Informing Better Business Decisions with Analytics
Analytics provide insights that add operational value to any industry.
In today’s global business community, the volume and complexity of customer and company data is continuously expanding. The prevalence of Internet of Things (IoT) and big data call for a more intelligent way to sort through this vast amount of information and find business patterns, trends and opportunities that otherwise go unnoticed. Business analytics give teams the power to unlock invaluable insights that maximize operational efficiency in every industry sector. When organizations have a well-planned analytics strategy, they are able to make better data-driven business decisions that empower success. Consider these examples of the power of analytics in action.
Lowering project expenses in transportation
Predicative analytics can help businesses find ways to save costs and create new business models, along with finding and exploiting patterns contained within data in order to detect risk and opportunities. This is making a big impact in industries such as transportation. For example, Total Cost of Ownership (TCO) is a key driver in the commercial trucking industry, but with varied inputs and massive amounts of changing data, calculating true TCO can be very difficult. Consider Daimler Trucks North American (DTNA): the company faced challenges integrating customer data sets into TCO calculations due to constantly changing file and data type formats.
PK solved this challenge by replacing its current workflows with an Alteryx analytics strategy that offered self-service BI, data preparation, data blending and analytics. This equipped DTNA with business intelligence that enabled TCO calculations without IT, lowered project expenses and created a more responsive, agile data environment.
Uncovering actionable customer insights in retail
Using analytics, companies can put insights from big data to work and better understand customers. A recent survey found that retailers are leaving nearly $150 billion on the table by not using customer data to their advantage. An excess of customer insights can result in data entry backlogs and issues with actionability. On the other hand, half of the companies that are employing data analyzation strategies, aren’t happy with them.
Consider the case of REI, one of America’s largest consumer co-ops, that wanted to better leverage its large volume of data to gain a better understanding of customer buying patterns and behaviors. PK helped REI make sense of its data through the implementation of an Alteryx self-service analytics solution that began with standardization. The data was then was pulled into a dashboard that gave C-suite executives daily snapshots into the health of the business. These daily reports allowed REI to quickly identify and respond to any changes or problems that would have previously gone undetected. PK then implemented geospatial capabilities and programming language R to gather, sort and analyze data such as how far customers lived from a store and what activities they were most likely to partake in based on purchases and activities. REI leveraged this data to implement business strategies that led to increased loyalty, customer engagement and sales. This customer data was then evaluated to determine total lifetime value, which allowed the company to target marketing activity to the most impactful buyers. PK’s analytics tools delivered invaluable insights that gave way to more informed business decisions for the retailer, such as the best products to offer and what locations would be ideal for store openings.
Just like Daimler and REI, companies are adapting analytics to better understand and solve tough business challenges. With the ability to selectively process, examine and translate a large volume of data into actionable insights, analytics help companies make better, quicker and more informed business decisions that save money, time and establish a better relationship with customers.