Editor’s Note: This is the first installment of a three-part series on how Big Data can be exploited to enrich market segmentations.
French novelist Marcel Proust is particularly famous for saying, “The real act of discovery consists not in finding new lands but in seeing with new eyes.” That sums up the way I see Big Data and its role in segmentation: It doesn’t necessarily point us toward undiscovered markets, but it can greatly enhance our ability to see our existing market in new ways.
How? I asked my colleague, Dr. Raymond Reno, to join me in creating some practical examples of how Big Data can sharpen your segmentation lens. In this first installment of our three-part series, we’ll show how Big Data can add value to creating market segments for a new product launch.
Finding the Lighthouse
Segmentation is frequently part of a company’s overall strategy to prepare for a new product launch. The identification of “Lighthouse Customers”—the early adopters who are likely first-users of a new brand, technology, product or service—can be a critical component of new product segmentations. So, too, is the identification of mainstream adopters, late adopters and those who likely will never be receptive to the new offering.
If your customer data or third party data includes purchase behavior over time, then you have a form of Big Data that can enhance your ability to develop targeting and messaging strategies. The basic idea is to analyze data from prior product introductions to explore questions like:
- Which consumers adopted new products, and at what point in time after the launch?
- At what rate did they adopt the new products?
- What was the impact to legacy products?
Understanding these patterns can provide clues into the likely adoption of your new product, even if the historical data comes from a somewhat unrelated category.
Big Data & the Pharmaceutical Industry
The pharmaceutical industry provides an example of how this works. Big Data in the form of physician prescribing records is available for all prescription drugs that are approved for use in the US. By examining physicians’ past prescribing behavior during a launch phase, we can begin to identify candidates for likely early and later prescribing behavior of the next new drug. Some physicians seek first-hand understanding of new drugs while others delay adoption until their peers have reported significant benefits or fewer side effects. By combining this understanding with other information, such as patient interaction style, approach to the disease state and ratings of currently available therapies, we can create a complete and effective segmentation story that supports development of successful launch strategies.
This same approach can be applied in other industries as well. The trick is combining Big Data with attitudinal data to enhance our understanding of likely adoption patterns to help brands prepare for successful product launches.
Our next post will focus on how Big Data can enhance segmentation by identifying targets for special programs, such as rewards programs, medical adherence programs or low-income bill payment assistance programs. Contact me or Ray to discuss how you can incorporate Big Data into your segmentation planning, or browse the many segmentation posts on this blog.
Special thanks to Dr. Raymond Reno for his contributions to this post.