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Data Driven Decision Making

We’re in the middle of a very strange period. Public information is simultaneously more abundant and more obscure than ever before. In parallel, emotion continues to cloud understanding and get in the way of rational decision making. So we wanted to put together some thoughts on how we can leverage data to create security and predictability in our decision making.

Create a Hypothesis: What Do You Expect To Happen

Every decision needs to be preceded by a theory involving its outcome. For example:

I think that if we had more leads we’d make more sales.

– A Solid Business Leader

This way, other stakeholders can ask questions like:

  1. What percentage of our leads are currently converting to sales?
  2. How many more sales would we make if we doubled our lead volume?
  3. What would it cost for us to acquire more leads?
  4. How many more leads do we need to make a measurable lift in our sales?
  5. What is the risk of keeping our lead volume the same and focusing elsewhere?

All of these questions can be answered with existing data, and hypotheses. Which lead us to our next thought:

Create a Record Of What Was Discussed

This creates accountability to the decisions we make. If, for example, we hypothesized that 200% more leads would result in 200% more sales – and after those leads are acquired the change in business performance is negligible – it’s appropriate to review the hypothesis made beforehand in an effort to identify where things may have gone wrong. This helps in two key ways:

  1. It prevents organizations from making the same mistake twice
  2. It enforces rational caution in decision making

When there is no record or accountability for the decisions we make, we’re more likely to guess wildly and risk considerable resources and time testing something unfounded. For example, we worked with a business that was so married to bringing one product to market that they burned over two million dollars and two years trying to get it off the ground before admitting it wasn’t their most compelling offer. In this case, had there been a) a hypothesis b) a recorded discussion and c) a record of who bet on what, the very same product might have been more efficiently evaluated.

Plan Based On What You Know

Finally, resist the urge to ‘do something’. It’s true that action is critical to success, but misguided action is the most expensive mistake any organization can make. Misguided action costs time, money, human capital, and creates immense emotional stress on the operating team. Add in a lack of accountability, and we wind up with an arduous and unsuccessful effort that everyone involved feels responsible for.

Instead, look at what has been proven, what remains unknown, what hypothesis was wrong, and where there might be an untapped opportunity. This way even unsuccessful tests are able to narrow the scope of the unknown and create an obvious direction for future work.

Today, in business, Public Relations – specifically earned media coverage – is often an epicenter of poor planning and reactive thinking. Business leaders want to ‘do something’, agencies or internal teams lack the material they need to do effective work and fear asking stakeholders for more material, and often there is zero reflection on the data received or hypothesis made.

A discipline like this that often seems so obscure and difficult to measure is just as easy to operate on data as any other controlled test: Journalists give binary responses (I’ll cover it or not interested), the business has a finite number of known offers and value propositions, and team members have limited time. In this case:

Time + Narrative = Coverage

– The PinchForth Team’s Theory

Narrative, in this case, would be any combination of business offers or value propositions and supporting data to create a story thatjournalists might find appealing. Time can be any interval the business can afford (one week?), and coverage is obvious to measure. So the work left to be done is assembling the resources available (offers, value propositions and data), hypothesizing on what might be the most relevant and appealing to journalists, and doing some outreach over the fixed amount of time.

This would quickly allow any organization to efficiently deploy resources, and learn from the work done – avoiding repeated mistakes. It’s one way to turn something often considered difficult to measure into a very tangible, scientific endeavor.

Pay Attention to Data, Ignore Editorializing

Today, around the world we’re watching leaders in business and politics rush to ‘do something’ without accurately communicating the risks, the expected outcomes, considering the costs, or even reviewing the complete effects of these decisions. This creates uncertainty where there should not be any. Even when overcoming an unknown or seemingly immeasurable obstacle, when our resources and time are finite we need to prioritize (through hypotheses), discuss the expected outcomes, record what is discussed, and then review both the learnings and the hypothesis before making the next decision. This converts even the most seemingly unknown or insurmountable challenge into a clear target overtime by using data to rapidly eliminate unknowns, and ultimately reveal effective procedures.

Without a record of what was said and what was hypothesized however, there can be no accountability, and it’s much easier to repeatedly make decisions that do a multiple more harm than good.

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