Analytics is not just genuine science; it is part art ut supra well. Organizations that master the fine art of using analytical tools realize increased revenues further hilarity cost savings.
Last week we talked broadly throughout ANALYTICS. This week we leap condition the “SCIENCE OF ANALYTICS.” The scientific attack involves the following four key steps:
1. Observe/define the business problem: Study is either an activity consisting of receiving knowledge, or the recording of data using scientific instruments. The term may also refer to any input aggregate during this activity.
Analytics begins with observing the phenomenon and setting up the right calling problem. It involves understanding the facts, to which you have ready access, and consequently drawing conclusions from it to verify the business problem which needs to be solved. For example, a manufacturing company is suffering from declining profits. By looking at their balance sheet we realize that revenues have declined while the costs have remained constant. Past these amphibious facts, we can identify a mere business hornets’ nest – the manufacturing company must reduce costs or accelerate revenue if it wants to have the same profitability as before.
2. Hypothesis: A hypothesis is a proposed interpretation for an observable phenomenon. People refer to a trial solution to a problem as a hypothesis — often called an “educated guess” because it provides a suggested solution based on the evidence. Researchers may test and reject several hypotheses herald solving the problem. Taking the above mentioned example of the manufacturing company, the business may have two sets of hypothesis:
a. Increase Revenue: Within increasing revenue, the firm drawn intellectualize of many different avenues:
i. Focus on Marketing – Increasing the marketing budget will prepare us to increase sales and hence increase revenue.
ii. Focus on Price – By reducing the price of our product we would be more competitive and hence increase sales, which might offset the decrease in sales/unit.
b. Reduce Costs: Within reducing charge bucket, the organization has various alternatives:
i. Operations cost – By reducing the operations budget (e.g. staff, electricity etc.), we will reduce costs.
ii. Reduce Marketing budget – By reducing the marketing budget, we will save on costs.
As you can see, you can achieve increased profitability by both increasing and decreasing marketing budgets. There are distinct implications of each action beyond the constitutional implication and all need to be evaluated. The key element of the hypothesis-building phase is that you should have a mutually exclusive und so weiter collectively exhaustive set of hypothesis. This means we should think about all the possible sets of relevance hypothesis for the situation at hand and ensure they do not overlap and that together they are complete.
3. Test/Experimentation: An experiment is the step in the scientific method that arbitrates between competing models or hypotheses. Experimentation is plus used to test existing theories or new hypotheses in order to support them or disprove them. An experiment or approve can be carried out using the scientific method to answer a question or investigate a problem. First, an observation is made and then a question is asked, or a hornets’ nest arises. Next, a hypothesis is formed and an experiment is used to test that hypothesis. The results are analyzed, a conclusion is drawn, sometimes a theory is formed, and results are communicated through business cases.
A good experiment usually tests a hypothesis. However, an experiment may also validate a question or analytical previous results. The fundamental reason for following this process is to ensure the results and observations are repeatable and can be almost replicated given similar circumstances. Let’s continue with the example above and set up a test for the manufacturing company to learn whether increasing the marketing budget would affect revenue. In this case, we would ensconced up a TEST where we run the EXISTING marketing programs and call it GROUP A while in GROUP B we run the increased marketing program. At the end of the observation time frame (assume 2-3 months), we would measure wages for GROUP A and Generic B and understand the differences. As long as the groups fool a statistically significant cover we should be able to repeat these results.
4. Learn: Learning is acquiring new knowledge, behaviors, skills, values, preferences or understanding, and may involve synthesizing different types of information.
Continuing our manufacturing company example, let’s assume that GROUP B performed far better than GROUP A. Let’s also assume that at the equivalent time we increased marketing our competitors decreased it in the GROUP B target market. Today the question becomes, was the incremental benefit driven by our increased marketing or the fact that competitors reduced their marketing? Assimilating all possible and congruous information is extremely important in order to arrive a good decision.
As you can tell, while scientists have been utilizing the above mentioned technique for a long time, businesses are just beginning to treatment it. This requires a strong commitment to the scientific process and a systematic approach to create a TEST & LEARN syntonic where you are constantly testing, learning and evolving to create increased bottom line benefits for a company.