Proximity Targeting is a marketing technique that uses mobile location services to reach consumers in real-time when they are around a store location or point of interest. This is done by defining a radius around a specific location. If a consumer has opted into location services on their mobile phone and enters within this radius, proximity targeting helps in triggering an advertisement or message to consumers in an effort to influence their behaviour. This can be combined with the ability to purchase impressions through programmatic ad platforms that are powered by real-time bidding which can help businesses formulate the right strategy of influencing their users on a particular geographical area. They can build user groups based on certain characteristics (such as neighbourhoods, demographics, interests, and other data), and subsequently launch another campaign that targets anyone which those characteristics. The growth of mobile devices has led to enormous data generation which offers tremendous potential when used effectively for business. Thus we need an efficient platform where we can process such huge data efficiently and with minimum latency and cost. This talk describes MIQ's journey into building a fast and scalable processing platform using Big Data, delivering faster and actionable insights for Proximity targeting which has empowered the creation of a product generating ~30 million dollar revenue on a year to year basis.

1. Proximity Targeting
at Scale
Presented By :
Rohan Raj ( Lead Engineer, MIQ )

2. 2
Agenda
Proximity Targeting
Tech Ecosystem,
Challenges &
Optimizations
Results

3. 3

4. 4
Activating Marketing Intelligence
through AiQ
AiQ is our technology that provides
modular, API-based analytics
services to rapidly build data solutions
for successful real-time business
outcomes.
As a result, we take a process that
might normally take a couple of
weeks and reduce time-to-value to a
couple of hours.
CONNECT
DISCOVER
ACTION
Onboard, unify and store any dataset,
making data organized, useful and
meaningful
Perform advanced analytics and process
datasets for insights & algorithmic
deployment
Export decisions to your
marketing technology platform
AiQ

5. 5
Daily Scale
genda
80 Billion Ad
Impressions
5000+
Strategies
10+TB
Data
900,000
CPU mins
1000+
Campaigns
750 million
users

6. 6

7. 7
Proximity Targeting & Hyperlocal

8. 8
11:00 am
Your Location
9:00 am
Coffee Shop
10:00 am
Competitor
Location 1
Customer Data - Without a Story
[random & disconnected]
6:30 pm
Gym
1:00 pm
Competitor
Location 2

9. 9
11:00 am
Your Location
9:00 am
Coffee Shop
10:00 am
Competitor
Location 1
1:00 pm
Competitor
Location 2
6:30 pm
Gym
Coffee enthusiast and
maybe stays in the vicinity of
the coffee shop to pick it up
before he goes on with his
day
User visited the Client
location at 11am along with
a bunch of other competitor
locations, so looks like he was
actively shopping
Competitors who
offer similar
products like the
client who
customers also visit
Time when he visits the
gym if we wanted to
capture time to target
users to consume fitness
related products. He
travels 10km to get to the
gym from the coffee shop
SATURDAY
Customer Data - With a Story

10. 10
Motion - Capabilities
User Journeys Audience Targeting Visit Trends : Unique, Repeat
Precise Uplift Rank Store (Footfall insights) Competitor conquesting

11. 11

12. 12
Big Data Processing Ecosystem

13. 13
Major Technical Challenges how
we overcame these

14. 14
Geocoding
OLC
● It’s designed to be used as a street
address. (similar to phone numbers)
● Not able to provide needed precision,
when radii around POI varies widely.
● Nearby places don’t have shared
prefixes.
● Looks Like this - 87G8Q257+5QP
Geohash
● Scalable and efficient for
programmatic usages and joins.
● Provides the needed precisions when
size of POIs or target physical stores
vary enormously.
● Hierarchical
● Nearby places have shared prefixes.
● Looks Like this - dr5ru7v

15. 15
Managing Parallelism - Skew
cost is involved

16. 16
Managing Parallelism - Overheads

17. 17
Latency numbers (Humanized) ….

18. 18
Optimizations- Joins, Shuffles & More
Registration Make Model Engine_size
AB12CDE Ford Fiesta 1.0
FG23HIJ Ford Fiesta 1.1
KL34MNO Ford Fiesta 1.5
PQ45RST Nissan Qashqai 1.6
UV56WXY Hyundai i20 1.4
ZA67BCD Ford Mondeo 2.0
Make Model Engine_size Price
Ford Fiesta 1.0 10110
Ford Fiesta 1.1 2500
Ford Fiesta 1.5 13653
Ford Fiesta 1.4 16700
Ford Fiesta 1.2 8965
Ford Fiesta 1.6 7320
Nissan Qashqai 1.5 14567
Nissan Qashqai 1.6 11432
Partition 1
Partition 2
Partition 3
Partition 4
...
A
B

19. 19
❑ Shuffle partitions
○ spark.sql.shuffle.partitions = 200
❑ Broadcast joins
❑ Salting
❑ Databricks skew.hints df.hint("skew", "col1")
○ df.hint("skew", ["col1","col2"])
○ df.hint("skew", "col1", "value")
from pyspark.sql.functions import broadcast
result = broadcast(A).join(B,["join_col"],"left")
Optimizations- Joins, Shuffles & More

20. 20
Cluster optimization & Persistence
● Spot Nodes
● Heterogeneous clusters
● EBS-autoscaling

21. 21
Results
20K $ → 4.5K $
Cost
21 Hours →3 Hours
Time
30 M $ / yearly
Revenue generated
Cluster
optimizations
shuffle
partitions
geohashingskew & join
optimization

22. 22
❑ https://medium.com/miq-tech-and-analytics/geohash-vs-open-location-codes-understanding-the-tw
o-geocoding-techniques-in-advertising-space-8002452201c8
❑ https://medium.com/miq-tech-and-analytics/understanding-customer-lifetime-value-in-retail-62eb62
f44994
❑ https://medium.com/miq-tech-and-analytics/decision-trees-optimizations-for-programmatic-media-buying-e
77e4410bfe8
❑ https://medium.com/miq-tech-and-analytics/meet-jarvis-79ad50f3ccb8
❑ https://medium.com/miq-tech-and-analytics/https-medium-com-nagaraj-mediaiq-how-we-use-s3-select-for-
schema-validation-and-filtering-data-at-miq-52cf036bf9be
Further Readings ...

23. 23
Thank You
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