The modern data landscape is intense. More data is being generated than ever before along with unprecedented ways to collect and analyze it.
The array of methods for using data analytics gives companies the potential to improve every angle of their organization, whether it’s detecting operational inefficiencies, generating ad-hoc analyses, or understanding their customers on a deeper level.
But having the potential and fulfilling that potential are two different things. Let’s delve deeper into these initiatives below.
Answering Questions as They Arise
Per a ThoughtSpot case study, the credit card division of a fortune 500 financial services company was looking for ways to put their massive trove of credit card transaction history to actionable use and boost their bottom line. The issue they kept running into however was a rigid business intelligence tool that most employees couldn’t use to analyze data on their own. Each time an employee had a question, a request was submitted to an analyst. Building reports to satisfy these requests took as long as two weeks, an eternity in today’s fast-paced business ecosystem.
Now, each employee uses search-driven analytics to instantly analyze any question that comes to mind—resulting in a 360-degree understanding of customer behavior and patterns which has allowed the credit card division to sell their products faster and more effectively.
Considering that data employees are some of a company’s most expensive hires, it doesn’t make sense to bog them down in tedious work. The internal benefits of deploying a self-serve ad hoc reporting tool are three-fold: it frees up data teams to focus on more important tasks, increases data adoption among employees and improves overall company productivity through real-time decision-making.
Detecting Operational Inefficiencies
No matter how much time and money a company invests in their operational processes, details will be missed, and oversights will occur. According to a Datameer report, OPower, an energy management company, was having trouble incorporating the amount of data they collect into their product: a mix of big data and behavioral science that helps utility companies across the globe reduce energy consumption and improve their customer relationships.
OPower serves 93 different utilities and 32-million consumer households. On any given day, the company gathers 7 million data points to provide households with reports on how they can lower their energy consumption. The SQL database the company was using wasn’t agile enough to incorporate all the data points into each analytic report, resulting in a massive waste of resources. Through migrating their data infrastructure away from SQL to an open-source platform gave product managers and data engineers more tools to access and analyze data. The shift considerably reduced the amount of time required to access data and put the 7 million data points to good use, ultimately lowering energy consumption by $500 million and CO2 output by 7 billion pounds!
Developing Deeper Customer Segments
Gathering data to build and refine customer profiles is nothing new in sales and marketing endeavors, but even with meticulous effort and valuable data put to good use, most customer profiles are too broad to resonate with individual consumers. Using big data analytics tools, Dell was able to get a clearer view of customers and leads by tying customer transaction records with social media profiles and email addresses, allowing automated insights to get closer in offering personalized promotional offers with better chances of converting.
The best part is that the process is cyclical: each campaign is more informed than the last, since these deeper customer segments continue to get more granular. Dell managed to double their closure rates and accurately identify twice as many people who responded to promotional programs than before.
The beauty of data is that it can be used to improve various facets of a company’s operation, but only with good data and the right tools in place.
How does your company use data and what kinds of improvements have been made through your analytics findings? Curious to hear other success stories in the comments!