Getting Organized: Paid Search, User Intent & The Search Funnel

There is no shortage of literature covering:

  • How to organize a paid search program to reflect your site map and product offerings
  • How to maximize quality score
  • How to categorize queries by user intent (informational, navigational, transactional)

Yet, I have not found any literature addressing how to put those three concepts together.

In a world where more and more campaign management solutions allow search marketers to allocate revenue across impressions and clicks involved in a conversion, it makes sense to organize a paid search program to fully leverage attribution modeling.

With the right structure, you can fully measure the interaction between various sections of a website, as well as the interaction between informational, navigational, and transactional queries; as well as branded and non branded keywords to get a good understanding of consumer behavior and brand awareness.

Taking a step back, user intent can be broken into three categories:

  • Informational queries look for a specific fact or topic, and account for roughly 80% of all search queries
  • Navigational queries involve locating a specific website, and account for roughly 10% of all search queries
  • Transactional queries look for information related to buying a specific product or service and also account for roughly 10% of all search queries

More details about user intent can be found in “Determining the informational, navigational, and transactional intent of Web queries” by Bernard J. Jansen, Danielle L. Booth, and Amanda Spink.

For the purposes of this article, let’s break down a site map into three levels (there could fewer or more levels, but the logic remains the same):

  • Category: for instance “office supplies” and “breakroom supplies” are category-level keywords.
  • Sub-Category: for instance “binders” and “clipboards” are sub-category keywords within the “office supplies” category.
  • Product: for instance “ideastream storage clipboard” is a product-level keyword within the “clipboards” sub-category

It makes sense to differentiate Brand vs. Non-Brand for several reasons: to maximize your impression share, monitor specific KPIs, implement a specific bidding strategy, use sitelink ad extensions, etc…

As a result, with the opportunity to build a paid search program from scratch based on current knowledge and tracking tools it would make sense to organize your paid search campaigns into all possible combinations of site map levels and brand types.

There are 18 campaign types:

  1. Non-Brand | Category | Informational
  2. Non-Brand | Category | Navigational
  3. Non-Brand | Category | Transactional
  4. Non-Brand | Sub-Category | Informational
  5. Non-Brand | Sub-Category | Navigational
  6. Non-Brand | Sub-Category | Transactional
  7. Non-Brand | Product | Informational
  8. Non-Brand | Product | Navigational
  9. Non-Brand | Product | Transactional
  10. Brand | Category | Informational
  11. Brand | Category | Navigational
  12. Brand | Category | Transactional
  13. Brand | Sub-Category | Informational
  14. Brand | Sub-Category | Navigational
  15. Brand | Sub-Category | Transactional
  16. Brand | Product | Informational
  17. Brand | Product | Navigational
  18. Brand | Product | Transactional

Why such a complex structure? What are the benefits of such a structure?


The above structure is pretty self-explanatory – you can guess where a given keyword should reside by answering three simple questions: Does it contain a brand term? Does it contain a category or sub-category or product name? Does it contain an informational or navigational or transactional term?

Quality Score Optimization

You can write more specific ad copy based on user intent. It is a valid test to rotate informational ad copy for informational searches, and so on. It can only help the CTR and the Quality Score – while it might hurt the conversion rate. The right balance can be determined via a proper A/B testing procedure.

Regardless, this structure should help determine relevant keywords/ad copy combinations, landing pages at every stage of the search process/buying cycle.

Search Funnel Analysis

A clear structure helps effectively measure all interaction at the campaign level – making it much easier for all major tracking solutions to provide granular insights.

More specifically, we expect informational searches – where most of the search volume sits – to be entry points in the conversion funnel, while navigational and transactional searches are more likely to convert.

Whatever you are expecting or assuming, this logic will help measure what is actually happening in your account.

Budget Allocation & Impression Share Optimization

A clean structure makes it easy to ensure brand campaigns have unlimited budgets and a high impression share – or at least a high exact match impression share, see this article for more information about impression share data.

Also, you can temporarily limit the daily budgets or even pause “Non-Brand | Informational” campaigns if your budget is limited, or if these campaigns are not meeting efficiency goals.

Site Link Ad Extensions

Relevant sitelink extensions can be built across all category and sub-category campaigns (not so much for product campaigns) based on top-selling products, top-rated products, current promotions, and so on.

This structure is theoretical and should be made more specific to every advertiser’s strategy / website / product offering.

For example, you might want to break down this structure even further by platform (desktop, mobile, tablet), or by geo, or both. The point is that your paid search program should be organized in a systematic manner allowing clear and concise insight into the consumer buying cycle and allow easy optimization based on consumer behavior.

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

Related Topics: Channel: Analytics | Search & Conversion


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.

Connect with the author via: Email | Google+


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