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Revenue Per Click (RPC) Model:
How to Generate More Revenue Per Click?
Internet marketing produces huge volumes of granular clickstream data that can
answer key questions, target your customers more precisely, and increase your business KPIs. For example, how can you generate more revenue per click (RPC)?
Unfortunately, for too many marketers, the answers to these questions require
levels of time and expertise that are often in short supply. Traditional analytic approaches are too slow, less accurate, and too expensive to generate both the short-term lift and long-term results that marketers need – now, more than ever.
Goals
The search aims to drive greater efficiency
-
Better keywords (KW) /match type selection
-
Improved bid management
-
Optimized user experience
-
Increasingly refined targeting
FocusKPI provides advanced RPC models for revenue generation.
-
Spend Allocation
What it does
-
Efficiently distributes spend across Search Engines
-
Maximizes spend within engines on keywords with the most elasticity
How it works
-
Keywords clustered are created from extensive data mining
-
Clusters based on behaviors and results (clicks, revenue per click, etc.)
-
Utilizes linguistic relevance
-
-
Predictive models are built using regressions of historical spend and revenue data
-
RPCi = f(x1, x2, x3, …, xn)
-
CPCi = f(RPCi)
-
-
Target spend level n is chosen based on clients business objectives
-
Each dollar (1 through n) is allocated to the portfolio with the highest Marginal ROI
-
Ensures that no dollar could be spent more efficiently within Paid Search
-
Calculations
-
In this case, for a KW portfolio, RPC is the rank of KWs’ value which serves as the foundation for bidding. It’s a balance of the following factors or objectives through various data mining techniques such as regression, segmentation, time-series model, etc.
-
Managing Longtail as well as Head KW.
Spend Allocation Optimization
SAM employs marginal analysis to optimize marketing spend.
RPC Optimization
RPC employs a range of tools for predictive power, flexibility, and scalability: segmentation, regression, and learning algorithm
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Internet marketing produces huge volumes of granular clickstream data that can
answer key questions, target your customers more precisely, and increase your business KPIs. For example, how can you generate more revenue per click (RPC)?
Unfortunately, for too many marketers, the answers to these questions require levels of time and expertise that are often in short supply. Traditional analytic approaches are too slow, less accurate, and too expensive to generate both the short-term lift and long-term results that marketers need – now, more than ever.
Goals
The search aims to drive greater efficiency
-
Better keywords (KW) /match type selection
-
Improved bid management
-
Optimized user experience
-
Increasingly refined targeting
FocusKPI provides advanced RPC models for revenue generation.
-
Spend Allocation
What it does
-
Efficiently distributes spend across Search Engines
-
Maximizes spend within engines on keywords with the most elasticity
How it works
-
Keywords clustered are created from extensive data mining
-
Clusters based on behaviors and results (clicks, revenue per click, etc.)
-
Utilizes linguistic relevance
-
-
Predictive models are built using regressions of historical spend and revenue data
-
RPCi = f(x1, x2, x3, …, xn)
-
CPCi = f(RPCi)
-
-
Target spend level n is chosen based on clients business objectives
-
Each dollar (1 through n) is allocated to the portfolio with the highest Marginal ROI
-
Ensures that no dollar could be spent more efficiently within Paid Search
-
Calculations
-
In this case, for a KW portfolio, RPC is the rank of KWs’ value which serves as the foundation for bidding. It’s a balance of the following factors or objectives through various data mining techniques such as regression, segmentation, time-series model, etc.
-
Managing Longtail as well as Head KW.
Spend Allocation Optimization
SAM employs marginal analysis to optimize marketing spend.
RPC Optimization
RPC employs a range of tools for predictive power, flexibility, and scalability: segmentation, regression, and learning algorithm