A large wireless phone service provider was concerned with the number of customers it was losing. Every customer that is lost costs the company $53 in monthly revenue. Although that seems small on a customer-by-customer basis, in total, the company was losing millions of dollars each month. Using advanced analytics it was able to develop an attrition model to predict which customers were most likely to terminate their contracts. In doing so, the company developed a model to cross-sell, helping it to retain customers by providing products, services, and other incentives targeted to their profiles. This program improved the retention rate and contributed to an overall savings of $6.7 million.
That is the type of success story common among companies that have deployed advanced analytics to better understand their data. Advanced analytics is a business-focused approach, comprising techniques that help build models and simulations to create scenarios, understand realities, and future states. Advanced analytics utilizes data mining, predictive analytics, applied analytics, statistics, and other approaches in order to allow organizations to improve their business performance.
Driving a wide range of applications, from operational applications such as fraud detection to strategic analysis such as customer segmentation, advanced analytics goes deeper than traditional business intelligence activities into the “why” of the situation, and delivers likely outcomes. By allowing business managers to be aware of likely outcomes, advanced analytics can help to improve business decision making with an understanding of the effect those decisions may have in the near future.
Challenges of Advanced Analytics
However, it is not uncommon to encounter problems along the way as you implement advanced analytics projects. One of the potential difficulties involves managing and utilizing large volumes of data. Businesses today are gathering and storing more data than ever before. New data is created during customer transactions and to support product development, marketing, and inventory. Social media is being mined to uncover customer sentiment. And, many times, additional data is purchased to augment existing business data. This explosion in the amount and type of data is one of the driving forces behind analytics. The more data that can be processed and analyzed, the better the advanced analysis can be at finding useful patterns and predicting future behavior.
However, as data complexity and volumes grow, so does the cost of managing the data and building analytic models. Before real modeling can happen, organizations with large data volumes face the major challenge of getting their data into a form from which they can extract real business information. One of the most time-consuming steps of analytic development is preparing the data. In many cases, data is extracted, and a subset of this data is used to create the analytic dataset where these subsets are joined together, merged, aggregated, and transformed. In general, more data is better for advanced analytics. There are two aspects to “more data”: (1) data can increase in depth (more customers, transactions, etc.), and (2) data can grow in width (where subject areas are added to enhance the analytic model). At any rate, as the amount of data expands, the analytical modeling process can elongate. Clearly, performance can be an issue.
Challenges of Real-Time Analytics
Real-time analytics is another interesting issue to consider. The adjective “real-time” refers to a level of responsiveness that is immediate or nearly immediate. Market forces, customer requirements, governmental regulations, and technology changes collectively conspire to ensure that data that is not up-to-date is not acceptable. As a result, today’s leading organizations are constantly working to improve operations with access to, and analysis of, real-time data.
As good as real-time analytics sounds, it is not without its challenges to implement. One such challenge is reducing the latency between data creation and when it is recognized by analytics processes. Time-to-market issues can be another potential pitfall of an advanced analytics project. A large part of any analytical process is the work involved with gathering, cleansing, and manipulating data required as input to the final model or analysis. However, this up-front work is essential to the overall success of any advanced analytics project.
Advanced Analytics Deployments Offer Productivity Gains
The end result of deploying advanced analytics is increased productivity with the ability to gather and analyze large volumes of data to deliver faster, more-effective business decisions.
Indeed, the whole big data phenomenon is largely focused on the analytical processing that is conducted on the data. But data need not be “big” in order to implement advanced analytics at your organization. And, ignoring the value that advanced analytics can provide your organization is not an option … because you can bet that your competition is not ignoring it!