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Finding frequently asked questions in Google with Natural Language Processing

It's pretty clear that Google searches are a key way people find businesses.  35% of referral traffic comes from search, versus 26% from social, a difference that appears to be growing.  

  • Ben Batorsky
6 min read
models

Talks on probabilistic modelling from tech @ ThriveHive

In the past month or so we've been giving talks and workshops on one of our projects to predict customer spend on different digital marketing services.  Briefly, the project aimed to use

  • Ben Batorsky
1 min read
models

Interpreting machine learning models: Picking low-hanging business insights, Part 3: Getting results

In the previous blog posts we talked about interpretability in machine learning and how to set up your machine learning question.  We also discussed a bit about the application to ThriveHive's data.

  • Ben Batorsky
7 min read
models

Interpreting machine learning models: Picking low-hanging business insights, Part 2: Asking questions

Interpreting machine learning models: Picking low-hanging business insights Part 2: Asking questions In the previous blog post we examined the importance of interpretability in machine learning models and how to interpret the parameters

  • Ben Batorsky
5 min read
models

Interpreting machine learning models: Picking low-hanging business insights, Part 1: Linear thinking

ThriveHive has been putting out a couple of exciting infographics based on some of our work here on the Data Science team.  I thought it might be worthwhile to dig into the

  • Ben Batorsky
5 min read
natural language processing

Identifying product mix, part 2: Implementation

In the previous post, we outlined one of the major challenges facing analytics here at ThriveHive; segmenting a business population that may not fall neatly into existing classification systems.  We came up

  • Ben Batorsky
5 min read
natural language processing

Identifying product mix: A bottom-up approach to segmenting a small business population

Part 1: Set up Typically methods for segmenting businesses have relied on standardized industry classification systems (e.g. NAICS).  However, the limitation of a top-down industry classification system is that it does

  • Ben Batorsky
3 min read
natural language processing

How the Words Used in Email Subject Lines Relate to Open Rates

 Previously, we found that the association between subject line length and email open rate varies by industry. In this blog post, I will use the same generalized linear model (GLM) to examine

  • Ben Batorsky
3 min read
email

What Impact Do Email Subject Lines Length Have on Open Rates?

  Previously, we found that ThriveHive customers' email campaigns achieve open rates significantly higher than the industry standard. This difference, however, varies by industry. For example, Computers & Electronics and Entertainment & Events

  • Ben Batorsky
3 min read
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