The Perks of Data Science in Marketing

The Perks of Data Science in MarketingSmarter Marketers, Smarter MarketingOleksii KharkovynaBlockedUnblockFollowFollowingJul 6Now we are observing a turning point in marketing: Data Science and Machine Learning are already available not just to large-scale businesses.

New solutions appear one after another, advertising campaigns are becoming denser, and marketers sometimes are even turning into programmers.

The truth is, the words Data Science, Big Data, Machine Learning have been present in marketing for years.

Many have heard them but only a few have seen them.

Naturally, e-commerce has become one of the most advanced industries in this area.

Right now decoding huge chunks of data is a mammoth task.

This is where Data Science can immensely help.

First and foremost reason why Data Science has enormous value for marketing is a possibility to extract meaningful information from data and discern the right insights.

In this post, I will try to showcase what does it mean and what is the real power of Data Science for Marketing.

What Mechanisms Data Scientists Use for Marketing1.

Regression analysis to predictive analyticsRegression analysis is a powerful tool for marketers that is a part of predictive analytics.

In simple words, data scientist conducts a regression analysis to spot the strength of similarities between specific customer variables with the buying of a particular product.

There are three primary classes of predictive models based on a regression analysis that are actively used by well-known companies like eBay and Amazon:Cluster models (segments) — Used for customer segmentation; algorithms segment target groups based on numerous variables, everything from demographics to average order total.

Common cluster models include behavioral clustering, product based clustering (also called category-based clustering), and brand-based clustering.

Propensity models (predictions) — Used for giving “true” predictions about customer behavior.

Common models include predictive lifetime value; the likelihood of engagement; propensity to unsubscribe; propensity to convert; propensity to buy; and propensity to churn.

Collaborative filtering (recommendations) — Used for recommending products, services, and advertisements to customers based on a variety of variables, including past buying behavior.

Common models (like those used by Amazon and Netflix) include up-sell, cross-sell, and next-sell recommendations.

Instead of watching past behavior to predict what a consumer will do next, predictive models can forecast consumer trends with much more accuracy.

Data Science makes possible for marketers to take this information and plan strategic campaigns around it.

For example, by dint of analyzing the history of purchases, you can predict when a person will be out of washing powder or contact lens solution and reminds him to buy it with a personal discount.

Or when buying a particular product to offer him related products.

For example, if a person bought a phone, offer him new headphones.


Data visualization to bringing the right productData visualization is a valuable tool that not only appeals to the eye but can be used to inform, inspire and guide actions based on customer behavior (and other business information).

For example, a brick-and-mortar marketing team might use all the information available on customers to make data-based decisions about which products and services are best to bring to market.

By using data visualization to shows which types of customers live in a store’s neighborhood, teams can hone in on important guiding questions: Do they buy more hard goods or soft?.Is there an age-range density that shows what should be stocked?.Does the desired product make-up change as you move towards or away from competitor locations?3.

Automated consumer support and botsIn order to reduce the cost of user support, automatic support systems, bots, and chat rooms are being actively used.

In order to make communication as comfortable as possible, bots are trained on request history, and this helps, using artificial intelligence, to make the machine’s answers as correct as possible corresponding to the request.

As a result, such communication increases customer loyalty.

Also with the help of bots, you can automate routine processes.


Computer VisionLarge retail chains use the analysis of goods on the shelves to forecast purchases.

When the cameras fix the missing items on the shelves, this helps to optimize purchases and reduce storage and logistics costs.

It is also possible for trade lines to teach the scales to determine which fruit or vegetable you are weighing and to automatically get a price tag without entering a name or code.


Text analysisText analysis allows you to select from comments or other arrays of text similar in meaning.

For example, it can help find paid comments for marketplaces and improve the quality of product reviews.

Top 10 Advantages You Can Get with Data ScienceThe most powerful data scientists are those who act as bridges between insights and people.

Well, let’s take a closer look at ways to Implement Data Science in Marketing:# 1 More accurate personalizationLeads are generated based on a deeper understanding of the client’s profile, whereby it is possible to apply the correct message across, to the right audience at the right time.

Data Science allows matching all touchpoints across all devices to individual profiles.

This creates a true 360° unified profile view that provides actionable insights for each customer.

# 2 More precise results measurementCurrently, a common marketing practice is to measure the results of a campaign after it ends.

At that point, marketers have either met client goals or they have not.

This practice, however, is outdated and, thanks to data tools, marketers can measure how campaigns are working in real-time and adjust tactics accordingly.

For example, if a certain project aims to gain X amount of marketing-qualified leads, marketers can measure how far along they are at various points of the campaign instead of simply looking at the end.

# 3 More powerful media buyingThanks to machine learning and the improvement of algorithms, a large number of guesses and erroneous hypotheses are excluded when working with programmatic.

# 4 Clarity of separationData helps to concentrate on the most useful and effective information.

Thus, you can segment and group directly those you need to target.

# 5 Effective Email campaignsData science can be used to figure out which emails appeal to which customers.

How often are these emails read, when to send them out, what kind of content resonates with the customer, etc.

Such insights enable marketers to send contextualized email campaigns and target customers with the right offers.

# 6 Omnichannel experienceData-driven is the road to cross-channel communication with the consumer when the message remains consistent and one through all points of contact.

# 7 Better definition of the customer experienceMany modern brands want to receive feedback from the user regarding the service or interaction experience.

Having real data about the behavior to identify weaknesses and strengthen them much easier.

# 8 Advanced Social Media MarketingNowadays, customers are highly active on social media sites like Facebook, LinkedIn, and Twitter.

Marketers can use data science to see which leads are exploring their social media page, what content they clicked on and more.

With insights such as these, they can formulate a proper social media engagement strategy.

# 9 Improved product developmentA data-driven approach to the development of a new product or service minimizes the risk that it will fail.

Companies can learn to better understand their target audience, which leads to the creation of a product ideally suited to market conditions.

# 10 Optimized Paid SearchWith Data Science opportunities you can analyze your preferred customers based on the types of keywords they search for and consider what the competition is targeting as well.

Leverage this data to position yourself at the top of the most relevant search results and drive valuable traffic to your company.

Do You Really Need a Data Scientist?To understand whether you need a data scientist or not is simple.

If any of your departments keep records of something, then you are already collecting data.

These can be: logistic flows, stock balance, purchase data, sales statistics, contact support, product descriptions, user accounts, purchase history, calls, product characteristics, video from cameras — all of these are data files that can potentially be used for creating solutions based on Data Science.

This will help to better understand customer needs, increase the average bill, make additional sales, retain customers, optimize purchases, reduce costs and thereby increase profits.

Wanna know how to find or build a data science team?.Read my previous post that will help you o this matter — AI & ML Capability: How To Develop It For Your Own Business.

Final Word: The Future of Marketing with Data ScienceIt’s quite normal to ask now questions like ‘Will data teams replace marketing teams?’.

If you are curious about this question, here is my answer.

Approximately 10 years ago, all marketing was based on the assumption that girls love pink and boys love blue.

Like I’m exaggerating a little bit but that’s kind of how I feel about it.

The data in marketing has been accumulating for decades, but only now has come to the balance of quality and quantity that it is simply impossible to ignore.

But does this mean that Data Science will replace marketers?When there is a strong leap in the development of technology, many are wondering if this or that innovation will replace human labor.

To ask such questions is quite normal and even necessary.

But not everything is as simple as it would seem, because the emergence of something new does not always mean the extrusion of another.

The reality is, Data Science will not replace marketers, Data Science will free marketers from routine operations.

People are always getting better at generating hypotheses and creating something new.

So, the future of Marketing + Data Science is not a competition but collaboration.

Hope you liked this article.

Feel free to share your ideas, thoughts, and suggestions.

Inspired to learn more about AI, ML & Data Science?.Check out my Medium and Instagram blog.

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