What is Your Data Maturity?

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An engagement with Axis isn’t simply an analytics project – we are fully committed to holistically improving your ability to compete with analytics. We don’t only help find the diamond in the rough, we work hand-in-hand with you to help capitalize on the entire mine. The Data Science Practice at Axis is dedicated to helping you achieve the end game of analytics maturity.

Why is this important?

Data-driven decision making is a cultural shift in many organizations. Whether they admit it or not, many organizations still primarily rely on ‘gut-based’ intuition and snub the idea of leveraging data to support smart decisions.

At Axis, we meet you where you are on the analytics maturity curve even if it means starting from ‘data chaos.’ We help build use cases and deliver strategic wins necessary to generate momentum and drive the institutional shift towards data-driven decision making and fact-based management. Our professionals help you make the journey from the chaos of the analytically impaired to the foresight of the analytically empowered.

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What is Data Science?

“What is Data Science and why should I care?” is a common question asked by someone unfamiliar with analytics. Consider that all decisions are made based on analysis of available information.

When we speak of Data Science, we speak of gathering, analyzing, and displaying information in a way that augments the ability to make better decisions. This task is impractical, if not impossible, without the structure and power afforded by modern computing and analytical constructs. So when you hear, “Data Science can help you be successful,” what that really means is, “You can improve your business decisions by leveraging additional information.” This is a derivative of the principle that accurate information allows for better decisions and the certainty that your competitors are trying just as hard as you to find an edge to steal market share.

For decades, analytics practitioners have combined computer science with math and statistics to achieve scientific insights that were unattainable by human minds alone. Today, it is rapidly becoming standard across industries to leverage analytical methods, algorithms, and procedures to solve business problems. A modern data science team has competencies in both computer science and statistics, as well as the business knowledge to effectively apply analytical models to business use cases using scalable technology.

At Axis, we push the frontier of data science by creating advanced visualizations that accelerate the business value of analysis and smart decision making. This addition to the standard data science formula shortens the time from analysis to action by presenting insights in ways that are easy to digest and understand.

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Example Use Case

The cell phone industry is extremely competitive and mature in the United States. Almost all new customers are won from other mobile carriers. As a result, efforts to grow revenue come not only by enticing consumers from other carriers, but also by reducing churn to hold onto existing customers. Thus, understanding the drivers of customer churn is critical in this industry.

Climbing the Analytics Maturity curve, the first step is to tame the chaos of raw data. Customer, transactional, and service data must be collected and securely stored. Form is achieved when high quality data is stored and governed in a single location, and is available to analysts and decision makers. Insights can then be generated by applying business intelligence and visual analytics practices and tracking churn KPIs such as the number of lost customers by time and segment. Only then can foresight be attained by applying advanced statistical models that predict the likelihood of a customer to leave.

The Data Science Practice at Axis works with clients to achieve foresight from chaos. At the final stage, the cell phone carrier predicts which customers are at risk for churn. The company then takes targeted, preemptive action based on customer habits and behaviors to prevent defection. The decision makers initiate more focused, less expensive, higher return churn reduction campaigns directed at high risk customers to improve results while reducing costs. Predictions are also fed back into the CRM system to initiate automatic promotional emails. This has a significant, direct impact to the bottom line and the company’s strategic position.