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‘Non-users’ are not alike! . Using Intention to create finer segments. Problem Statement. We currently undertake segmentation based on behaviour We compare users as a group with non users as a group and identify determinants that are significantly different between the two groups.

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non users are not alike

‘Non-users’ are not alike!

Using Intention to create finer segments.

problem statement
Problem Statement
  • We currently undertake segmentation based on behaviour
    • We compare users as a group with non users as a group and identify determinants that are significantly different between the two groups.
    • For example, there are only a few who have gone through Male circumcision in Swaziland. They are the ‘users’. As of now, the rest of non circumcised men are all classified as “non Users”.
    • The ‘non user’ group is seen as one monolithic group and is expected to share similar characteristics.
    • Is this accurate and appropriate?
problem statement contd
Problem Statement contd.
  • Currently, non users can be a sizeable majority and it is likely to be quite a varied group. Variations in the group mean that there could be different reasons for doing or not doing the behaviour.
  • In an ideal world, we would like to know the barriers and motivators for each person and then design communication messages that are specific to them. This approach however can be quite impractical.
  • Considering non viability of an individualized approach, a campaign targeting an ‘average’ non user may seem the only way. A more like a one size fits all approach.
  • Is there a happy medium between individualized messages on one side to a one-size -fit- all approach on the other? Finer segments totalling 4-6 may provide this
identify sub groups within non users
Identify sub groups within ‘Non Users’
  • Cluster Analysis
    • We use SPSS Cluster Analysis to identify subgroups within our ‘non user’ population. We use their propensity and preparedness towards Male Circumcision to identify sub groups (clusters).
questions used to construct clusters
Questions used to construct clusters

Propensity

  • I have thought about MC I think it might be a good idea for me
  • I have thought about MC and I think it is NOT a good idea for me
  • I have thought about MC and I will never get circumcised

Preparedness

  • I have thought about MC and collected some information about it
  • I have talked to someone who went for MC about his experience
  • I have talked to a doctor / nurse/ service provider/ counselor about Medical MC
  • I have talked to a male family member about MC
  • I have talked to a partner about MC
  • I have talked to my mother/family member about MC
  • I would like to talk to someone who has gone through MC

For each respondent we asked whether the statement applied to them or not. Conducted in Swaziland.

cluster analysis
Cluster Analysis
  • We use K means clusters in SPSS to construct 5 clusters among non users.
  • K means divides sample in to K (K = 5 here) groups and makes sure that the distance between the clusters is maximized and distance within clusters in minimized.
  • In other words, it put the birds with same feathers together.
  • In the data set, for each respondent, it indicates which cluster he/she belongs to.
clusters formed
Clusters Formed
  • Cluster 1 : The Dead Against
  • Cluster 2: Wake Up folks
  • Cluster 3: Positive but need push
  • Cluster 4: Champions
  • Cluster 5: Super Champions
  • Cluster 6: Already Circumcised
size of the clusters
Size of the clusters
  • First thing to note is that the size of the segment “Dead Against” is only 10%
    • From the pretest study done a year ago, the size of this cluster has gone down (though not strictly comparable, it indicates positive changes in Swaziland on issue of MC).
  • ‘Wake up folks’ are essentially undecided and constitute about one fifth. About the same have already got circumcised.
  • The remaining 54% of the population are quite positive about MC (‘Champions’, ‘Super-champions’ and ‘positive but need push’)
next we plot each group s readiness to mc

Next, we plot each group’s readiness to MC.

For next slide please switch to Slide Show mode

so what
So what?
  • The average hides big differences.
  • All ‘Non users’ are not the same and assuming them to be similar can lead to erroneous conclusions.
    • Same message (and same determinants) are not likely to work for each of these groups.
    • Our communications can be improved if we have this information and if we target messages according to the cluster.
    • It is also very useful to know what proportion of our target audience falls in these categories. In this case, it gives an idea of your ‘pipeline’. The size of each cluster can also help in prioritizing which clusters to target.
next step
Next Step
  • We need to understand from each of these clusters what are their main barriers?
      • Since they are all non users, traditional segmentation of users vs. all non users will not work well.
      • However, each cluster could be compared with users; one at a time. That is compare Users with Serious Contemplators; compare Users with Dead Against and so on. We now present results form this analysis.
recommendations
Recommendations
  • For Dead Against group: Social norms and Knowledge seems to be the key. Their knowledge levels of benefits of MC seems to be much lower.
  • More ‘Knowledge’ is needed for 'wake up folks’ group as well. However, they do not feel the pressure of social norms as much. This group believes far lesser in the 'increased sexual satisfaction' benefit that everyone else seems to.
slide21

Those who are positive but need some push- they need to be reassured of fears and QoC concerns regaridng the

recommendations1
Recommendations
  • Groups 1 to 3 do still need social support. Champions and Super champions have already secured enough social support.
  • Fears relating to the MC process are important and relevant to each group.  
    • These fears were much lesser among the users. Use testimonials as far as possible. Also, leverage the fact that every group does want to talk to someone who has gone through MC.
recommendations2
Recommendations
  • When talking to super champions, talk about their remaining fears about the operation Show how they can get appointments.
  • For champions, do the above and also show what are different places where they can go. Emphasize the transport service that you offer.
general recommendations for other cases
General Recommendations (for other cases)
  • This presentation showed segmenting the non users based on their preparedness for MC. This may or may not be possible in other cases.
  • It is not to say that a ‘user-non user segmentation’ is not useful. It is most useful when the diversity in the groups (especially among non users) is low.
  • Other ways to reduce this diversity are:
    • Identify variables that may bring diversity within the target audiences. Segment separately for them.
    • For example, we may divide target audience in to narrow groups based on key demographic variables: Rural vs. Urban; Men vs. Women; Young vs. Old; Consistent users vs. Inconsistent users; Unmet need for spacing vs. Unmet need for limiting; and so on.
    • A ‘user-non user segmentation’ can be undertaken within the group e.g. filter in only respondents from rural areas and then compare a rural user with a rural non user.
optional other uses psychographic segmentation
(Optional) Other Uses: Psychographic Segmentation
  • The SPSS Cluster Analysis method outlined here can help in psychographic segmentation too.
    • This involves dividing the target audience in alike segments based on some key psychographics attributes. These attributes could be perception of and reaction to risk, attitude towards pleasure, attitude towards tradition/modernity, morality, own agency, support seeking, independence, masculinity and so on. These can be quite close to the archetypes that are developed in FoQuS.
      • Based on these selected attributes, the population can be divided in to specific clusters of like minded people.
    • The assumption is that like minded people are likely to have similar needs; similar determinants of promoted behavior; and also likely to like and react to our communication in a similar way.
      • This knowledge will help with brand promise and positioning.
      • Will also be really useful for the programs that have same target audience for multiple interventions e.g. talking to men 20-40 years old for VCT, Condoms, MC and MCP. We may not require to do separate TRaCs for each of them.
cluster analysis1
Cluster Analysis
  • We use K means clusters in SPSS to construct 5 clusters.
  • K means divides sample in to K (K = 5 here) groups and makes sure that the distance between the clusters is maximized and distance within clusters in minimized.
  • In other words, it put the birds with same feathers together.
  • In the data set, for each respondent, it indicates which cluster he/she belongs to.
syntax for cluster analysis
Syntax for cluster analysis

QUICK CLUSTER @1582Thought @1583GoodIdea @1584NotGood @1585WillNever @1591TalkToFriend @1592TalkToNurse @1595Girlfriend

/MISSING=LISTWISE

/CRITERIA=CLUSTER(4) MXITER(10) CONVERGE(0)

/METHOD=KMEANS(NOUPDATE)

/SAVE CLUSTER DISTANCE

/PRINT INITIAL ANOVA CLUSTER DISTAN.

What is the right number of clusters? Usually it will be somewhere between 3 and 6. It depends a lot on the data. And getting the right number of clusters is an iterative process. First try with four clusters. Cross tab the cluster membership across the classifying variables and assess if the clusters seems distinct enough from each other. Then, try with 5 clusters, take cross tabs again and assess if five clusters seem more distinct from each other. Also check what is the size of the cluster. If the cluster size is too small 5% of sample or lower, it may not be worthwhile to keep that as a separate cluster (you may choose to merge with one of the clusters if they seem close to one of them. A better option could be to filter them out. Also look at the dist ance from cluster center. If the distance is too far off then you may want to filter some of the cases to get more distinct clusters.

If you have good data and if you have chosen the initial variables (on which the classification is done well) then you will find

slide37

Another motivation for this study is thus:

  • Adoption of a behaviour is an end result and only visible outcome. However persuasion need to happen to move people along m;. The process to get to end can be quite different
  • Behaviour change in context of MC (and IUD and perhaps even injectables adoption), where the beahviour is required to do once or at fairly long intervals, there is a latent process underway