Calculating the Cost of Customer Acquisition

Published Winter 2013

Establishing the ROI across analog and digital media can be difficult.

Over the past decade we have been mapping and modelling CPC data on behalf of our clients and, although there is significant variance in the performance of individual campaigns, we have established a generic rule of thumb that allows us to estimate the historical and future performance of a client's campaign based on a few simple metrics.

The calculator below is a streamlined version of our methodology. To facilitate a cost comparison with traditional database marketing campaigns it includes a comparative analysis with the DMA's published average of Direct Mail Campaigns.

Would you use it to plan your next campaign? No. But it will allow you to explore the relationship between the price paid for CPC advertising and the price of the goods and services and services being advertised.

Product Costing

Cost of Goods & Handling ($):

RRP ($):

Adwords Campaign Data

CPC ($):

Bounce Rate (%):



Revised Traffic Acquisition Cost

Effective CPC ($):


Est. Gross Margin (after Advertising Costs)

Search ($): Display ($): Direct Mail ($):

Return on Advertising Spend

Estimated Cost per Response

Search ($): and Display ($): Direct Mail ($):

Estimated Revenue per $ Invested

Search ($): and Display ($): Direct Mail ($):

Estimated Profit per $ Invested

Search ($): and Display ($): Direct Mail ($):

#Data + #Insight = #Edge

Disclaimer

The use of these calculators, charts, visualisations, data or any information shall be at the user’s sole risk. Such use shall constitute a release and agreement to hold harmless, defend and indemnify Digital Partners from and against any liability (including but not limited to liability for special, indirect or consequential damages) in connection with such use. Such release from and indemnification against liability shall apply in contract, tort (including negligence of such party, whether active, passive, joint or concurrent), strict liability, or other theory of legal liability; provided, however, such release, limitation and indemnity provisions shall be effective to, and only to, the maximum extent, scope or amount allowable by law.

Questions of accuracy

Tymbals is an experiment in machine learning and statistical modelling of small data pools. Tymbals is still under development. It is still learning. Tymbals is Beta - i.e. Pre-release.

The probabilities and outputs (e.g. calculators, charts, visualisations) will evolve and change as the system ingests more data.

Tymbals is a probability model. The results generated by Tymbals are market estimates based on the cummulative value of the data within the distributed data pools.

Privacy

All data inputs are automatically added to the learning pool from which Tymbals models are generated.

If you do not want your data added the data pool do not use Tymbals

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