Classic Data Science Shapes Era of Hyper-Personalized Services

November 12, 2020

Aliaksandra Hancharova
Head of Data Science Technologies Department
IBA Group

Humankind’s unrelenting march in our digital transformation journey has created a data explosion that gave rise to data science.

Digitizing legacy analogue information and the digitalization of business operations and human interactions continually add to data volumes. Statista forecasts that the world will create 59 zettabytes of data in 2020. That figure will likely increase to 149 zettabytes by 2025.

Data science behind data analysis

Yet, data alone is insufficient to deliver a competitive business advantage in the digital age. Companies must analyze the massive data sets at their disposal to succeed in the digital economy. They can do so by leveraging data science, machine learning and artificial intelligence (AI).

Using unprecedented computing power, data scientists can create algorithms which can automate tasks. They can also analyze and predict behavior and identify anomalous activity at scale. 

These capabilities have created unprecedented strategic business opportunities. They also produce transformative business models that cater to a shifting consumer landscape.

The need for behavioral data

Specifically, modern digital-savvy consumers demand personalization. This applies to every digital and physical business touchpoint, interaction and experience.

Audience segmentation has, therefore, become vital to identifying customers. It also helps to select the most appropriate and relevant way to interact with them.

In this regard, customer behavioral data has become a much higher value asset to marketing and sales departments than demographic segmentation capabilities.

Segmenting customers based on behavior creates powerful opportunities. Companies can reach and engage with customers based on what they do, as well as who they are.

Understanding consumer behavior patterns allows businesses to address specific customer needs or desires. This can happen at the appropriate moment in their customer journey. Insights regarding customer engagement preferences help to reach consumers via their preferred channel.

Leveraging classic data science

To unlock these capabilities, businesses must leverage classic data science, machine learning and deep data analytics. These solutions extract valuable insights and intelligence from customer data. These insights inform and shape the personalized interactions that are relevant to the individual in the moment based on their behavior.

Applying this intelligence also helps businesses respond to changing customer needs, preferences and expectations in real time, which can vary depending on time and context.

And with every transaction and engagement, organizations gather more data. With the right solution, companies can use this information to inform future sales and marketing decisions.

Machine learning can also use this data to predict sales. Sales forecasting can identify benchmarks. It can determine incremental impacts of new initiatives and it can help to allocate resources in response to expected demand. Projecting future budgets requirements also becomes more accurate.

The ability to analyze other important business metrics like customer churn also helps to retain customers, which positively impacts the bottom line. 

Companies can also use data-driven marketing mix modelling solutions to leverage customer information. These insights help companies optimize their marketing budgets. The results include betters returns on investment and increased profits.

Protecting consumers from fraud

However, in this world dominated by online and digital engagement, security has become a top priority. Today, machine learning algorithms are also successfully employed for fraud detection.

Machine learning models learn from patterns of normal behavior. This enables them to predict rare or exclusive events. Fraud is one example as it might occur once in millions of transactions. Importantly, this happens in real-time. This enables fraud prevention rather than retrospective fraud identification. 

As such, embracing classic data science and its many applications offers immense benefits. Those that lag in their adoption risk losing ground to their competitors. These “laggard” companies will become irrelevant in the modern digital marketplace.

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