At forward-thinking companies around the globe, data science and analytics are upending the old way of doing things. Businesses are marketing, buying, selling, and improving operations in ways that simply weren’t possible a few years ago. Advanced insights have never been more accessible — even to those without a formal data science background.
Explore how four companies in a variety of fields — from U.S. broadcasting to commercial real estate — are using advanced analytics to get an edge on the competition.
Transforming customer churn to return with predictive analytics
Well-known research from Bain & Company shows that increasing customer retention rates by just 5% can increase profits by 25%–95%, which is why a popular human resources software company makes customer satisfaction a priority. Using data from their Net Promoter Score (NPS) survey, they built a predictive model to identify customers likely to be dissatisfied with the company. The model not only helps the company segment customers into cohorts of promoter, passive, and detractor, but also helps identify detractors before they churn (and tell all their friends). Thanks to their predictive model, they can identify four out of five at-risk accounts before they churn — making it a powerful tool for retention. Instead of waiting for a customer complaint, account managers can use data to convert a potential detractor into a promoter and proactively make sure clients are happy.
Company: Human Resources Company
Goal:Ensure employees receive paychecks and employers stay happy with their services.
Solution: Built a predictive model using their Net Promoter Score (NPS) survey to identify customers likely to be dissatisfied with the company and intervene accordingly.
Results:The company can identify at-risk accounts with 83% accuracy, allowing account managers to proactively reach out to customers rather than waiting for a customer complaint.
From black box to glass box: Real estate predictions get clear
CBRE, the world’s largest commercial real estate services and investment firm, saw a lack of access and transparency in commercial real estate data and set a goal to take analytics out of the black box. By providing business intelligence to the organization, they knew they could identify the best investment opportunities on the market. Using spatial analysis and predictive analytics, they gathered data about the growth of residential, commercial, restaurant, recreation, and retail spaces for their target areas to create forecasts. CBRE can now make decisions backed by insight, giving their brokerage team a unique advantage when making purchasing choices.
Goal: Provide business intelligence for the organization, including identifying the best investment opportunities currently on the market.
Solution: Used spatial analysis and predictive analytics to gather data about the growth of residential, commercial, restaurant, recreation, and retail spaces throughout target areas and create forecasts.
Results: The data science team can now provide the brokerage team with unique insights about properties being considered for purchase.
Broadcasting company uses spatial and predictive tools to keep the music playing
A leading U.S. broadcasting company takes their radio quality very seriously. To identify problem areas, they spend thousands of hours driving around major cities to collect data about the quality of their satellite radio signal. Through machine-learning powered clustering tools, their Systems Engineer combined, reduced, and filtered 178,599 data points to identify 22 clearly defined trouble spots. By leveraging K-Centroids Diagnostics and other predictive grouping building blocks, the company can automatically analyze signal power, signal quality, and cellular power to help identify the probable cause of a signal problem. This process takes less than one minute and 31 seconds, saving the team hours of work. By leveraging spatial and predictive tools they’ve gained deeper insights into their data and saved time by scaling and automating their processes. Rather than manually crunching data, the Systems Engineer team has freed up time to investigate problem areas and recommend solutions.
Company: U.S. Broadcasting Company
Goal: Provide listeners a clear signal, no matter where they go by identifying locations with signal problems.
Solution: Leveraged machine-learning powered clustering tools to combine, reduce, and filter 178,599 data points to 22 clearly defined problem areas.
Results: With automated processing, the company's Systems Engineer team now has time to investigate problem areas and recommend solutions, rather than manually crunching data.
Rain, shine, and a chance of fluctuating interest rates: Financial forecasting that considers internal and external factors
Cetera Financial, one of the largest independent financial broker-dealers in the U.S., knows that nothing exists in a vacuum, including financial forecasts. They realized that to achieve better and more accurate revenue and expense forecasts, they would need to consider both internal and external factors. Now their financial forecasts are based on 10 to 20 internal and external drivers, including economic changes, raw materials, supply changes, inflation, and seasonality. They went beyond traditional internal data sources and created predictive models that accounted for macroeconomic factors. Cetera executives can now easily see the effect of each factor (internal and external) on the brokerage's revenue.
Company: Cetera Financial
Goal: More accurate financial forecasting.
Solution: Created a new analytics engine that considered 10-20 internal and external factors.
Results: Company leaders have more reliable 5-year forecasts and are able to see the effect of each factor on Gross Dealer Concession.
No matter what field you’re in, advanced analytics can be a game changer. See how modern analytics can give your company an edge.
Melissa is an advanced-analytics advocate who dreams of democratizing data science. A former research physicist and business analyst, she’s seen the business and technical sides of data work and believes all analytics practitioners have more in common than they realize.