This is a guest article by Theodor on how to Compare Sales of One Product with Another
Ok, now here’s one for you.
Suppose you’d like to come up with a sales report on different products, comparing their evolution on the same period of different years (say Jan ’09 vs. Jan Jan ’10). At the same time, you’d like to keep an eye on their yearly trend (entire 2009 vs. entire 2010).
No big deal, you’ll say, but here’s the twist: the products have not been available for the entire time span taken into consideration. Let’s say you’ve only had Product 1 available for sale for Feb ’09 onwards, while it had been discontinued from October ’10. If you’re really looking for a Like-For-Like (LFL) comparison, you’d only want to compare the months where you have data for both years. It’s false to claim you’ve had a sales boost of 300% when you entered the market with Product X in October 2009, selling 1000 units over 3 months and compare that to the full results of 2010, when you’ve sold 3000 units. In the first scenario you were averaging some 333 units/month, while later you’ve dropped to a mere 250/month. Nothing to brag about there, is it?
Ah, but we also have different product classes. One is aimed for the high-profile buyer (A-Class products), the second for the middle level (B-Class) and so on. Given that different products were added to each class’s portfolio and then later discontinued, we should see the total LFL development of each product class in the same graphical representation.
Hold on another second. One country is defining its quarters as Jan-Mar, Apr-Jun etc, while other might relate a quarterly result to a specific day in the company history (such as the company launch date, or since the new CEO took over or whatever). Wouldn’t it be nice to be able to compare equivalent datasets in any user-defined time span?
So how do you compare sales of one product with another?
Now I’ve always said that the second hardest thing mankind has ever done was to send men on the Moon and safely return them home. That’s only because the MOST difficult thing in the world has become to compare apples with apples. There are so many subtle differences between one dataset and the other (even though they both relate to the same source), that if one reporting template is to have a long life, it should first and foremost come with the built-in ability to allow the end user to drill down through the data and change criteria in order to get relevant results for today’s issue. And all that will change tomorrow, as there will lay a new and unexpected issue on the table.
With that in mind, when I create my templates I follow the self-made golden rule (which later I found many others have applied for themselves long before I knew Excel ever existed) – keep the raw data in one sheet, preferably hidden; use a second sheet for calculation, ALWAYS hidden, and provide a simple and useful graphical interface for the end-user in the third sheet. This will avoid any mishaps such as “Could you please put your formulas back in, I donno which button I pressed and….!!”
Comparing Sales of One Product with Another – Demo:
First see the demo of this technique. Then, we can learn how I created it.
Coming to the attached example – which only works in Excel 2007 and later, by the way:
- Your data is in sheet ‘data’, ordered by product and timeline (Jan-Dec, 2009 and 2010). I’ve created the values using the =randbetween() formula, and then copy-pasted the values only so they will not change anymore.
- To keep things more clear, I’ve placed the calculation formulas in the same sheet as that with the graph, just so you can compare values and figure out formulas more quickly, without having to switch between sheets all the time.
How the Sales Comparison Chart is made?
Now, to bring up values of a particular product, I’ve created a list in C44:C70 (values in column B are just for guidance). We can compare two products, which can be chosen from a couple of drop-down boxes linked to cells B6 and B8. Here’s where the values in column B help: basically, they tell me which item index from the drop-down corresponds to a product. I then placed the same item indexes in data!A7:A46. This is all because I am lazy and I find the sumifs() formula a blessing: all I have to do now is to add up the results that correspond to (1) the chosen Product in the drop-down, which is looked up by the index, and (2) the year, which is in data!E6:E45. [More on INDEX Formula]
An alternative in Excel 2003 would have been to concatenate the value of “Product 1″&”2009” for example, to get a unique identifier and not return the sales value of 2010 by mistake. Then vlookup() after the concatenated value. [Related: How to lookup based on multiple conditions]
These calculations are placed in ‘Yr sls’!F51:Q54. Note there’s an initial IF() there, to only display the values if the respective month is selected. There are two sliders up in the second row, which can help you ‘cut’ your desired portion of the year for comparison.
‘Yr sls’!F61:Q68, using sumifs() again, I added the sales values for each product class. Finally, in ‘Yr sls’!F45:Q48 are the final calculation, where if an item index lower than 8 (corresponding to Product 1) is selected, the values in F61:Q68 are brought up, else the values in F51:Q54.
So now we see our resulting values above the chart, in cells F6:Q9. The deviation is calculated in F5:Q5. But for the yearly totals, I only want to compare apples with apples, i.e. months in which sales have been recorded in both years. For that I used cells U6:AF9. The totals in R6:R9 are based on these isnumber() results. This allows you to have the exact deviation between similar months over an user-defined time span.
Ok, time to close. But not before your boss knows the exact portfolio of each product class! Look shortly in data!B6:B45. This is where, using countif(), we have the number of occurrences for each product class. Knowing that product class “A” will be repeated say 3 times, we’ll use this knowledge to look up the third occurrence of “A” and bring up the product next to it. Now take a peak in sheet “Legend”. Knowing we have to lookup for A 1, that’s how I wrote the formula. But also knowing that “A” will be repeated twice for each product (once for 2009, another for 2010) and not wanting to see duplicates in my product list, there’s a very simple solution: just use odd numbers!! This will only bring up every 2nd occurrence of a product. As I said, I like it simple 🙂 I just left the numbers in C5:C15 visible so you don’t have to fish around for them, the rest are simply I the same color as the background. A bit of conditional formatting does the rest.
Of course, before presenting this to any decision maker, you’d hide the rows and columns they’re not supposed to touch and present them with a clean looking table.
Download the Excel Workbook:
Click here to download the workbook with this example. Examine the formulas and chart in “Yr Sls” worksheet to understand how this is weaved together.
[Added by Chandoo]
Thank you Theodor
Thank you so much Theodor for teaching us some valuable techniques on how to compare apples with apples. I am sure our readers will find these ideas very useful.
If you like this post, say thanks to Theodor.
Do you compare & analyze sales data?
I do this all the time. As part of running my small business, every couple of months, I would take up sales data and see if something odd is going on. I make line charts comparing sales of this year with previous year, understanding the overall trend and compare one product with another.
What about you? Do you analyze sales data? What techniques do you use use? Please share using comments.
Learn more from these pages:
If you work a lot with data & do similar work as above, go thru these articles to learn more.
19 Responses to “How to Distribute Players Between Teams – Evenly”
An excellent solution, especially for large data sets.
Another solution without using solver would be to assign the player with the highest score to Team 1, the 2nd to team 2, 3rd to team 3, 4th to team 3, 5th to team 2, 6th to team 1, 7th to team 1 and it continues. This method would end up with a Std Dev of 0.001247219. This works best with a distribution with lower Std Dev for the dataset.
Full Disclosure: this is not my idea, remember reading something a few years ago. Think it may have been Ozgrid
thinking back I now remember why I read about it. About 10 years back I had to distribute around 300 team members into 25-30 odd teams. Used this method based on their performance scores. I used the method I described to do this and the distribution was pretty fair.
Solver would have saved me a ton of time though 🙂
I think the issue with you first Solver approach was that you took the absolute value of the sum of team deviations (which should always be zero except for rounding) instead of the sum of the absolute values (which is a reasonable measure of how unbalanced the teams are).
Here's another simple algorithm you could use: you start from the top (with players sorted from high to low), and at each step allocate the next player to whichever team has the smallest total so far. You can implement it dynamically with some formulas so it will update automatically when the data changes.
If the scores were more widely distributed (so that this might end up with not all teams the same size), you could add a constraint to only pick among the teams which currently have fewest players at each step, or just stop adding to any team when it hits its quota.
When I tried it on the sample, I got the three teams below, with a STDEV of 0.000942809 (i.e. about half of what Solver got to).
Team 1: John, Hugo, Tom, Josh, Eric, Zane, Charles, Andrew
Team 2: Barry, Michael, Kenny, Joe, Xavier, Patrick, Oliver, William
Team 3: Henry, Steven, Ben, Frank, Kyle, Edward, Cameron, Lachlan
Thanks for sharing!
Hi,
I was looking at all the solutions and this is closest to what I intended to do. I am dividing a bunch of players into 3 soccer teams. Players availability is also a factor while deciding the teams.
So the steps the excel needs to do is as follows:
1) In availability column if "yes" go to next
2) Equally divide 'Goalkeepers', 'Strikers', 'Defenders' basis their quality
So the end result gives each 3 teams a balance of players playing at different positions.
Can this be done on Google spreadsheet with only availability as an input from the user and rest calculates by itself.
Sorry for asking such a pointed question, but I have been struggling to find a solution for it for sometime now!
Hi Ishaan,
I am working on a similar problem at the moment, so I am wondering if you ever found a solution and if you are willing to share what you did.
Hi everyone, this is a variation of the famous Knapsack Problem https://en.wikipedia.org/wiki/Knapsack_problem.
I had to use a VBA implementation recently as part of a problem, where we ar trying to allocate teams of an organization into different locations (we are a large company with many different team). The goal was to optimally allocate teams to individual buildings without putting too many teams into one building and not splitting teams apart.
As we had around 400 teams of different sizes, solver couldn't handle it anymore. Luckily there is a Knapsack algorithm implementation in VBA readily available on the internet :).
I also went with a heuristic approach first!
An interesting mathematical solution but what if Eric and Xavier can't stand each other or Patrick is best friends with Steven - the real life problems that effect "even" teams.
@Joe
You can add more criteria like
If Eric and Xavier can't stand each other
=OR(AND(E15=1,E16=1),AND(F15=1,F16=1),AND(G15=1,G16=1))
It must be False
If Patrick is best friends with Steven
=OR(AND(E5=1,E17=1),AND(F5=1,F17=1),AND(G5=1,G17=1))
It must be True
Note that the 2 formulas above are exactly the same
except for the ranges
One must be True = Friends
One must be False = Not Friends
Nice Post!
Just one question What if number of players are not even or equally divisible.
Nice post Hui!
I download your workbook and just try to change in options the Precision Restriction from 10E-6 to 10-8 and the Convergence from 10E-4 to 10E-10. The process take almost the same time, but the results was great.
The standard deviation I got was 0,000471.
Team 1: John, Tom, Kenny, Frank, Eric, Xavier, Edward, Zane
Team 2: Steven, Hugo, Ben, Joe, Josh, Oliver, Cameron, William
Team 3: Barry, Henry, Michael, Kyle, Patrick, Charles, Andrew, Lachlan
Great application of Solver! Thanks for the link!
Great explanation. Well done... However, I tried with 6 teams of 4 players and solver never did finish.
How about vba code for the same data set.
I have 3 column A B C wherein A has text and B has number Wherein C is blank. And in C1 been the header C2 where I want the name to come evenly distributed the number which is in Column B.
My Lastcolumn is 1000.
Sorry if I'm being slow here, but how is 'Team Score' calculated? I've gone through the explanation several times but it seems to just appear.
@Hrmft
This process uses the Solver Excel addin
Solver is effectively taking the model and trying different solutions until it gets a solution that meets all the criteria
Then solver puts the solution into the cell and moves to the next cell
So yes it appears to "just appear"
Hi ! Thank you so much ! Works great 🙂
I cannot get the fourth Equation to work in my excel spreadsheet
You have =($E$2:$G$25=0)+($E$2:$G$25=1)=1 as a SUMIF solution, I have, =($F$2:$H$13=0)+($F$2:$H$13=1)=1 as my solution but it does not work. The only thing I changed is the ranges. Any suggestions?
Thank you.
Jim
I cannot get the fourth Equation of TURE or FALSE statements to work in my excel spreadsheet You have =($E$2:$G$25=0)+($E$2:$G$25=1)=1 as a SUMIF solution, I have, =($F$2:$H$13=0)+($F$2:$H$13=1)=1 as my solution but it does not work. The only thing I changed is the ranges. Any suggestions?
Sorry I left some of it out in the previous question,
Thank you. Jim