Why Marketing Data Science Is the Fastest Growing Force in 2022

Today especially, customers value a personalized approach. Personalization is no longer just a trend, but something you take for granted.

Marketers are tasked with capturing the attention of customers. Whether we’re talking about a small store or IKEA, perfect personalization is essential. The good news is that technology is advancing and allows you to understand exactly what your audience needs.

Over the past decade, information consumption on the web has increased manifold. Now, there are over six billion devices connected to the internet, about 2.5 million terabytes of data generated every day, and about 1.5 MB of data created every second for each person.

With this pile of data, marketers and business owners need to figure out how to personalize a product, when to offer it to a customer, and how to make the buying process easier. This is possible with the help of data science.

Data science plays a growing role in all aspects of our lives. It’s also the biggest driver of marketing innovation at the moment, fueling advances in automation, artificial intelligence, machine learning, and virtually all marketing technologies.

So, it’s only natural that interest in data science and related fields is growing.
Let’s take a closer look at what data science is and how it works.

What is data science?

Data science is an interdisciplinary field that mixes age-old scientific methods and processes with algorithms and cutting-edge systems to create meaning and applicable knowledge from unstructured data.

What about data science and marketing? The advent of data science has enabled deeper analysis, effectively using data processing techniques to optimize your marketing plan and better understand your customers.

AliExpress, Uber, and Airbnb, for example, are all using data science to tailor company plans to fit their customers’ desires and habits and ultimately, solve tasks: ordering a cab, booking an apartment, or buying a light fixture.

How does data science work?

One of the biggest problems businesses face today is that they’re overwhelmed with data, most of which they don’t even need. The first important step is to identify the data that is valuable for your goals.

Then, it’s all about creating the right algorithms to collect and process that data (with the help of automation). This is the same kind of data science that creates Google’s deep learning algorithms, unmanned cars, and other technological innovations in artificial intelligence.

Why will data science for marketing matter in 2022?

In addition to the aforementioned unmanned cars, there are a lot of reasons why marketing data science will play such a pivotal role in 2022. It’s important to understand that the technology used by data scientists has evolved rapidly over the past 10 years. Now, you don’t need huge teams of scientists, data collectors, and data analysts to get information about your audience and website visitors. Thanks to the automation tools available today, as well as recent advances in machine learning and artificial intelligence, small businesses and marketers can now do data science in a way that only the largest organizations could afford before.

This has really leveled the playing field in terms of businesses using data science to drive productivity and growth. Those who don’t respond to this opportunity soon enough will be left behind. When competitors already have the exact data on how to help their customers, the rest will be wasting money on strategies that don’t work. While some already know exactly what their client needs, others will be guessing how to please their consumer.

What is machine learning?

One of the most popular technologies, artificial intelligence (AI), was made possible by data science, in addition to a list of other incredible technologies.

Machine learning is part of artificial intelligence and uses algorithmic models to identify patterns in data sets. Even relatively simple machine learning algorithms can analyze volumes of data and draw conclusions. For example, they can determine which combination of post-sale interactions is most likely to lead to a second purchase. The more a machine learns, the more sophisticated it becomes.

How is data science related to artificial intelligence?

Without data science, the practical concept of artificial intelligence would not be possible. Scientists are still actively working on artificial intelligence. Their goal is to create technology that will mimic or even improve human cognitive thinking and decision-making. Any artificial intelligence application relies entirely on data and the system’s ability to interpret it.

So, while not all data science is artificial intelligence (or machine learning), AI would not exist without the theories or practices created/discovered within data science.

What is the difference between data science and data analytics?

Data analytics is the specific process of analyzing data to extract useful information. Traditionally, it’s been a manual process performed by data analysts, but things are changing thanks to automation, machine learning, and artificial intelligence.

Nowadays, analytics can be used by both analysts and algorithms. The advantage of algorithms is that they can process data on a massive scale, while humans retain the cognitive advantage of being able to think out of those algorithms’ borders.

Analytics is one of many processes in data science. Its purpose is to present raw data in a way that makes it easier for you to view, understand, compare, and retrieve useful information.

Generally, the overall goal of data science is to help you improve business results by increasing productivity and enabling you to test different methods.

The key is that you get all the answers from reliable data, usually in large volumes, that you would never be able to handle without data science or the latest technologies associated with it (namely, automation and machine learning).

How can data science help businesses?

Generally, data science can help improve any area of business where relevant data is available. It’s not even limited to marketing. For example, a business owner can use data to test whether overtime really helps a business do more, determine the best wages to pay employees, or try out flexible work models.

How can data science help marketers?

Accelerates campaign planning

Marketers can plan campaigns faster and easier thanks to data science. That’s all because data can be collected and analyzed much more accurately and efficiently. These same processes, which marketers perform manually by conducting the analysis themselves, can be accomplished several times faster with data analysis.

Optimizes budgets

Data science in digital marketing allows you to see and analyze ROI (return on investment). Technology can be used to analyze a campaign, get the percentage of people involved and trends in their behavior. It’s important to determine and verify what works best at a given point, and data science is right for that.

Provides real-time data

Typically, marketers collect customer data after each campaign is over – and data science makes it possible to do this non-stop. This is especially important for exploring new opportunities, predicting trends, and beating the competition.

Increases loyalty

Loyal customers are the ones who help sustain the business, and they cost less than new customers. Data science allows you to improve services for existing customers, thereby increasing their loyalty.

Reminds your customers about the brand

For example, McDonald’s installed smart billboards in 10 British cities: the ads on them changed depending on the traffic on the roads. When there was a traffic jam, the image changed and hinted at stopping for dinner at the restaurant: “Stuck in traffic? There’s light at the end of the tunnel.” This means that with the help of data analysis, you can offer your product to consumers at exactly the right moment for them.

Gauges customer reaction

For example, Snickers implemented this idea as part of its “You’re not you when you’re hungry” campaign. The system analyzed users’ social media posts and gauged their moods. The more negative and angry the posts were, the more Snickers was discounted. This remarkable solution could only come to fruition thanks to data science.

Data science mechanisms for marketing

Regression analysis

Regression analysis is a powerful tool for marketers that is part of predictive analytics. Simply put, a data scientist performs regression analysis to determine the degree of similarity between specific customer variables and the purchase of a specific product.

Instead of observing past behavior to predict what consumers will do next, predictive models can forecast consumer trends with much greater accuracy. Data science allows marketers to use this information and plan strategies around it.

For example, by analyzing purchase history, you can predict when a person won’t have dishwashing detergent or contact lens fluid and remind them to buy it at a personal discount. You can also simply offer them related products when they buy a certain item. For example, if a person buys a phone, offer them cases or other accessories.

Data visualization

Data visualization is a valuable tool that not only attracts attention but can also be used to inform, drive, and guide actions based on customer behavior.

For example, a marketing team can use all available customer information to make data-driven decisions about which products and services are best brought to market. By using data visualization, marketers may find out what types of customers live in the store’s neighborhood and what kinds of products they buy.

Automated customer support

Automated customer support systems, bots, and chats are actively used to reduce the cost of customer support. To make communication comfortable, bots are trained on the history of requests, and this helps, using artificial intelligence, make the machine’s answers as accurate as possible, corresponding to the request. As a result, such communication increases customer loyalty. You can also use bots to automate routine processes.

The big picture

Predictive analytics is a tool that allows thousands of successful companies to process a lot of data and find out if they should gamble on certain assets or if it will only bring them losses. By analyzing customer behavior, you can easily understand their needs and therefore, keep your business afloat.

With digitalization, and personalization increasingly important to potential customers, it’s more important than ever to properly utilize data science to resonate with your target audience.

This blog was co-authored with Dmytro Sokhach: Dmytro is an entrepreneur and the 6-Figure Flipper Club member. Founded Admix Global (web agency) that builds websites, makes them profitable, and sells them as business

by Roy Emmerson

Co-founder at TechTimes.com

Roy is a tech enthusiast, twin dad, programmer, and co-founder of TechTimes.com with a background in marketing at Itrate.co.