Advancing Digital Marketing with Data Science

Traditionally, the data that we once harnessed was mostly small in size and structured.

Before, our data could be assessed using simple Business Intelligence (BI) tools. However, today, it has morphed into mostly unstructured or semistructured data. In other words, our data can no longer be processed by these simpler BI tools.

Due to the volume and complexity of the data we collect, the analytical and algorithmic tools which analyse, process and decipher it need to be just as complex and advanced.

Enter: data science.

Data science gives marketers access to helpful groups of data aggregated through a variety of channels including organic (website analytics/SEO), email marketing, social media, and more. But before we get into it, let’s first define what data science is.

 

What is data science?

Data science is the combination of various algorithms, tools and machine learning principles to expose hidden patterns from raw data to be able to make informed decisions. Data Science not only carries out exploratory analysis for data insights but also uses various advanced machine learning algorithms to pinpoint the occurrence of a specific event in the future.

The science is primarily used to make both decisions and predictions using predictive causal analytics, prescriptive analytics and machine learning.

 

Predictive casual analytics

This model can predict the possibilities of a particular event in the future based on data. This can help an organisation prepare for, and respond to, a variety of future outcomes that when applied correctly can lead to strong competitive advantage.

 

Prescriptive analytics

A prescriptive model uses the data collected from predictive casual analytics and goes even deeper into the potential results of certain actions. Prescriptive analytics can not only make its own decisions, but it can also modify those decisions based on dynamic parameters. This analytics model both predicts and suggests a range of prescribed actions and associated outcomes.

 

Machine learning

A subset of AI, Machine learning algorithms improve automatically through experience and by the use of data. Machine learning models can determine future trends by finding out the hidden patterns within a dataset to then make meaningful predictions.

 

Optimising digital marketing with data science

 

Search engine optimisation (SEO)

 

• Data science: Advances in data science are quickly changing the way website traffic is optimised. Data scientists are building on search engine capabilities by data accumulation, evaluation, and reaction.

 

• Machine learning: ML utilises algorithms to calculate trends, merit, and other characteristics when assessing SEO. Machine learning both customises personalised searches to increase click-through rate and aids in pattern detection to identify duplicate content or spam.

 

Email marketing

 

• Data science: Projects in data science enable digital marketers to estimate what buyers like, how they prefer to shop, and when they’re most likely to make a purchase. For instance, eCommerce sites use data science to generate highly targeted, personalised emails containing product suggestions that cater to a customer’s specific taste. Hence, data scientists help drive email marketing future sales based on a customer’s shopping history. Also, data science can determine how often customers make purchases so that marketers can find the most favourable time and frequency to send emails about their products.

 

Customer segmentation

 

• With data science models, marketers can divide customers into categories based on their preferences, buying choices, region, gender and age. As a data scientist, you can generate an email segment that sends select offers, new products, company news and other personalised information to your segmented customers for maximum results.

 

Optimising marketing campaigns with data science

 

Data scientists gather demographic data that enables marketers to segregate and test various prospects for their marketing campaigns to determine which is the most effective. Test emails can be sent about multiple messages and offers, and the click-through rate can then be monitored to calculate marketing campaign performance.

 

Not only do these marketing tools learn, adapt and improve according to customer behaviour, but they also have algorithms that instinctively adjust the timing, content and personalisation of said campaign.

 

Conclusion

Largely, with the help from machine learning and data science, email marketers have been able to improve their messaging and content based on buying power. Besides, search engines like Google have been able to improve the overall user experience due to data collected on previous searches and click-through rates.