Market research is the foundation of any strategic decision, whether it is a pivot, creating a new product, retargeting your customer base, or long term company vision. According to the Economist Intelligence Unit, "60% of professionals [sic] feel that data is generating revenue within their organizations and 83% say it is making existing services and products more profitable." To provide the right recommendations, market research relies heavily on data, a copious volume of numbers, measurements, observations, groups, words and more. Gathering data is already a tremendous task which needs a solid plan of execution, but what do you do with the data once collected? Converting the data into useful, relevant and insightful information for implementation would be a nice start. Perhaps you could show some PDA, Protano’s Data Analysis.
The Protano’s Data Analysis was created by Marco Protano of The Nurture Company for processing information into digestible bits with the end results being actionable for decision makers.
- Data – it’s pretty straightforward; simply amass information until you have enough to answer any specific question and unforeseen ones. You want the data to be raw, and untouched by human interpretation. This is the point where you are only listening and taking notes.
- Insights – after the data has been sorted, this is when the human touch of interpretation and grouping the gigabytes occur. Never delete the original data as humans can make mistakes, which would require revisiting to the virgin data. Discover correlations, causations, hidden meanings, and other nuggets. Having this down will help in the implications to your recommendations.
- Implications – In this last step, you already have the person on the hook to prep up for the recommendations by showing the supported data and insights radiating from it. Implications sole purpose are to reflect the previous steps, and draws them into what could potential level up.
Although the framework seems simple at first sight, it creates a funnel to distinguish priorities in data collection, relevant data points, potential correlation, causation, and assess the need for further investigation. No matter the software implemented, the framework allows researchers to narrow the specific details in order to debunk assumptions, support hypothesis, and give strong recommendations.
Netflix is the perfect example of how big data influenced first leap into creating television shows. Since Netflix has a massive data on the types of shows their customers were streaming, they noticed large in three key items: Kevin Spacey, the original House of Cards, and director David Fincher. Given the insight of these three major points, it led to their large, calculated gamble in the bid war for purchasing the rights against cable network juggernauts. Now it is Netflix’s highest rated show with several accolades over the past 3 years.
Side note: Don’t jump from data to implications as it interrupts the natural, data-driven story mojo for the recommendations. Imagine the PDA as mechanisms to build towards the climax of the story, which in this case would be the amazing recommendations backed by real data.
The biggest takeaway is to ensure your data is pushed through the funnel in order to make appropriate endorsements and decisions.