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Customer Story

Fender Brings Customers on the Guitar Journey with Alteryx + AWS + Tableau


Fender Key Stats

Industry: Manufacturing

Department: Analytics

Region: United States

Data stack: AWS, Alteryx (Designer, Server and Intelligence Suite), and Tableau


Fender Musical Instruments Corporation (Fender) designs and creates the iconic guitars played by the likes of Jimi Hendrix, Nancy Wilson, Kurt Cobain and H.E.R. In fact, Fender guitars, amps, and pedals have been played on more stages than any other company in the industry.

In addition to the legends, Fender is on a mission to support music enthusiasts at any stage of their musical journey. But even the most enthusiastic guitar owners admit learning to play can be challenging.

That’s why Fender empowers guitarists to triumph over challenges and transcend barriers, fostering an environment where their musical aspirations can flourish and innovation can thrive. As Fender guides beginners on their guitar journey, it uses data analytics to enhance the customer journey.

Meghan Gohil, Automation and Data Visual Manager at Fender, works with a team of esteemed data analysts that support cross-functional departments like marketing and finance to make intelligent decisions. Combining Alteryx with AWS and Tableau, along with various data sources, Gohil’s team drives business impact on everything from marketing campaigns to supply chain optimization to subscription revenue.

Understanding today’s guitarists: An optimized data stack enables richer customer insights

Today’s musicians are a diverse, rapidly changing demographic (Fender’s own research found that 50% of the guitar-buying market is made up of beginner female guitarists). To understand its customers on a deeper level, Fender implemented an analytics process that gives them a rich view of their market.

Fender’s team works with data from multiple sources: Microsoft SQL Server, Excel spreadsheets, Google Sheets, CSV files, and more. The team also has access to TomTom and Experian data through Alteryx’s data packages, helping Fender enrich datasets with demographic information.

The team primarily uses Amazon Redshift, a powerful data warehouse for storage and computation, and needed a way to optimize reporting and visualization with Tableau. Before Alteryx, the team would output Redshift data directly to Tableau, and reports took a long time to generate. With millions of records and unaggregated data, the process was slow.

Once the team adopted Alteryx, runtimes were significantly faster. A typical query that might take three minutes now ran in seconds. One Tableau report took almost a minute and a half to load when doing it straight from Redshift. Venkat Ravikumar, an analyst on the team, created a workflow in Alteryx that aggregated the data, reducing the load time to under 10 seconds.

The time savings allows the team to get deeper insights. “If you’re someone that has been writing in Python and SQL and you switched to Alteryx, you’re able to be more efficient with your time with a higher output,” Gohil said. “It also allows us to have more detailed analysis.


We can get really deep in the weeds because we’re not dealing with lines and lines and lines of code, which means we can focus on truly understanding our players and their needs.

Meghan Gohil

Automation and Data Visual Manager at Fender


Today, the team uses Alteryx Designer and Server to build and automate workflows, deployed on an AWS environment. Gohil has over 100 workflows built with Alteryx Designer, with an array of use cases across line-of-business teams.

Predicting demand with a machine learning model

When Fender creates a new guitar – such as a model in the Fender Artist Signature Series – the team needs to forecast how many guitars to manufacture.

It’s an important call to make: Too many guitars can also mean excess inventory. It’s costly to store guitars, in part due to the climate control required to protect the delicate instruments. But if they make too few guitars, they miss out on potential revenue when demand peaks.

To predict demand, the data team uses the Alteryx Designer Intelligence Suite to create a machine learning model. They leveraged social media data from platforms like YouTube and Spotify to gain deeper insights into the popularity of each artist. The model also includes factors like how long the artist has been in the market and the price point of the guitar. The predictions augment the decision-making with an intelligent data-driven approach.


Prior to Alteryx, decisions had been made based on gut feeling. This way, we are able to get closer to a more scientific way to do it.

Meghan Gohil

Automation and Data Visual Manager at Fender


Analyzing customer lifetime value: When earning a lifetime customer means creating a lifelong guitar player

For any product, the customer journey has some friction. But when your customer journey is the guitar journey? It’s even more challenging. The guitar is hard to master, and beginners will often abandon the instrument in the first 90 days, even before they get good enough to buy more lessons and equipment.

Fender’s goal is to encourage guitar adoption so players become valuable lifelong customers. Fender Play, Fender’s online subscription-based platform for guitar, bass and ukulele, utilizes strategically curated video lessons and is one way Fender provides a path to playing.



To find out which videos drive viewer engagement and lead to conversion, one of the analysts on Gohil’s team, Misho Galbo, created a workflow in Alteryx Designer that aggregates millions of records.

From there, the team can slice and dice the data in different ways to explore, investigate, and ask questions. How many videos do people watch before they subscribe? What types of videos hook people, and which ones drive them away? “Because it’s a workflow in Alteryx, it’s easy to go in and look at the data in different ways,” Gohil said.

In 2021, PreSonus joined the FMIC portfolio of brands whose flagship software product is a digital audio workstation (DAW) software used to record music. PreSonus is another subscription-based offering with opportunities for optimization. Analyst Jon Driscoll uses Alteryx Designer to create a churn analysis and customer lifetime value calculation that multiple departments use. The analysis helps teams understand how well PreSonus is performing, determine the right price point, and more.

A versatile instrument that’s also easy to pick up

Like the guitar, Alteryx has range. Its flexibility allows for creativity and experimentation, with expansive use cases beyond prep.

“It’s a Swiss Army knife. It’s not just an ETL tool. It’s a machine learning tool. You can run predictive analysis directly in Designer. There are all kinds of connectors, so you can connect to a wide variety of sources. You can blend it all,” Gohil said. “Other products are not as complete as Alteryx is.”


It’s a Swiss Army knife. It’s not just an ETL tool. It’s a machine learning tool.

Meghan Gohil

Automation and Data Visual Manager at Fender


And also like the guitar, a data analytics tool is usually challenging to pick up. But with Alteryx, the Fender team got up to speed fast.

As someone who previously used SQL, Gohil appreciates that Alteryx is designed for simplicity and ease of use. “Alteryx is much more user-friendly, so we’re not spending as much time thinking about and writing arcane lines of code,” Gohil said. For instance, “we’ve simplified hundreds of lines of SQL code into four Alteryx nodes.”

Fender and Alteryx are both committed to helping beginners achieve outcomes sooner than later. Alteryx makes it faster and easier to accomplish a first use case, and Fender uses data insights to get guitarists to their first song.

Fender Play’s tagline, after all, is “The easiest way to learn how to play.”

Ready to rock ‘n’ roll? Start your journey here.


3 Reasons Fender Uses Alteryx + AWS + Tableau

Faster workflow times

Alteryx drastically improved runtimes by optimizing the AWS + Tableau data stack.

Versatile use cases

Fender uses Designer for everything from automating reports in Tableau, to building a predictive model.

Deeper-level analysis

Because Alteryx saves time building workflows, the team can get deeper into the questions they want to answer and make tangible business impact.


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