How to build customer-oriented applications using third ...
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© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved. How to build customer-oriented applications using third-party data
Transcript of How to build customer-oriented applications using third ...
© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
How to build
customer-oriented applications
using third-party data
© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Mohsen MalikAWS Data Team Lead,
Customer Advisory
Attendees will learn:
• How location data is used to improve in-app experiences
• How Nextdoor uses point of interest (POI) data to help
improve global business data coverage and quality,
discovery, verification, and onboarding experiences
• How Nextdoor uses POI data to improve lead generation of
local businesses and grow page claim rates
• How to provide personalized recommendations using
Amazon Personalize
• How to discover, find, and use third-party data with
AWS Data Exchange
© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Today’s Speakers
Josh Cohen
Senior Vice President
Product
Daniel Gray
Vice President
Solutions Engineering
Rahul Sureka
Engineering Leader
Angel Goñi Oramas
Enterprise Solutions
Architect
Mai Nguyen
Operations Manager,
Global Product Operations
© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Customer acquisitionCustomer engagement
and retention
Customer loyalty and
satisfaction
Improving customer experiences with data
© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Improving customer experiences with data
1 – Improving the Customer Service Experience, Harvard Business Review, 2021
2 - 25 Mobile App KPIs and Metrics You NEED to Track, Buildfire
3 - 68 Personalization Statistics Every Digital Advertiser Must Keep in Mind, Instapage, 2019
4x
16%
download
potential
Higher
LTV
© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
• Financial and
purchase
• Psychographic
attitudes
• Mortgage and
property
• Lifestyle
• Travel
• Credit card
• Debit card
• Point of sale data
by merchant
• Brand
• SKU
• Loyalty rewards
• Traffic
• Downloads
• Time in-app
• In-app purchases
• Search
• Geographic
• Points of interest
• Visits
Common types of third-party data used to
improve in-app experiences
Transactional Viewership and
usageLocationDemographic
© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Identify best audience traits and
behaviors – employ look-alike
modeling for new customers
Demographic
Using third-party data to deliver better appsExample – expanding into new markets with a delivery service app
Transaction
Food delivery app(third-party data customer)
Learn major spend
categories of target customer
to monetize promotion
Improve geotargeting and
shorten delivery timesLocation
Food delivery app(third-party data customer)
Viewership
and usage
Measure process trends for
benchmarking and
competitive analysis
© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Source
and store
Ingest
and query
Analyze and
visualize
Develop and
operationalize
Amazon NeptuneBuild and run graph apps
AWS Glue Amazon Athena
Amazon QuickSight
Amazon SageMaker
Amazon PersonalizeDevelop custom experiences
Amazon ConnectOmnichannel customer service
Amazon Fraud DetectorDetect issues and predict risk
Simplified access, synthesis, and analysis of
datasets with AWS Data Exchange
Amazon S3
AWS Data Exchange
Amazon Redshift
AWS Data Pipeline
© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Demographic TransactionViewership
and usageLocation
Access to a diverse selection of data providers and
products in AWS Data Exchange
Add image and crop to red guides
Foursquare TodayImproving In-app Customer Experiences with Location Data
Co
nfidential ©
Fo
urs
qu
are
20
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Aren’t you the check-in app?
Confidential ©
Fours
quare
2020[Getting Location Right]
We are today’s leading, independent location-based platform
We’ve been powering the places in your
pocket for years. Ever typed a venue in Uber?
Add a geofilter to Snapchat? Geotag a tweet in
Twitter? Snap a photo with a Samsung phone?
That’s Foursquare.
and a developer community of over 200,000.
Trusted by the best in tech
Co
nfidential ©
Fo
urs
qu
are
20
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13
What makes us unique
14B human-verified check-ins, self refreshing map
with millions of always-on ground truth signals each
month. Stop detection.
500M devices globally, 100M+ places globally, 900
venue categories, 2.4M updates monthly.
From our double opt-in consent measures, to
partner audits and data supplier review programs, to
ongoing regulatory compliance, respecting
consumers’ data privacy rights is a non-negotiable.
Customers can readily procure and ingest our
data through the AWS Data Exchange, and build
cloud-native solutions downstream.
Product quality and accessibility is not tethered to
Walled Gardens. Customers can build with our data
with scale and flexibility in mind.
Accuracy
Scale
Privacy-first
Accessibility
Independent
Confidential ©
Fours
quare
2020
Confidential ©
Fours
quare
2020/products
Foursquare Products
Places
Places (or POI) provide precise
firmographic details, such as venue
name, address, and category as well
as rich content attributes (photos,
reviews, tips).
100M+ Points of Interest Globally
200+ Countries and Territories
900+ Venue Categories
Visits
Visit feeds provide a granular, high
quality daily feed of all visits to certain
categories, chains, or individual
venues.
3B+ Monthly US Visits
10K+ US Chains
4M+ US Venues
Confidential ©
Fours
quare
2020
Nextdoor Use Cases
The Opportunity: Nextdoor developed a need to expand their global Point of Interest (POI) data to readily map
local businesses to neighborhoods in select countries, like the US, Germany, Canada, Denmark and the U.K.
The Solution: Foursquare Places data, which was purchased through AWS Data Exchange, enabled Nextdoor to
surface local businesses and their associated attributes (venue name, LatLong and hours of operation, for e.g.), on
a global scale in their app.
[Use Case]
In-App Local
Business
Discovery
Direct/Email
Marketing
Onboarding &
Verification of
New Businesses
Foursquare Data supports several downstream use cases:
Confidential ©
Fours
quare
2020
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Key takeaways
Location data has become more important than ever.
Foursquare Places – uses a breadth of sources,
ensures accuracy and provides data richness to get
location data right.
Various use cases – location data is incredibly valuable
for major brands like Nextdoor for any number of use
cases, including improving in-app user experiences.
Global POI reach – use the world’s most extensive POI
data set to fill in the gaps.
Access data seamlessly – easily access and subscribe
to Foursquare’s data through the AWS Data Exchange.
[Key Takeaways]
Confidential ©
Fours
quare
2020
Thank you
Foursquare AWS Data Exchange
integration at Nextdoor
Background
Organization Pages are critical part of Nextdoor app. It’s foundational in ensuring a thriving
marketplace between neighbors and organizations.
For an Organization:
• Organization owners showcase their business and build a brand.
• Promote their products and services on Nextdoor to increase awareness, loyalty, and revenue.
For a Neighbor:
• Find an organization in order to get a job done.
• Interact with business owners.
• Recommend them to other neighbors.
Foursquare X Nextdoor Requirements
Coverage in all countries where Nextdoor operates.
Parity with APIs from other providers
• Search for a point of interest (POI) by name or partial name, in a location.
• Fetch data on a specific POI.
Provide latitude, longitude, address at minimum for POIs
• Auxiliary information (phone number) very important.
POI must include local businesses, and more.
Organization Onboarding and Verification
Businesses find value in by promoting their businesses on Nextdoor. Foursquare address,
email and contact data helps us in onboarding and verifying the Organization.
Claim
Flow
Create
Content
Find
Business
Verify
Business
FSQ Data
Nextdoor members search for
businesses and find local businesses
recommended by their neighbors
Foursquare data helps us in increasing
coverage in search results
Discovery
AWS Data Exchange
Prior to AWS Data Exchange, to integrate with vendors, we need to build custom
pipelines. Some vendors provided the data to us on their Amazon S3 buckets and FTP
servers. It required a lot of time and effort for each integration. We need to manage
permissions, writing custom code to bring in the data into our data lake.
With AWS Data Exchange, the process is highly automated. Once Foursquare
uploads a file to AWS Data Exchange, it automatically appears in our Amazon S3
buckets. This saves us a lot of time and effort.
Our data pipeline:
Foursquare uploads monthly places data file to AWS Data Exchange
We are using Auto-Export option in AWS Data Exchange to download Foursquare data file
to Nextdoor’s Amazon S3 bucket automatically
We then transform this raw data and ingest into our places catalog
This information is surfaced to Nextdoor’s users
Offline jobs
Production
AWS Data Exchange Amazon S3
Lead Generation with Foursquare Data FileThe Foursquare monthly data file unlocked the opportunity for Nextdoor to leverage
lead generation campaigns to acquire new SMB customers.
Monthly cadence and easy data ingestion ensure that we always get the most current
business data
Foursquare data file expanded existing business catalog by severalfold
POI attributes unlock the ability to target specific verticals with messaging that resonate with local
businesses
We leverage email and direct mail channels using Foursquare data to drive customer acquisition.
Email Marketing
Generate better top-of-funnel brand awareness with the
Foursquare audience
Increase lead conversion and ultimately drive revenue
Develop strategic, personalized and educational
acquisition campaign
Improve targeting and positioning for SMB verticals
(i.e., home services, restaurants)
Cross-promote to both neighbors and organizations
to drive product adoption and higher customer LTV
Direct Mail
Direct mail serves to reinforce Nextdoor’s
core brand value as the local neighborhood
platform
Leveraged Foursquare POI addresses for
direct mail campaign to prospective
customers
The Foursquare AWS Data Exchange integration was
an important value-driver for Nextdoor in terms
of customer growth, onboarding, and discovery.
What is AtScale?
29
AtScale is a semantic layer for business intelligence and data science programs pushing all compute down to data in Snowflake.
Presents a consistent set of business metrics for BI and Data Science teams to
consume from with tools of their choice.
Accelerates end-to-end query performance while pushing down compute to
Snowflake.
Establishes an integration layer within the enterprise data fabric to support
analytics discoverability, governance, and security.
AtScale Customers: Who’s Using a Semantic Layer?
FINANCIAL SERVICES
HEALTHCARE
CPG/MANUFACTURING
OTHER
RETAIL
Simplify blending of
3rd party data with
1st party data –
eliminate data
preparation.
Visualize data with
common BI
platforms (Tableau,
PowerBI, Looker,
Excel)
Expand feature
library for data
science applications
Simplify evaluation
of a new data sets.
Applying Semantic Layer to Data-sharing: Why
AtScale simplifies and streamlines data sharing for BI and Data Science teams
Applying Semantic Layer to Data-sharing: How
AtScale Universal Semantic Layer
Step 1. Connect your data source and start modeling Step 2. Start analyzing in any BI tool
Amazon RedshiftData warehousing
The Data & Analytics Flywheel
Traditional
w/ Semantic Layer
Legend
Data
Model
Analyze
Consume
Insights
5b. New, enriched data made available for further analysis
5a. More decisions made
2. Logically modeled,, “analytics ready” data
4. More people able to leverage “analytics ready” data via many tools (incl Excel)to make decisions
3. More time spent on analysis, less on data prep = more actionable data
Decisions
Access
1. Agile access to “live” data w/o ETL
© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Q&AJosh Cohen
Senior Vice President Product, Foursquare
Daniel GrayVP Solutions Engineering, AtScale
Rahul SurekaEngineering Leader, Nextdoor
Angel Goñi OramasEnterprise Solutions Architect, AWS
Mohsen MalikAWS Data Team Lead,
Customer Advisory
Mai NguyenOperations Manager, Global Product Operations, Nextdoor
© 2021, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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