Think about the end before you begin

Over the last century, generations of marketers have seen the rise of new media frontiers from radio, to cable TV and the Internet and quickly learned how to harness their potential. But even a few years ago, very few of them would have imagined the possibilities available today where almost every action leaves a data trail. So, the marketing industry is quickly evolving beyond relying on a conceptual and intuitive decision making process towards more systematic and evidence based one. It begins to resemble design engineering methodologies — putting together concepts, data, models, and knowledge while employing an elaborate analytical process. The end result produces more effective marketing, better understanding of the customer’s journey and improved product designs.

Abundance of Data

Today, marketers have astronomical amounts of historical and real-time data, knowledge and expert opinions at their disposal.  How can one organize these often disparate resources to process and model them to fuel enhanced decision making? It becomes less a question of what to do, but rather how to act faster and better than the competition. At the same time, the explosion of data accompanying the digital and online growth challenges the limited processing capabilities of our brains. As a result, the mere access to more data may not result in better decisions.  However, the key is the ability to distill the vast amounts of data into insights for decision makers that lead to real business outcomes.

Marketing analytics, as a systematic, model-enabled process, can help make informed marketing decisions in a timely fashion resulting in better outcomes. However, to successfully implement a new analytics program, one should fully understand the business implications then be prescriptive in their goals, audience, and execution. Too many organizations fail in implementing analytics by either going too broad with their scope or by not understanding the business problem. Here are a few basic guidelines, that can help plan and execute a successful marketing analytics program.

Think About the End-game – at the Beginning

Focusing on the processes, mechanics and technology is a common mistake. The key to success is understanding the goal of the analytics and the business problem it can solve. You may start with answering a few clarifying questions: Who in the organization is likely to benefit from the outcome of the analytics? Are we resolving issues where the judgement seems inadequate? Is the goal to explain the past events—what and why something happened, or forecast what will happen?

Short Line to Business Value

Focus on a subject area, decisions or issues that have a good chance of quick and demonstrable success. The successful application of analytics that favors a negative decision (e.g., elevating the risk of launching a new marketing campaign) will have less impact than the application that favors a positive decision (e.g., introducing a new product). Getting the management buy-in for in a project like that, greatly increases your chance to prove the analytics be positive agent of chance.

Keep it Simple

As a rule of thumb, start with a problem that is understandable and familiar. Look for areas in which the development costs are low compared with their potential benefits. Database marketing is a good example, where determining how to best identify and target the most profitable customers leads to a high-quality marketing segmentation. Imagine the power of the message “the analytics will help target customers who, based on their behavior, are more likely to respond favorably to our future selling efforts.”

Everybody wins: the sales team, the management team, the process improvement team and ultimately, the customer.

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