We all know that marketing messages compete with one another for the attention of our customers. Are you accounting for the interactions between marketing campaigns in deciding who is sent what?

Revenue cannibalization between marketing campaigns exists when one customer receives multiple offers with similar content or multiple offers within a short period of time.

"Business as usual" for marketing typically results in your best customers receiving every available contact. Modeling revenue cannibalization between contacts empowers marketers to make better circulation decisions for over-promoted customers without impacting purchasing.

Revenue Cannibalization Model

There are three components of the Cannibalization Model:

1. Timing Effect
2. Marketing Effect
3. Product Effect

Timing Effect compares the revenue shelf life of each contact to the others. Using contact date and weekly revenue patterns of any contact pair, the amount of revenue exposed by each contact to the other is computed for the Timing Effect. More overlap during an active purchasing period indicates higher cannibalization.

Marketing Effect compares the promotional elements of each contact to others. Each contact is described by a handful attributes, such as its title, brand, media, channel, pricing focus, seasonal focus, and promotional focus. The more similar these elements are between contact pairs, the higher the cannibalization.

Product Effect compares the merchandising category mix of each contact to others. Each contact is described by product content with a demand mix across standard product categories. Similar product offerings between a pair of contacts indicate higher cannibalization.

These three components are then combined to arrive at the overall cannibalization impact to revenue. Similarity between contact timing, promotional elements, and merchandise mix result in higher revenue cannibalization.

An Example

Below is a real example of one marketer’s Cannibalization Model. This example has ten marketing offers (A through J); it shows how a customer’s predicted demand performance is impacted if that customer were to receive other offers.

Reading across the first row of the above matrix: If a customer is targeted to receive offer A, then you should decrement the predicted demand for offer B by 18%, offer C by 26%, offer D by 29%, etc.

Let’s assume the expected sales per mailing of our sample customer is represented by the chart below:

Now let’s apply the Cannibalization Matrix to our customer example. Assume our customer is receiving offer A. The chart below shows the adjusted sales per mailing estimates given the customer receives offer A. The revised performance is computed from taking the first row of the matrix and applying those entries to the forecasts. For example, the customer that was expected to generate $2.00 if mailed offer B would now be expected to generate $1.64 (18% less than $2). Offer A impacted the revenue estimate of offer B.

Let's continuing the process one more step. Assume our customer is now targeted to receive both offers A & E. We then apply Row E cannibalization matrix entries to the revised performance estimates to generate the demand figures cited below. For example, offer B drops from $1.64 to $1.12 due to the 32% cannibalization effect of E on B.

The Benefits of Understanding Cannibalization

There is enormous value in knowing the Cannibalization Matrix for your contact plan:

• Make wiser contact decisions for your customers.
• Eliminate wasteful circulation and shift circulation to more incrementally valuable customers.
• Find monster contacts, the ones dominating others by eating away at their revenue.
• Find bullied contacts, the ones being dominated by others and losing the most revenue.
• Learn about cannibalization hot and cold spots to improve the overall saturation of your contact plan.
• Find any hidden gems within your contact plan – those independent contacts that are isolated and not impacted by their neighbors.
• Explore making changes to your contact plan and simulating the impact to saturation.
• Add a new contact, drop an existing one, or modify the timing or content of an existing one then see the impact to neighboring contacts and the overall saturation.

About Randy Erdahl
Randy is an analytic marketing champion and is Clario’s Executive Vice President, Optimization Solutions and co-founder. As the executive leader of optimization solutions, he provides vision and direction for Clario Stream Solution development, as well technical leadership to sales, marketing and direct client interaction.