Leveraging analytics to ace new product launches
Blog Posts:Mu Sigma
Published On: 20 February 2015
More than 20,000 new products are launched every month in the CPG market alone. However failure rates for these products are notoriously high, and this issue is prevalent across industries. Consider the infamous case of the Ford Edsel, an epic failure caused in part by the marketing team’s failure to spend as much time analyzing market needs as it did the name of the car.
So how can marketers leverage data analytics to improve the odds of success for their product launch? Here are some key tenets:
Fail fast, cheap and often: extreme experimentation is the key
When launching a new product, marketers need to maintain a mindset of experimentation. We recommend breaking your execution into many smaller tasks to enhance the odds of success; this also reduces the risk of failure in the overall plan while a corrective course can be charted sooner. Create a hypothesis for each task and design an experiment to test the hypothesis. This will help the team quickly understand what is working and what is not.
Hunt, gather and farm for data
The biggest challenge for most companies planning to launch a new product is a lack of data to enable efficient decision making. Often, they over rely on internal data, narrowing the scope of analysis and insights being generated. It’s crucial for companies to expand their data universe — complementing internal data with external data — to make analysis more comprehensive.
Internal data ― Gather & leverage: In the case of a new product launch, historical sales data may not exist. However, in most cases, you’ll have data on a similar product line. Evaluating that data can provide invaluable insights for the new product launch.
External data ― Hunt & procure: External data is available in different forms and sources. Keep watch for relevant data that can be used for your analyses. For example, Google search trends can give a good idea of both time and spatial spread of demand, at no cost. Likewise, for a pharmaceutical drug manufacturer, procuring data from specialized vendors about physician prescribing patterns might prove very useful.
New data ― Create & farm: This refers to creating new data that doesn’t exist either internally or externally, but can complement existing data. For instance, a software company that just launched a program can capture information regarding prospect customers and potential leads from their website, say from registration forms that capture essential information of website visitors on to the product page. By inserting relevant fields to capture the visitor’s name, organization and contact details; and by using analytics to store and leverage this data, the company can figure out how to target prospective customers better.
The end objective here would be for organizations to carefully plan which data to gather and hunt and which to farm. You should dedicate investments, realign your resources and strategize sufficiently to cover all three aspects.
How is your organization leveraging analytics in its product launches? Would you say your company is behind, right on or ahead of this curve?