How To Estimate Incremental Revenue Opportunities With Impression Share Data

When looking for opportunities for growth, most search marketers try to find ways to estimate how much they can scale up their paid search effort, and what would be the impact on ad spend, revenue volume, and efficiency.

There are lots of different ways to go about this – one way I’d like to cover in this post is to leverage impression share data. We’ll see how to aggregate and dig into impression share data, and how to estimate incremental revenue opportunities based on those impressions missed due to insufficient budget. Due to the length of this post, I thought I’d cover those impressions missed due to insufficient rank in a separate article.

Note that a similar logic can be applied to AdWords impression share data and Bing Ads share of voice data.

Getting A High-Level View Of Your Impression Share In Paid Search

Impression share data is currently available at the campaign/ad group levels in AdWords, and account/campaign/ad group/keyword levels in Bing Ads. Whatever level you are interested in, the same logic can be applied to aggregate impression share data up to the account level or any label/dimension you like.

The metrics I will refer to in this post will be:

• AdWords Search Lost (budget)/Bing Ads Impression share lost to budget (%) = Lost IS (Budget)
• AdWords Search Lost IS (rank)/Bing Ads Impression share lost to rank (%) = Lost IS (Rank)

Say we want to analyze the below campaigns in AdWords – and we want to determine what is the combined impression share. We need to aggregate the impression share data by weighting the IS data by the number of impressions of each individual campaign.

More specifically, the aggregated IS in this example is: (120,457*98% +58,789*68% +78,456*71%) /(120,457 +58,789 +78,456) =83%

Generally speaking, if n indicates individual campaigns (or ad groups), the formula to determine the aggregated IS is:

The same thought process can be applied to the aggregated Lost IS (budget). The aggregated Lost IS (budget) is then (120,457*0% +58,789*68% +78,456*71%) /(120,457 +58,789 +78,456) =2.7% Hence, a similar formula to determine aggregated Lost IS (budget).

Same thing for the Lost IS (rank). The aggregated Lost IS (rank) is then (120,457*2% +58,789*20% +78,456*29%) /(120,457 +58,789 +78,456) =14%

Now that we can aggregate IS data, we can visualize the data at different levels — for example, by country and by product category/country:

Determining The Room For Growth

One can easily determine the total number of available impressions in the marketplace if you were not missing any impressions due to neither insufficient budget nor rank. This provides some hard numbers as of the actual room for growth.

For instance, if your IS was 68% and you got 58,789 impressions, then the number of available impressions in the marketplace was 58,789 /68% =86,454.

You can generalize the calculation to determine available impressions — for instance, by country and product:

Estimating Incremental Revenue Opportunities Based Off Lost IS (Budget)

As for those campaigns capped due budget, the logic is relatively simple. Assuming you are not changing your bids/creatives/landing pages in the meantime, one can expect stable CTR, CPC, conversion rate, and average order value (AOV) for those incremental impressions:

• Incremental Impressions (Budget) =Available Impressions * Lost IS (budget)
• Incremental Clicks (Budget) =Incremental Impressions (Budget) *Stable CTR
• Incremental Cost (Budget)= Incremental Clicks (Budget) *Stable CPC
• Incremental Conversions (Budget) = Incremental Clicks *Stable Conversion Rate
• Incremental Revenue (Budget) =Incremental Conversions *Stable AOV

Getting back to my initial example, if a campaign generated 58,789 impressions and had a IS of 68% and a Lost IS (budget) of 12%, then the total number of available impressions is 86,454, and the corresponding number of incremental impressions due to budget is 12%*58,789 =10,375. This particular campaign would generate an additional 10,375 impressions if opening up the daily budget.

Assuming the CTR and CPC are stable on those incremental impressions, then the number of incremental clicks due to budget for this campaign would be 10,375*3.4% =353. This campaign would generate an additional 353 clicks.

Assuming the conversion rate is stable on those incremental clicks, then the number of incremental conversions due to budget would be 353*2.1% =7 (after rounding this value). We can also assume the AOV is stable at \$612 and come up with hard incremental revenue numbers.

At a high level, you can use the same methodology across all campaigns, aggregate the data, and come up with incremental conversion estimates by country or any dimension you are interested in.

Also, beyond the raw potential for conversion growth or revenue wise, search marketers typically look for the most efficient growth possible, which one can determine by looking at the incremental ROAS for this incremental revenue.

This is a first step to impression share analysis and potential budget reallocation. In the above example, the UK market seems to have lots of potential in terms of revenue volume, however the CA and FR markets seem more profitable, so you might want to invest more there first.

Again, since the same methodology can be applied at the country, product category, brand vs. non-brand levels, or any dimension you’re interested in – this analysis can help go beyond traditional paid search and make better business decisions based off not only current performance but also future growth.

Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Related Topics: Advanced | Channel: Analytics | How To | How To: Analytics | Search & Analytics

About The Author: is a Business Analyst in the Digital Marketing team at Adobe, providing advertisers and account management teams with data-driven and actionable insights on strategies to optimize their online marketing mix. One of his specialties is to develop tools and simulators for analysts to use, and executives to use for making business decisions.

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Paragraph after the aggregated IS formula. You applied the incorrect values to Lost IS (budget), using values from the IS column.

Agreed on the other comment regarding spend efficiency, but I give the author more credit. That kind of managerial thought is better left for another post. This is strictly about diagnostics, prior to discussion about whether or not to spend more based on other marketing metrics and objectives.

• Ben Vigneron

Maybe the scenario I am simulating here is unclear to you. More specifically, I am referring to those paid search campaigns which ARE capped due to insufficient daily budget. If the only thing you do is to uncap those campaigns, then you can expect stable returns while generating more volume, since you’re bidding on the same keywords at the same bids, triggering the same ads, redirecting to the same landing pages, and targeting the same audience (geo, language, etc..). This is a basic scenario and it seems to me that your comment refers to forecasting revenue growth using Lost IS Rank, i.e forecasting revenue growth for those campaigns NOT capped due to insufficient budget.

• Ben Vigneron

Good call, I meant: Aggregated Lost IS (budget) =120,457*0% +58,789*12% +78,456*0%) /(120,457 +58,789 +78,456) =2.7%

As for your comment about my conclusion, I totally understand that this analysis might not always be comprehensive enough to actually help make decisions in terms of budget re-allocation by market. There are so many potential variables involved, for instance the online to offline purchase rate or the return/cancellation rates might vary by country, in which case search marketers need to layer offline data on top of online data.

However, for the sake of keeping this post simple, I had to limit the number of specific assumptions and externalities. Then you can make this analysis more sophisticated by adding your own assumptions based off other marketing metrics and objectives.

• Nigel Haig

Makes sense though I agree with Sam M that the far bigger challenge is forecasting using lost IM (rank), given you’ll likely have to account for higher CPCs etc. I look forward to that article.

Regarding the debate on the assumptions used, I’d suggest it depends on how quickly your budget is being spent and your bidding options in terms of standard or accelerated. If you’re using standard delivery and you’ve enough budget for it to work then it’s perfectly reasonable to assume the performance levels will be the same. However if you’re using accelerated then it may be unwise to assume the same level of performance for the hours after the budget has been spent (likely to be at the end of the day), given user behaviour and competition levels are likely to be different at this time.

To find Aggregated IS, wouldn’t you want to use Impressions Available instead of Impressions Received? Using Impressions Available would give more weight to the campaigns with a low IS and correctly calculate the amount of room for increased spend.

It’s too bad IS metrics are set to be retired in AdWords beginning on Feb 4.

Only the old IS columns will be retired. The new columns will still be available.

Loved your article, but the complexities of multi-touch attribution are sure to muck up your otherwise clean math and reasoning. We both know you can’t throw in the logic towel because it’s complex, and mentioning what the attribution model affects are, and how the may vary by country, is beyond the scope here. Not criticizing, wondering if folks will decide to allocate more budget to brand traffic, taking it from “lower” ROAS product ads. :-)

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