Data is Reactive. Research is Proactive.
Many people put a tremendous amount of emphasis on data.
Why?
There are many reasons, that all sound like “the numbers don’t lie and everyone else is doing it.”
What would you say if I said that data alone doesn’t help your strategy?
How about if I said it can hurt your strategy?
Why would I say that?
Because all data looks backward.
All data is focused on the past.
Your data is perfect insight into what people did.
The challenge of strategy is creating a better future.
There is tension in this idea summed up by Roger L. Martin’s question: “What would have to be true?”
The reality is data is the past.
Strategy is the future.
Change is constant.
As NBA legend, Pat Riley said, “The only thing that is certain in life is change.”
Our focus on the future mixed with constant change forces us to be skeptical about how much we rely on data.
Why?
Customer needs can change quickly.
Fads can come and go.
Competitors are constantly looking at ways to improve upon our offer to steal market share from us.
This is where research comes in.
Research is a bridge-building tool that connects the past, present, and future in an actionable way.
But be careful.
If you don’t do your research correctly, you’ll fall victim to “garbage in, garbage out” research that can stop your progress in its path.
Marketing Professor, Alan Andreason, shared an idea in the 1980s called “Backward Market Research.”
His idea boils down to three steps that create market research that will help you build a strategy that can deliver growth.
Those three steps:
- What is our hypothesis?
- What is the best way for these answers to show up?
- Design your research around those two answers.
Hypothesis
Everything strategy begins with a question.
Market research does as well.
All hypotheses are assumptions that we can test to figure out if they are true.
A good hypothesis compares two or more variables.
There are two types of variables:
- Dependent: These come from your observations.
- Independent: These are variables you can control.
Depending on your business, a good hypothesis might look like:
- Offering more items will cause our online purchase size to increase by at least 5%.
- Developing personalized sponsorship packages will allow us to charge a premium to local businesses.
- Influencer marketing can help us drive top-of-mind awareness by 10% or more.
Your hypothesis matters because it establishes the foundation of your research.
A hypothesis is built using a mixture of data combined with signals and inputs you gain from the world around you.
These signals and inputs may come from things such as:
- Conversations with customers or prospects.
- Observations of how customers are using your products.
- Questions that you receive over and over.
There is no perfect formula for creating a hypothesis.
It is trial and error.
This emphasizes the importance of coming up with a question, testing it, and using what you learn to move forward or to go back to the start.
Your hypothesis should:
- Focus your research.
- Help you generate new solutions.
- Provide answers for social phenomena.
However, the hypothesis is only the first step.
Research
Good research doesn’t come in a one-size-fits-all package.
That’s why the shape of your research questions matters. The answers you are looking for might come in many forms.
- Multiple-choice.
- Stacked rankings.
- Focus groups.
- Long-form answers.
The key is to let the hypothesis point you to the correct research tool.
Design
This lights the path to designing your research.
Why is the design so important?
Good research helps you gain insights that others don’t have.
Too often, businesses are operating from the same playbook.
That isn’t always a bad thing.
But it isn’t always a good thing either.
If everyone is looking at the same playing field with the same lens, you risk becoming derivative of each other.
Falling into the “Commodity Trap”, chasing each other down the hole of discounts, attempting to win on price.
There are never real winners in a price war.
Designing good research follows a predictable path.
Start small.
Take your hypothesis to one person or a small group.
Test your thinking.
Validate the question.
Or, if it doesn’t hold up, throw it out.
The stakes are low.
Don’t look at a failed hypothesis as a loss.
Celebrate recognizing its defectiveness when you haven’t spent lots of time and money on it.
Go back to the drawing board.
If your hypothesis is valid, move up and test it with slightly larger groups such as focus groups or small gatherings.
The same process follows.
Validate the idea or not.
Again, don’t hesitate to dump a bad hypothesis.
If your question deserves more research, move to larger sets of your target market with tools such as surveys.
At each step, validate your question, and decide on next steps.
This process can open the door to unique insights.
These insights can help you find market space, opportunities, and offerings.
The choice isn’t data or research.
We need them both.
Data shows us what happened.
We mix that data with our insights and observations to ask a question.
This question creates the foundation for our research.
This research builds a bridge that connects us to the past meeting the present and pointing toward the future.
This is part of my ongoing series of posts about strategy and creating new opportunities.
Founder & CEO, Group 8 Security Solutions Inc. DBA Machine Learning Intelligence
6moThank you for your share!
Scale Revenue | Enabling SME and enterprise companies to secure, scale and retain high-value client accounts with V.I.T.A.L. Method | Scaled previous business to £55m | Former FTSE 250 Sales Director | Author | NED
6moI enjoyed this Dave Wakeman, and the point you make. Whats your view on using data to predict patterns to inform the future?