Seven Steps to a Systematic Analytics Approach from Conceptual Ideas to Implementation
Besides the steps, going through this will also help understand how prioritize research questions, collection of high priority doable hypotheses, and define and acquire right data.
Business opportunities abound as the speed of how the consumers act and react to the markets and how the markets acts and reacts to consumers’ behaviors feed each other. These opportunities have their own life cycles , of creation and destruction.
There are two major challenges organizations face, and analytics as a strategy comes to the rescue.
(1) The complexity of business processes are connected to the changing environments of how consumers act and react, and in turn contributing to the new life cycles of value generation (consumer dynamics), the creation of new generations of products arising out of the old ones (product dynamics), and together how organizations, governments, and consumers push and pull to receive their share of the value in the market place (market dynamics). Yet organizations have to abide by the ethical considerations of operations.
(2) While these complex interactions are going on in the market, the previous generations of concepts of organizational and market efficiencies are becoming standard best practices in all organizations, eliminating any differences in uniqueness of products and services due to those best practices and associated market efficiencies.
The next generation of value differentiation among organizations seems to be being relevant, timely, and personal. That is being relevant to consumers in terms of right product, at the right time, with a right price on terms of personalized offers. While these concepts have been in vogue in the last 20 years, they have come to occupy a central place in the new highly connected, socialized real time world.
Connecting the ideas of being relevant, timely, and personalized with the dynamics of business processes mean we collect right data, make it available for analysis, do pertinent analysis, and use the insights and implementation of analytical results in the right way.
From the point of view of an organization that is trying to be competitive using analytics as a key strategy, the patterns of activities on how these opportunities are identified and leveraged are explained as a seven step processes, listed as (1) spotting the opportunities, asking right research questions, (2) identifying the verifiable hypotheses, (3) defining and locating the data sources, (4) defining and confirming the right measure to use, (5) preparing the data, (6) analyzing, and (7) presenting for implementation.
In the following, these concepts are explained.
1. Identifying and articulating the business opportunities
Each and every dynamism among consumers and markets may look like a great opportunity, but not all opportunities are the same in terms of value it can bring to the organization and the ripeness of the market situations that will yield itself for value creation. The interesting thing is identification of right opportunity itself is a mini data analysis problem. Typically organizations use return on investment to rank opportunities and among the highly ranked opportunities, select the one that is ripe for execution and implementation, popularly stated as picking low hanging fruits.
2. Converting business/process related verifiable research questions leading to verifiable, computable, and attributable hypotheses
Continuing on the path of analytics as a key strategy, once we identify the right opportunity to pursue, we have to unpack this organizational opportunity into components of analytical steps. The first step is asking right research questions from different angles and spotting the most probable and most valuable questions that would help us seek for more valuable details that can be attributed to consumers’ behaviors so that consumers can be engaged in a right way through various campaigns. See the reference at the bottom, on developing great research questions. There is a systematic collection of thoughtful activities moving from business process questions to verifiable hypotheses.
“Hypotheses are suppositions or proposed explanations made on the basis of limited evidence as a starting point for further investigation” (Google definition)
For example, if our limited historical observation points out that the minimum monthly sale happens during the holiday season, a season where consumers spend the maximum amount of disposable income, it is a missed opportunity for the organization.
However, it could be because our products and services are not tuned to the holiday season (product dynamics), or because we are not having the right campaigns during those months (consumer dynamics), or because we are not matching the competition efforts (market dynamics).
This is a missed opportunity with maximum possible value to the organization because the whole economy is buzzing with almost one fifth of spending by consumers during the holiday season.
The testable hypotheses are,
– There are high value consumers who are looking for our products during holiday season
– Our products captures or represents holiday moods and sentiments
– Our average marketing efforts in terms of time and money, for high value customers are lower than our competition
– The average marketing reach out by channels of communication to our high value consumers need to be matching at least the levels of what the competition is spending.
Note that hypotheses are the links that explicitly connect the data to the research questions and hence to the opportunities. The opportunities are realizable in terms of being relevant, timely, and personalized, if the measurement arising from important hypotheses are estimated for each and every one of the consumers, truly becoming a one to one marketing solution.
3. Defining data sources
4. Defining a right measure to use
5. Extracting, Transforming, and Loading for analysis
7. Stating conclusions including the levels of uncertainty in conclusions and caveats
Once we settle on our hypotheses, we are ready to define the right measure and the right data. Most of the times, the challenges of locating right data is not that much compared to not giving enough careful attention to defining the right measure. Continue in the next section, …