Archive for the ‘Uncategorized’ Category

What is Impression Share?

June 27, 2011 Leave a comment

Impression Share is basically defined as the ratio of the number of times that an advertiser actually gets an impression to the number of times he expects an impression.

When can an advertiser expect to get an impression?

Each time there is a search by a user on the keyword that an advertiser is bidding on, the advertiser can expect to get an impression

Why isn’t impression share 100% for always?

There multiple reasons for an advertiser not getting an impression on which he has bid, a few of which are listed below

a. Budget Exhaution

b. High Competition Bids

c. Ad not clearing thresholds for ad quality

At what grain is the impression share computed?

Impression Share can be calculated at any level – customer, account or campaign. It can also be calculated at the AdGroup level but Google Adsense does not go below the campaign level.

Why would I be interested in Impression Share as an advertiser?

Placing a bid on specific keywords shows your awareness of user base. It would be highly important for you to understand why your ads are not showing up whenever a user is typing the keyword. Once you know what is causing less than 100% impression share, you can take necessary actions to improve performance. Microsoft’s AdCenter will in fact filtering out your ads if not many users click on it. Thus, you would be able to understand whether your keyword choice is correct and reallocate budget, if not.

What are the other metrics which are closely related to impression share?

Click Share and Revenue Share provide another perspective to advertisers an can help by looking at the problem from a different angle.

Categories: Uncategorized

What is Churn Analysis in the Paid Search Domain?

June 23, 2011 Leave a comment

Customer Attrition or Churn is a major cause for concern in the Paid Search Domain. Many a times, customers do not have account managers taking care of them, especially if they are low value customers. For the publisher, it becomes very important to quantify churn in order to understand the incentive involved in appointing an account manager for a set of low value customers. As the customers are of low value, their ad campaign portfolio is not very diverse and single account managers can handle lots of accounts. Is there a good ROI in appointing account managers to such accounts. The answer to this question lies in estimating the potential value that a customer can bring had he not churned out.

Churn refers to the notion of advertisers losing interest in the publisher. Analytics is all about quantifying notions and the notion of churn can also be easily made tangible. As an advertiser loses interest, his performance slowly begins to deteriorate. There are 2 definitions of Churn that I can think of.

1. Impression Based Churn – In this scenario, an advertiser’s budget may run out and he may not care to replenish. He would stop showing any impressions.

2. Click Based Churn – Click based churn is a super set of impression based churn. This is because it is not possible for customers not getting impressions to get any clicks. But, apart from this set of customers, there may also be others who receive impressions but may not attract any clicks. This could happen because these advertisers are bidding on the the wrong kind of keywords. Hence, they show up in contexts, that the user is not interested in.

In some platforms like Microsoft’s AdCenter, if a customer stops receiving clicks, he would also soon stop receiving impressions. The delivery algorithm is designed in such a way that a user gets to see only relevant ads and hence an ad listing not receiving clicks in spite of being served on the publisher, may soon be tagged as irrelevant.

Once we have established the above two definitions, the focus now should shift to how one can estimate Churn. For all my explanations, I would only be refering to impression based churn in order to avoid confusion. All that I explain below can be applied to click based churn also.

The first step in churn estimation is to select a time period of inactivity for the advertiser. Let us start with 1 week. and count an advertiser as churned if any of his listings do not get served for a period of 1 week. We could then take historic data of all advertisers available with us and then count for every month, the number of customers who did not perform (receive impressions) for a period of 1 week ending in that month. We could repeat this exercise for 2 weeks, 3 weeks and so on. It would not be sensible to go beyond a period of 24 weeks or 6 months in the paid search domain.

Let us assume that an Advertiser called XYZ banking served a listing on Google on a particular date in Jan 2010 and he stopped having any impressions till August 2010. This particular advertiser would be counted as churned, if we were considering a 4 week definition of Churn. Though we count him as churned, he has in fact not churned, because he started getting impressions in August 2010. Therefore this customer is not actually churned.

Consider the following graphPercentage of Customers Actually Churning


The x-axis represents the time period of inactivity and the y-axis represents the percentage of customers who actually churned. For eg. , we see that when we consider 2 weeks as the definition for Churn, only 60% of the customers who we counted as churned have actually attrited. The remaining 40% came back after having no activity for a period of 2 weeks.

There would be a point of saturation where, even if we increase the time period of inactivity considered for defining churn, the number of customers, actually churning out does not increase by much. This time period ,for which if a customer does not receive impressions, can be considered as the cut-off of inactivity for an attriting customer. There is little chance that he may come back after this period of no ad servings. The account managers can then take action on such customers much in advance of the end of this time duration of inactivity to understand the reasons and also make recommendations that are mutually beneficial to both the publisher and the advertiser.

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Categories: Uncategorized