A large wireless phone service provider was concerned with the number of customers it was losing. Every customer lost costs the company $53 in monthly revenue. Although the revenue looks small on a customer by customer basis, with a large customer base the company was losing millions of dollars each month. Using advanced analytics they were able to develop an attrition model to predict which customers were most likely to terminate their contract. In doing so, the company developed a model to cross-sell helping them to retain customers by providing products, services and other incentives targeted to their profile. 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 follow organizations to improve their business performance.
Traditional business intelligence enables us to understand the here and now, and even some of the why, of a given business situation. Advanced analytics goes deeper into the “why” of the situation, and delivers likely outcomes. Although advanced analytics cannot infallibly predict the future, it can provide models for judging the likelihood of events. 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.
Advanced analytical capabilities can be used to drive a wide range of applications, from operational applications such as fraud detection to strategic analysis such as customer segmentation. Regardless of the applications, advanced analytics provides intelligence in the form of predictions, descriptions, scores, and profiles that help businesses better understand customer behavior and business trends.
Issues in Deploying Advanced Analytics
When implementing advanced analytics projects it is not uncommon to encounter problems along the way. 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. And many times additional data is purchased to augment existing business data. This explosion in the amount of data being stored 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 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 data set 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.
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 and with access to and analysis of real-time data.
For example, consider a financial services provider that is confronted with detecting and preventing fraud. Each transaction must be analyzed to determine its validity. The retailer waits for approval while this is done in real-time. But if you err on the side of safety, valid transactions may be declined which will cut into profit and perhaps more importantly, upset your customer. The advanced analytics approach leverages predictive analysis to scrutinize current transactions along with historical data to ensure transactions that may appear suspicious aren’t the norm for this customer. The challenge is doing this in real-time.
Today’s nimble organizations need to assess and respond to events in real-time based on up-to-date and accurate information, rules, and analyses. Real-time analytics is the use of, or the capacity to use, all available enterprise data and resources when they are needed. If, at the moment information is created (or soon thereafter) in operational systems, it is sensed and acted upon by an analytical process, real-time analytics have transpired.
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. As much of 60% to 80% of the man-effort during a project goes toward these steps. This up-front work is essential though to the overall success of any advanced analytics project.