"Business executives don't need to be fluent in data science, it is enough to be literate".
"Because the velocity of data is increasing and will always increase, the need for data literacy is increasing and will always increase. This does not mean that to be a successful executive you have to become a data scientist – quite the contrary. It means that in order to be a successful executive, you need to understand how data is turned into action, be familiar with the methods of data science and data-scientific research, and be able to think strategically about how to use data to create value for your business."...from a blog from Shelly Palmer titled, "Data Literacy Will Almost Make You Invincible"
3 Steps to Data Literacy1. Understand how data is turned into action
Forbes published an article written by McKinsey titled Four Steps to Turn Big Data into Action. “Picture a factory. Insights are products—goods that are valuable because they are useful; data is the raw material from which the products, the insights, are made; and front-line operators are the consumers, or the people who need and use the product.
Here are the 4 steps to get there.
- Have a clear understanding of what you want to achieve…decide what questions your business needs to answer and the actions you want those answers to enable.
- Source the raw materials
- Produce insights with speed
- Deliver the goods and act…making sure that insight driven actions require a clear understanding of what front-line managers can actually use.”
Applying these four steps to merchandising displays:
Step 1(understanding what you want to achieve):
- Make every display program better the next year than it was the previous year
- Know the program sales lift for each program
- Know the on selling floor execution rate for each program
- Reduce inventory markdowns caused by display programs not making it to the selling floor or having low sales lift.
Step 2 (source the raw materials):
- Insights about display performance start with knowing cumulative sales lift. In addition, you can know daily performance as well by store.
Step 3 (produce insights with speed):
- Take advantage of daily data by making changes while the program is live. Determine what you want to do while the program is live and affect individual stores by notifying store managers tasks they should do on any given day.
Step 4 (deliver the goods and act):
- Increase collaboration with your retail partner by gaining agreement on what you both want to achieve and specifying steps you both agree to take.
2. Be familiar with the methods of data science and data scientific research
Shelly Palmer’s blog on Data Literacy Basics:
“While there are countless ways to analyze data, there are three basic steps to the process of turning information into action:
- Transform: Processing, enrichment and aggregation
- Learn: Regression, clustering and classification
- Predict: Optimization and simulation
These steps are explained in detail in “What Do You Do with Data?” In practice, you don’t need to know how to do any of this; you just need to know exactly what each step is and what it is for.”
Shelly advises those seeking literacy to “Get in the Game.” Inject yourself into the process.
Applying data literacy basics to merchandise displays:
- Ensure your years of experience about what drives success is factored into the data science.
- Your knowledge of store size can impact the likelihood of a display being put on the selling floor, because you know smaller stores may not have room in the aisle.
- You also know some brands respond to promotion better that other brands, so it would be helpful to notify an individual store manager when a display program is selling well in other stores but remains in that store manager's back room.
3. Think strategically about how to use data to create value
Shelly Palmer suggests that “Turning data literacy into invincibility will evolve from your understanding of how best to combine 1st, 2nd, and 3rd party data, data scientific research and your business knowledge to turn information into action.”
- You can think of 1st party data for display programs as the retailers unit and dollar sales by SKU by store.
- 2nd party data is the CPG data about which store participates in each display campaign and what the start and end dates are for that campaign.
- 3rd party data is Shelfbucks MEASURESM data about the sales lift by display program in the aggregate and by store and the on-selling floor data by program and by store.
Over time, there is always the possibility of additional data from other sources: for instance, daily weather by store etc.
Applying strategic thinking to display programs:
Think about bringing a continuous process improvement approach to decision making about display programs:
You will have data about:
- Program sales lift and on floor execution rates
- Use this data to reallocate dollars the following year that aren’t competitive in sales lift
- Use data to improve SKU mix the following year in cases where sales are dominated by one or two product SKUs
- Develop dexterity in using an interactive dashboard to rank program success and retailer on floor rates by display program
- You'll never be at a meeting again, where someone says, "Which programs worked and which didn't."...You will all know!
You can learn about how predictive algorithms work by subscribing to a podcast by one of Shelfbucks' employees. This podcast is a great example of the power of data science: Predicting the Unpredictable in iTunes…the 6th podcast on the list.
Making your displays invincible is within your reach with