#SMX #23B2 @PravinThampiIncrease revenue and reduce cost faster

Intelligent Bidding Using

Non-SEM Sources

#SMX #23B2 @PravinThampi

100s of thousands of such deals

Over a 1MM Ads Active/day in US

Bid computation is complicated by additional modifiers– RLSA– Device– Location

SEM @ Groupon: Handle Deals that have different locations, prices and time (expiry)

#SMX #23B2 @PravinThampi

100s of thousands of Slot Machines

Unknown probability of success

Target for rate of return on spend

Limited time window for slot machines

Existing Solutions– Head Vs Tail Models– Statistical Models– Machine Learning Models

Bidding Challenge: Identify the deals that generate max revenue before the deal expires

#SMX #23B2 @PravinThampi

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Non SEM SEM

Sample Data

Use performance from free sources

Strong correlation with paid sources

Free sources Data (Direct/SEO) : leading indicator of performance for deals

Outside the box: Leverage all sources of info

#SMX #23B2 @PravinThampi

Increase/Decrease bids for Deals based on their overall performance

Factor Calculation: 1+(VPC_deal - VPC_avg) / VPC_avg

Less than 1 for below avg performers, greater than 1 for above This factor was multiplied on top of existing bid

calculation Scaling multipliers & upper/lower thresholds can be

used to adjust sensitivity

Simple Model: Best model, used a linear model

*VPC: Value per click/visitor

#SMX #23B2 @PravinThampi

Best deals started rising to the top faster Higher bids saw more conversions Saved cost on low potential deals Maintained ROAS Targets

Double Digit lift in performance!!

#SMX #23B2 @PravinThampiLEARN MORE: UPCOMING @SMX EVENTS

THANK YOU! SEE YOU AT THE NEXT #SMX