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Posts Tagged ‘Analytics’

www.ibm.com ibms data analysis expertise helps clients improve the speed and quality of decisions, while better understanding the consequences and business outcomes of those decisions.

Predictive analytics is a solution used by many businesses today to gain more value out of the large amounts of raw data by applying techniques that are used to predict future behaviors within an organization, it’s customer base, it’s products and services. Predictive analytics encompasses a variety of techniques from data mining, stastics and game theory that analyze current and historical facts to make predictions about future events.

The benefits of implementing predictive analytics is undeniable. There are countless documented case studies and success stories where predictive analysis yielded a substantial return on investment, helped companies optimize existing processes, provided a better understanding of customer behavior, identified unexpected opportunities, and anticipated problems before they occurred. But with all of the benefits associated with predictive analytics, there are many challenges that accompany becoming an analytics-driven organization.

The perceived complexity is the largest challenge facing executives today. The cost of implementation is a close second. While these are legitimate fears, many tools are being developed to simplify the process and establish transparency from the complex formulas and statical modeling. It is, however, up to organizations to educate themselves on the basics and concepts of predictive analysis in order to fully utilize these tools.

Another challenge, which is more technical, is the traditional approach of having analyst explore data sets by saving data and manually applying relationships in order to make predictive assumptions. While this can work at a basic level of predictive analytics, predictive analytics at it’s most effective application requires extremely large amounts of data and thus is best suited for analytics platforms wih parallel processing, which support custom analytical applications that query data using SQL.

This brings us to another challenge with implementing predictive analytics in your organization, and that is managing the enormous data volumes associated with it. Some organizations known to apply leading edge analytical techniques, are gathering perabytes (that’s approximately 1000 terabytes, or 1 million gigabytes) of data. While these amounts of data require costly data warehouse upgrades, it enables organizations to form very comprehensive analytics and it enhances visitor/customer experience by providing targeted, customized marketing and services.

But with these large amounts of data and data storage comes the challenges of producing the platform for processing this data with complex formulas at fast rates. Because of this, analytic platforms often run off massively parallel processing (MPP) databases. MPP databases coordinate processing of a single program by more than one processor by dividing up parts of a program into several processors with their separate memory and operating systems. But many organizations that cannot afford MPP databases, instead implement analytical platforms as data marts to off-load complex processing.

While these challenges to indeed appear to be complex, the important thing to know is that if you have the architecture to support it, there are several tools out there that take out the complexities and applying predictive modeling.

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Product Description
You have more information at hand about your business environment than ever before. But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool. In “Competing on Analytics: The New Science of Winning” , Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-… More >>

Competing on Analytics: The New Science of Winning

Companies struggle to find out what makes their customers “tick” while budget constraints make finding the best customers and prospects more difficult. Without a way to manage and utilize the collected customer’s information effectively, companies are finding themselves prisoners of their data warehouses and databases since data in and of itself has no value. This is where the advanced marketing analytics turn data into action by targeting the right individual with the right offer or promotion with the right message and analytics has become an integral component of the marketing business.

The New Analytic Paradigm
The idea of developing a model for a campaign or program simply can’t work in today’s environment. To address this, firms have developed few models, which provide knowledge about customer’s likeness to purchase, expected churn rate, time frame and lifetime value. The mechanism of marketers used in distinguishing advanced information about customer’s behavior and using this knowledge in all marketing and sales activities is more important. The challenge of providing advanced analytics not only requires a new paradigm, but a renewed focus on the infrastructure and platform requirements that can get the modeler out of the business of fixing data issues on the task at hand; creating accurate predictions of customer behavior.

The Application of this Advanced Knowledge
Marketer can better manage the entire marketing lifecycle from planning to measurement, with advanced knowledge about customers. After new paradigm, a proper platform must be put in place to handle the entire analytical modeling process. All analytical model development, scoring, accuracy checking, performance verification, and integration with marketer toolset need full support of campaign, measurement, ad-hoc and reporting platforms. the analytical capability and integration must be truly automated, saving the modeler over 80% of the time in dealing with data integration and scoring issues and letting the advanced analytic resource focus on providing value – by developing analytics that can help support and drive the business.

Best Practices with Advanced Marketing Analytics
Redistribution of contacts from one customer group/segment to another during early marketing planning process of marketing strategy is the most common best practice by using advanced analytics. By adjusting the marketing plans and strategies while early planning process for increase in return on marketing investment by more than 20% with a clear picture of expected results and during execution stage of each program, marketers evaluated results against expected results provisioned by the advanced analytical platform and reallocate contacts while programs are in market based on the evaluation results.

By leveraging the advanced analytical platform, marketers can now optimize offers on inbound channels like customer service and inbound sales and can provide straightforward recommendations to sales or service representative in call centre or at the retail service desk. Optimum use of advanced analytic platform can create lift of over 25% and can provide justification for the marketing platform with in 45 days. These recommendations are often two to three times more likely to be accepted by the customer as a result of the unique combination of the advanced analytic platform and the timing of the offer for customer initiated conversation.

There was a time not too long ago when it was cool to have a web site. You didn’t have to worry about issues such as usability, conversion, and flow because the web was not as competitive as it is today. If you had a website that looked nice, you were in business. Let’s face it even if you had an ugly site you were still in business. If you got business from the site nice if not you were happy too. In the beginning most people didn’t really believe they could make money on their website.

Today the web offers huge opportunities for business to grow and increase their revenue. Hence, the web has become fiercely competitive market place. In such competitive environment, you have to know what works and what doesn’t. Without measuring KPIs (Key Performance Indicators) you are not giving your company a chance to understand your business.

Measuring performance of traditional marketing such as magazine or post card mailer advertising is very difficult. We can say it worked or it failed based on sales, but we don’t know the details such as what part of the ad was effective. How often people looked at the ad and for how long?

With web analytics, we can get a greater understanding of where and how effectively we spend our advertising dollars. Web analytics plays a strategic role in measuring success and alerting companies to vulnerabilities within their online environments. The value of web analytics solutions is not in their ability to collect data, but to measure customer behavior that provides a basis for replicating success or driving change.

Among companies that utilize analytics in this way, 75 percent increased new visitors to their sites, 78 percent improved average visit duration and 65 percent improved returning visitors. Eighty-three percent increased page views per visit, 68 percent improved conversion rates and 64 percent improved the number of visits per existing customer.

The value of analytics is not just in the measurement of the data but also in the ability to act on the results. KPIs should drive action. Every KPI we measure should correspond to action. Creating an effective web analytics strategy requires more thought than money.

Digital marketing is impossible without KPIs. Without KPIs, you are spending on marketing because it feels good. With proper metrics, you spend money because it makes sense.

Web analytics is easy according to every company that sells web analytics solutions. Visit their websites and they’ll tell you loud and clear that web analytics is a walk in the park. Web analytics is not easy.

KPIs have to be part of your strategy. It is not an isolated project within a company. Web analytics should be part of your business analytics efforts. Just like you measure your sales and your revenue, you should measure your web KPIs.

KPIs should be more about people and strategy than technology. Many marketers shy away from web analytics because it sounds like something an engineer would enjoy doing. Web analytics is about measuring consumer behavior. Web analytics is one of the most effective tools to grow your business.

Web analytics is turning raw data into actionable items. Many marketers build their own marketing programs based on information about other companies’ marketing history. Don’t make that mistake. What works for them is not necessarily going to work for your organization. General market research studies provide you general information when you need information specific to your business. Web analytics is the tool for that.