Data Science in Marketing: Best Use Cases

Today, there can be no marketing without data science. In fact, data science in marketing is a cornerstone for at least several types of online promotion, just to mention performance marketing and marketing analytics. In modern times, marketing teams extensively use data science in marketing analytics in order to improve their efforts. And since data science in marketing has many tremendous use cases that can really make a difference in your company, we decided to take a closer look at them.

In this article, we are going to think about how data science for marketing is changing modern promotion and sales departments. For starters, let’s think for a few moments about data science in marketing analytics.

Data science in marketing analytics

When it comes to modern online marketing campaigns, marketing analytics is an absolute foundation. Every online campaign, especially conducted with the performance marketing techniques, has to be thoroughly measured in order to optimize it and tweak its efficiency.

Not that long ago, marketing teams were simply responsible for buying ad space in magazines or on billboards. They created an ad, and published it, that’s it. Indeed, life was much simpler 🙂

However, at some point, people started to realize that the Internet should be the primary marketing tool. This is primarily because it’s available worldwide, but more importantly, it offers much more extensive analytics capabilities.

This means that marketing departments can now measure almost every single aspect of their campaigns, just to mention ROI or cost of one click (CPC). And this is what data science in marketing analytics is all about.

Because we now have access to such extensive marketing information, we can make our campaigns and ads much more effective and appealing to the given target audience.

With data science in marketing analytics, you can:

• Fully measure the effectiveness of your marketing campaigns.
• Determine what you can do differently to get better results.

What is data science in marketing analytics used for?

Generally speaking, data science in marketing analytics has two major goals:

1. To sell more (primarily by improving their conversion rates and by gaining more visibility in the various online marketing channels, especially Facebook Ads and Google Ads)
2. To lower marketing expenditure (by optimizing not just the campaign itself but also the cost-per-click rates)

64% of marketing leaders report that data-driven strategies are vital in today’s economy[1].

In mid-July 2020, Invespcro.com published an infographic containing statistics and trends for data science in marketing analytics. And, as we can see here, data science in marketing is primarily used in seven crucial aspects of online sales and marketing:

• Customer journey analysis: 63% of surveyed companies utilize this strategy
• A/B testing: 60%
• Website personalization: 46%
• Customer segmentation: 55%
• Usability testing: 55%
• Cart abandonment analysis: 47%
• Copy optimization: 44%
• Online surveys: 34%
• Expert usability reviews: 28%
• Competitor benchmarking: 21%

As you can see, these elements play a crucial role when it comes to marketing efficiency. Through data science in marketing, companies try to understand their customers, track their competitors’ activity, test various options and versions of their ads (A/B tests), and segment customers into specific groups.

Now, let us take a look at specific aspects of data science in marketing, and see how this solution can be used to improve your marketing activities.

Data science in marketing: Predictive analytics

Predictive analytics is a process based on utilizing AI and machine learning models in order to measure marketing and market activities to spot trends and correlations. With this knowledge, companies can try to predict the future, or at least the direction in which the market will go.

Companies and marketing agencies frequently use predictive analytics in order to:

• Analyze customers’ behavior
  With ML algorithms, you can spot correlations and patterns in customers’ behavior. This knowledge can be used to predict future purchasing tendencies, which is extremely helpful when you run an online store.
• Prioritize leads
  Here, especially sales departments are interested. For obvious reasons, not every lead or prospect will become a paying customer. That’s why companies use ML and AI to try and predict which leads are the most prospective ones.
• Enhance product design
  With predictive analytics, large corporations can use their knowledge about customers and the market in order to devise a product or a service that’s more tailored to the market’s current needs or trends.
• Improve targeting
  Similarly as in leads prioritization, the predictive analytics apps can show you the most prospective target groups and customers so that you can concentrate your efforts on them.

Of course, you have to realize that predictive analytics is no magic wand. It’s not some kind of ready-made tool that can be purchased, downloaded, and installed on your computer. Predictive analytics algorithms have to be built almost entirely from scratch to suit your organization’s needs and expectations. That’s why it usually takes at least several months to implement a fully-fledged predictive analytics algorithm in your company.

Data science in marketing: Content optimization

“Content is king”–how many times have you heard this statement? But there’s some truth to it, as a properly designed content strategy can vitally help you with your SEO and sales efforts. How is that possible?

First of all, high-quality content is one of the fundamental factors in Search Engine Optimization. That’s why companies run blogs and other sections on their websites containing articles, tutorials, and guides.

And secondly, if you produce content that’s adjusted to your customers’ needs and provide answers to the questions they frequently ask, the probability that they will buy something from you increases.

With data science in marketing, you can optimize the content on your website, and various ML/AI algorithms can help you estimate which types of content, questions, and headlines are most likely to become popular among your target audience. This way, you produce content that’s both Google-friendly and user-friendly.

Data science in marketing: Budget optimization

As you probably know, the vast majority of rates and fees in online marketing can be easily optimized. The way to achieve this marketing budget optimization is through intelligent solutions. By analyzing marketing data, data scientists can build a marketing spending model to use your budget better. For instance, such a model can help marketers distribute their budgets across various locations, channels, and mediums in a more effective way and optimize them for their key metrics.

Data science in marketing: Marketing channels identification

As we’ve already told you, marketing data science can help you indicate the most effective marketing channels that will turn out to be effective in your particular situation. This way, you can concentrate your budgets and efforts exclusively on channels that are worth your time. After all, having properly indicated marketing channels is half the battle, right?

For many companies, performance marketing is the best solution to grow sales and conversions. That’s because, in this approach, you pay just for the result (typically clicks, ad impressions, or other actions users can take).

Lead targeting and scoring

As you already know, not every lead or prospect will become a paying customer. That’s why companies use ML and AI to try and predict which leads are the most prospective ones. This so-called lead targeting/scoring allows you to determine factors that describe your current paying customers.

And, by looking at historical data, marketing data science can help you assess business requirements that have to be fulfilled in order to get a new customer.

Many organizations build even something called a predictive lead scoring system. It’s nothing else but an ML algorithm that’s based on marketing data science. Such a system, when designed correctly, is capable of calculating the probability of conversion for each potential customer. Based on this insight, you can easily divide your potential customers into groups that are worth (or not) your further efforts.

Customer segmentation and personalization

Segmentation is simply based on dividing customers into different groups. Segmentation allows you to match promotional activities to a specific target group. Segmentation helps define an effective marketing message, communication channels, and argumentation. It increases the effectiveness of the activities carried out, thus minimizing their cost.

On the other hand, personalization allows you to send personalized messages to customers and speak to them directly through a website or an app (e.g., a personalized email will be built based on interests/previous purchases of a given customer and will be addressed by name).

The main goal of personalization is to build lasting relationships with customers. Both in the case of segmentation and personalization, data science in marketing can help you make your efforts more efficient.

Sentiment analysis

Today, marketers want to understand more thoroughly what their customers really think about their brands and companies. And this is what sentiment analysis is all about. Various marketing data science tools use historical data (especially coming from various social media platforms) to analyze the connection between a brand and its customers.

This way, companies can also understand their customers’ beliefs, opinions, and attitudes. They can also monitor how customers react to different marketing messages. One of many tools that help you with sentiment analysis is Brand24.

Pricing strategy

Data science in marketing can help you in devising the optimal pricing strategy. There are many approaches to this question, and none is universal. That’s why retail and e-commerce companies use various tools that help them set their prices in the best possible way so that they’re both attractive to customers and profitable from the business point of view.

There are many price monitoring tools available online. For example, you can take a look at Price2Spy.com and Sniffie.io. Such tools, frequently available in the SaaS model, combine marketing and sales data coming from hundreds of online stores and show you the results.

Customer communication

Here’s another element of your marketing strategy that can be easily automated with data science in marketing. Today, chatbots, voicebots, and so-called personal assistants are more and more popular in online stores and mobile devices. Such tools help you communicate with your customers 24/7 without the need to hire more call center consultants.

Not that long ago, chatbots and voicebots could answer only the most straightforward questions and commands. They are now based on advanced machine learning algorithms that help them “learn” from each conversation and improve themselves in time.

As a result, modern chatbots are more advanced and can handle more and more complex questions.

Improved CX

Nowadays, customer experience (CX) plays a key role. Companies should create positive customer experiences by ensuring convenience, accessibility, and quick contact with the company. It is now the foundation of lasting relationships with customers and the way to build their loyalty. Indeed, customer experience is by far the most important aspect of every marketing and sales campaign. After all, no matter how great your ads are, if your customer service can’t cash checks your marketing writes, it’s all in vain. Your customers will be disappointed and go elsewhere.

According to Gladly’s Customer Expectations Report 2020, 79% of customers say personalized service is more important than personalized marketing.

Marketing data science can help you understand your customers and their needs in a more deepened way. As a result, you can offer the service they expect and meet their needs.

Digital marketing platforms

Lastly, we’d like to mention digital marketing platforms. There are many platforms available that help you make more informed marketing decisions and are based on data science in marketing. In this article, we want to give you two examples. First of all, we have Google Analytics. It’s a tremendous open-source tool that helps you understand your website visitors and track your online marketing campaigns. With Google Analytics, you can quickly optimize your campaigns and indicate the critical target audiences.

This tool is almost exclusively based on marketing data science, and it uses data that comes from Google and your website. The second example is Crazy Egg. It’s a data collection tool that allows you to gain insights into your customers through various testing capabilities. The service offers a range of tools, including heat maps, A/B tests, and user session recordings.

Marketing Data Science: Implementation Examples

Some of the aforementioned solutions have to be built for your company from scratch. It takes weeks to get everything up and running. But there are also tools and techniques where implementation can happen almost instantly. We want to show you two examples of marketing data science tools that can be implemented in your marketing department right away:

Language inderstanding and automated communication: Persado

It’s a marketing AI company that focuses on improving customer service through ML-fueled communication. They work on the AI platform of reference for the choice of words across the enterprise.

According to Persado, their ML algorithm applies understanding of language in order to create the best message to speak to customers across all channels. In many instances, all you need is a thorough marketing brief.

Persado’s Message Machine leverages the world’s most comprehensive marketing language knowledge base of over one million words and phrases, powered by AI and data science in marketing. According to their statistics, they’ve managed to increase their clients’ conversion rates by over 40%.

ML-Fueled marketing automation: Salesmanago

The days when marketing was done by hand are far behind in the past. Today, almost every marketing activity can be automated. On many occasions, this happens with the support provided by marketing machine learning and marketing data science. Take the example of SALESmanago Copernicus.

This solution is based on ML-powered technology that learns each individual customer’s behavior, predicts their purchasing behavior, and recommends personalized products. Thanks to data on other customers’ purchases with similar interests, the algorithm can predict what products you customers can be interested in[2].

Summary: The key ways of Data Science may be used in marketing

Here are some key ways data science is used in marketing:

• Recommendation Systems
  Companies use data science techniques like collaborative filtering and content-based filtering to analyze customer data and provide personalized product recommendations. This helps increase sales and customer engagement.
• Sentiment Analysis
  Data science models can analyze customer reviews, social media posts, and other unstructured data to understand customer sentiment towards a brand or product. This provides valuable insights to improve marketing campaigns.
• Customer Churn Prediction
  Predictive models can analyze customer data to identify those at risk of churning, allowing companies to take proactive measures to retain them. This helps reduce customer attrition.
• Customer Segmentation
  Clustering algorithms and other data science techniques are used to segment customers into groups with similar characteristics. This enables more targeted and effective marketing campaigns.
• Marketing Mix Optimization
  Data science is used to analyze the impact of different marketing channels, pricing, promotions, etc. to determine the optimal marketing mix for a business. This helps maximize marketing ROI.

Naturally, there are many more examples of such tools, but that’s a story for another time 🙂 However, if you are interested in marketing data science, drop us a line! We will gladly help you understand all the benefits of data science in marketing and help you devise tools and solutions that will make a real difference in your company. We are at your service!

Data Science in marketing – FAQ

What is data science in marketing analytics used for?

Data science in marketing analytics is primarily used to fully measure the effectiveness of marketing campaigns and to identify areas for improvement. It helps in determining what adjustments can be made to achieve better results, such as optimizing conversion rates and reducing marketing expenditure.

What are some key aspects of data science in marketing analytics?

Some key aspects include customer journey analysis, A/B testing, website personalization, customer segmentation, usability testing, cart abandonment analysis, copy optimization, online surveys, expert usability reviews, and competitor benchmarking.

How does predictive analytics contribute to marketing strategies?

Predictive analytics utilizes AI and machine learning models to analyze market activities and customer behavior. It helps in predicting future trends, prioritizing leads, enhancing product design, and improving targeting by identifying prospective target groups and customers.

What role does content optimization play in marketing?

Content optimization involves adjusting content better to suit the needs and preferences of the target audience. With data science in marketing, ML/AI algorithms can help identify which types of content, questions, and headlines are most likely to resonate with the audience, thus improving SEO and increasing sales potential.

How can data science optimize marketing budgets?

Data science can optimize marketing budgets by analyzing marketing data to create spending models that allocate resources more effectively across various locations, channels, and mediums. This optimization aims to maximize key metrics and improve overall ROI.

How does data science help in identifying effective marketing channels?

Data science helps identify effective marketing channels by analyzing relevant data to determine which channels are most suitable for reaching and engaging the target audience. This allows companies to concentrate their efforts and resources on channels that offer the highest return on investment.

What is lead targeting and scoring, and how does data science assist in this process?

Lead targeting and scoring involve predicting which leads are most likely to become paying customers. Data science utilizes ML and AI algorithms to analyze historical data and identify factors that indicate prospective customers, thus enabling companies to focus their efforts on leads with the highest conversion potential.

How does data science contribute to customer segmentation and personalization?

Data science helps in customer segmentation by dividing customers into specific groups based on various characteristics. This segmentation allows for more targeted marketing efforts and personalized messages, which can improve the effectiveness of marketing campaigns and increase customer engagement.

What is sentiment analysis, and how is it beneficial for marketers?

Sentiment analysis involves analyzing customer opinions, attitudes, and beliefs, especially on social media platforms, to understand their perception of a brand or company. This helps marketers gauge customer sentiment towards their products or services and tailor marketing strategies accordingly.

How does data science enhance customer experience (CX)?

Data science enhances customer experience by enabling companies to understand their customers’ needs and preferences better. By analyzing relevant data, companies can offer personalized services, quick responses, and convenient communication channels, thus improving overall satisfaction and loyalty.

This article is the updated version of the publication from Feb 8, 2021.

References

[1] MarketingEvolution. 50+ Digital Marketing Statistics You Should Know for 2021. Jan 22, 2021. URL: https://www.marketingevolution.com/knowledge-center/50plus-data-driven-marketing-and-personalization-stats-marketers-need-to-know. Accessed Feb 8, 2021.
[2] SalesManago. SALESmanago Copernicus – Machine Learning & AI. URL: https://pomoc.salesmanago.pl/machine-learning-ai-marketing/. Accessed Feb 8, 2021.