How to Apply Data Science Methods to Obtain Marketing Insights

Nick Brown
Published 08/09/2023
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You already know the power of data and how it is crucial to how your business operates and how efficient it is. With the amount of data being generated every single day constantly increasing, being able to sort the wheat from the chaff has never been more important, especially when you consider that a staggering 3.5 quintillion bytes of data are being generated every day.

As the amount of data has increased, the demand for data science has also grown. What is data science, though? Perhaps more importantly, how do you use data science to gain marketing insights and boost the various metrics that matter?

What is data science?

data science diagram

Data science uses modern tools to sort through large datasets and divide them into usable sets of data that give real insights into how your business is performing and highlight results that really matter. Many organizations collect huge amounts of data on their operations and audience. This data is often stored in a data warehouse or similar (What is a data warehouse?) and is added to continually. Most of this data is raw and does not offer the usability you need for detailed analytics. Data science can make it usable.

By converting raw data to insightful data sets, you have information that allows you to make planning decisions that will affect your future operations. It helps you make better strategic marketing decisions that you can constantly revisit and make changes to that optimize your marketing efforts across the various channels you use. Here are some different types of data science that are used:

1. Business Intelligence (BI)

This type involves the data scientist processing data so that you can use the information to make an informed decision. For example, a business call center could use a data scientist to analyze the huge amount of call data they collect so they can formulate strategies to outperform that competitor and provide better customer service.

2. Cloud computing



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With so much data being generated, a lot of work is now carried out in the cloud. Here, data scientists can work on everything from delivering services or designing databases in the cloud to sorting and analyzing that data or helping to store it.

3. Security work

A lot of your data is highly sensitive, and data scientists are often involved in cybersecurity work. This can involve everything from assessing how vulnerable a business is to cybercrime attacks to working with other cybersecurity experts to ensure a system is more robust.

4. Data engineering

There is a need for systems that collect data, store it securely, and convert raw data to more understandable information. Data engineers design these systems and ensure that they work correctly and that authorized individuals can access that information.

5. AI and machine learning (ML)

It would be almost impossible for all data science work to be carried out manually. Therefore, data scientists help design and “train” AI and ML systems to do large parts of the required work. They use info and algorithms so that their systems will react a certain way to different scenarios.

6. Market analytics

This can be of particular use to your marketing efforts. With this, data scientists focus on how your products or services (or those of a competitor) are performing and whether any particular events have affected sales or profits. They can also produce predictions for future performance.

How to Apply Data Science Methods to Obtain Marketing Insights


So, you know that data science can help you use your existing data and have better insights into the various marketing efforts you have. Data science and data engineering is now an integral part of any modern operational strategy and should be carefully considered if you’re not using it now. But what particular areas of marketing can data science best help with?

1. Segmentation

No matter how many customers you have, they will have different wants, needs, and behaviors. Good data helps you either segment (human-driven) or cluster (ML-driven) your customer base into groupings and can help inform the marketing messages you use to target each group. Depending on your business type and location, you may choose several identifiers, including:

  • Age and gender.
  • Geographical location.
  • Spending levels and regularity.
  • Historic buying patterns.

With segmented groups, you can better identify loyal customers and those with a high CLV (customer lifetime value). In turn, that allows you to focus marketing efforts on the groups that represent the best ROI. After all, there is little point in allocating a high marketing spend on customers who spend an average of $25 per quarter.

2. Content creation

What sort of content do your customers like? If you’re not entirely sure, then data science can help. Data and analytics can help highlight what content your customers love and what content they’re not engaging with. Analytics will help you create content that your customers will like and engage with more.

Of course, there are other elements to ensure your content is as good as it can be. By utilizing testing methods such as A/B or serial testing, you can focus on the minutiae, such as word choice, font, or image use. You can also look at testing and analyzing to see when your content is most effective on each platform.

3. Real-time insights

While historical data and forecasting can contribute to your marketing efforts, there are times you want to know what’s happening right now. Good data science can help provide you with real-time analytics so that you—or automated systems—can make instant decisions that may affect sales and conversion rates.

A good example would be monitoring a customer’s behavior when on your website. Targeted offers and ads can be sent to them based on that behavior to encourage them to make a purchase, not abandon their cart, or return to your site.

4. Target leads

Identifying qualified leads can be one of your marketing teams’ biggest challenges. Using data science with other tools, such as machine learning, you can analyze all your existing marketing data to better predict when a lead is ‘ripe’ for targeting and conversion.

Data science can help score the value of every lead and identify when best to reach out to them. For example, if a customer has been browsing various business phone-related products, it might be a good time to message them about offers on the call forwarding service you offer. Data science can remove the trial-and-error aspect of your marketing efforts and can lead to a dramatic improvement in sales.

5. Optimize your channels

Most businesses now work in an omnichannel world to reflect their customers’ preferences. However, people can behave differently on each channel, which can present a real challenge when it comes to messaging and other factors. Data science can help marketers identify and analyze those different behaviors and plan accordingly.

Knowing how your customers interact with you on platforms such as Instagram or Facebook means you can optimize each channel to best suit that behavior and deliver the messages people want to see or hear. It can also help you build better buyer personas so that you understand all your customers’ wants and needs.

6. Maintain loyalty and improve retention rates

You will know that long-term customer loyalty is good for your business. It can help reduce overall CACs (customer acquisition costs) and increase your CLVs (customer lifetime values). Data science can provide you with the analytics to identify what will make a customer remain loyal to you.

Those analytics can help highlight the best actions or offers for your existing customers. It can also help you predict how those customers may react to any proactive action by you. If they do choose to leave you, those analytics can also identify potential problems so that you can tweak them in the future.

7. Recommendations

Recommendations can be a great way of driving customers to different landing pages. In general, there are two types of engines behind such recommendations:

  • Content-based: This analyzes a customer’s browsing behavior to recommend similar products. However, this type of filtering will rarely upsell and will more likely offer a ‘like for like’ recommendation.
  • Collaborative: This analyzes the historical behavior of other customers and recommends products based on that behavior. The downside of this is that it may recommend products that do not meet the current needs of your customer.

The obvious answer is to use a hybrid approach, combining the analytics you have from the customer’s behavior with that of others so that you are offering a wider range of products with some upselling.

8. Predictions

By combining data science with ML (machine learning) models and AI, you can use predictive analytics to forecast what may happen if you take certain actions or what the demand may be for certain products. With so much data now available—and increasing every day—the accuracy of predictive analytics has never been better.

With good predictive analytics, businesses can:

  • Ensure good content is delivered to the right people on appropriate platforms.
  • Predict the potential success of any marketing campaign before you launch it.
  • Identify high-value customers, or those with a potential high CLV, and target them with relevant messages.
  • Know when cross-selling or upselling is more likely to succeed.

Transform Your Marketing Efforts

Of course, there are many more things to consider when approaching data science than just the analytics you will gain from it. From infrastructure management to machine learning tools that can help interpret the data you’re collecting, it should be well thought out and well-planned. The thing to focus on is that data science is something that can transform and boost your marketing efforts.

Data science gives you a better—and deeper—understanding of both existing and potential customers. Use these things well, and you’ll gain better marketing insights than you ever have before.


Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.