Using Data to Drive Strategy

How NetGalley uses data to frame decision-making and help books succeed

One of the core principles at NetGalley is our focus on data. We give publishers the tools to help them understand and use their data as strategically as possible, and we use data to guide the development of our service.

When we work with publishers on a strategic level, and as we continue to build new features in NetGalley, we are thinking about what data is most valuable to publishers. When we meet with publishers, we hear that you would like more ways to correlate your marketing efforts with activity, or better understand your audience. It’s important for us to know which metrics are most important to you in the pre-publication phase, and how you’re using your results to understand what’s working for your books.

As much as we are encouraging publishers to look at their own data, we are doing the same for ourselves.

And as much as we are encouraging publishers to look at their own data, we are doing the same for ourselves. 

Since early 2019, NetGalley has been working closely with Mandy Fakhoury, our on-staff data scientist, to delve into our own data. She has helped us examine how publishers are using the site, which tools are most used (and how effectively), and how industry trends are manifesting on NetGalley.

For example, with Mandy’s help, we learned which categories within the NetGalley catalog saw a lot of interest, but had relatively few titles listed. We shared the results of that research to encourage publishers in those categories to capitalize on what we learned.

Mandy shared a bit about her work as a data scientist for NetGalley and Firebrand, plus a tip about how lay people can become more comfortable using hard metrics to guide their decision-making.

What is the role of a data scientist? 

The role of a data scientist is extracting meaningful information from data. My job is focused on data management, modeling, and business analysis. The process for any data scientist is defining the problem, collecting the data, understanding and exploring the dataset, and analyzing and communicating the results and findings. Ultimately there’s a question to be answered or a problem to be solved, therefore the majority of my time is spent making sure the data is ready to be analyzed. The remainder of my time is spent on creating models or analyses that give insightful meaning or show certain trends that answer or solve the problem.

What have you observed about how publishing engages with data, compared  to other industries?

Publishers require data to make clear decisions to innovate and better serve their customers. Like  other industries, data is a crucial part of the decision-making process. As an example, most industries use historical trends which allows them to identify which areas have been successful and which ones need improvement. In the publishing world, historical trend is about identifying which genres yielded the most sales and which titles sold the most. 

Any favorite project you’ve worked on for either Firebrand or NetGalley?

I can definitely say I have learned more about natural language processing (NLP) and text analysis within both Firebrand and NetGalley. I have become more familiar with the data that goes into the publishing world. On the Firebrand side I have learned the trend of publishers owning or losing a buy button, as well as whether a sale price is different than the list price provided by a publisher among other insights. On the NetGalley side I have created a Word Cloud based on reviews from members [now live for NetGalley Advanced clients!]. Doing the Word Cloud, I got more comfortable using Flask as well creating applications in R Shiny. I have also gotten a deeper understanding of sentiment analysis as well as text classification and the quality of a text. Overall, every project teaches me something new and that is my favorite part; with data science you never stop learning.

Any advice for non-data scientists to become better at using hard metrics to guide decision-making?

An effective decision is made based on a blend of experience and data. The best approach is understanding your data, the behavior of trends, as well as your audience, and don’t let the data blindly drive your decision. 

Decision making is a critical aspect of success or failure. In this new era, data has become a key part of the decision-making process. Once a problem has been clearly defined, it’s a matter of collecting the appropriate data needed to answer our problem. Data provides us with the information that can be used and processed in different ways to make decisions. A big challenge is knowing how much to rely on the tools at your disposal and how much to rely on your instincts. An effective decision is made based on a blend of experience and data. The best approach is understanding your data, the behavior of trends, as well as your audience, and don’t let the data blindly drive your decision. 

A dedicated data scientist is invaluable to the growth of NetGalley for Kristina Radke, VP of Business Growth & Engagement. 

“Mandy is dedicated to helping us understand all of the activity on NetGalley and find real answers about the use of our site, both by publishers and members. It’s so important to how we plan NetGalley’s continued growth. A gut feeling just isn’t enough to make business decisions! I don’t want to just think about data and activity–it’s critical to my role to understand it and use it to get better and better. I love working closely with Mandy as she collects and analyzes our data, and making space to consider what that data means to us going forward.”


A gut feeling just isn’t enough to make business decisions! I don’t want to just think about data and activity–it’s critical to my role to understand it and use it to get better and better.

Lindsey Lochner, VP of Marketing Engagement, explains that publishers benefit from our increased investment in data analysis. 

“Lately we’ve been hearing more about data analysis from many of the publishers we work with, whether they have an official data scientist (or team) in-house, or their individual marketers have an increased focus on the data available to them. As our clients dig even further into their own data, it’s our goal to help provide some of the missing puzzle pieces that fit into the entire picture of how a book is performing. Mandy helps us visualize and analyze the specific activity and review data that we gather from our platform, so that we can present that back to publishers to help widen their scope.”

When we launched NetGalley Advanced in January 2019, we did so to give publishers even more early data. And as we continue to build out new features and functionality within NetGalley Advanced, every update we are releasing is to help publishers discover a deeper understanding about how you (and any associated imprints) are using the site. Publishers can see how members are interacting with their titles, and how their actions (both on and off NetGalley) affect NetGalley activity, and more.  

We’ve shared some of those data-driven best practices here on NetGalley Insights, including tips on how to develop a data strategy, how to best use the Snapshot PDF, and takeaways from high-performing marketing campaigns in our Proven Strategies series.

We’ll be sharing more dispatches from our internal research here on NetGalley Insights. 

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