Pandora's Blind Spot: Two Simple Ways to Make Your Digital Marketing Data Smarter

Last week in a flash of inspiration, I decided to to create a special station on Pandora. The basic premise would simply be women singers rocking out about powerful women's topics. I searched the existing channels for "women" and "female" and words related to "power" and came up with nothing. That was rather sad, but fortunately, on Pandora I can build my own channel. 

So I started with a series of "seeds" to kick off the kind of artists I had in mind. Seeds represent the core of the station. Pandora takes the profile of these songs and finds similar artists using the

music genome project

. Notice all my seeds all female artists. And I'm not exactly up-to-date on music which is why I use Pandora to introduce me to new artists. 

I needed to train my new station for two concepts.

Concept one: singer must be female.

This should be easy to train. Male or female are pretty clear concepts.

Concept two: singer's topic must be empowering. No whining.

This would be the more difficult concept to train because "empowering" is not clearly defined. I would have to cull this content over time. 

It was a decent start and now music is playing, great!

Pandora station seeds
Pandora station seeds

Original Seed song list

And then I noticed a series of male rap stars coming through. Now I've curated another channel called Bad Girlfriend which I use for working out that contains a bunch of these artists. So it's not completely unusual that these male artists might bleed through. Every time a male singer came up, I selected Thumbs Down or moved it to another channel which allows me to reject the song from this station, eventually "teaching" Pandora what the station should be. 

Pandora thumbs down songs.
Pandora thumbs down songs.

Thumb's Down list on new Pandora Station

I actually gave the thumbs down to so many songs while training the station that I received an error message from Pandora saying I'd exceeded my thumbs down limit for the day. Who knew? This told me the concept of "female singer" does not exist in Pandora's data. Any system that could tell the difference between male and female lead singers would have caught the pattern and it wouldn't have to be 

IBM's Watson

to do it. Which brings me to this fundamental tenet about data:

Data is only as smart as the information it carries. 

If you have ever tried to combine data streams from, for example, an agency's detailed paid search spreadsheet with basic web visit and page data, then this is for you. Data streams are like straws. They only carry what you put in the glass. If you want to extract value from your analytics, then think about adding these two essentials to any data stream: 

Essential #1: What is the purpose or goal?

Why did you send out this content in the first place? For digital marketing data, I recommend using a discrete set of 5-7 labels such as attract, engage, build loyalty or however you visualize your customer stages. In Pandora's case which is product data, I might tap the

7 universal emotions

designed to capture why someone chose to create a station.

Essential #2: Who is the target?

Who is the audience? This is especially nice for digital marketing data when you have multiple audiences such as business units or multiple customer types such as partners and prospects to track. In the Pandora product example, my target is female singers.

Adding more intelligence to the data can happen gradually over time so there's no need to think of every circumstance and drive your technical team crazy. Start with these two fundamentals and I guarantee it will immediately boost your digital marketing analytics results. 

And just for kicks, you can sample to the Fierce Powerful (women) Pandora station,

here