Why You Should Hire a Marketing Data Scientist

Most marketing teams are leaving a lot of money on the table by not leveragaing a Marketing Data Scientist.

According to Sitecore, the average US brand collects eight pieces of data per user, ranging from address to behavioural insights.

Brands are collecting an extensive amount of data at various stages of the customer journey. Data science helps us leverage this data into actionable insight that results in a greater return on investment.

Data science methods like machine learning, clustering, and regression have moved marketing from a creative domain to a scientific one.

By leveraging data science, marketing teams can extend their top-funnel approach to incorporate the full-funnel and uncover product and customer insights at scale in an unprecedented way.

To do this, growth marketers should understand what data scientists can and cannot do as well as some of the methods and how Marketing teams use Data Scientists.

What Data Science Is and Is Not?

There is a lot of confusion about what a Data Scientist does and does not do.

Specifically, people often interchange the terms of data science and data analytics.

The easiest way to differentiate between the two is that a data scientist looks to predict the future, while a data analyst looks to summarise the past.

Data scientists make predictive models using regression, machine learning, and other advanced statistical methods, while a data analyst uses descriptive statistics to analyse past patterns.

A data scientist is not a software engineer. Their programming ability is enough to run machine learning and statistical analyses they need using platforms like R, Python, and SAS, but not to develop software or manage infrastructure like an engineer would.

Data Science is the intersection between business expertise, programming, and statistics, where programming is simply a medium to derive insights using statistics and business or domain expertise.

The Data Scientist toolbox uses artificial intelligence and mathematical modeling to unlock a new set of insights.

A marketing data scientist can answer questions such as:

• Who are your most promising customers?
• What choice alternatives do consumers of your product have?
• How do people feel about your brand?
• What other products do your customers want to buy?

By leveraging the Data Scientist, a marketing team can eliminate waste and target customers in ways that are cost-effective and personalised.

Understanding Data Science Workflow

Understanding the data science workflow will allow your marketing team to communicate with the Data Scientist effectively.

After you have defined your marketing problem and gotten access to your data, the Data Scientist will perform some exploratory data analysis to get an idea of the right model to find the insight you are looking for.

This could mean testing models on historical data sets and measuring its accuracy or a variety of other methods to create a benchmark against which to measure the success of whatever model they pick. After the model is chosen, the data is formatted in a workable way.

This could involve figuring out how to deal with missing values, duplicates, or other variables that make the model harder to apply. The model is then run on a partition of the data in order to train it.

The method chosen will mold itself to the data and then will allow you to apply the model to any dataset with the same parameters. Finally comes fine-tuning the model. This means the model isn’t overfitted to the data and that it runs as it is supposed to.

Scenarios of Data Science in Marketing

Let us take a look at a scenario that most marketing professionals are deeply familiar with.

A company is spending a small fortune on marketing, and the ads are getting a lot of visibility, but the return on investment is nowhere near expectations.

Enter the Marketing Data Scientist.

Through data collected on the website and social media pages, the data scientist can understand the customer base’s demographics. This understanding goes beyond age, geographic location, and gender.

A simple basket analysis, wherein we analyse certain consumer behaviours’ occurrence, will give you details about what else this customer is likely to shop for.

While the market basket analysis has been employed for years by retailers, in the new age, it gives you insights beyond that for second order effects, say a customer buyers bread, we also categorise spreads.

It might give you a less intuitive, but equally actionable insight such as foodies are also likely to be home décor enthusiasts and are likely to watch, “Yoga with Adriene,” on YouTube (affinities produced with the Think with Google toolbox).

This allows you to market in new places where your client base is present, while still exposing you to a new audience, increasing your visibility without breaking the bank on marketing material.

Data Scientists Seek to Optimise Every Chance They Get

In Data Science and Marketing, the name of the game is optimisation. A Marketer is aware that business success is, in large part, driven by profitable revenue.

Tying a business’s marketing strategy to key performance indicators like customer lifetime value (CLV), incrementality, and cost per customer acquisition (CPA) is absolutely necessary for a competitive business landscape.

Businesses simply cannot afford to spend on marketing that does not contribute to their bottom line. A favorite tool of every data scientist and one that is absolutely necessary to the modern-day marketer to help with this is segmentation.

Some Key Benefits of Segmentation

• Helps determine market opportunities
• Tailor-made marketing initiatives
• Product development and design insights
• Pricing model insights
• No more boring personas

We can define segmentation as grouping or clustering of customers into groups based on different characteristics. Every marketer knows that different audiences respond to different narratives.

This is where our data science toolkit comes in.

Clustering customer segments together when you only have a few input variables is easy. However, the task becomes much more challenging as the number of variables grows.

Data scientists use a machine learning method called clustering to figure out where the segments really are.

Did I say no more boring personas!

How Does Clustering Work?

Clustering algorithms are unsupervised, meaning the algorithm figures out what variables are similar to each other without input from the user.

These clustering algorithms strive for the most mutually exclusive, collectively exhaustive segments. When employed correctly, this approach optimises clusters in the most efficient way possible.

It does not segment where segments are not needed and do not miss segments necessary for a targeted campaign. Not only are points within clusters similar to each other, the clusters themselves are dissimilar, meaning marketers can then tailor what each segment will respond to, rather than a generic campaign with low ROI.

This particular algorithm works by cycling through a series of cluster centers and finding which one is most mutually exclusive, and collectively exhaustive.

Funnel Optimisation Leveraging AI

Machine learning and artificial intelligence are how marketing reaches the full funnel. Marketing campaigns have traditionally focused on awareness, acquisition, and activation.

Through the use of data science, the growth marketer gets all the way down to retention, revenue, and referral. A business can forecast the customer lifetime value of new customers through the use of several machine learning and artificial intelligence methodologies.

Rather than just focusing on a first sale, growth marketers employ data science insights to help companies to target longer customer relationships.

Data science allows you to understand the customer journey in a much deeper way than previously possible. Where do your best customers come from?

Data scientists give you the insights to curate your marketing strategy to the customer that makes the most sense for you. Suddenly, your marketing team is directly impacting your revenue growth.

Machine learning can predict churn rates, helping you develop a strategy to target customers who are not as engaged with the brand as you would like them to be. Your marketing team is now working on retention.

This works all the way down to referrals. Artificial intelligence can help you determine which customers are influencers for your brand through qualitative analyses on the quality of content, brand affinity, and brand engagement. You can then target them to make their referral process simpler and more effective.

Insights and Experimentation

Equally important as predicting these segments is understanding "why". Marketing Data Scientists look for causal relationships that Marketers can then reverse engineer into an effective campaign. For example, let us hypothesise that clicks and conversion rate are positively correlated.

The Marketing Data Scientist can test if that is true with regression analysis, then the Marketer, in partnership with the Marketing Data Scientist, can come up with experiments to test which campaigns produce more clicks, therefore a higher conversion rate.

The Marketer is not just a creative, but a scientist, whose process involves constant experimentation.

The Marketer can communicate the pain points of a business, while simultaneously understanding the public sentiment and developing a marketing strategy around that.

Data science in marketing is about answering these questions in a more efficient and cost-effective way. Similarly, marketers understand the importance of the narrative. Studies show that a consumer is much more likely to remember an ad when there is a narrative attached to it.

Should You Hire a Data Scientist?

So, as a marketing executive, where should you start?

It is just as important to recognise where your company is not ready to employ artificial intelligence as it is to recognise where it is necessary.

Someone considering hiring a data scientist should be asking themselves questions like do I have enough data? Is that data sourced in a way a Marketing Data Scientist can access? An early-stage startup may not have the infrastructure or volume of data to necessitate hiring a data scientist.

A large enterprise may need to consider it's data pipelines before it can consider hiring one. Understanding what a Marketing Data Scientist can and cannot do is essential in deciding whether one is right for your team.

Maybe your company has some need for data science tools but has not employed them before. As is in marketing, when thinking about where to integrate data science and data science marketing tools into your marketing strategy, it is often best to capitalise on the low hanging fruit first.

Certain tools and methods are easy to implement using data your company is already likely to have. For example, your company has likely already done demographic research and has the raw data somewhere.

Clustering is a natural next step from there. Similarly, most businesses that are past the startup stage have the data to do at least some churn rate prediction, and from there, they can take steps to minimise it.

But ideally you look to target the right customers for your strategy or better still reverse engineer that with the clustering.

Once your business has a big enough pool of data, you can venture into natural language processing and sentiment analysis to understand how consumers feel towards your product.

Here Are Some Examples of Marketing Data Science

Real-Time Growth Experimentation

Using data to analyse customer sentiment about product or brand attributes is becoming a core competency for today’s marketing team.

The key differentiator here is that scenarios and experiments can now be tested in real-time rather than in retrospect or on an intermittent basis which means that today’s brands have an opportunity to immediately engage with and connect with their customers in ways that they were never able to achieve before.

Channel Optimisation

So what do we do with all of this insight?

The answer is to make better decisions within your marketing.

Here are a few practical applications that data science can help marketers trim the fat on their campaigns and better understand and target their customers.

By taking a look at where your greatest conversions are, you can choose what channels to use to bring your product to market.

Data Science can help you automate this process and make sure you are always getting the greatest possible ROI.

Targeting and micro-segmentation:

Conducting statistical analysis of structured and unstructured data sets allows data scientists to organise and re-arrange data in ways that reflect creative or content performance and inform creative executions against micro-targeting campaigns.

This helps marketers deliver hyper targeted messaging and personalised offerings to smaller, highly focused consumer groups.

Customer Persona Development

Marketing and data science both take common approaches in their strategies, making assumptions, then validating or invalidating them.

Data science can help you test the research and assumptions you make to develop who your customers are and then pivot if need be.

Once you have a solid understanding of who your true customer persona is, the data will show you deeper insights about what channels they prefer and what content they are likely to respond to, hence further increasing marketing efficiency.

Lead Targeting and Lead Scoring

Seasoned marketers know that a business or product does not have just one customer persona, but several. The problem often arises that businesses are not sure which one will provide the greatest ROI.

Data science allows you to track which customers have the greatest lifetime value (LTV) and then create a model to rank and target leads by LTV or any other KPI that makes sense for your business.

Sentiment Analysis

Sentiment analysis is a marketer’s natural best friend. Any marketer knows that the most important trait a marketer should possess is empathy. Sentiment analysis allows you to collect data at scale to help you empathise with the customer.

It allows you to monitor their reactions and beliefs towards the information they receive and gives you feedback on content and how people are engaging with your campaign.

Your customer’s initial reaction when they find your social media accounts or website can go a long way to shaping how they feel about your brand view, even before they have experienced your service. This reaction is often shaped by multiple factors including reviews or social posts and comments.

Sentiment analyses are typically set up by data engineers who assign specific values (negative, neutral, or positive) to individual words, to give each piece of content a score based on the user’s reactions in the comments.

This same method can be applied to emails, reviews, and even phone conversations using speech-to-text analytics. There are also plenty of social media listening and monitoring tools that offer this type of analysis as an out of box service.

Product Development and Pricing Strategy

Data science will help you match the right product with your customer. By looking at insights given to you by customer persona data you can perform various clustering analyses to see what else they are likely to buy and what price they are likely to buy it at.

These insights let you know exactly what your customer is looking for both from your current collection and give you data to develop new products they might be interested in.

Real-Time Data Insights

Data science also gives you the power to communicate with your customers quickly based on real-time data.

For example, a marketer may want to target customers who have delayed flights. Data science allows you to find customers who fit the mold and market to them immediately.

This helps marketers improve their customers’ experience by further personalising content.

Hire Reed Iredale: Marketing Data Scientist

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