True value of Analytics? It is the "How much impact?" stupid!

 More often than not, the analytics users and developers focus on answering "what" questions: "what happened?" or "what is happening?", depending on the nature of data being handled, be it historical or real-time streaming. Regardless of the temporal aspect, the "what" analysis provides only a small part of a bigger story. Don't get me wrong; the "what" analysis is a much required foundation for further deeper analytics. But as a business analytics professional, if you provide insights to your key stakeholders (executives, operations managers, etc.) from only the "what" analysis, you are shooting yourself in the foot!

So, what would complete the analytics story?

A primary question that has to be addressed by the analytics for creating greater business value is: "How much impact is there on my key business outcomes?". The business outcomes for the impact analysis can be financial metrics such as revenue and costs, and qualitative yet valuable metrics such as the customer experience and brand value.

For example, if your job is in product analytics, it is not enough to measure the "what has been happening" or "what happened" trends of product awareness, trial rate, conversion, purchase rate (first or repeat), etc. It is also not sufficient to perform quantitative analytics on various stages of consider-evaluate-buy-experience-advocate-bond-buy(repeat) consumer journey. It is a must to mine deeper insights by measuring "how much impact" those trends have on the business itself. If your job is in web analytics or any other domain monitoring and analyzing various KPIs, it is not enough to measure "what is happening (in real-time)" or "what has been happening" or "what happened". The value of such "what analysis" is akin to the skin deep beauty.

The same applies even if your job is to develop analytics tools/platforms that help users perform the above variety of analytics. If the product (tool or platform) you are building enables the users to conduct only the ""what" analytics, you are doing a huge disservice to your customers (assuming the customers are still buying that stuff!). And, if you are the customer exploring the analytics tool to buy, you have the right to demand more than the "what analysis" capabilities from your potential vendor. Remember, the right to remain silent does not apply here!

Now, if it is the degree of impact on the business outcomes that is of higher importance, how can one connect those business outcomes to the metrics being actually measured and analyzed? That's where the business domain knowledge comes into play. For example, see the DuPont model for the Retail industry. The DuPont model establishes a relationship among various value drivers with the financial levers which in turn are connected to the business outcomes. On the other end, the measurable KPIs (for example: page visits, visitors, sessions, duration, bounce rate, etc. in web analytics) can be (and dare I also say "must be"!) quantitatively connected to the value drivers. That can be done through various statistical and/or machine learning techniques applied on the historical data.

Such "How much?" analytics with established relationships to assess the degree of impact being made on the business outcomes, are the ones that truly generate the business value.

In fact, the process of establishing the relationships among various metrics and outcomes through "How much?" analytics will become a valuable foundation for "Advanced What Analytics" such as predictive ("What might happen?") and prescriptive ("What should happen for optimal outcome?") for more actionable insights.

That is a nice circle of "What - How Much - What" analytics to complete.

Your thoughts please!

Acknowledgment: DuPont model for Retail developed with inputs from my ex-colleagues (Bernadette, Kim, Robyn, and Victoria), the true industry experts! Any errors are mine.