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...?
@carmenmardirosSunday, 17 February 13
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
Classify Keywords by Intent
“where do I find reviews for communication apps ipad for kids [brandname]”
Brand name
@carmenmardiros Brand name replaced for client protectionSunday, 17 February 13
Classify Keywords by Intent
“where do I find reviews for communication apps ipad for kids [brandname]”
Brand nameCore Product interest
@carmenmardiros Brand name replaced for client protectionSunday, 17 February 13
Classify Keywords by Intent
“where do I find reviews for communication apps ipad for kids [brandname]”
Brand name
Intent to buy
Core Product interest
@carmenmardiros Brand name replaced for client protectionSunday, 17 February 13
Criteria
Classify Keywords by Intent
“where do I find reviews for communication apps ipad for kids [brandname]”
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
@carmenmardirosSunday, 17 February 13
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).
@carmenmardirosSunday, 17 February 13
• 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
@carmenmardirosSunday, 17 February 13
• 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?
@carmenmardirosSunday, 17 February 13
• “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
‘Brand Unaware’Queries
Not Yet Customers Existing Customers
HelpMesRepeat Buyers
N/A
Interest Match =
How much value?
@carmenmardirosSunday, 17 February 13
• 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
@carmenmardirosSunday, 17 February 13
• 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
DefiniteConvertibles
Vague Potential
No or Low Potential
Broad Interest Match+ Product Interest Match
= High Potential@carmenmardiros
Sunday, 17 February 13
Navigational queries = Vague Potential
Classify the Brand Aware by Potential Value
HelpMesDefinite
Convertibles
High Potential = Consideration/Evaluation
Intent to Buy or Try
RepeatBuyers
Unknowns
@carmenmardirosSunday, 17 February 13
• 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
‘Brand Unaware’Queries
Not Yet Customers Existing Customers
HelpMesRepeat Buyers
N/AIrrelevant Stumblers
PossibleConvertibles
Definite Convertibles
DefiniteConvertibles
Unknowns
‣ Refine further by proximity to conversion (Consideration, Intent to Buy, etc)
@carmenmardirosSunday, 17 February 13
• 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)
@carmenmardirosSunday, 17 February 13
• 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)
@carmenmardirosSunday, 17 February 13
• 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
Create Custom Dimensions (NextAnalytics)
@carmenmardirosSunday, 17 February 13
• 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
Create Custom Dimensions (NextAnalytics)
@carmenmardiros Brand name blurred for client protectionSunday, 17 February 13
4. Aggregate the Data Using Pivot Tables
Create Custom Dimensions (NextAnalytics)
@carmenmardiros
Without logic and connections between keyword groups the above is just a bunch of labels
Sunday, 17 February 13
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
Create Custom Dimensions (NextAnalytics)
@carmenmardirosSunday, 17 February 13
6. Use Slicers to Peel The Onion Further
Create Custom Dimensions (NextAnalytics)
IF NOT (Active Use OR Evaluation OR Purchase)THEN ?
@carmenmardiros Brand name blurred for client protectionSunday, 17 February 13
• 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?
@carmenmardirosSunday, 17 February 13
• 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
Sunday, 17 February 13
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