Advanced Data Analytics & ROI
ROI data & advanced analytics are certainly not the most popular marketing terms out there but when it comes to investing a lot of money in a project, you would most likely want to be 100% that you are going to see the return on your investment (ROI).
Playing it big means risking. And while risk by itself is a noble thing (nothing ventured, nothing gained, so they say), you don’t want to rely on your business hunch alone.
That’s when advanced analytics and ROI data come in – to sift through every meaningful piece of information that is available and help you make the right decision that would be based on solid logic, scientific methods, and concrete numbers or facts rather than a guess.
What Is ROI
ROI stands for «return on investment», and it is one of the cornerstone metrics of investment and business in general.
When talking about an online business, we use ROI to see how much money we will gain from investing in a particular venture, campaign, or product/service.
A very broad, general formula for calculating ROI looks like:
ROI = (Gains from investment – Cost of investment) / Cost of investment
Obviously, any ROI lower than 0% means that not only you have not gained anything but rather lost some. However, an ROI that’s more than 100% is quite possible – Forrest Gump had himself quite an ROI on the shares of some «fruit company» (that turned out to be Apple).
Of course, it is not always that easy in real life. Sometimes you invest gradually and in varying portions, which makes it more difficult to calculate. Plus, you need to be aware of the fact that $100 you invest today is not going to be $100 a year later. It will probably be $99.xx, depending on the inflation rate.
There is a question that is more interesting than «What is ROI?» – this question is «How exactly does ROI help?». Well, if the ability to make evidence-based decisions is not enough for you, here are some more benefits of ROI for an online business:
- Allows to quickly analyze the performance of projects
- Helps in making comparison between different investments or projects
- Gives a powerful insight into meaningful business data
- Assists in financial planning and budgeting
Being as great as it is, ROI, nevertheless, has its weaknesses, mainly when it comes to the overall assessment of an investment success. Not everything can be measured in dollars (or money, in general) plus the very definition of profit (or gains) is quite broad making it hard to be consistent with it at times.
Big Data ROI
Big data is an ambiguous term that may refer to either of the following:
- large amounts of data that traditional processing application software is unable to successfully handle
- using predictive analytics, user behavior analytics, or other advanced analytics methods for extracting a particular value from the data
Sooner or later, it would have come to this - the data we have on our hands is more than we can handle. Likewise, the information we want to learn from the data that’s on our hands is more than it could give us. Looks like a stalemate.
To resolve this stalemate appeared big data. Knowing what your customers and clients ARE up to now is good and all helpful but knowing what they WILL BE up to in the future provides for a very lucrative competitive advantage.
The term «big data ROI» comes up when you need to evaluate the potential financial outcome of a major project that involves big data. Needless to say, it is a hugely sophisticated task with so many elements in the equation that sometimes you can’t even seem to find the equation itself.
We are guided by a simple principle: every piece of data, big or small, has to make sense and serve a particular business purpose. Therefore, the less data you analyze, the more precise your results will be.
Advanced Data Analytics
Much like big data, advanced data analytics comes into picture when regular analytics is simply not enough. For example, when you need to create a description for a customer who would most likely churn after the first month of using your product.
The term «advanced data analytics» itself refers to the set of techniques used for processing and modeling internal and external data in order to generate insights that could drive business-improving decisions.
The practical value of advanced analytics is not always obvious. Instead, it gives you a nutritious food for thought that can result in a meaningful decision much later.
Love it or hate it, advanced analytics is sometimes the only viable method of getting some business-relevant information that could not be obtained otherwise.
Common techniques that are often attributed to advanced data analytics are:
- Descriptive analysis (understanding the underlying cause of certain processes or actions)
- Predictive analysis (analyzing a huge amount of hidden relationships between different factors and outcomes to produce a forecast for the future based on the existing data)
- Simulation (imitating a possible action or process in order to see what the outcome may be)
- Optimization (creating a few possible solutions for the problem and then choosing the optimal one)
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