How Analytics & Data Science Improve Your Business Efficiency
Corporations, particularly those that are focused on making sales to a broad audience- as opposed to selling a small number of large ticket items- have always had a keen interest in crunching numbers related to the way customers interact with their brands to boost sales. This is what we might refer to as data analytics. But many organizations opt to take their efforts a step further using data science.
What Is Data Science & Analysis?
Techopedia defines data science as:
“[…] a broad field that refers to the collective processes, theories, concepts, tools, and technologies that enable the review, analysis, and extraction of valuable knowledge and information from raw data. It is geared toward helping individuals and organizations make better decisions from stored, consumed, and managed data.”
The term was once commonly known as datalogy, but since the idea of studying massive amounts of data for use by marketing entities became a much more practical reality due to AI and machine learning, a more accessible term, “data science” has become the norm.
While the definition of data science covers analysis, we would do well to consider data analysis as defined by Techopedia. They describe it as;
“[…] qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.”
The Growth Of Data Science & Analytics As A Practical Tool
Traditionally, these tools were used to create new constructs from existing data. But the ability of information technology to gather and generate actionable data has long outpaced our ability to use it. Just analyzing all the data has become a growth industry all its own.
However, modern data creation and capture capabilities are moving data science and analysis beyond its traditional usefulness as a tool for creating new theories and into the more practical realm of direct organizational management. To put it plainly, data science and analytics are now able to be used to actively fine-tune and adapt marketing, business practices, and more to make business processes more efficient.
Forward-thinking organizations are taking advantage of the universal optimization methods which we will discuss below. They are real-time reporting and existent data interpretation.
Businesses with significant customer interactions, whether those interactions happen in real life or online are benefiting the most from real-time reporting, (RTR). RTR has the benefit of being immediately actionable, giving public-facing merchants the ability to optimize sales processes as quickly as they can recognize them. As markets grow accustomed to RTR driven competition, you can expect to see companies prioritizing better response times.
RTR makes customer interaction reports more meaningful by enabling service reps to gain a fuller understanding of customers as they interact with them. Consider how many times you’ve called a customer service hotline only to be put on hold, subjected to an infinite number of questions, and then put on hold again. This is the traditional version of RTR. But, of course, there’s nothing “real-time” about it. Today, the question session of a service call can be done while the customer interacts with the customer service rep.
This makes customer service faster- and less annoying for the customer- while at the same time being much more informative for the merchant. It’s a win-win situation, and everyone gets what they want. On top of that, as the customer- you might find yourself getting what you want even faster the next time you call if the company in question makes good use of the data your call provides them with. And that’s just a start.
Existent Data Interpretation
Real-time reporting is great for generating and leveraging micro-scale interactions, and that makes it an excellent tool for companies to make turn-on-a-dime policy changes. But some problems call for preemptive solutions. In other words, RTR is a great way to learn from the mistakes you make on the person-to-person level- but Existent Data Interpretation, (EDI) can help you avoid those problems altogether. But not only that, EDI predicts the future on the organizational level.
The purpose of EDI is to construct predictive models that will help organizations avoid problematic customer(s) relations incidents, for example, that could be avoided.
On an organizational level, EDI can give you the ability to retool your assets to take advantage of seasonal opportunities. Those familiar with the real estate market sales cycle, for example, (discovered in the 1930s by economist, Homer Hoyt), will appreciate the ability of EDI to develop very long term predictions that few would be able even to imagine. Today, most real estate professionals are unaware of Hoyt’s discovery, and those who re-discover it, and properly leverage it, become tycoons.
Hoyt came up with his real estate cycle long before data technology enhanced the abilities of business people. Imagine how many Hoyt-like predictions could become a reality with the aid of these technologies and methods. The possibilities are, frankly, unlimited.
Data-Driven Results In A Tech Powered Market
Imagine you’re that point-of-sales level clerk. You’re armed with RTR tools (or your equipment is) which makes you infinitely more valuable to the organization. On top of that, you’re also guided by endpoint EDI constructs that help you avoid known sticking points that could kill a sale, spoil an event, or disrupt essential opportunities.
That’s the way these technologies would be marketed to a small business owner. But the real power of Data Science and Analysis will be felt most at the organizational level, driving new efficiencies that could hardly be imagined a decade ago. Real change will occur as the result of upper management decisions derived from big packages of interpreted data, unraveling major areas of business-process streamlining that will revolutionize the way you do business.
Jumping The Learning Curve With Big Data Analytics Services
The full capabilities of any new technology are beyond the ability of any business to realize without expert help. The recent history of Internet-driven tech has proven that outsourcing is the key to leveraging cutting edge data products and services. Analytics and Data Science are no exception.
Forward-thinking organizations are not struggling to create their big data and analysis products on their own or forming internal departments to do the job from scratch. Competitive organizations are outsourcing their significant data needs.
Until these technologies and methods become ubiquitous- big data services, big data offerings, and big data consulting are full-time jobs- best served by the professionals. You need to work with a Big Data Consulting and services company that is already ahead of the curve.
That’s where the next generation of data science marketing professionals come in. You’ve got the products and the services people want- and prominent data consultants have the predictive constructs science to make your brand go viral.
At this point, the real competition is down to who will access these cutting edge data services first.