If you are trying to set up bidding in paid search, it’s important that you have a strategy in place before you implement it. The most relevant questions that you should ask when developing this strategy are as follows:

  • How many bidding strategies do I need?

  • What cookie window should I use?

  • Should I use a revenue-attribution model and move away from a last-click model?

  • How can I get the most of long-tail/low-volume keywords?

1. Ensure Your Account Is Ready For Bid Automation From An Editorial Standpoint

Although this isn’t exactly related to the actual bidding strategy, but you need to be sure your quality score is at an optimum level before you try scaling up your account. Since Ad Rank= Max Bid*Quality Score, you’ll have to maximize your quality score in order to minimize the average cost per click at more aggressive positions. Basically, you will need to address the account structure and test some ad copy first.

Before moving on, you will need to make sure that your ad group structure has been stable for a few weeks so you can obtain enough historical data at the keyword, ad group and campaign levels.

2. Break Down Your Paid Search Program Into Multiple Keyword Buckets

Unless you have a very basic paid search program, more than likely you will be bidding on different kinds of keywords with different performance levels. You certainly don’t want to optimize your branded keywords the same way you would bring your generic keywords words since the average positions in CPCs are typically quite different from each other.

To be specific, the buckets of keywords that you have created can be defined based on the effective they are, as well as the number of clicks needed to make statistically significant big decisions. This correlates to the average conversion rate.

For instance, we could have something as follows:

  • Bucket #1/Brand keywords: $1 CPA, 10 clicks to get a conversion

  • Bucket #2/Generic keywords: $10 CPA, 25 clicks to get a conversion

  • Bucket #3/Competitors’ keywords: $20 CPA, 30 clicks to get a conversion


Once you’ve got to find amount of buckets in your account, you can dive even deeper into consumer behavior, lifetime value (LTV), and actual bid rules for each of your individual buckets.

3. Analyze Consumer Behavior Trends For Each Bucket

You’ll need to determine the average amount of like time from impression / click to conversion and the average number of clicks per sale for each individual bucket.

To be specific, it could make sense to determine the number is days and clicks it will take to get 90 percent of all conversions. As an example:

  • Bucket #1/Brand keywords: 90% of all conversions occur within 60 days and 1.5 clicks. You want to use a 60-day cookie window and a custom revenue-attribution model reflecting multiple touch points.

  • Bucket #2/Generic keywords: 90% of all conversions occur within 90 days and 2.2 clicks. A 90-day cookie window and a custom revenue-attribution model seem relevant.

  • Bucket #3/Competitors’ keywords: 90% of all conversions occur within 90 days and 2.5 clicks. You want to use a 90-day cookie window and a custom revenue-attribution model.

There really isn’t anything you can do about these trends in Adwords since the default cookie window is set to 30 days.  The last-click model is the only revenue-attribution model currently available.

Although, if you are using more sophisticated technologies, you’ll have the ability to set custom cookie windows and revenue attribution models for every bucket of keywords. This is by far a more sophisticated way to optimize assisting/upper-final keywords.

4. Determine The Lifetime Value For Each Bucket

Although brandon keywords: normally offer form generic and competitors terms at first sight, the LTV can be significantly greater for generic terms. Depending on whatever reporting sweet you are currently using, there’s a chance you will be able to pull user-centered reports as opposed to the word centered reports. It would be a good idea to determine the number of conversions and associated amount of revenue by a unique user instead of analyzing the number of keywords/clicks involved in a conversion.

For example, here are some types of trends you can expect in your account:

  • Bucket #1/Brand keywords: $80 revenue by unique user

  • Bucket #2/Generic keywords: $120 revenue by unique user

  • Bucket #3/Competitors’ keywords: $100 revenue by unique user

These LTV numbers, in conjunction with the average order value, these numbers can help you adjust your CPA/return on advertising spending targets from a meeting and strategy standpoint.

As another point, high LTVs are usually correlated with a high percentage of new customers vs repeat customers.

5. Build Out Your Bid Rules Logic For Each Bucket

With a better understanding of each bucket of keywords in terms of visually and consumer behavior, you can build custom did rules logic:


In this example, it would be a good idea to build a minimum of one bid rules logic for every bucket.  These bid rules would be aimed at LTV adjusted CTA targets, and would be using the cookie thresholds and cookie with those determined earlier.  Assuming the technology you are using allows you to do this, you can adjust long tail keywords by clustering the data from the keyword level up the ad group, campaign, or even account level.

In general, you want your bids to reflect key word level performance while looking at longer time ranges and less granular statistics.  With this, you will probably end up with the following cycles of bid decision:


Although these are just basic examples, you will probably end up with more buckets and/or more cycles of bid decisions, even though this is a reliable logic in general.