Data Science in Marketing

The informational age we live in right now is filled with data of all kinds. Much like in the case of solar energy, a huge share of this informational flow is neglected, ignored, and thrown out of the window. This is utterly wrong, since the data users provide on themselves are so generous that it can skyrocket your profits up to the moon. The problem is… these data are to be collected and processed, which is not an easy task. Let’s get to know why, as a marketer, you might want to learn to deal with big data and how to harness this power for your benefit. 

Definition of data science

Data science is an interdisciplinary study of large data volumes. Ideally, data science provides a holistic, thorough, and refined look at a raw dataset, so it is convenient to work with the data. It borderlines a whole plethora of other scientific fields like cybernetics, statistics, mathematics, linguistics, and even cryptography at times.

Although data scientist is a separate profession, the related skills are invaluable for many other fields, marketing included. In the digital world we currently live in, people share tons of data, which, when left unaccounted for, are an immense loss. If you think about it, there is no other way to predict future events without prior research.

We know that fire burns after having been burnt ourselves, people might be reluctant to swim in water after being close to drowning, or kids start to control their behavior after being punished. When we apply the same logic to a larger scale, we get the professional investors making predictions, based on the research of past asset prices. Or an underdog sportsman trying to take down a reigning champion, by examining his weak spots. So why not to apply the same logic to marketing?

Benefits of data science for marketing

You might have never thought about it, but as a marketer, you have been applying data science all this time. Ask yourself a question, why and how do you optimize a campaign? Obviously, male enhancers do not interest the female part of the audience, so you do not waste resources on the meaningless targeting this segment. Harnessing the power of data science can help your marketing endeavors enormously:

• Saving money and efforts on trial-and-error marketing
• Targeting only the most relevant customers, willing to make a purchase
• Increasing a customer’s lifetime and retention rate
• Quick learning from customer’s feedback
• Finding the correlations between seemingly unrelated factors
• Predicting the most popular products in the future
• Optimizing one’s funnels and campaigns
• Converting more leads with precise cross and up-selling

Data science is like a map, which you can use to know exactly where you are going to. However, this map ought to be drawn first.

Related technologies and approaches

Besides mastering advanced functionality of some basic software like Excel, you are going to need to get a massive pool of data. Of course, over time, you will get the results of your advertising campaigns, but the process can be facilitated further.

Crowdsourcing

You might have heard about crowdfunding, which is about a crowd financing some start-up. Well, crowdsourcing involves the crowd too, but this time around they provide some data, insights, and ideas. Take, for example, LEGO, which launched a dedicated website for users’ ideas on what the next LEGO set is to look like. To some extent, this can be treated as user-generated content, since the company encourages exactly that.

As a marketer, you can encourage your audience to share feedback on a product. For example, name like 10 features, e.g., price, convenience, design, and ask to estimate each on a scale from 1 to 10. It is a very basic form of crowdsourcing, which does its job.

ETL: mix & integrate the data

The data you receive are not always homogenous. In fact, more often than not the information is heterogeneous, obtained in various formats: text, numbers, photos, videos. Processing the data like that is akin to comparing bread against the Large Hadron Collider.

This kind of dataset is to be unified, but transforming one type of data into another manually is a time-consuming process. Enter, the Extract Transform Load (ETL) technology, which is one of the most popular solutions of this kind. With the help of ETL, you can relatively easy unify all the formats and start processing the data, without being worried about making a mistake and interpreting the data the wrong way.

This way, you can bring comments and scale rating to the same format to assess the mood of your clientele. ETL helps to incorporate all the deals, actions, and data on clients in a convenient database. Moreover, the technology is capable of enriching one piece of data with the information from a totally different source or do the opposite and erase the non-essential data for better decision-making.

Statistics

Statistical analysis is invaluable when it comes to finding the correlations, based on certain formulas. Statistical methods can help to:

• Find the most significant factors
• Establish the parameters as a reference point for future assessments
• Predict the campaign results
• Discover the distracting factors
• Make decisions better

It also leads to micro-segmentation, based on behavioral factors and demographics. Micro-segmenting the audience is helpful to fine-tune a campaign and squeeze out every little penny. In for a dime, in for a dollar, hey?

Machine learning and neural networks

While neural networks have not achieved the complexity of a human brain yet (probably for the best), AI ability to self-learn is a huge campaign booster. You might be able to tell that ZorbasMedia, ZM TEAM, and ZM HR are the same entity, but a primitive AI will not treat it so. Teaching the AI to establish such correlation is what machine learning is about.

Take for example ChatGPT, its vast knowledge and communicative capabilities are based on a huge pool of data. It is able to provide more or less intelligible answers, thanks to the prior experience. It does not mean the answers are necessarily correct, though. An AI can be taught to distinguish human faces and tell right away whether it’s a man or woman, which is handy for greater targeting. Machine learning helps to:

• Target a very niche-like audience, interested in a very specific product, e.g., mass tort
• Assess a lead’s readiness for a target action
• Lower the bounce rate
• Plan the budget ahead
• Increase the audience’s engagement
• Improve email open rate

Predictive analytics

Basically, the ability to make predictions about everything related to marketing. The range of its applicability is impressive:

• Consumer behavior analysis: search for any correlations between consumer’s behavior and demand
• Grouping the leads: thanks to the AI, you can lower the cost of targeting disinterested leads and improve the UX by delivering only relevant offers
• New product release: based on the previous interactions with the audience, you can predict whether the new product to be received positively or not.
• Analysis of a market basket: AI can find which products complement each other or answer why users abandon their carts, so you can capitalize on the info
• Campaign optimization: besides achieving high level of personalization, AI is helpful to get the data for quick campaign establishment

In marketing, every second counts. So, the ability to process a large pool of data and deliver robust predictions quickly is the key to succeed in such a highly competitive profession. Rapidity is essential for getting leads early on, before someone else converts’em.

Conclusion

Data science, machine learning, artificial intelligence, and other related concepts are by no means a replacement to human efforts. After all, the AI is yet to reach the same level of creative problem-solving. But the AI is undeniably better suited for standardized tasks, which is where data come in handy.

Thanks to computers and machine learning, you can process unbelievable amount of data and find vague correlations that can make all the difference. Terabytes of data flow around you right now, and ignoring such an abundant stream is a crime against your profit. Harness it, and get even more profit. Any initial costs of obtaining a software for data science is worth it, since the results outweigh any investments.