Data Science for Marketing: How to Get Started
From math and advanced computing to data engineering, data science involves an array of expertise areas to create a thorough, holistic look into raw data.
Implementing marketing data science in your digital campaigns may sound like a tricky business. With an increasing number of companies integrating machine learning and AI into existing marketing, however, gaining deeper insights from your data might be easier than you think.
Here’s what you need to know to get started.
What Is Data Science in Digital Marketing?
For a long time, the data that we had was mostly small in size and fairly structured. Because of that, it could be analyzed using simple BI (business intelligence) tools.
Today, however, things are a little more complex. As the business environment becomes more and more digitized, the data that we have is becoming less structured and BI tools are no longer capable of processing its volume and variety.
Here’s where data science comes in.
Data science is a forward-looking, scientific approach that blends in various algorithms, tools, and techniques to extract meaning and insights from raw data.
Although often used interchangeably, data science and machine learning are not quite the same thing. Data science tackles big data and includes data cleaning, preparation, and analysis. Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data.
At its core, data science provides answers to open-ended questions as to “what” and “how” events occur. Implementing this approach helps digital marketers to better understand their customers, maximize work efficiency, improve automation and decision making, and detect various patterns in consumers’ online behavior.
Data Science Workflow
Before rushing into data collection and analysis, it’s important to understand – and follow – the data science workflow.
A data science workflow is used to define the phases of your data science project. A well-defined workflow provides valuable guardrails to help the data science team successfully plan, organize, and implement each project.
There are several well-known data science workflow frameworks that can be utilized during data science marketing projects.
Cross-Industry Standard Process for Data Mining ( or CRISP-DM) is one of the more popular frameworks.
Typically, the main phases of the data science lifecycle include objective definition, data preparation (including data cleaning), model building (to train and test the algorithm), deployment, and monitoring.
Each phase has its own predefined tasks and deliverables (e.g. documentation and reports). In addition, there are frequent opportunities to go back to a previous step and/or evaluate the progress of the project against its original objectives.
With a strong data science workflow, you can make sure that business objectives are properly addressed throughout the project, but also adapt and change the objectives accordingly based on new findings.
Defining Your Objectives and Understanding the Data
Before you get started, it’s imperative to define your objective, understand the requirements and specifications of the project, and establish priorities.
When defining the objective, you will also want to frame the business problem and formulate the initial hypotheses you’d like to test. Questions to consider when defining the problem include:
- What problems are we currently facing and what problems are we looking to solve?
- What insights do we want to explore further?
- What challenges are our current customers facing when using our product/service?
During this first phase, you should be starting with the initial data collection, familiarizing yourself with the data, and identifying data quality problems. During the process, various interesting subsets might surface, helping you to better formulate your hypothesis.
In the data preparation phase, you’ll need an analytical sandbox – a separate area of your data warehouse where you can do experimental/development work on your analytics system, ideally during the entire duration of your project.
Furthermore, make sure to explore, preprocess, and condition data, as well as perform an ETLT (extract, transform, load, and transform) before you move on to the next phase.
Model Planning and Building
During the planning stage, you should determine the techniques and methods you want to use to draw the relationships between variables.
The model building phase is where the best-fitting model is chosen and the dataset is split into training and testing sets. Next, the model is taught patterns from the training set and tested. Machine learning techniques, such as feature selection, principal component analysis, and clustering algorithms are utilized at this stage.
At this phase, your goal is to create the delivery mechanism that will help you get the model out to the users or another system. Depending on your project, this could mean getting your model output in a dashboard or scaling it to the cloud to a larger user base.
Regardless of your end goal, remember that you should continuously monitor and evaluate your workflow. This will make it easier to identify any regression and stability problems as soon as they surface.
Clustering in Data Science
In the simplest of terms, clustering in data science is a machine learning technique by which data points are grouped into a single cluster. Data points that are in the same cluster should have similar features and properties. Likewise, the data points in different clusters should have different features and properties.
Clustering algorithms are a family of unsupervised learning methods. This means that the algorithm is not presented with labels, leaving it to find structure in its input on its own. Unsupervised learning can serve as a goal (i.e. discovering hidden data patterns) or as means towards future learning.
Before you run a clustering algorithm, the nature of the clusters, as well as the optimal number of clusters in the data set, are often unknown.
Generally speaking, the main purpose of clustering is to determine a model that describes the clusters in a single dataset (and only this dataset).
Consider the graph below. The clustering algorithm groups the raw data into three clusters, optimizing for minimum distance between the individual data points and maximum distance between clusters.
Overall, there are a number of different clustering algorithms that data scientists use to group data points. Some of the more common ones include K-means clustering, mean-shift clustering, and hierarchical clustering.
In digital marketing, identifying similar groups of data in a dataset is particularly beneficial for marketing analytics and customer segmentation.
7 Ways to Use Data Science in Marketing
Although many tech giants are already using data science for marketing, many businesses are still navigating this new space.
To help you get a deeper understanding of how data science can work for your digital strategy, here’s an overview of seven of the top application of data science in marketing today:
Table of contents:
- Customer Segmentation
- Sentiment Analysis
- Channel Optimization
- Marketing Funnel Optimization
- Lead Targeting and Scoring
- Predictive Analytics
- Pricing Strategy
One of the most prominent advantages of data science (and its clustering algorithms) in digital marketing is customer segmentation. In a nutshell, customer segmentation is the division of potential customers in a given market into separate groups.
Data science clustering simplifies the process and assists marketers in creating specific strategies and tactics for each segment based on distinct characteristics (e.g. demography, behavior, or purchasing power).
For example: if you’re planning to launch a product targeting millennials in Europe, creating an effective targeting strategy for each consumer would be a considerably difficult task.
By using data clustering, you can identify factors that are common in the millennial population and cluster consumers having similar characteristics. Effectively, this allows you to scale up your digital strategy towards a cluster rather than a single consumer, and – in the long run – implement it into your SaaS remarketing strategy, too.
Sentiment analysis is a text classification technique that lets you understand the sentiment of your customers towards your business, product, or service. It involves sorting the sentiment behind data such as social media conversations, feedback, reviews, surveys, and customer support conversations.
Data science and machine learning algorithms are wonderful tools for determining the emotional tone behind these sentiments, as well as sorting data at scale and providing real-time analysis. Leveraging these algorithms enables businesses to work more efficiently, with more accuracy, and towards more useful ends.
Data science in marketing offers access to data groups collated through a number of different channels including social media, organic, and email marketing.
By analyzing prospects’ online interactions within those groups, data science helps to make connections, create pathways, and identify missed opportunities on the channels that are most popular within your target audience.
Even more, it lets SaaS brands uncover what is bringing the desired outcomes and how they can best quantify their success on different channels.
Marketing Funnel Optimization
Traditionally, marketing campaigns have focused on awareness, acquisition, and activation. Through the use of data science, we can now get all the way down to revenue, retention, and referral.
Marketing data science can be used to attract the right customers at the top of the funnel, predict customer action and learn how to engage at the middle, as well as retain customers and predict the probability of further purchases at the bottom of the funnel.
Data science algorithms are further capable of predicting churn rates (the number of customers lost in a predetermined time frame). This means that marketers can create more effective strategies that specifically target customers who are more likely to stop engaging with the business in the near future.
What’s even more, through qualitative analyses, machine learning can determine your brand’s best ambassadors, allowing you to make the referral process more simple and effective.
Lead Targeting and Lead Scoring
When it comes to digital marketing for SaaS, generating quality leads is a vital first step towards landing loyal customers.
Because there are a lot of moving parts in a lead generation strategy, it can be difficult to determine which parts of the campaign are working and which need some improvement. Here’s where data science can play a key role.
Analyzing collected marketing data allows data scientists to predict which offers will be most attractive to different customers at different times. This lets you create great offers for all different stages of the buying cycle and improve lead quality.
Data science in marketing can also help in qualifying leads quantitatively (i.e. lead scoring). It allows you to simplify and take the subjectivity out of the process, as well as truly understand which leads have the best chance of converting.
With data science, the potential value of each lead can be scored based on factors like the characteristic of the customer segment they fall into and the behaviors of similar customers based on historical data of closed leads and their outcomes.
Predictive analytics brings together data mining and machine learning models to predict the possibilities of a particular event in the future that might affect your customers or your business.
Predictive analytics and data science offer a solution to businesses that are overflowing with data but are struggling to turn it into useful insights.
Using historical and current data, data scientists can identify trends and predict the probability a customer will perform a certain action, such as canceling their subscription.
Data science models excel at integrating new information and detecting emerging trends and demands. This opens up an attractive opportunity for SaaS businesses looking to determine an effective customer-oriented pricing strategy for their products and services.
Overall, data science can benefit your pricing strategy by providing you with valuable information on the elasticity of the demand (i.e. how customers (will) react to different pricing) and the best prices for your business based on its goals.
Key Benefits of Using Data Science for Marketing
There are numerous advantages for SaaS brands that are ready to embrace data science as part of their digital marketing efforts.
Data science algorithms help understand trends in consumer behavior so that we can better predict how valuable these prospects might be now and over time. In addition, leveraging data science in SaaS allows your business to:
- Deliver more personalized customer experiences and react promptly to users behavioral changes
- Analyze more data in less time
- Quickly adapt to new data
- Save resources by automating marketing processes and minimizing trial-and-error marketing plans
- Increase customers lifetime value
- Target the most valuable customers and reach the right users
Level up Your Data Analytics Strategy
At the end of the day, data science, machine learning, and AI technology have brought in revolutionary changes in digital marketing.
Data science is steadily shifting from an interesting, high-tech whim to a soon-to-be indispensable tool for all SaaS businesses. Its models are enabling us to glean new actionable insights and better understand our target audience.
Ultimately, excluding data science from our marketing strategies could be a costly decision to make.