Monday, December 3, 2012

Using Analytics for Superior Outcomes

Korea Times
November 30, 2012

The ability to use quantitative data to shape decisions and outcomes has become a key source of competitive advantage over the past decade. With IT practically ubiquitous, firms of any size can harness data to get smarter about customer behavior, the supply chain, product development, talent management and other areas of business.

Data are proliferating in volume and type, including video, audio and web data that wasn’t readily extracted even five years ago. International Data Corp. estimates the digital universe is doubling in size every 18 months. But for most companies, data remain an underused and underappreciated asset.

Recent Accenture research indicates eight out of 10 of companies are not achieving desired goals, largely because they have not developed an analytical capability to manage the vast quantity of information available.

Accenture research confirms that high-performance businesses—those that substantially outperform competitors over the long term and across economic, industry and leadership cycles—are five times more likely to use analytics strategically compared with low performers.

High-performing companies such as Procter & Gamble and Tesco, for instance, have made the discipline of analytics central to the execution of their strategy. Nonprofit organizations can also parlay analytics into vital public information. Google uses search engine results to predict flu outbreaks by county across the United States and provides that information to the federal Centers for Disease Control, which can dispatch flu vaccines to those areas.

Most high-performers are using analytics to optimize their core processes. Scheduling analytics at Cemex, for instance, helps the firm deliver cement to construction sites within a specified 15-minute window. That allows Cemex to charge premium pricing in a commodity market.

Analytics have other uses as well. It can integrate datasets from separate functions to balance apparently competing objectives. For example, a procurement employee may be pushing for early payments to get discounts from suppliers. But a finance colleague wants to hang on to cash as long as possible. With an analytics capability in place, a company can find the precise point that ensures the benefits to both sides of the equation are balanced.

The discipline of analytics is well suited to a world where different regions are growing at different paces, and have quite different regulatory and political risks. Managing a multinational portfolio of businesses requires executives to anticipate changing supply and demand conditions in many different markets at once. This means developing data-based insights that answer the question: What are our best next steps?

How to make better decisions

Some high-performing companies are establishing real differentiation in analytics by deliberately closing the decision process loop. They turn raw data into insight and use the insight to shape business processes, which then generate better decisions.

Redesigning the decision process involves taking each process relevant to solving a problem, embedding analytics in the processes and linking the reengineered processes more tightly together.

The next step is a checkpoint to determine the effect of the decision: Did the organization achieve the right outcomes? If not, where did something go wrong in one of the three components? It’s essential to close the loop this way because if you can quickly discern where the problem occurred, you can make rapid adjustments by changing a key metric, an analytical method or the deployment of resources.

Analytics become even more powerful with a cross-functional approach because most business problems touch multiple areas of a company. At a health payer, for example, the traditional batch claim processing provides poor customer service, increases administrative costs and drives poor cash management.

A better solution, real-time adjudication, is complex enough that it requires analytics and collaboration among several areas—such as prioritizing transactions, retraining technicians, and shifting resources away from adjustments and appeals and toward customer service at the provider’s office.

How to do it

Building an advanced analytical capability to support improved decision-making is not easy. Companies may struggle to generate insights from their technology investments, connect the insights to the relevant processes, and then link them to tangible business outcomes. While each company has its own unique set of challenges, decision process optimization tends to run up against several common problem areas:

Focusing on the wrong metrics. Most firms establish a large set of metrics, but they often lack a causal mapping of the key drivers of their business, which a small set of metrics should track.

Inability to validate insights. Data analysis usually generates many possible insights. What’s tricky is to validate them across functions to identify the most useful insights. There is no formula for validation, but rather a review of the statistical analysis through the filter of management experience.

A business-to-business manufacturing company, for instance, was preparing to launch a new product. Detailed customer research showed a shift in preferences among small to medium-size businesses toward preparing more of their own products for export. Also, recent cuts in staff meant the end users put more value on the product’s ease of application. Each of these insights was double-checked and deemed important enough to reformulate the product.

Faulty execution. After a key insight has been selected, there are many possible actions to take. Should you change pricing, reconfigure the sales force or adjust the supply chain? The most common failure in this regard is that managers choose not to make a decision at all, fearing the implications of making a wrong decision.

Yet choosing the right action in a timely way is essential to success. Accenture research shows that one attribute shared by high-performing companies is the speed with which managers make decisions, typically in close proximity to their customers.

Cultural resistance. Many managers, while reluctant to say so, rely primarily on intuition and experience rather than fact-based analysis. A recent Accenture survey found that 40% of business decisions are still made based on judgment alone, partly because of the absence of good data.

While experience and intuition are valuable assets, they remain limited until combined with relevant data. Executives and managers thus must increasingly be fluent with analytics. They should understand the models underlying decisions, as well as the assumptions behind the models. And at all levels of the organization, the structure of incentives and rewards should encourage people to use analytics in their day-to-day business processes.

From “so what” to “now what”

As managers become more fluent in analytics and rigorous decision making, they can address progressively more sophisticated questions. They can more accurately predict what customers will buy, where supply chains could break down and which employee will become a top performer.

UBS Investment Research, for example, has started incorporating proprietary analysis of satellite images of the parking lots of Walmart into its earnings estimates. UBS bought its satellite data and analysis from a startup called Remote Sensing Metrics LLC, which built a model for how customer flow correlates to quarterly earnings.

By counting the cars in Walmart’s parking lots month in and month out, Remote Sensing Metrics analysts were able to predict customer flow. From there, they created a mathematical regression to come up with a more accurate prediction of the company’s quarterly revenue each month.

Similarly, a major U.S. music distributor used predictive analytics to address a sudden spike in demand for the CDs of one of the artists in its back catalog. How could it ramp up production to meet immediate needs without creating excess inventory in the future?

The company’s analytics engine used data from the supply chain, finance and the internal customer relationship management system. In short order, the music distributor pinpointed the source of greatest demand, and could make timely decisions about where to boost production and where the most cost-effective and profitable locations were to locate inventory.

Every corner of the business stands to benefit from predictive analytics and more informed decisions. So how can companies achieve that level of sophistication? The route to building an analytical capability that can improve decision making will depend on the level of analytical maturity currently within the organization.

A consumer packaged-goods company accustomed to innovating through market basket analytics will have a different set of issues, challenges and questions than a bank that may not even know its credit exposure on a daily basis. An electric utility accustomed to doing two physical meter readings per year will likely not be prepared to take advantage of the rollout of a smart grid that allows for several meter reads per hour. Therefore, a useful first step is a diagnostic to determine the company’s current maturity and where the gaps lie.

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