Estimating the revenue impact of direct mail (catalog) and email campaigns over an observation window is complicated by a few factors.
- Purchases may not be linked to a specific promotionor offer code
- Prospects may be targeted with multiple campaigns;difficult to isolate response to specific campaign.
- Purchasers may choose not to click through campaign email.
Furthermore the marketing manager needs to consider the point that the incremental revenue impact has to separate out the purchases from customers who would have made purchases even had they not been targeted with the campaigns. The simplest and most reliable method for the estimation is described below.
A few operational guidelines
- The method should be usable across both direct mail and email campaigns.
- The method should be robust and easy to automate – necessary conditions for basic process intelligence
Design: The incremental revenue impact of marketing campaigns is estimated by multiplying the count of the opted-in customers with the differential in the revenues per contact for the observation of the opted-in customers from the not opted-in customers.
In the example above, the incremental revenue impact is 5,789,779 x ($0.10-$0.06)= $232K.
Why it works:
- The method assesses the influence of the campaign but does not wade into the weeds of which campaign, which offer code. (It does not matter).
- The critical factor is if a customer is part of the opted-in list or not. The working assumption is that by being in the opted-in list the marketing manager will make at least one attempt in the observation period to appeal to the prospect. (There is no excuse not to).
- The method lends itself to further sophistication via segmentation. In the example below, the “Thrifty Browser” would be targeted with a different type of campaign than the “24 Carats”. Their respective Revenues per contact are $0.16 and $7.20 respectively. Marketing campaigns are typically assigned at a segment level and have different costs. The segment-wise separation is useful for improving accuracy in estimating marketing ROI. So if the manager spent $10K to market to the “Thrifty Browser” segment, the ROI would be (283,279 x $0.07)/$10,000 = 1.9.
[Aside: The above screengrabs are taken from an implementation of the Polytab marketing intelligence solution]. To see how the calculation is done manually, download the worksheet by clicking on the button below.
For more guidance on picking the segmentation scheme that works best for you, click this article on the seven kinds of segmentation every marketer must consider.
About marketing intelligence automation: Tactics such as the above are critical to a marketing manager. The method described above is not perfect by any means but the measure is reasonably robust and has been proved to be reliable. Furthermore, once standardized the intelligence can be embedded into the marketing process. This helps the marketing manager get reliable decision support on a near real time basis. To learn more about marketing intelligence automation, check out Polytab.
This series will cover the following topics. Please subscribe to the blog to stay current. Or if you have a different question you’d like to see addressed, please drop us a note.
- How to optimally allocate the marketing budget.
- How to factor in program fatigue and cannibalization in marketing campaigns.
- How to estimate the revenue impact of social media campaigns
- How to estimate customer lifetime value
- How to map market research against your customer universe
- [Posted in 02-2013] How to estimate revenues on upcoming marketing campaigns.
- [Posted in 02-2013] What is the campaign management process as followed by industry leaders
Make marketing easy
A shameless self-promotion. You can create all the process components in-house but you need active participation from analysts to make it happen. You can get it all in a slick interface with Polytab marketing intelligence automation. Use the button below to register for an info-session.