What do the movies “Stealth, The 13th Warrior,” “Heaven’s Gate” and “Speed Racer” have in common?
While they all may be movies that you’ve never heard of, they’re also movies made with massive budgets because the heads of their studios predicted the films would rack in wads of cash.
But they’ve all turned out to be box office disappointments.
This is the type of scenario that movie studios – and a myriad other organizations – could potentially avoid by using big data analytics and data discovery to predict how the market will react to their offerings, argues Scott Schlesinger, Capgemini’s head of business information management, in a Harvard Business Review post.
“Analytics allow studios to go beyond simple focus groups or established financial modeling to determine how audiences might respond to a given film,” Schlesinger adds. “It’s all about identifying patterns in past data, melding them with current data points that are readily available, and then taking action to improve business performance.”
In addition, it’s important for a studio to base its analysis on an unbiased assessment of the factors that have been found to be significant in predicting the performance of a movie, he says. A studio could look at all the action adventure films released in a certain time frame starring a certain actor and then sort by when the movie was released and where, as well as how it has performed.
“In the future, movie studios will find it a rarity to see their films labeled a ‘bust’ on their quarterly sheet,” Schlesinger notes. “By planning releases in a more systemic way, predictive analytics will be considered just as important as the producer, director, and actor who make a film a blockbuster.”
While companies don’t typically have Hollywood-size budgets to invest in new product development, big data analytics is key to predicting and grabbing market opportunities and threats, adds Dominic Barton, global managing director and head of the analytics practice at McKinsey & Company.
Companies can use big data analytics and data discovery to bolster core operations or create new lines of business, he notes. For example, an insurer could use big data analytics to boost underwriting performance and to develop new risk-based businesses.
One particular retailer has even used big data analytics to fend off a competitor, according to McKinsey. The competitor was threatening the retailer by using consumer data gathered through online purchases to effectively generate recommendations to its customers. The retailer itself began using data and analytics to improve its own operational performance, Barton notes.
“The retailer . . . gathered volumes of data but wasn’t using it to potential,” he adds. “This information on product returns, warranties, and customer complaints contained a wealth of information on consumer habits and preferences. None of the information was integrated with customer identification data or sufficiently standardized to share within or outside the company. Happily, the company had a team that could help solve these problems: in-house data analysts whose siloed efforts were underused.”