The document outlines a structured approach to analytics in business, highlighting the key steps: observing and defining the business problem, formulating hypotheses, testing through experiments, and learning from results. It emphasizes the importance of identifying relevant hypotheses that cover all possibilities and using the scientific method to validate them. By applying these techniques, organizations can increase profitability through informed decision-making based on data analysis.

1. By Rahul Nawab
Academy for Decision Science & Analytics
www.adsa.in
Academy for Decision Science & Analytics http://www.adsa.in

2. Analytics is not just pure science; it is part art as well. Organizations that master
the fine art of using analytical tools realize increased revenues and enjoy cost
savings.
The scientific approach involves the following four key steps:
 Observe/define the business problem: Observation is either an activity
consisting of receiving knowledge, or the recording of data using scientific
instruments. The term may also refer to any data collected during this
activity.
Analytics begins with observing the phenomenon and setting up the right
business problem. It requires understanding the facts, to which you have
ready access, and then drawing conclusions from it to identify 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. Through these two facts, we can identify a simple business
problem – the manufacturing company must reduce costs or increase
revenue if it wants to have the same profitability as before.
 Hypothesis: A hypothesis is a proposed explanation 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 before solving the problem. Taking the above mentioned
example of the manufacturing company, the business may have two sets of
hypothesis:
 Increase Revenue: Within increasing revenue, the firm might think of
many different avenues:
o Focus on Marketing – Increasing the marketing budget will enable
us to increase sales and hence increase revenue.
o 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.
Academy for Decision Science & Analytics http://www.adsa.in

3.  Reduce Costs: Within reducing cost bucket, the organization has
various alternatives:
o Operations cost – By reducing the operations budget (e.g. staff,
electricity etc.), we will reduce costs.
o 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 several implications of each
action beyond the primary implication and all need to be evaluated. The
key element of the hypothesis-building phase is that you should have a
mutually exclusive and collectively exhaustive set of hypothesis. This
means we should think about all the possible sets of relevant hypothesis
for the situation at hand and ensure they do not overlap and that together
they are complete.
 Test/Experimentation: An experiment is the step in the scientific method
that arbitrates between competing models or hypotheses.
Experimentation is also used to test existing theories or new hypotheses in
order to support them or disprove them. An experiment or test 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
problem 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 test a question or test previous results. The fundamental reason
for following this process is to ensure the results and observations are
repeatable and can be closely 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 set 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 revenue for GROUP A and
Academy for Decision Science & Analytics http://www.adsa.in

4. GROUP B and understand the differences. As long as the groups have a
statistically significant size we should be able to repeat these results.
 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 same
time we increased marketing our competitors decreased it in the GROUP B
target market. Now the question becomes, was the incremental benefit
driven by our increased marketing or the fact that competitors reduced
their marketing? Assimilating all possible and relevant information is
extremely important in order to reach 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 use it. This
requires a strong commitment to the scientific process and a systematic
approach to create a TEST & LEARN environment where you are
constantly testing, learning and evolving to create increased bottom line
benefits for a company.
Academy for Decision Science & Analytics http://www.adsa.in