Episode 330 Your Dashboard May Be Your Biggest Blind Spot
We were paying more than $100,000 for what was supposed to be a better source of truth.
But the reporting we were producing for Wall Street did not match what other analysts were seeing.
When I dug into the custom database, I found missing items, dramatically overstated items and understated items.
The software was working. The data looked professional.
And the answer was still misleading.
That is the blind spot I want to talk about today. The Welcome to the Bulletproof Your CPG Brand podcast.
I’m Daniel Lohman, founder of Retail Solved, and this is the show where we help entrepreneurial CPG founders protect runway, improve execution and compete smarter.
Today, we are talking about why more data does not always create more clarity.
This is Part 2 of the CPG Decision Clarity Series. Follow Bulletproof Your CPG Brand now so you do not miss next week’s episode on how to start with the decision instead of the spreadsheet.
Let’s roll up our sleeves and get started.
Most CPG brands do not have a reporting problem. They have plenty of reports.
Sales reports. Retailer portals. Distributor reports.
Broker updates.
Trade-promotion recaps. Deduction reports.
Syndicated data.
Shopper data.
Inventory reports.
Forecasts.
Dashboards.
Spreadsheets.
Sometimes, the business has so many reports that different departments can enter the same meeting with different versions of the truth. I see this all the time.
Finance has one answer. Sales has another.
Marketing sees something different. The broker has their own interpretation.
The retailer portal appears to be saying something else entirely.
And after everyone reviews the data, the team is still debating what to do next.
That is not necessarily a reporting problem. It is a decision problem.
And when the margin for error gets smaller, decision quality matters more.
Your report may be technically accurate.
Your dashboard may be functioning exactly as it was designed.
Your software provider may be delivering exactly what the contract promised.
And the business can still make the wrong decision. That is the uncomfortable truth.
More data does not automatically create more clarity I want to be careful here.
I am not criticizing data scientists.
I am not criticizing business intelligence teams.
I have worked with some exceptionally talented analysts. A strong finance and BI team is a real advantage.
But CPG has complexities that do not always fit neatly inside a standard model.
Retail data is messy. Distributor data is messy. Retailer portals are messy. Syndicated data has gaps.
New products do not always appear where they should.
A pack size change does not always get connected to the old pack so it does not report prior sales right away.
Items get classified differently across platforms and definitely not the way customer shop.
Promotions do not always line up with the weeks shown on the report. Retailer definitions do not always match the brand’s definitions.
The same product can serve multiple shopper need states while being placed inside only one database category.
The tool may not understand that.
The person who built the report may not understand that. And that is not because they are careless.
It is because understanding the software is not the same as understanding how a shopper, retailer, category, broker, distributor and brand interact.
That is where context matters. The $100,000 source of truth
Years ago, I was responsible for producing reporting that ultimately went to Wall Street.
The company had invested more than $100,000 in a custom database. It was supposed to provide a cleaner and more precise view of the business.
But something did not feel right.
Our reporting did not align with what other analysts were providing. The numbers were not reconciling the way I expected.
So I started digging.
Some products were dramatically understated.
Some were overstated.
Some items that appeared in the standard database were missing from the custom database.
There were gaps that could materially change how someone interpreted the performance of the business.
I ended up spending at least 20 hours a month cleaning it up, reconciling the differences and trying to understand what was really happening.
Think about that.
The company had paid more than $100,000 for something intended to create a better source of truth.
And the tool still needed someone who understood the business well enough to recognize when the answer did not make sense.
That is the lesson.
The software was not necessarily broken.
The people were not necessarily doing bad work.
The problem was that the business was trusting the output without enough operating context.
I do not share that to brag about finding a database issue.
I share it because these blind spots exist in every business. They exist inside inexpensive spreadsheets.
They exist inside retailer portals. They exist inside custom databases.
They can even exist inside the most respected platforms in the industry.
I have found issues in tools built by companies considered the gold standard.
In one situation, correcting a problem they did not know existed helped uncover a new revenue source and a stronger point of differentiation for them.
The point is not that every platform is bad. The point is that no platform is perfect.
And when the margin for error gets smaller, you cannot afford to confuse a polished report with a complete answer.
This is especially true for smaller brands that don’t have the luxury of purchasing a custom database. So, they are forced to look at their portfolio in isolated segments - even when the items are right next to each other on the shelf. I have a solution for that. That is why this episode is so important.
Blind spot number one: the data is accurate but incomplete This is one of the easiest problems to miss.
Every number shown on the report may be correct. But what is not shown?
Perhaps an item is missing. Perhaps a retailer is excluded.
Perhaps distributor shipments are being mistaken for consumer purchases.
Perhaps promotional timing is not aligned correctly.
Perhaps a product transition is splitting performance across two UPCs.
Perhaps one report captures natural-channel performance while another captures conventional retail differently.
Perhaps the brand is celebrating sales growth without seeing the margin that was sacrificed to generate it.
An accurate number can still tell an incomplete story. And an incomplete story can lead to a bad decision.
Blind spot number two: the data is organized around the database—not the shopper
This is one of the most important lessons I learned in category management.
Databases are not always organized the way shoppers shop.
With every brand I’ve worked with, products were often merchandised together at shelf.
A shopper could stand in front of that section and see a group of products serving a related need.
But inside syndicated data, those products could appear across many different categories.
On a recent project, the items were merchandised together in the same shelf form the shoppers point of view were but they were in 17 different categories in the standard database. Let that sink in a moment.
The database saw individual classifications. The shopper saw a solution.
That distinction matters.
Because when data is organized around a generic category structure, it can hide the relationships that actually influence shopper choice. All of the items within a category as the shopper see it can interact. Sales from one item can boost or blunt other items when the product assortment is changed. That matters a lot when working with retailers.
I built a robust custom tool that brought multiple data sources together and reorganized the information through a shopper-first taxonomy.
We created retail-ready views and deeper analytical tools that allowed us to look at products, categories and opportunities in a more useful way. A taxonomy is the data hierarchy - how the data is organized. Simply put, when a shopper stands in front of a category, what influences their decisions. Shoppers see only the snack category while the databases break each section into their own distinct category.
That work helped us identify whitespace for a reimagined chip before launch.
It helped us think about flavor. Size.
Retailer fit.
Category placement. Competitive framing. And shopper need state.
We were not simply asking:
“Where do chips sell?” We were asking:
“Where does this product fit in the shopper’s life, what is it competing against, and how can it create a new reason to buy?”
That is a very different question.
And the quality of the question changes the quality of the tool. The question is not only whether the data is correct.
The question is whether the data is organized in a way that reflects how the shopper actually makes a decision.
Blind spot number three: every department is optimizing in isolation This happens constantly.
Sales is trying to grow revenue.
Marketing is trying to increase awareness. Finance is protecting margin.
Operations is managing inventory.
The broker is trying to close distribution gaps. The distributor is trying to manage movement.
The retailer is trying to grow the category and protect productivity. Every group can be doing its job.
Every group can hit its individual metric. And the business can still become weaker.
A promotion may grow sales but compress margin.
A new retailer may expand distribution but create costly execution problems.
A new SKU may increase the brand’s total sales while stealing volume from an existing item.
A marketing campaign may generate attention without creating repeat purchase.
A sales team may win more doors without securing the right items in those doors.
A deduction team may recover money without fixing the process that created the deduction.
That is what I mean by optimizing in isolation. The dashboard says one area improved.
But the larger business did not. Retail does not operate in isolation. Shopper behavior influences velocity.
Velocity influences retailer confidence. Retailer confidence influences distribution. Distribution influences inventory.
Inventory influences promotion performance. Promotion performance influences margin.
Poor execution creates deductions. Deductions affect cash.
Cash affects runway. Everything is connected.
The reporting system needs to help the team see those connections.
Blind spot number four: the report arrives after the decision has already been made
A lot of reports are rearview mirrors.
They show what happened last month. Last quarter.
Last promotion. Last retailer review.
That information matters.
But the shopper already made their decision. The retailer already made their decision.
The promotion already ran. The cash already left.
The deduction already appeared.
The inventory already went out of stock.
The opportunity is not to eliminate historical reporting.
The opportunity is to use history to improve the next decision. That is where many dashboards fall short.
They summarize.
They display.
They organize.
But they do not help the team reroute.
The best decision tools should help answer: What is changing?
Why is it changing? Where is the risk?
Where is the opportunity?
What do we need to do before the next event, shipment, retailer meeting or commitment?
Reports show what happened. Retail Clarity shows what to ask next. This is why I built the Retail Clarity Framework.
It is a way to move beyond one-dimensional reporting. There are four lenses.
The first is the internal lens. What happened?
Sales.
Velocity.
Distribution.
Margin.
Trade spend.
Deductions.
Inventory.
Retail execution.
These metrics tell us what the business did.
They matter.
But they are only the beginning. The second is the shopper lens. Why did it happen?
Did the brand bring in new shoppers? Did repeat purchase improve?
Did the shopper respond to the promotion—or merely buy earlier than they would have?
Did price sensitivity change?
Did the product solve the same need the shopper originally hired it to solve?
Did the shopper move to private label?
Did they move to a different size, flavor, format or channel? The third is the category and competitive lens.
What influenced the result?
Did a competitor launch an item? Did the retailer change the shelf?
Did another brand fund a promotion?
Did the category definition miss a new source of competition? Did distribution move?
Did the retailer change its pricing strategy?
Did a new item take space or visibility?
Did the brand’s assortment become inconsistent across retailers? The fourth is the predictive lens.
What should happen next? Where should we invest?
What should we stop funding? Which gaps matter most?
Which retailer deserves attention? Which item needs support?
Which promotion window creates the best opportunity? Where is margin leaking?
What can we prevent before the next short-pay, out-of-stock or missed commitment?
That is the difference between a report and a decision tool. A report gives you information.
A decision tool helps the team choose an action. Start with the decision—not the spreadsheet
One of the biggest mistakes teams make is beginning with the data. They open the file.
They pull every available field. They create charts.
They add filters.
They build a beautiful dashboard.
Then they ask what they should do with it. I would reverse that.
Start with the decision.
What question does the business need to answer?
Not:
“What data do we have?”
Ask:
“What are we trying to understand?”
Are we trying to determine which promotions create profitable growth? Are we trying to find the most valuable distribution gaps?
Are we trying to identify why one retailer is underperforming? Are we trying to understand where deductions originate?
Are we trying to determine which items belong in the core assortment?
Are we trying to see whether a new product created incremental demand or transferred volume from somewhere else?
Are we trying to help the COO see what is driving profit and loss? Once the decision is clear, then determine what data is required. That sounds simple.
But it changes everything.
Five questions to ask before trusting your next report
Take one report your team reviews every week or every month. Ask these five questions.
First:
What decision is this report supposed to make easier?
If the answer is vague, the report may be informational rather than operational.
Second:
What is missing from the picture? Items.
Retailers. Channels. Timing.
Margins. Promotions. Shopper behavior. Competitive activity. Execution.
Third:
Does the structure reflect how the shopper and category actually work?
Or is the business simply accepting the category definitions that came with the database?
Fourth:
What other part of the business could change the interpretation?
A sales increase can look different after trade spend. A distribution win can look different after velocity.
A promotion can look different after incrementality.
A deduction can look different after the root cause is identified.
Fifth:
What action should become clearer after reviewing it?
If everyone leaves the meeting with another request for analysis, the tool probably has not finished its job.
A decision tool should reduce debate—not create more of it That does not mean everyone must agree immediately.
A huge blindspot for brands is when each department and manager wants to look at their differently. Same data but from their perspective. This the the fastest way to increase decision confusion and this always sends teams down competing rabbit holes.
Strong teams should challenge assumptions.
But the tool should help the team focus the debate. It should make the important exceptions visible.
It should expose gaps. It should show priorities.
It should help the team see where to look first. And it needs ownership.
One of my favorite parts of building tools for brands is training the team to maintain them.
I do not want a company dependent on me forever just to refresh a spreadsheet.
I want the system to become part of how the team works. That means the logic must be clear.
The taxonomy must be understandable. The process must be documented.
The users need to know what the tool can answer—and what it cannot. Because software alone is never the full solution.
A strong tool combines:
The right data.
The right question.
The right business context. The right owner.
And the discipline to keep it current. Your reporting may be the bottleneck
Founders often assume the bottleneck is a lack of data. So they purchase another platform.
They request another dashboard. They add another report.
They ask the broker for another spreadsheet.
But what if more reporting is making the business slower?
What if the team is spending hours reconciling numbers instead of making decisions?
What if every department is looking at its own version of performance?
What if the reports explain the past but never help protect the next promotion, shipment or retailer conversation?
Then the bottleneck is not access to information. The bottleneck is decision clarity.
When the margin for error gets smaller, clarity becomes your competitive advantage.
Not because clarity guarantees the perfect decision. It does not.
But it gives the business a better chance to see risks sooner. To spot opportunities earlier.
To stop funding what is not working. To protect margin.
To reduce preventable leaks.
And to focus scarce time and resources where they can create the most value.
Doing this alone will give your brand a combative edge because this most brands don’t have the discipline to bake this into their SOP.
The practical next step
Here is what I want you to do this week.
Choose one report your team relies on. It may be a sales report.
A promotion recap. A broker update.
A distributor report. A deduction tracker. A category review.
An executive dashboard.
Then ask:
What decision is this supposed to help us make? Does it answer that question?
What does the team still debate after reviewing it? What part of the business is missing?
And what would we need to see earlier to make a better decision next time?
The unresolved question is the opportunity.
That may be where your next decision tool should begin.
This is also the best was to identify, quantify and plug decision leaks in your business.
Consider this:
I recently created a Decision Tools page at RetailSolved.com/ DecisionTools.
You will see examples of tools designed to help CPG teams understand sales, distribution, promotions, deductions, retailer performance and category opportunities more clearly.
These are not generic dashboards.
The goal is to turn messy retail information into a practical next decision.
If your team has a report it relies on but still debates what to do next, send me the report—or simply describe what you are trying to understand.
Tell me the decision it does not answer.
I will let you know honestly whether a custom decision tool makes sense.
Again, that is: RetailSolved.com/DecisionTools
The spreadsheet is not the product. The decision is.
To recap
Your dashboard may be working exactly as designed. Your data may be technically accurate.
And the answer can still be incomplete.
Do not assume that more data automatically creates more clarity. Start with the decision.
Understand what is missing.
Organize the information around the shopper, the category and the way the business actually operates.
Then build the tool that helps your team see what to do next.
Because when the margin for error gets smaller, clarity becomes your competitive advantage.
Thanks for listening to the Bulletproof Your CPG Brand podcast.
If this episode made you think differently about one of your reports, share it with someone on your team.
Then ask them one question:
What decision does this report still not help us make?
Connect with me on linkedIn and message me. I read every one. I’ll see you next time.