Optimal Frequency

1 Introduction

First transaction after running EM campaign really counts.

Determine the optimal frequency and impose a sensible cap, which enable us decrease cost per sale.

This study focuses on optimal frequency from a direct response standpoint, namely, how to increase the efficiencies of a campaign to deliver leads and sales. After analyzing campaign data, we are able to look into the impact of frequency on redemptions and sales.

2 The wrong path to optimal frequency

A consumer who redeemed after the third mails, but subsequently receives other mails. Attributing the redemptions to the total mails would grossly overestimate the level at which consumer redeemed.

We are able to identify the most common frequency level prior to redemptions. Since the vast majority of mails from consumers who never redeem are ignored, realistic redemption rates are impossible to estimate.

3 The right path to optimal frequency

Cumulative redemption rates reveal the true optimal frequency level. By looking at cumulative mails and redemptions, a model is crated of how redemptions are harvested with each incremental mail. In effect, this methodology simulates what would have happened had the campaign been frequency capped at different levels.

The redemption rate on the first email was the highest, though the first three** all had at least 100% lift on average. At any given moment there are only a fraction of consumers who will immediately respond to your solicitation. Thus, a direct marketing campaign’s performance will depend on its ability to maximize reach at the optimal frequency level and boost the frequency of consumers that have only seen a few mails.

4 The most efficient frequency vs. the most profitable frequency

The frequency level with the highest response rate may not necessarily be the same frequency level to maximize your profits. There will always be a trade-off advertisers have to manage between efficiency and volume. Restricting frequency to only one email per consumer might achieve a lowest possible cost-per response, but you may end up with a very low total number of responses.

5 What this means for marketers

It is important for advertisers to recognize and react to the amount of money being wasted on excessive high frequency users. The culprits are not the consumers who consume for, five, or six mails, but rather the thousands of consumers who receive hundreds of mails without any response. We suggest basic frequency caps. Imagine what a frequency cap could mean when a consumer receives 1000 emails – a cap at 10 emails would drive reach to at least 100 additional potential customers.

It is significant knowing how various caps will impact campaign performance, monitoring where gross waste is significant, understanding whether negotiated caps are truly in effect, which will help in planning and buying media more intelligently.

Quantify the trade-offs between frequency levels and response rates.

Quantify how much pricing premiums for capped inventory are actually worth.

Strategically pick frequency levels that maximize total response yields, while still meeting the cost per response goals.

Identify customer purchase frequency increased as a result of a specific marketing campaign.

Identify email campaign assisting direct mail campaign for in-store purchase.

Typical visit frequency

Consider the case of a user who deletes cookies every day.

If a particular site has a group of very addicted uses who return frequently, even if a small number delete cookies daily, the result will significantly inflated numbers of cookies relative to the number of actual people who visited the site. In such cases it is not unusual to see an average of two or more cookies for every user over the courses of a month.

Of course, most users only result in one cookie, but a small number generate many cookies.

Controlled experiments have been done by economists and social scientists to show the effect of user pass exposure. While the results from the studies differ in different scenarios, largely it has been shown that with increased past exposure the user is more like to response positively. Intuitively, past exposure to an ad might help in several ways such as increased brand awareness and familiarity with the product, or even an increase probability to the user to notice the ad. At the same time, some studies have also show an “ad fatigue” effect where users might tired of an ad if is displayed too often.

Some site tend to have mostly passers-by, i.e., visitors that only go to the site once over a given time period. No impact on the total number of cookies for the site.

There exist some addicted users who visit particular sites frequently, which leads to significantly inflated frequency numbers of addicted users relative to the number of average people who visited the sites.







The frequency distribution of user visits RON is skewed, that is, a small portion of users are frequent visitors, while the remaining are infrequent visitors. Hence, the sample size available to estimate item affinity per user is small for a large number of users.