t boil the ocean. Take incremental steps with consistency over
the long run
• Data agility is a competitive advantage
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