Companies are increasingly adopting zero-based budgeting practices— where departments must ask for and justify every dollar spent. As a result, teams have to be increasingly thoughtful and strategic about the budget they request and its potential ROI. This is particularly pertinent for research and insights teams where traditional research methodologies are expensive and the unfortunate alternative is oftentimes doing no research at all.
As such, zero-based budgeting has a large impact on innovation; that’s why Quirk’s and GutCheck partnered up for a roundtable webinar to discuss some effective research solutions and the tradeoffs associated with each. Sitting on the roundtable discussion included our very own Chief Executive Officer Matt Warta, Chief Research Officer Renee Smith, Chief Product Officer Keith Johnson, and Product Manager Matt Lucas.
Big Data is Heading the Way
The discussion began with an evaluation of the current landscape for innovation through market research in a zero-based budgeting world. While there are many tradeoffs between DIY, traditional and agile methodologies, one solution is leading the way—big data. Industries in customer relationship management (CRM), media buying, and marketing analytics have been utilizing big data to automate their data collection for the past 5 years. Today, big data is bigger than ever with more people engaging and utilizing tools that offer big data integration. However, “incorporating big data is a marathon, not a sprint” as Keith explains, and companies have to take the right steps before making the leap. Before getting started, you’ll want to familiarize yourself with the 4 V’s of big data:
The biggest challenge when it comes to data is the “veracity” of it. Because there is so much and such a variety of data, it can be difficult to assess its accuracy and application to your business. Discerning the signal from the noise is where most innovation teams will spend their time interpreting the data. While big data can answer when, where, and what, it can’t answer why. Integrating primary research, particularly with an agile methodology that can keep up with the speed of big data, will help to analyze and connect the dots— easier said than done. Getting expert help to find accurate and focused data relevant to your objectives is critical to successfully using big data as a solution.
Machine Learning Is on the Horizon
While it’s still early in its application, machine learning has been around since the birth of computers. In its simplest form, it takes predictive modeling and adds a feedback loop: an algorithm that can learn and adjust itself. A specific example of machine learning today is Facebook’s newsfeed. What users like, read, react to, or follow determines what content they see on their feed in the future. As user preferences change, so can the algorithm and what content is ultimately shown. Utilizing machine learning as the means to collect big data will eventually lead to the most real-time, living and breathing data.
You’ll still need human expertise to draw on in-depth insights, but eventually big data and machine learning will work together to create a seamless and automated process for gathering the most accurate data. While the adoption of technological solutions for innovation is on the rise, not every business can easily adopt them, and they may have to take some steps before they’re ready. Take the time to evaluate how these solutions apply to your business and the tradeoffs associated with a variety of different research approaches by watching the full webinar below.