Brand Promotion

Brand promotion is a common marketing strategy intended to increase product awareness, customer loyalty, competitiveness, sales and overall company value. In the AppStore, this is usually done through advertising in other mobile devices, either through banner, video, or interstitial ad units.

(Another form of promotion is to get featured by Apple or some other popular site; however, most indie developers don’t have access to this kind of network, so for the purposes of this post I’m just going to focus on brand promotion through advertising).

There are a ton of mobile ad networks available. However a mobile ad campaign can be a very costly. In fact, I had one sales rep tell me that a typical campaign budget needs at least $30K in order to make a noticeable impact.

Therefore the question faced by marketers before they decide whether or not to run a campaign is – does the incremental volume from promotions justify the cost? And perhaps as a secondary question, does the promotional sales “lift” differ across marketing channels?

Fortunately both questions can be answered with a ROMI (return on marketing investment) analysis.


A ROMI analysis provides a direct link between marketing and shareholder value. It can tell you if promotions increase or decrease profits, and also what types of promotions are the most profitable so the marketer can make proper resource allocation decisions.

How does a ROMI analysis work? Roughly it uses data on sales generated by marketing investments, such as promotions, and leverages tools such as regression analysis to predict the incremental volume for a proposed marketing investment. The following outlines a general tutorial for ROMI analysis:

Return on Marketing Investment Analysis

Step 1: Run a log-linear regression model correlating sales, price, and your marketing promotions. If you’re comparing multiple channels, you can add each channel as a dummy variable in your regression. Typically competitor prices should also be factored into the model.

Step 2: Calculate lift factors from the regression estimates, based on the base price and predicted increase in sales from the promotion.


Step 3: Make a table comparing the economics of the trade promotions.

Given your regression estimates and event structure, you can calculate a product margin and predict baseline sales units using your base price and zeros for all promotion activities. You then calculate total units sold (lift factor * baseline units), and find the incremental volume sold (total units – baseline). Once you know incremental volume, you simply find your incremental contribution (margin promoted * incremental units) and sum up all of the various costs associated, such as fixed costs of the promotion and variable costs (price cut * baseline sales, which represents the opportunity cost of the promotion). Determine your event gross contribution (incremental contribution – event cost) and arrive at your ROMI by dividing gross contribution by event cost.

It’s a bit time consuming the first time, but once you have this set up, you can use it to evaluate and compare any number of promotional activities related to app marketing.

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