Are You Ready For the Anlaytics Fast-Lane?

Data and analytics are everywhere, with numerous examples of how the right analysis yields significant lifts in marketing or operational efficiencies. The returns are, without doubt, measurable and worth the investment. However, a word of caution for the leaders – don’t be swayed by case studies and peer recommendations, or assume that employing an analytics firm will yield similar results for your business. Being ready to utilize the feedback in a timely manner, with a clear implementation plan, is equally critical to realizing the ROI on your analytics investment. Here are a few questions to ask yourself, before embarking on an analytics project.

1. What is the problem statement? Be clear with what question you need answered. It could be; defining the target audience for your product or/service, understanding customer engagement with your product, improving acquisition effectiveness with better offers, identifying churn propensity by customer cohorts, etc. The key is to explicitly state the goal, stay focused and avoid the “noise.” Defining a clear problem statement upfront is crucial to staying focused and not be distracted by a lot "interesting" findings that are bound to pop-up.
2. Do you have the right data set? Work with your analytics and IT experts to identify the right metrics needed to answer the above questions. A third party perspective is always recommended in such instances, since it helps question the status quo and is not bound by what is familiar. Start as comprehensive as possible, since analytical modeling often throws new data dependencies that may not have been obvious. A holistic view of data points available from the data warehouse go a long way in defining the problem statement.
3. Is the organization ready to ingest the analysis? The best time to use the analysis is “now.” I have often contended that analysis based on historical data is like playing catch-up. But, with predictive modeling we can project certain behaviors with a fair degree of certainty. The imperative hence is, that an organization has the operational capability to act quickly on the recommendations (marketing & sales changes, product updates, online experience edits, etc.). Invest in the back-end systems that can adapt and learn from the new programs, or else, run the risk of being obsolete.
4. Do you have dedicated personnel to guide the process? This is the most important determinant of success, and perhaps the most overlooked as well. A well defined problem statement, predictive analytical models and process efficiency cannot be achieved unless we have the right analytical minds leading and nurturing the program. As an organization, we need to recognize the need for analytics leader who has the resources and can rally the operational teams to achieve the desired outcomes. The ROI of analytical projects depends on this critical investment, just as it does on problem statement and analytical modeling.

Analytics and data modeling empower the businesses, and to stay competitive, businesses need to equally weigh continuous innovation and implementation. Rapid deployment is as critical for success, as is harnessing and modeling business metrics!