The term analytics refers to the applied use of statistical techniques to gain understanding and value from structured and unstructured information in enterprise, line of business, departmental and third-party databases and repositories. As the research firm Gartner Inc. has described, "Analytics leverage data in a particular functional process (or application) to enable context-specific insight that is actionable."

Analytics measure ratios and percentages, often employing complex mathematical algorithms to understand relationships within and among data sets. Statistical methods can be applied to both historic and near real-time data for separate or combined purposes. Business analysts, Web analysts, marketing managers and other users employ analytics for various business purposes. For example, financial analytics reveal trends and deliver what-if scenarios that affect the planning, budgeting and forecasting process. Customer relationship management (CRM) and marketing analytics employ data mining and predictive methodologies to address customer propensities to buy or churn and create measures of lifetime customer value. Web analytics are used to understand customer behaviors and optimize product and service offerings, marketing and sales campaigns. Still other analytic applications have an ancestry in search engines and address unstructured information through text mining.

In recent years, the evolution of technology and simultaneous growth in data availability have enabled a revolution in customer-focused decision-making. Decisions have become increasingly data-driven and the time frame for these decisions continues to accelerate. Marketing Analytics is the science that connects the vast amount of data with intelligent marketing decisions. Marketing Analytics compiles inputs from all parts of the business, processes all relevant linkages and uses this new information to drive optimal marketing decisions. A company's Analytic Maturity is a measure of the extent that the key business components (Data Integration, Decision Making and Operationalization) are fully integrated across the enterprise. An analytically mature company is much better positioned to conduct effective marketing analytics.

Successful customer acquisition and retention depend upon customer understanding. Customers' needs, wants, sensitivity to offer details, channel preferences, price sensitivities, risk characteristics and demographics are all important factors in the creation or maintenance of a profitable relationship. Finding all the relevant data is one of the biggest ongoing challenges for marketing analytics.

There are several categories of data that can lead to an understanding of customers or prospects, and how they might respond to new products and offers:

·         Past marketing efforts - How have they been reached in the past? What were the results, including whether they responded, how much they spent, how long they remained a customer, exactly what they bought, whether they met risk criteria, etc.

·         Existing customer relationships - Depending upon the business, individual customers may have many interactions that provide a lot of useful information: transaction history, levels of spending, payment history, etc.

·         Third party data on customers and prospects (whether or not they have already been contacted) - Demographics (both neighborhood and household), credit information, shared information from other marketers, and additional information that can be inferred (for example, a recently married couple might soon be in the market for baby products or a a prospect with little or no debt and a good income might be in the market for investment products.).

·         New data generation - Using focus groups, surveys or test marketing (this is a form of scientific experimentation and can be very valuable because it is controlled). It can be used to test the effectiveness of product design, message, channel and price; compare different settings of these parameters; identify overall optima or customize offerings to specific segments (also identified through the same experimental process).

Whatever the data source, marketing analytics describes the approach to appropriate analysis and interpretation that drives effective marketing decisions. This is a complex multifaceted process whose components include:

·         Structuring the data - including integrating data from multiple sources

·         Understanding the data - including profiling and exploration of relationships among all variables

·         Preparation for analysis - including systematic approaches to missing imputation, outlier treatment and derived variable creation

·         Statistical data analysis - including selection of techniques that enable reliable conclusions in the specific context

·         Validation - including confirmation of initial modeling results through testing on independent samples

·         Interpretation - including extrapolation from analytic results to broader business applications

·         Implementation - transforming insights into business decisions

Marketing Analytics impact information management and decision making throughout any organization. But a one-size-fits-all solution is not appropriate. The key dimensions of an analytically mature solution to marketing analytics for companies in different industries are quite different with respect to the optimal degree of integration of data across departments and processes, the need for decisions to be centralized vs. decentralized and the extent and time scale in which operational issues need to be closely linked to one another.

Marketing Analytics to the Rescue: The Next Big Thing?

CRM has become today’s business buzzword. Despite $125 billion spent on CRM initiatives over the past five years, nearly 70 percent of companies have yet to realize positive return on their investment. Yet, a new savior appears emerging from the ashes: marketing analytics – a.k.a. the next "big thing."

Since 1996, most CRM spending focused on information-centric technology components such as data warehousing, data aggregation and segmentation, content management and reporting; or operational customer touch point elements such as contact center management, sales force automation, personalization or marketing automation. Analytics may be the missing link that allows these organizations with millions in sunken investment to reach the CRM "promised land."

Analytics Technology

Analytics and supporting technology are not the panacea to customer loyalty. Analytics are most successful when applied in the context of a solid strategy that considers the people, processes and technologies necessary to grow valuable customer relationships. These solutions can serve as effective enablers when applied in the right way. Moreover, the vast improvements in analytics technology no longer mandate the need for an advanced statistics degree to run modeling tools.

Why Now? Why Today?

The strategies, processes and technologies used to identify, acquire and retain profitable customers, as we now know it, are changing. Companies battle each other for buyer attention by barraging consumers with an unprecedented number of messages across every conceivable medium.

One recent study by NFO Research suggests that the typical American consumer absorbs fewer than two percent of messages cross channel. Budget-strapped marketers are desperately seeking ways to both through the buzz and sustain the attention of profitable customers through messages delivered at the right place, time and through the right channel.

In the big budget heydays of the 2000s, marketers could experiment with unproven tactics. Customer churn in the days of the booming economy was less of a crisis. Those days are distant memory, and marketers need to be smarter and hit their target audience with a precision that would make a highly trained marksman jealous.

Information Everywhere and not a Drop to Use!

Companies struggle to find out what makes their customers "tick" through surveys and offers. New customer-facing technologies have made the opportunity larger but situation even worse because customer data can be captured from so many touch points. Unfortunately, without an adequate way to manage this information, companies are now finding themselves prisoners of their data warehouses.

The situation may not be quite that dire, but the predicament of having lots of information without an effective way to utilize it is certainly a very real business issue. Until recently, companies have not been able to integrate the intelligence from the data from their operational touchpoints, such as their call centers and Internet site. Data in of itself has no value. This is where analytics enters the picture – analytics turn data into action.

The Analytics Solution

While analytics is not the cure-all for all of marketing’s woes, it certainly enhances a marketer’s ability to reach their target customer in the most effective and efficient manner. Today’s analytics tools provide organizations with the means to evaluate data and create easy-to-use conclusions. More importantly, a key benefit to utilizing analytics is that it helps companies measure the previously immeasurable.

Many CRM projects have failed because they lacked consideration of financial objectives. They sought to "improve customer satisfaction" rather than solve more specific, measurable points of pain (i.e., reduce call transfers, increase repeat buys, decrease product X attrition by X percent).

The repeat buyer rate metric – considered the Holy Grail for marketers – indicates the significant value associated with using analytics to target loyalty marketing and cross-selling efforts. A recent report published by McKinsey Consulting indicates that 80 percent of direct marketing budgets will go towards post-acquisition loyalty marketing between 2002-2005. The advantage of analytics in allowing companies to focus their spending on the areas of greatest return fundamentally shifts the direct marketing industry away from "blind" acquisition toward "intelligent" retention and value expansion.

There are many tangible examples of how organizations successfully used analytics to create these types of gains. One telecommunications company implemented churn and customer value models deployed through its call centers. By targeting high-value customers at risk of switching to a competitor and routing calls to its best service representatives, the company experienced a 37 percent increase in revenues, 12 percent increase in per customer profit and a 17 percent reduction in churn. A bank implemented a similar program through the Web that drove a 29 percent increase in customer self-service usage, a 51 percent increase in customer satisfaction levels and a 64 percent decrease in attrition – huge savings for them and better service for their customers!

Take the Good with the Bad

Analytics is all about math. Yet, it is sometimes hard for more experienced business people to trust the mathematical "black box" rather than their professional judgment. They may think that they "know their customer" and can out predict a model based on experience. More often than not, their experience is biased – and wrong.

With the advent of advanced tools like neural networks that continually "learn and objectively adapt" based on actual customer behaviors, analytics are 40-60 percent more accurate that judgmental decision making alone. Typically, this is an organizational and change management issue, requiring structure, standards and policies necessary to remove the guesswork from these 21st century marketing best practices.

Honesty is the Best Policy

Another frequent objection to the application of analytics is the potential for infringement on consumer privacy. This gained steam in recent years with intrusive advertising and e-mail spam, made worse by identity theft incidents where hackers gained access to social security and credit card numbers. The consumer community is generally leery about the potential for information abuse and the "big brother" connotations inherent in predictive analytics.

Some companies are seeing great success in merely "asking" for permission in bold print (note: not fine print hidden at the bottom of a Web form). They also offer to share the results of analytics with their customers, grant customers access to their own data and explicitly refuse to sell the results to others for their own selfish marketing purposes – especially to outside third-party solicitors. This helps establish trust with the consumer and creates a comfort level to share more information without fear.

The Bottom Line

Analytics targets the right individual for the right offer or promotion. Keeping that customer happy and loyal is an ongoing effort made consistently by your organization at any possible opportunity. The more intuitive you can be about your customer’s needs and wants, the more cared for the customer will feel and the better his or her perception of your company’s customer service level. It’s this trust that establishes the most lasting loyalty. Analytics is the tool that will help you create it and has never been more user friendly and affordable. However, the best analytics tools are only half as good as those used in conjunction with a solid customer strategy.