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San Francisco | New York1.877.AMPUSH.1 www.ampush.com
OmniValue:
LTV Modeling Solution
Alpha: Version 1.0
• An average user approach to LTV looks at all
customers to determine the average customer’s
value, and uses that to inform CPA goals.
• However, this approach is skewed because people
have different:
	 • Propensities to purchase different products	
	 • Purchase patterns over time
• With a single CPA target, we end up over-paying
for low-value customers, breaking even on
mid-value customers, and not acquiring enough
high-value customers.
• CRM data provides a limited set of attributes to
cohort customers against.
√ OmniValue combines data from CRM, publishers,
and data partners to cohort customer LTV across all
attributes (i.e. age, gender, interests, behaviors,
and device).
√ With more granular cohorts, OmniValue can
dynamically adjust the media optimization and
bidding strategy per cohort
√ Individualized bidding strategy per LTV cohort
drives higher profit per cohort
Use Cases
√ Audience segmentation: Create goals per targeting
segment based on historic LTVs
√ Product prioritization: Understand which product(s)
historically drive high value customers
√ Re-Engagement: Prioritize customers to re-engage,
and understand the value of re-engaged purchasers
AVERAGE USER	 LTV PROBLEM
OmniValue SOLUTION
CRM Data Partners Publishers Lifetime Value
+ + =
CRM Data Partners Publishers Lifetime Value
+ + =
0.3M
$350
LTV
1.3M
$90 LTV
3M
$75 LTV
$33M
PROFIT
Varying LTV Tiers Singular Bid Net Yield
$90
CPA
0.3M
$350
LTV
1.2M
$90 LTV
3M
$75 LTV
$112M
PROFIT
Varying
LTV Tiers
Calibrated Bids
Per Tier
Increased
Net Yield
$180
CPA
$80
CPA
$70
CPA
0.00%
50.00%
100.00%
150.00%
200.00%
250.00%
0 20 40 60 80 100 120
ROAS
Days Since Firest Purchase
One Time Purchaser Repeat Customer (4+) Repeat Customer (2-3) Historical LTV (aggregate)

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OmniValue LTV Modeling Solution

  • 1. San Francisco | New York1.877.AMPUSH.1 www.ampush.com OmniValue: LTV Modeling Solution Alpha: Version 1.0 • An average user approach to LTV looks at all customers to determine the average customer’s value, and uses that to inform CPA goals. • However, this approach is skewed because people have different: • Propensities to purchase different products • Purchase patterns over time • With a single CPA target, we end up over-paying for low-value customers, breaking even on mid-value customers, and not acquiring enough high-value customers. • CRM data provides a limited set of attributes to cohort customers against. √ OmniValue combines data from CRM, publishers, and data partners to cohort customer LTV across all attributes (i.e. age, gender, interests, behaviors, and device). √ With more granular cohorts, OmniValue can dynamically adjust the media optimization and bidding strategy per cohort √ Individualized bidding strategy per LTV cohort drives higher profit per cohort Use Cases √ Audience segmentation: Create goals per targeting segment based on historic LTVs √ Product prioritization: Understand which product(s) historically drive high value customers √ Re-Engagement: Prioritize customers to re-engage, and understand the value of re-engaged purchasers AVERAGE USER LTV PROBLEM OmniValue SOLUTION CRM Data Partners Publishers Lifetime Value + + = CRM Data Partners Publishers Lifetime Value + + = 0.3M $350 LTV 1.3M $90 LTV 3M $75 LTV $33M PROFIT Varying LTV Tiers Singular Bid Net Yield $90 CPA 0.3M $350 LTV 1.2M $90 LTV 3M $75 LTV $112M PROFIT Varying LTV Tiers Calibrated Bids Per Tier Increased Net Yield $180 CPA $80 CPA $70 CPA 0.00% 50.00% 100.00% 150.00% 200.00% 250.00% 0 20 40 60 80 100 120 ROAS Days Since Firest Purchase One Time Purchaser Repeat Customer (4+) Repeat Customer (2-3) Historical LTV (aggregate)