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.
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 helps digital marketers move beyond broad demographic targeting and one-size-fits-all campaigns. It enables them to understand their audience, predict their needs and behaviors, and deliver hyper-personalized experiences.
If you want to apply data science in your marketing efforts, explore our PPC solutions.
See where this is going? Data science is your ticket to marketing success. With data science, you won’t be throwing marketing dollars into the wind; instead, you’ll be investing them where they’ll have the most impact. Plus, it allows you to stay ahead of the curve by predicting the future.
Let’s start with data science in marketing! There is a wide variety of activities involved in data science. It not only involves data analysis but also includes data collection, data cleaning, machine learning, predictive modeling, and the development of algorithms. Data scientists often work on complex problems, such as predicting customer churn, optimizing marketing budgets, etc., requiring extensive data exploration to inform strategic decisions.
Data analytics in marketing focuses primarily on data analysis and reporting. It involves gathering data, cleaning and transforming it, and generating insights to support marketing decisions. Data analysts typically work on well-defined questions, such as assessing the performance of a marketing campaign, measuring ROI, or segmenting customers based on purchase behavior. The goal is to provide marketers with easily digestible information for day-to-day decision-making.
Data analytics primarily deals with historical data to assess past performance. While it can provide insights for future strategies, it’s not as focused on predictive modeling as data science.
Which one to choose? You can leverage both if you want to gain a better understanding of your audience.
But if you need to predict future trends or optimize marketing campaigns using advanced algorithms, data science is the way to go. On the other hand, if your focus is on monitoring and improving the performance of existing campaigns, segmenting customers, and creating data-driven reports, data analytics is more suitable.
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.
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:
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.
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.
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.
Read more about the most important machine learning algorithms.
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 eleven of the top application of data science in marketing today:
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.
By analyzing customer data and segmenting the audience (based on psychographic, demographic, geographic, and behavioral data), marketers can create AI-driven user journeys, personalized product recommendations, email content, and advertisements responding to a specific customer pain point. This customization enhances the customer experience, fosters brand loyalty, and increases sales.
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.
Data science helps marketers understand which types of content resonate with their audience. Analyzing data on content performance can help marketers refine their content strategy, create more engaging content, and drive higher levels of user engagement.
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.
Attribution modeling assigns value to each marketing touchpoint in the customer’s journey, helping businesses understand the impact of different marketing channels and campaigns on conversions, which can better guide your marketing budget allocation.
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.
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 can estimate the potential revenue a customer will generate over their entire relationship with a brand using its first-party data. It can also predict when customers are likely to churn (stop using a product or service) based on historical data. This information helps marketers prioritize customer acquisition efforts and focus on high-value segments.
In our Paris Talks Marketing episode with Nikolay Stefanov and Paris Childress, we discuss more on how to get started with a model predicting customer lifetime value:
Market basket analysis is somewhat like peering into a customer’s shopping cart to understand their preferences and behaviors. With market basket analysis in marketing, businesses can make smarter product recommendations.
For instance, by analyzing the purchase histories and browsing behavior of customers, digital marketers can recommend complementary or related products to shoppers.
The same is true for website content. Marketers can deliver more personalized content recommendations by analyzing user behavior, such as the articles users read, the videos they watch, or the products they view.
In terms of dynamic pricing strategies, using market basket analysis will help marketers examine which products are frequently purchased together. Businesses can then adjust pricing to encourage bundling or cross-selling.
For example, offering a discount when customers purchase both a laptop and a laptop bag can incentivize customers to buy more.
Businesses can analyze customer data in real-time and respond to inquiries, whether it’s through chatbots, live chat support, or social media responses. This allows brands to engage in personalized conversations with customers, offering tailored product recommendations, addressing specific concerns, and providing relevant information.
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.
Real-time interaction uses natural language processing (NLP) and machine learning to engage with customers and prospects in real time. NLP and machine learning algorithms empower real-time interaction with the ability to understand and respond to natural language queries and inputs.
Explore our Paris Talks Marketing podcast episode with Amit Levi of Anodot to learn more about how to leverage machine learning:
Businesses can analyze customer data in real-time and respond to inquiries, whether it’s through chatbots, live chat support, or social media responses. This allows brands to engage in personalized conversations with customers, offering tailored product recommendations, addressing specific concerns, and providing relevant information.
Real-time interaction extends to email marketing, where NLP and machine learning can be used to personalize email content based on user behavior and preferences. Automated email responses, triggered by specific actions, enhance user engagement and nurture leads through the sales funnel.
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:
Here’s why it’s important to increase and predict customer lifetime value:
Let's explore some real-world examples of how several brands have successfully utilized data science strategies to optimize their marketing campaigns:
Salesforce, a leading customer relationship management (CRM) SaaS provider, uses data science to implement predictive lead scoring models. These models analyze lead attributes and behaviors to predict which leads are most likely to convert into customers. This helps sales teams prioritize their efforts and focus on high-potential leads.
Mailchimp, an email marketing platform, employs data science to predict optimal send times for email campaigns. By analyzing user data, including when subscribers are most likely to open emails, Mailchimp's algorithms help users schedule emails for maximum engagement and impact.
Google Analytics, a widely used web analytics tool, relies on data science to provide detailed insights into website performance. It tracks user behavior, traffic sources, and conversion paths, enabling companies to make data-driven decisions about their online marketing strategies.
Netflix leverages data science to enhance user experience. Their recommendation system, powered by machine learning algorithms, analyzes viewing history (including what you’ve watched and how you’ve rated it), user behavior, and preferences to suggest personalized content to each subscriber. This data-driven approach keeps users engaged and ensures they discover content tailored to their tastes.
Amazon uses data science for pricing optimization and product recommendations. They continuously adjust product prices based on factors like demand, competitor prices, and user behavior. Additionally, Amazon's recommendation engine suggests products to customers based on their browsing and purchase history, contributing significantly to their sales and cross-selling efforts.
Spotify employs data science to curate personalized playlists and the popular "Discover Weekly" feature. Algorithms analyze users' music preferences, listening history, and behaviors to create playlists that cater to individual tastes. This keeps users engaged and encourages them to explore new music.
Coca-Cola employs data science to monitor and analyze social media and online conversations about their products. By conducting sentiment analysis, they gain insights into customer perceptions, allowing them to respond to trends, address concerns, and adjust marketing strategies in real time.
As the importance of data-driven decision-making grows, many marketers find themselves drawn to the world of data science. Marketers have traditionally focused on creative aspects, market research, and campaign management. However, the digital age requires marketers to work with data analytics, customer insights, and data-driven strategies.
So, can a marketer become a data scientist? And what skills do you need to become a data scientist? Let's explore:
1) Transferable Skills & New Technical Skills Requirements:
Marketers possess several skills that align well with the requirements of data science. These include critical thinking, problem-solving, creativity, and a deep understanding of customer behavior. Marketers are also skilled communicators, which is essential for conveying insights drawn from data.
But to transition into data science, marketers also need to acquire new technical skills. This includes proficiency in programming languages, knowledge of data manipulation and analysis libraries, and familiarity with machine learning algorithms. Key areas to focus on include:
2) Data Collection:
Marketers are already accustomed to collecting and working with customer data for segmentation, targeting, and personalization. Data scientists take this a step further by designing and managing data pipelines, cleaning and preprocessing data, and developing predictive models.
3) Pursue Advanced Degrees (Optional):
Consider pursuing a master's degree or certification in data science or a related field to deepen your knowledge. If you combine marketing and data science skills, exciting career opportunities can open up for you.
Marketers who become proficient in data science may find themselves in high demand for roles such as marketing analysts, data-driven marketing managers, or customer insights specialists.
While becoming a full-fledged data scientist may require significant time and effort, many marketers choose to become hybrid professionals.
They leverage data science skills to enhance their marketing capabilities. This creates a unique blend of marketing expertise and data-driven insights.
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.
Ready to run data-driven marketing? Learn how to build a data strategy.
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