Reddit Roundtable: Data Science In Digital Marketing

In a nutshell, data science gives answers to the things we don’t know that we don’t know. (Whereas data analytics tends to solve problems for questions we know we don’t have the answers to).

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PRACTICAL USES OF DATA SCIENCE IN MARKETING

A few months ago I wrote about some practical applications of data science in marketing, including social media mining, persona development and data-led creative. On Reddit, the discussion focuses more generally on the conceptual application of data science to marketing.

One of the most interesting takes was data science as an additional skill to any digital marketer. Programming can be a substantial barrier to entry in learning data science but there are a number of more visual tools that abstract away the need to code. A few of my personal favourites are Exploratory (a UI built on top of R, without needing to know R), KNIME (a visual data science pipeline tool), and Orange (a data mining and visualisation tool). Getting to grips with tools like these will afford you a significant advantage. For example being able to identify patterns of customer behaviour that lead to conversion events, which you might already be looking at manually, case by case. Being able to take advantage of machine learning will allow you to do it at a much greater scale.

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Really though, you don’t even need to go as far as learning how to use these tools, especially if you already have data scientists in your marketing department. The best thing you can do as a digital marketer in a world of data is to become knowledgeable in how data science works generally. This will enable you to have productive conversations with your data team to work together to develop valuable solutions to real business problems. You’ll quickly become the data science team’s best friend.

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DATA STORYTELLING

Storytelling goes beyond marketing. It helps customers engage with your brand and clients buy into your narrative. Data scientists tell stories with data by combining a strong narrative with strong visuals, usually following an experiential escalation from initial question through methodology and insights to recommendations.

At the peak of data storytelling lies data journalism – telling complex stories through engaging data visualisations. Data analysis can feel a lot like journalism – interrogating data, following your intuition, chasing leads. Cleaning data is where your inner journalist emerges and you begin to develop an intimate knowledge of your data. It is hard because it always takes longer than you might expect, it’s not something you can outsource; it’s a critical part of the data science process and one that ultimately pays off when bringing everything together into a story.

Users often turn to Reddit in search of data journalist recommendations. One of my favourites, recommended in the thread below, is Chartr – an online newsletter sharing punchy bitesize data stories.

Who is your favorite Data Journalist? from r/datascience

GOOD DATA SCIENCE TO EXCELLENT

A recurring topic is the step change between average and excellent data science. Programming skills, statistics concepts and familiarity with machine learning frameworks are the main focus of a lot of online data science learning material. These hard skills are vitally important, especially as data science is increasingly integrated with software engineering, deploying models and working with native cloud architectures. However, the ability to communicate the value of their work and then deliver on it, is undeniably even more important.

Data science can be extremely complicated and often people will have no idea what you’re talking about, which is why communication is incredibly important. Those who are great communicators have the ability to to distill complex problems into simple ideas.

The idea of not letting “perfect” get in the way of “good enough” is a popular point as well. It aptly denotes the difference between data science in academia and pragmatic data science in industry. Marketing is fast paced and not the place for a two year-long scientific study of user behaviour.

Finally, not enough can be said for subject matter expertise. An excellent data scientist should have a penchant for understanding the domain and the data before diving into any analysis-type work. Like anything else, data has context. Naive assumptions and working blind will only lead to false positives and shaky conclusions.

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ARE YOU READY FOR MACHINE LEARNING?

Some data scientists can be their own worst enemy, desperate to work on state-of-the-art deep learning projects but a total Goldilocks when it comes to finding the right data source. This is the “Instagram vs. reality” of data science. Real data is messy, unorganised and often smaller than you might like.

While jumping straight to hiring a team of expensive machine learning engineers without a clear strategy would be a mistake, it is not difficult to drive value from clean and meaningful data.

It’s a communication thing. Machine learning for machine learning’s sake is not likely to be productive and there are always reasons why you shouldn’t do something. But in these scenarios suggesting different approaches rather than shooting down ideas is better for everybody. Machine learning and AI are often spoken about at conferences like the second coming of Jesus. So it’s not surprising then that management and decision-makers have such high expectations and excitement for its prospects.

Ultimately, data scientists want to do machine learning and management want to say they do machine learning, so it’s about doing more with less, than being stuck reporting all day.

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