The cost of content is the most basic level of measure in the content management space. If there is one basic measure that library services, the core of content management, should bring to any installation is a reduction in the Cost of Content. By simply calculating the Cost of Content before and after the installation the basic ROI for the deployment begins to be identified. But Cost of Content is also a component of several other calculations, like Content Valuation.
It’s very simple to understand yet very few people have ever tried to calculate this value. The concept is not complex but the question always comes about as to how much time specifically to allocate to each task and what to add together. But the most basic calculation has always been the best for me.
Part of the series Calculating the Return on Investment with ECM.
I like to look at the cost of content as the amount of money spent from the time that content is first created until it is read by any individual through normal distribution channels. What this does is focus on the unique value a content object creates to an organization. It also allows the costs around the way content is consumed to be calculated separately, which is an import part of further analysis which I will cover later.
Cost of Content can easily be stated as:
Cost of Content = (Annual Authoring Costs + Annual Review Costs ) / New Objects per Author
Annual Authoring Costs
One can quickly jump to the thought that authoring costs needs to simply look at the salaries of the entire authoring community. While this may get a rough number it is not entirely accurate. It’s easy to exclude vacations and holidays, but typically the rest of the time is best calculated developing estimations with the authors using polling. It is rare that one can get the “real” hours used to develop documents, but if they are available it’s best to use them.
To most accurately calculate authoring costs, I typically ask the author to break their weeks into percentages based on four categories:
- New Document Authoring (including meetings and research)
- Document Revisions (including meetings and research)
- Reviewing Documents (not authored by the individual)
- Non Document Time (team status meetings, etc.)
Keeping with three categories usually keeps this as a simple task for users without adding to much complexity. I also like to include meetings and research time as this is an important part of the process. It is also common that in future calculations, assuming pre- and post- installation, research time may also decrease.
It is also important not to focus on a single author. Several authors should polled on their opinions of the breakdown. Both junior and senior authors should be used as when available new authors as well.
Another interesting calculation that can be made if this data is collected into several collections is the calculation for System Ramp Time (brining a new user to junior level status). To do this simply collect enough polling data from each group and keep the results separated. But for Cost of Content you should use all numbers.
Once the poll results are collected simply average the times for New Document Authoring. Then use this percentage to work against either the fully loaded annual costs of the employee or simply just the salary, just be sure to be consistent. Finally multiply by the number of Authors.
Annual Salary * % Time Spent Authoring * Number of Authors
$50,000 * 75% * 10
Annual Review Costs
Typically reviewers already have in their mind how much time they spend on reviewing documents. They either have an estimated percentage of their time that they spend every week reviewing documents, if volumes are high. Or if volumes are low, they feel they know the average number of hours spent on each document. If using hours spent you will also need to ask for the number of documents reviewed each year. When reviewing the Review Community, make sure to only use one or the other of the numbers.
First of course average their polled results. Then just as with Authoring, make sure to use the same number as before and to use fully loaded costs or salary. With percentage, simply calculate the percentage and multiply by the number of reviewers.
Annual Salary * % Time Spent Reviewing * Number of Reviewers
$100,000 * 10% * 15
If you are able to get percentages from everyone then simply average the hours and then calculate the average costs.
(Annual Salary / (52 weeks * 40 hours)) * Review hours per week * Number of Reviewers
($100,000 / 2,080) * (4 * 52) * 15
$48.07 * 208 * 15
New Objects per Author
New Objects is simply the number of new objects created by the authoring team in a year. This can include all authors or be broken down into junior and senior authors, just remain consistent.
Following the statements above, the average author is paid $50,000 each year while the average reviewer is paid $100,000. Authors feel they spend 75% of their time authoring new documents while reviewers feel they spend 10% of their time reviewing documents.
(Annual Authoring Costs + Annual Review Costs) / New Objects
((Authoring Salary * Poll Results * Authors) + (Reviewer Salary * Poll Results * Reviewers)) / New Objects
(($50,000 * 75% * 10) + ($100,000 * 10% * 15)) /150
($375,000 + $150,000) / 150
$525,000 / 150
From this example three important metrics are discovered.
- Cost of Authoring = $375,000
- Total Cost of Content = $525,000
- Average Cost of Content = $3,500
After installing a CM system or making improvements, the Cost of Authoring should decrease if volumes stay the same. This is typically the most visible number on the bottom line as it’s mostly salary information.
Very similar to Cost of Authoring, Total Cost of Content is another number that could easily be tracked from the bottom line. Again this is made up of salaries but now adds the reviewers.
The Average Cost of Content is the best metric to catch improvements to the system as this shows were efficiencies have been gained in the system. In an improving system, Average Cost of Content would only be expected to increase if the quality or the size of content is changing.
Note On Working with Polling Data
One important lesson I’ve learned in working with polling data is to regularly update the owner that will be consuming the ROI data. Polls are not scientific but really an informed guess. Unless you’re ready to sit beside someone with a stop watch or have them log their individual time, this is probably the best number you will get.
I actually calculate the results first and then validate the poll with the owner. By calculating first I am not biased by preconceived notions. And by presenting the results after collected, the owner can make their own decision on the information.
Typical answers to be expected are:
- “Ah, yes” – This means the results that your polling is inline with what was expected. This means that your polling data could enforced preconceived opinions. But it could also mean that the preconceived opinion is so ingrained that that was the only obvious result, so validate your results.
- “That’s not right” – This means that your polling is not near the preconceived opinions. Validate your results and review the polling data with the owner, possibly involving one of the more trusted individuals polls.
- Neutral Response – Just because the numbers seem to fit doesn’t mean you should not validate your results. When I get a neutral response from the owner, I make sure that they are in agreement with the number before moving forward.