Prioritization is about maximizing the value you provide to your customers. When you have multiple sets of customers with different priorities, what do you do? You could try and find the lowest-common-denominator, and please everyone a little bit. But that would be the wrong thing to do – by trying to please everyone, you fail to delight anyone.
Setting Up A Prioritization Example
To keep it simple, imagine that your market can be divided into three distinct groups. You would represent each of those three groups with a persona. While working with customers in each group you identify that each group has a distinct set of priorities. You have five capabilities you want to introduce – represented by five features. You will be able to release one capability (feature) per release. Keep it simple for now by assuming they all have the same cost and time to implement.
Each group ranks the features 1 to 5. Each group ranks them differently.
One client I worked with would create a simple spreadsheet like the one above. Each group has a column, and each feature has a row. The number in each cell is the sequence of that feature, in priority order, for each group. That client would then add an “Aggregate” column, representing the sum of the values. My client would then sort the spreadsheet from lowest to highest aggregate value.
Feature 3 would be in the first release, then feature 2, feature 1, feature 5, and finally feature 4.
Each of the three most important features for any given group is highlighted in green, with descending intensity. This makes it easy to see that (for this example data) each group gets their most valuable feature in one of the first three releases. It takes until release 4 for all three groups to have their “top 3″ implemented.
You can call this the lowest common denominator approach. Relative value is averaged out across all of the groups. Each group has an equal say in what gets released when. One problem is that each group is (almost never) exactly as important as all of the other groups.
Prioritizing Features By Customer Importance
This approach, flaws and all (we’ll come to that) could be improved by taking into account the relative importance of each group. Consider adding a weighting value for each group. That weighting might reflect the size of the market segment, the strategic value, or a combination of factors that cause one market segment to be more important than the others.
The chart above shows the same example, except now groups 1,2, and 3 have an importance measure of 10,20, and 40. The aggregate value column now incorporates that weighting. Instead of just adding up the sequence values for each feature, the values are first multiplied by the importance of their group.
Counter-Intuitive Math
You would think that since the smallest numbers come first, you might want to divide by the relative value of the groups instead of multiplying. When you aggregate the sequencing, higher numbers (lower priorities) tend to push features to the bottom of the list, and lower numbers (higher priorities) tend to pull numbers to the top of the list. If you divide by the relative importance of each group, instead of causing their inputs to bubble to the top, you end up reducing the relevance of the inputs from that group. By multiplying you make each group’s inputs relatively more significant, as the relevance of the group increases.
From the color coding, you see thta group 3′s influence is greater than either group 1 or group 2′s influence. Also note that (for this example), group 2′s most important feature is scheduled ahead of group 3′s third most important feature – even though it is also the most important capability to group 1. The aggregate value column is now sorted, using the same approach as before, but with a weighting that favors the more important group.
The Problem With This Approach
The approach looks pretty clever, and it is not horrible. What it is is mediocre. And it encourages mediocrity. First, you get mediocrity because a feature that is “kind of appreciated” by all groups will get a lower aggregate value (and therefore a higher priority) than a feature that is incredibly super critically urgent for one group but wholly uninteresting to another.
Consider feature F1. It is (because we get to make up the examples here) critically important to Group 1. It is also completely irrelevant to group 2. This is because the people in groups 1 and 2 are very different. They care about different things. Because group 2 is uninterested in feature 1, group 1 has to wait until the fourth release to get it. Bummer for those losers in group 1.
Now consider feature F5. Group 1 doesn’t care about it, and they get it in release 2. Those guys are probably sharpening pitchforks and lighting torches.
They don’t get what they really want until release 4, and you can argue that this is ok because the other groups are more important. But you haven’t set expectations well. Group 1 thinks their inputs are fed into the mix, and your roadmap is for them just as much as it is for everyone else. Turns out, this approach is not very good for setting expectations with group 1.
Another Manifestation of The Problem
Now look at the flip side. Your most important market segment, group 3, does not get what they want in sequence. To keep our example exhilarating, imagine that each shaded feature (sequence 1,2, or 3) is a “must have” for the group that ranked it. No group will go live until at least the 1,2, and 3 (shaded) features are implemented. That means that group 3 – your most important market segment – cannot go live until release 4. Group 2 can go live after release 3. But group 2 is not the most important group. You tried to satisfy the biggest, most valuable group first – and it did not work.
Why? Bad math.
You cannot use “sequencing alone” to represent the value that each feature provides to each group. For one group, maybe only the first two features have a lot of value, and the other three are all relatively worthless. You get a form back from them that shows 1 through 5. But it does not show that the value of (the features sequenced first and second are ten times the value of the other three features.
Solving the Problem With Good Math
There is a solution. But it is so much more labor intensive that most teams won’t do it. You have to determine the value of each feature to each market segment. You can’t use sequence or priority as a proxy. It has to be a number. Then you can aggregate.
The Philosophical Problem With This Prioritization Approach
There is still a philosophical problem with this approach, even if you did solve it with the right math. As we mentioned before, you are creating a mediocre – and therefore sub-optimal – solution by aggregating (and effectively averaging) the value of each feature to each group. Those features that are “kinda nice for everyone” will beat out the features that one group is really passionate about, but other groups don’t care about. If you take this approach, why bother to even define personas – you’re subconsciously ignoring those insights. Market segments are dominated by the companies that put out the killer features. It would be “killer” for some users if their car could go offroad. And killer for others if it got great mileage. Another group might really care that it is showy and fast. If you prioritized a feature that helped each of those goals somewhat (say, really good tires), how passionate would your potential customers be? The offroad guys want a roll cage. The eco-hawks want a super-efficient engine. And the “all hat no cattle” guys want a wing on the back – and it should come in yellow.
You may find that your segments are so different that you need to treat them as different markets, or at least market different products to them. Otherwise, you end up with a car that goes offroad but cannot climb a 40 degree slope, that gets 25 miles to the gallon, and, while yellow, does 0 to 60 in 7 seconds. You don’t delight anyone. How much penetration will you get in any of those markets?
Not much.
The Better Way To Prioritize
Pick your most important market. Do the most important things to them first. Then go to your next most important market. Repeat. Your prioritized road map will look like the following:
Group 3 – your most important market segment – goes live as soon as possible. After release 3. Your next most important market segment goes live next. Until you run out of features and segments. Note – make sure you continuously reprioritize. You may, for example, discover a new feature that lets you double your penetration with group 3. You should probably do that before finishing a feature targeted at group 1 – even if group 1 is not launched yet.
Conclusion
Do the most important things, for the most important markets, first. Then add the next most important thing, after you learn through your incremental release plan, even if it means delaying the launch in a lower-priority market segment, in exchange for higher returns from an already served segment.
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