Learn how to get the most from your historical keyword data. We’ll answer questions like: “Which long tail keywords have the best conversion potential?” and “Which keywords need their own landing page?” Experience level: Intermediate Target audience: Affiliates/Publishers Niche/vertical: Keywords Jeremy Bencken, Founder, Wordloop (Twitter @Bencken)

1. Keywords Hiding in Plain Sight
Using Ngram Analysis and Bayesian Filtering to Find
High Conversion Keywords

2. Would you rather
be this guy…
@bencken

3. …or this guy?
@bencken

4. My Background
Performance-Based Content Marketing for B2B Link Building Tools to Help You Scale
@bencken

5. A Friend’s Video Content Site…
• 26,700 distinct kws
per month
• 90% 1-entrance kws
• 3% of kws converted
• 61% of conversions
from kws under 10
entrances
@bencken

6. Organic Entrances vs. Keywords
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7. Which keywords drive conversion?
@bencken

8. Approach #1
N-GRAM ANALYSIS

9. Ngram Analysis
B D
dvd ripping software for mac
A C
ID 2-word ngram (bigram)
A dvd ripping
B ripping software
C software for
D for mac
@bencken

10. @bencken

11. n-gram Analysis Pros/Cons
• Pros
– Find major phrases that drove traffic and
conversions
– Indicator of what phrases Google is ranking
you for
• Cons
– Hard to compare keyword conversion with
varying volumes
– Ignores long tail data
– Chick and egg problem
@bencken

12. Approach #2
BAYESIAN FILTERING

13. Bayesian Filtering
• Same technology that
detects spam
• Based on probability that
words are associated with
conversions
• Finds the phrases and sub-
parts associated with
conversions
@bencken

14. Google Bestows Upon Us “Prediction”…
• You need…
– Conversion data at kw level
– Excellent Analytics
– Google Cloud Storage
– Google Prediction
• Steps
– Download your keyword data from GA
• Beware of sampling, English only, Same medium, More is better
– Organize keyword data into 2 columns:
number,”keyword”
• Number: 1 = converted, 0 = no conversion
– Upload to Google Cloud Storage https://developers.google.com/
– Train a new Predict model prediction/docs/hello_world
@bencken

15. Example Training Data
0,"best dvd ripping software"
0,"dvd ripping software"
0,"book"
0,"sell videos online"
0,"best free dvd ripper"
0,"turkey"
0,"master cleanse blog"
0,"a book"
0,"dvd ripping software for mac"
0,"learn economics market structure online"
0,"sell videos"
0,"tropic thunder"
1,"chemistry: videos of fission and fusion"
0,"dvd ripping software mac"
0,"selling videos"
0,"prono videos"
0,"special days in december"
0,"selling videos online"
0,"list of december holidays"
0,"grim reaper"
@bencken

16. @bencken

17. Let’s Ask a Question…
Question: Would calculus or algebra search traffic perform better?
algebra
calculus
Answer: calculus searches convert better!
@bencken

18. Prediction At Scale…
So what?
Prediction Value * Google AW
Search Volume = Potential
1) Screen all your top keywords
and ngrams via the model.
2) Screen all those single-visit
keywords for opportunities.
3) Screen suggestions from 3rd
party tools.
Filter performance:
97.6% accurate, 2.2% false positive, .2% false negative
@bencken

19. @bencken

20. Keyword Tools
• Adchemy • MergeWords
• Adgooroo • Market Samauri
• Adwords Keyword • NicheBot
Tool • Searchmetrics
• ConcentrateMe • SEMRush
• Ispionage • SEOBook
• KeyCompete • Spyfu
• Keyword Discovery • Ubersuggest
(Trellian) • Wordstream
• Keyword Elite 2.0 • Wordtracker
• Keyword Spy • Wordze
@bencken

21. Thanks!
Jeremy Bencken
Founder, Wordloop
jeremy@wordloop.com
@bencken
@bencken