getting to the people behind the keywords

Getting to the People Behind the Keywords

How to Use Semantic Meaning to Identify and Optimise for Different Intentions

Carmen Mardiros@carmenmardiros

Sunday, 17 February 13

How Many of You Hate Keyword Analysis?

@carmenmardirosSunday, 17 February 13

• Why I hate keyword analysis:

‣ Tedious, grunt work

‣ “Aha” moments get lost, fit in the big picture - Constant danger of drowning in data

• Why I love keyword analysis:

‣ Unparalelled insight into the business and its audience

‣ Voice of Customer at large scale

‣ Search is top channel for most websites so insight into a huge chunk of your audience

Common Keyword Analysis Techniques

• Long Tail vs Head

• Branded vs Generic

• SEO: Navigational, Informational, Transactional

• Highest volume keywords

• Top converting keywords

“So what?”@carmenmardirosSunday, 17 February 13

• No difference between Long/head or there is a difference but that requires more investigation

‣ Meaning and intent trumps Long Tail classification.

• So what if they’re brand aware/unaware? What does this mean about what they’re here to do?

• “Top keywords” leads to obsession over metrics rather than visitor satisfaction and doesn’t say anything about topic/interest coverage

There Must Be a Better WayIsn’t there...?


Classify Keywords by Intent

“where do I find reviews for communication apps ipad for kids [brandname]”

@carmenmardiros Brand name replaced for client protectionSunday, 17 February 13

• “I’ll just break the keywords into groups of intent based on words they contain”.

• All discussions I found online suggest you do this.

• It makes sense



Brand name




Brand nameCore Product interest




Brand name

Intent to buy

Core Product interest


Criteria



Brand name

Intent to buyInformational

Core Product interest Qualifier

Logic is solid, putting it into practice for 1000‘s keywords is hard@carmenmardiros Brand name replaced for client protectionSunday, 17 February 13

• Keywords easily fall into different categories depending on how you look at it.

• Breaking keyword portfolios into logical groups (segments) gets messy and complicated.

Step 1

Step Away From The Keywords and focus on the People


People-focused Keyword Analysis

1. Identify expected search behaviour based on Intent and Lifecycle stage.

2. Build way to segment data.

3. Establish target behaviour (the goal).

4. Analyse actual behaviour (the reality).


• Use gap between 3 and 4 (what I hope is happening when these Personas visit the website and what is actually happening to identify areas requiring optimisation)

Identify Lifecycle Events that Mark Crucial Behaviour Shifts

.....................................................

Becomes Brand Aware

UnawareLoyal

Customer

Becomes Customer


• These lifecycle events translate across industries and businesses.

• Indicate different interactions with the website, familiarity with the website, different reasons for visiting.

• Those attitude changes translate into data: recency, frequency, engagement, conversion rates are different

Map Search Behaviour to Customer Lifecycle Stages

‘Brand Aware’Queries

‘Brand Unaware’Queries

Not Yet Customers Existing Customers

N/A

?

?‣ What is the Potential Value to become customers?

‣ Brand Unaware = “fresh blood” into the business

HelpMesRepeat Buyers

What keywords are these people likely to search for?


• “Not yet customers” likely to form the bulk of the traffic

• Will include a variety of stages, different overlapping types of intent, varying degrees of Potential Value to become customers

• Potential more important in Brand Unaware as that’s fresh blood, category keywords

Does Interest Match What Business Offers?Potential Value is determined by

Interest match and Interest strength

No or Low Potential

Vague Potential

High Potential

High PotentialN/A Vague Potential

No Interest Match=

No potential value

‘Brand Aware’Queries




N/A

Interest Match =

How much value?


• Interest match alone is not enough, it’s just the first step

• So what? -

‣ If the No/Low potential segment is high it skews the data (also indicates crap marketing)

‣ Some Potential and High Potential have different profiles and respond to messaging differently

Brand UnawareDetermine Interest Match and Strength

Broad Interest Match:

“Language development”“Learning difficulties”

Product Interest Match:

“App for Ipad”“software”

Irrelevant Stumblers

Possible Convertibles

DefiniteConvertibles


• The point: you have at least 2 subgroups of people based on their potential. You would reasonably expect them to respond differently to different types of content and to have different conversion rate.

‣ First query type only: possibly target audience, touches on what the business has to offer but is not a great fit because it doesn’t express interest in product

‣ Second query type only: irrelevant for the business, not a good match at all.

Brand UnawareDetermine Interest Match and Strength

Broad Interest Match:

“Language development”“Learning difficulties”

Product Interest Match:

“App for Ipad”“software”

Irrelevant Stumblers

Possible Convertibles


Vague Potential

No or Low Potential

Broad Interest Match+ Product Interest Match

= High Potential@carmenmardiros


Navigational queries = Vague Potential

Classify the Brand Aware by Potential Value

HelpMesDefinite

Convertibles

High Potential = Consideration/Evaluation

Intent to Buy or Try

RepeatBuyers

Unknowns


• No Irrelevant Stumblers, if they used brand name they have at least some interest

• Definite convertibles - evaluation, trial purchase etc

• Unknowns - Branded queries without any additional qualifiers.

‣ You cannot determine their intent from keyphrases used, you can only determine it based on website interaction.

‣ Their behaviour is likely to be different: more pages seen, quicker browsing as they intended to navigate a known website to perform a task.

What Behaviour Do You Expect and Target From Each Persona?

N/A‘Brand Aware’Queries




N/AIrrelevant Stumblers

PossibleConvertibles

Definite Convertibles


Unknowns

‣ Refine further by proximity to conversion (Consideration, Intent to Buy, etc)


• Why I split my keyphrases by Brand - Different kinds of personas and potential found in Brand Aware and Brand Unaware

• Reasonably expect different behaviour from the Possible Convertibles compared to Definite Convertibles

Step 2

How to Apply This to Large Sets of Data(the good stuff)


• I tried keyword classification first and I drowned in my segments, time for another approach

Early Grunt Work

• Get (clean) list of all keywords, create frequency tables for all 1, 2, 3 words combos*

• Google Adwords Keyword Tool

• Group them into broad buckets (building blocks):

‣ Brand terms

‣ Interest match and strength - Broad interest, Product interest terms

‣ Proximity to conversion - Qualifiers, Evaluation, Buy/Try terms, etc

‣ Existing customers - HelpMes, Repeat Buyers terms

*Text editor+Regex+Excel orhttp://www.hermetic.ch/wfca/wfca.htm (thanks to Charles Meaden for the suggestion)


• Hard work but part of it gets reusable for different clients, industries etc - You’re building up a reusable framework which you can reuse and improve over time.

Create Custom Dimensions (NextAnalytics)

1. Create copy of Keyword column

@carmenmardiros Brand name blurred for client protectionSunday, 17 February 13

• My workflow involves NextAnalytics to pull data via the API from Google Analytics

• Their “fix” feature is gold for creating custom dimensions. I use it for any dimension I may want to apply a higher level grouping for.

2. Create Custom Dimensions Rules using Regex



• If column includes one of the predefined terms, the value in that cell gets replaced with “Product Interest”.

• If there is no match then cell left blank.

3. Run Regex Search and Replace on Keyword Columns



4. Aggregate the Data Using Pivot Tables


@carmenmardiros

Without logic and connections between keyword groups the above is just a bunch of labels


5. Create Conditional Custom Dimensions

• I started with how I expected people to use Search based on Intent and Lifecycle stage

• The data corroborated my theory

IF Broad Interest Match AND Product Interest Match THEN Definite Convertibles



6. Use Slicers to Peel The Onion Further


IF NOT (Active Use OR Evaluation OR Purchase)THEN ?


• Helps determine other intents you may have missed by excluding the already defined intents from your data. Slicers and pivot tables make it amazingly easy.

• For Brand Aware queries I keep going until I end up with navigational queries (brand name without any other qualifiers)

Summary

• What search behaviour do you expect based on intent and lifecycle stage?

• Use tools to apply that logic at large scale, and improve it over time.

How Do You Go About It?


• Contributions from #measurecamp audience:

‣ @james_cornwall - Add internal search data as well into Persona definition

‣ @yalisassoon - Identify behaviour of Personas and then use machine learning to identify other visitors with very similar behaviour which may fall into the same category

‣ Validate the assumptions with qualitative Voice of Customer to complete the picture

Thank You

Carmen Mardiros

Please get in touch with questions and comments:[email protected]

@carmenmardiros


getting to the people behind the keywords

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communication apps ipad