The homelessness problem is too complex to expect that there can be a comprehensive solution. To get a handle on it we need to partition the homeless population into segments with unique needs and preferences. Once the uniqueness of each segment is known, we can determine programs and services tailored to the specific needs of individual segments. Precise segment definition will help to determine what segments should be targeted first, the definition of solutions in each segment, staffing, distribution, budgets and many other factors. The idea is to break a problem that is too big to solve into smaller problems where there are chances for solutions.
Below I present a technique that is much like one that I developed years ago to help my management consulting clients in the US and Europe understand their markets. It included a tool, in the form of a matrix, which makes it easier to identify market segments in terms of their dimensions. The technique yields meaningful and precise definitions of segments of the homeless population. This will help in determining those segments in which homelessness relief projects should focus their efforts – the parts of the homelessness problem that need most to be solved or have some other quality that make them priorities. Once defined segments are described and analyzed and criteria are used to select from among them those that will be addressed.
In segmentation, we seek to characterize groups of the homeless,
- with similar or related characteristics,
- who have common needs and values,
- which will respond similarly to specified solutions, and
- which are large enough to be important to address.
It is important that each segment defined is homogenous, its members sharing a key characteristic that differs from that of all other segments.
For example, it’s likely that the homeless population might be partitioned by demographics such as gender, age, income, and life stage. But this may not be precise enough for selection of the segments on which to focus. So, other dimensions are considered, such as geographical location or reasons for being homeless.
Each dimension should be partitioned into components, to bring further understanding. In the partitioning it is important to include all possibilities, even if you have to name one of the components “Other”.
The reason for this is that when you select components to be included in a dimension of a segment you are actually excluding all the other components in that dimension.
Some categories of dimensions that might come into play include:
- What kind of solutions homeless might accept.
- Location preferences of homeless. Some homeless want to be within reach of the public transportation or job opportunities.
- Why they find themselves in a homeless situation.
- Their demographics
Each dimension is then broken down into components.
Here are some examples of the components of some demographic dimensions:
- Age: Under 6. 6-11. 12-19. 20-34. 35-49. 50-64. 65+
- Sex: Male; female, homosexual, other
- Social Situation: Alone, With spouse, With spouse and child, With another person(s)
- Annual Income: Under $3,000. $3,000-$10,000. $10,000-$20,000. $20,000+
- Occupation: Managers, clerical, craftsmen, farm-workers, retired, students, other
- Education: Grade school, some high school, high school, some college, other
- Religion: None, Catholic, Protestant, Jewish, other
- Race: White, black, Chicano, Asian, other
- Nationality: US, British, French, German, Italian, Latino, Japanese, other
I developed the Homeless Population Segmentation Worksheet to facilitate the definition of homeless population segments.
In this sample, homeless population segments are defined by selecting one or more components from each of the nine dimensions. Selections are in yellow.
This homeless population segment consists of:
Healthy Hispanic male farm workers, between 20 and 34 years old, living alone, with a grade school education earning between $10,000 and $20,000 annually but can’t afford to pay rent.
Other selections from each of the dimensions will yield additional candidate homeless population segments.
Once segments are defined in this way, the analyst should adjust the segment definitions for size and reasonableness. This can be done by adding or subtracting components from each dimension. Adding a component in any of the dimensions – say adding “less than grade school” to the Education dimension of the segment just defined – increases the “size” of the homeless population segment, (where size means the number of homeless in the segment). The modified (larger) segment definition would then appear as,
Healthy Hispanic male farmworkers, between 20 and 34 years old, living alone, with a grade school education or less earning between $10,000 and $20,000 annually but can’t afford to pay rent.
Eliminating a dimension is equivalent to including all of the components of that dimension. So, if we eliminated the Occupation dimension, the above segment would appear as,
Healthy Hispanic males of all occupations, between 20 and 34 years old, living alone, with a grade school education or less earning between $10,000 and $20,000 annually but can’t afford to pay rent.
Eliminating a dimension could result in a segment that is too broad for meaningful solutions.
However, having too many dimensions, where they may not be meaningful, could lead to homeless population segments that are too small to be of interest. If the total number of component selection options, across the dimensions, is quite large, the resulting number of candidate segments may be too large and result in loss of focus.
There may be other tests for reasonableness of homeless population segment definitions other than size. Here are some criteria to be applied when defining segments:
- The segments should be measurable in terms of size and ability to reach.
- The homeless project team should have a capability to reach and serve the segment.
- The number of segments should match the project team’s capabilities.
Here are some advantages of the above approach to homeless population segmentation
- First of all, it challenges planners to define each dimension with precision.
- This, in turn, leads to greater understanding of the segment, thus making it easier to decide on the parts of the problem that should be pursued.
- A proliferation of types of homelessness becomes obvious, and planners tend to focus on a few.
- This precision also helps in profiling priority homeless populations segments; i.e., describing them in greater detail so different parts of the project team can be focused more effectively.
Once the segments are deemed appropriate, they must be described, analyzed, and evaluated for inclusion in a homeless relief project. We won’t go into that at this time.