The title isn’t entirely true.
You can run a brand with spreadsheets to a certain point. Almost comfortably, even.
You can input and aggregate your sales/marketing channel dailies, get a ballpark on some KPIs, and do some ad hoc analysis for monthly or quarterly reviews.
But there will be a point in your growth journey where it will no longer be possible.
Here are 3 major reasons spreadsheets fail.
(BTW, we’re a data and analytics platform, founded by analysts. We love Excel and GSheets. We just don’t think spreadsheets should be the complete basis of a serious data strategy.)
The performance limits of Excel and your computer
This one section could be the entire article, really.
In the pursuit of analytics, Excel or your computer will eventually crash. Which crashes first is largely to do with how powerful your computer is.
Excel is just not built to handle the data and analytics loads of brands.
Frequently (and infuriatingly), this often happens in the middle of a particular analysis or more complicated metric calculation.
For instance, calculating customer lifetime value can be a full-day process, and Excel might crash in the middle, which may leave you starting from square one and wanting to play Toss the Computer™.
And even if your spreadsheet(s) or computer don’t crash, they might just be unbearably slow, which means it’s going to suck up a lot of…
Relying on spreadsheets is likely already costing you hours every week.
Granted, it may be only an hour or two per week if you’re just inputting Shopify or marketing data into a performance tracking spreadsheet.
But if you have more sophisticated spreadsheets, or you have some hardcore analytics to run, you’re definitely feeling the burn of Excel Hell.
And if you aren’t yet, you will.
Then there’s Amazon.
Amazon’s UI, although miles ahead of where it was a few years ago, is still a nonstarter for meaningful reporting and analytics. Translation: building your own reports in some way outside Seller Central is necessary.
Unfortunately, building those reports in spreadsheets is a guarantee to invite major pain-in-the-assery into your life.
Pain-in-the-assery (n.): The state of managing Amazon data in spreadsheets. Associated with yelling at computer screens.
There are at least a half dozen reports to download, every day, from a single selling region, and if you’re running an international Amazon business, that number is multiplied by the number of regions.
And all this is only covering daily reporting.
You’re probably all too familiar with weekly, quarterly, and annual reporting data presentations to leadership, investors, and board members.
“I need to create a big report? Goodbye, Thursday and Friday.”
It adds up, doesn’t it?
How much time do you lose on downloading, uploading, copying, pasting, pivoting, and aggregating in a given calendar year?
^ Based on our own experience and talking to merchants, it could be, genuinely, an entire month.
And to make matters worse, all the time that you spend with manual data work may not even align with what your coworker has been working on in their manual data work, because spreadsheets tend to be quite personal and based on your own world, workflow, and preferences.
Which means your brand struggles with…
Arguments about definitions of metrics, people reporting different numbers over different time periods—these are data governance problems.
Employees working in their own spreadsheets invariably will invariably lead to having those problems. Or, if multiple employees are working in the same spreadsheet, they may be aligned with each other, but that team may be on a totally different page (or, in this case, spreadsheet) from another team.
As part of improving data governance, brands need to align around a data dictionary, which is a master document with definitions of and calculations for every single business metric. For instance, you may be using a platform for your subscription program. It will define subscription revenue one way, you (the reader) may define it in a slightly different way, and your supervisor may define it in yet another way.
Poor data governance is bad for everyone.
It reduces productivity, creates friction, and leads to misalignment between teams.
This can be catastrophic, especially if there are multiple misalignments.
If marketing defines CAC in one way, and finance defines CAC in another way, what does Allowable CAC look like in practice?
Finance says X is the Allowable CAC, Marketing says Y is.
Marketing substantially overspends. The company doesn’t have enough money to make a large enough purchase order to cover retail needs.
A retailer gets pissed off and issues a massive chargeback. Now, the brand loses even more money.
Stranger things have happened.
Worse things have happened.
And these problems can harm or wreck an org.
Kicking Spreadsheets (Mostly) to the Curb, and Running Better* Analytics
*Better, as in: faster, automated, and sophisticated.
We’ve reached the point of the SaaS blog where we offer our solution. Yay!
As you may or may not have noticed, we’ve peppered in some testimonials about our data/analytics platform (we are, and this is true, extremely subtle).
Daasity was founded to, among other reasons, substantially reduce consumer brands’ reliance on spreadsheets, in order to drive their business forward.
On the Excel limits/time-in-spreadsheets front: We make analytics that are impossible in spreadsheets or other data tools possible.
Even basic LTV may be a full day exercise in Excel, but in Daasity, it’s just logging into the app. Out of the box, you can analyze LTV in several ways, but here is a report showing LTV by first SKU purchased (clearly indicating a great Acquisition SKU opportunity):
On the data governance front: By aligning the org around data in a visualization tool connected to Daasity (we support Looker, Tableau, and Power BI out of the box), everyone is always on the same page, using the same definitions. And if you have different definitions of metrics than we do, you can customize them in the platform.
Learn more about us here: Daasity.
Or, if you’d like to read more of our content, we welcome you to: