The Great American Payday Prepare for a (Relatively) Bumpy Ride.pdf
TMP March 2015
1. Future leaders
Promotion & Recruitment report
March 2015
* In this report you can find information about results of March
promotion and recruitment of AIESEC in Poland
2. Dear AIESEC in Poland,
With huge pleasure, we would like to inform you about results of March
2015 promotion and recruitment in AIESEC in Poland – our third Future
Leaders Recruitment. As members of AIESEC we spent a lot of time in
preparation to this process, we were promoting FL for long hours,
preparing a lot of assessement sheets, providing knowledge about
this process to LCs, but now we should be proud that we gave an
opportunity to so many young people who want to be part of our
amazing organization and achieve with us 3582 exchanges.
We want to thank all LCVP TMs and LCVP Marketings for your work,
engagement and effort during these intense months of preparation
and work. We are really proud of you !
Yours,
Ola & Kacper
National Recruitment Coordinator & Future Leaders Brand Manager
5. OVERALL RESULTS
We had 1296 applications
We allocated 671 new members,
including 4 returnees
We allocated 214 TMP-TMPs
General we allocated
889 people
12. NPS score for MC & NST
evaluation of cooperation with VPs MKT commission
37,5
My evaluation of Task Force
During Task Force we delivered everything we planned.
As a follow-up, countdown campaign posts and
sponsored post templates were created.
Task Force participants were also responsible for
materials feedback and they did a great job.
25. LCs results
Note that:
LC Warszawa UW and LC Olsztyn did not fill in the Landing
Page views from their channels (LC Olsztyn did not use bit.ly
to collect the data) - I calculated it as averave number, basing
on national performance. That’s wht their Landing Page views
statistics may be different from the real ones
LC Wrocław UE, LC Wrocław UT, LC Poznań, LC Katowice and
LC Warszawa SGH did not use landing page/stopper using
landing page/promoted both landing page and eRecruiter links
directly - so their data is probably not accurate.
29. Conclusions
LCs results:
My only conclusion is - we can predict (and as an effect. plan)
the traffic we need to generate fo get planned number of
applications. If you do not have results - you need to improve
the traffic on your channels, there is no other way to get
resulst! As there was no exception from this rule (and
conversions can confirm that) - you can plan it in advance
(plan shares by your members, posts on your local FP etc.) to
generate traffic.
LCs online performance reflects overall performance.
31. Distribution of applications in each sub product
Subprogram
No. Of Rejected
after CV
No. Of rejected
after RB
No. Of accepted No. Of apps
Conversion of
RB/app
Conversion
app/accepted
Social
Entrepreneur
106 207 358 671 84,20 53,35
Marketing 60 95 144 299 79,93 48,16
Business Sales 45 114 175 334 86,53 52,40
Numbers
Charts - applications
n=1304 n=677
32. Targeting in each sub product
These charts give you a clue about proper targeting and
communication of our subproducts.
As LCs have different approach towards rejecting candidates after the
application (only age is the common factor when rejecting candidates)
wa cannot draw valuable conclusions about targeting nationally.
In case the share of rejected after the RB is big, then we can do
better with communique of the subproduct (in all 3 subproducts; we
should focus on a way of communicating what each subproduct is,
focus on its uniqueness) to get more candidates from ‚Rejected after
RB’ to accepted.
n=671
n=299
n=334
33. Charts - conversions
As previously said, wa can’t draw proper
conclusions from app/RB conversion charts due
to differences in recruitment process in LCs.
Most of apps we had from SE sub product,
which also had the best conversion rates in
terms of the no. of people who past RB/no. of
application (second chart)
It means we did a better job in terms of
targeting the talents of SE than we did in other
two sub products.
The worst conversions we had in Marketing
subproduct - as we need a lot of Marketing
talent, we should approach these people in a
different way as targeting, promotion channels
and communique of the subproduct needs
improvement.
34. Distribution of each customer profile
Profile
No. Of Rejected
after CV
No. Of rejected
after RB
No. Of accepted No. Of apps
Conversion of
RB/app
Conversion
app/accepted
Culture,
language &
humanic studies
61 109 185 355 82,82% 52,11%
Artistic studies 2 6 17 25 92% 68%
Engineers
studies
24 77 99 200 88% 49,5%
Medical &
nature studies
17 30 32 79 78,48% 40,51%
Business studies 85 176 313 574 85,19% 54,53%
Marketing
studies
22 18 31 71 69,01% 43,66%
35. Customer profile
73% of our applicant and new members major in business,
culture, language & human studies, there are around 15% of the
applicant and new members are engineering students.
Looking at the share of people who got accepted, the situation is similiar.
Also looking at the marketing & artistic studies profiles share - we can
see that we didn’t have enough applicants in this case, so both targeting &
communique needs improvement.
Also, profiles distribution is varying in different LCs (due to many factors),
that is why we cannot get a lot of valuable inputs nor conclusions on
national level.
What we can do as an organization is to specify which profile is the
best for us basing on performance and leadership pipeline of our
current members - and then go hunting for this talent.
n=1304
n=677
36. Due to differences in recruitment process in LCs, share of rejected after CV does not give us many
inputs (besides Marketing - where the share is much bigger than in other profiles, which means
we did a bad job with targeting)
Big share of rejected after RB tells us that we could have done better with proper communique of
the product to the profie (in Medical & nature studies and Engineering studies profiles)
Targeting in each profile
n=355 n=71 n=79
37. Charts - conversions
As mentioned, app/RB conversion does not reflect whether we did a
good job or not on national level due to differences in recruitment
process in LCs.
Convsersions of applied/accepted in Medical & Nature studies and
Marketing studies are very low, comparing to other profiles. As
mentioned before, this indicates that we could have done better with
targeting in these profiles.
We had only 25 applications (1,9% of total applications) from Artistic
studies profile - that is why we should not look for the why behind
such high app/accepted conversion.
In case of students profiles you need strong cooperation on TM-MKT
line - as some of the profiles tend to be more effective and stay in
organization longer than others (it depends strongly on your LC
experience)
In some cases, we also need some ‚specialized’ people taking over
some specialized roles in LCs, eg. marketing peeps, designers, analytics
etc., that is why both targeting and communique of our product is
important (so we get the talent, and the talent can develop his/her
specific skills in our organization). In this case you also need strong
cooperation on TM-MKT line.
38. Distribution of customer age
Age
No. Of
Rejected after
CV
No. Of
rejected after
RB
No. Of
accepted
No. Of apps
Conversion of
RB/app
Conversion
app/accepted
Share of apps
>18 1 9 9 19 94,74% 47,37% 1,45%
18<x<19 2 18 9 29 93,10% 31,03% 2,21%
19<x<20 37 104 221 362 89,78% 61,05% 27,59%
20<x<21 31 96 191 318 90,25% 60,06% 24,24%
21<x<22 27 73 115 215 87,44% 53,49% 16,39%
22<x<23 30 60 67 157 80,89% 42,68% 11,97%
23<x<24 31 39 43 113 72,57% 38,05% 8,61%
24<x<25 22 14 16 52 57,69% 30,77% 3,96%
25<x<26 39 3 5 47 17,02% 10,64% 3,58%
39. Charts - conversions
There are not many conclusions here. Besides
the fact that basing on organizational needs -
the younger people we have, the better it is
for the organization
Please remember the primary targets will be
1st or 2nd year student, who will be more
likely to continue in the organization.
In some cases we tend to recruit a bit older
talent (3rd, 4th year of studies) for specific
position or specific area in the LC. This is
understandablec and is probably the why
behind such high conversions in 21<x<22 and
22<x<23 groups of age.
40. Distribution of each channel
Channel
No. Of Rejected
after CV
No. Of rejected
after RB
No. Of accepted No. Of apps
Conversion of
RB/app
Conversion
app/accepted
Fanpage AIESEC 33 68 83 184 82,07% 45,11%
FB groups 42 75 64 181 76,80% 35,36%
AIESEC Website 14 23 38 75 81,33% 50,67%
Posters 8 6 15 29 72,41% 51,72%
Leaflets 5 12 16 33 84,85% 48,48%
Radio 1 2 2 5 80,00% 40,00%
Press 1 0 1 100,00% 0,00%
Friend from
AIESEC
61 140 304 505 87,92% 60,20%
Friend not from
AIESEC
22 31 69 122 81,97% 56,56%
Event organized
by AIESEC
7 9 22 38 81,58% 57,89%
AIESEC
University/AIES
EC Native
School
2 7 6 15 86,67% 40,00%
Other 12 35 38 85 85,88% 44,71%
GC/GT 4 7 20 31 87,10% 64,52%
41. Promotion channels
39% of applicants G2K about us from their friends in
AIESEC. All online channels brouhgt us as many as 34% of
all applications. This gives us 73% of all applicants.
Looking at the share of people accepted - it is similiar. 73%
of all accepted people G2K about us from online channels
and friends from AIESEC.
n=677
n=1304
42. Channel effectiveness
These 2 charts show us how efficient our channels
were.
First two are Facebook - fanpage & FB groups.
Fanpage is a good channel for promotion. We did
reach proper target with it (only 18% of rejected
apps after application review). Share of applicants who
were rejected after RB was big though - which means
we need to improve communication on local fanpages.
FB groups has big share of applications. Although a lot
of people were rejected after RB - which means the
communication was poor. In many cases people
shared Future Leaders landing page directly.
Recommendation for future is to share content from
local fanpages (together with graphics) to make it more
attractive and increase the reach of your fanpage.
n=184
n=181
43. Channel effectiveness
Website - a lot of people went through the
website. As previously, the communication there
was good as we can see on the chart. We also
reached proper target, but we can work more on
communication (as the share of people who got
rejected after RB is big).
Event proved to be very effective.
Recommendation is to organize events to attract
applicants with specific skills we need (eg. IT,
marketing).
Press - we had 1 applicant who marked ‚Press’
as the channel he/she G2K about Future Leaders
(and the person didn’t eventually get accepted). It
means that it is a channel to be explored.
n=75
n=1 n=38
44. Channel effectiveness
Friends from AIESEC - the channel had the
biggest share of applications and turned
out to be the best one overall. It is
important to internally educate members
about the product. Still, I think we should
aim to get at least 80% conversion from
this channel.
GT/GC - we had only 31. It has the best
accepted/applied conversion of all
channels we used - so we need to explore
cooperation with LCVPs OGX (Integrateg
Experience) more to get more applicants.
Friends not from AIESEC provided us with a
lot of valuable applicants - which is a
surprise. This means that Future Leaders is
getting more and more recognizable.
n=505
n=122 n=31
45. Channel effectiveness
Posters, leaslets - they have good conversions, but are
not providing us with enough applicants.
AIESEC University/AIESEC Native School - people who
tahe part in these projects are generally a god target,
so small share of applicants is a surprise and a thing
to develop during the next campaigns.
Radio - we had only 5 applicants, no conclusions.
n=15
n=29 n=33
n=5
46. Charts - conversions
Friends from AIESEC provides us with the greatest amount of
applications and with huge accepted/app ratio - which gives us
the conclusion that education of our members is crucial.
Also, targeting and building proper communication is important
(see conversions of FB groups - many people applied and
invited for RB but eventually not accepted in comparison of
Fanpage - not as many people got invited for RB but way more
people got accepted). Although, both need further development
to reach at least 50%.
GC/GT people are also a good investment, although we did not
have many applicants from GC/GT. Development of IxP can
provide us with many valuable members, so we need to
develop this channel.
Use of AIESEC University/AIESEC Native School participants is
also a way to get more valuable applicants, so in the next
campaign we can think about that.
FB groups - this channel has a big share of applicants and very
low conversions. LCs can analyze content posted/shared during
their campaigns and make a good use of this channel during
the next campaign as well.
47. Distribution of customer motivation
Motivation
No. Of Rejected
after CV
No. Of rejected
after RB
No. Of accepted No. Of apps
Conversion of
RB/app
Conversion
app/accepted
Teamwork 6 24 28 58 89,66% 48,28%
Self-
development
84 186 313 583 85,59% 53,69%
Professional
Experience
45 77 111 233 80,69% 47,64%
International
Experience
48 83 159 290 83,45% 54,83%
I have lots of
free time
4 8 8 20 80,00% 40,00%
Meet new
people
24 38 58 120 80,00% 48,33%
48. Customer motivation
45% of applicants did so for self-development, which is
the biggest share of customer motivation. Getting
Professional Experience and International Experience
gained respectively 18% and 22% of applicants.
This means two things. First is that now we have to provide them
with the experience they applied for ;-) and another thing is that
during future campaigns we can emphasise these more, to reach
more valuable applicants.
Suprise is that only 4% of people applied to gain team work
experience - which is one of our unique values as an organization.
In the future we also should emphasise it more.
n=1304
n=677
49. Motivation vs. success
Generally, we can see that candidates with expectations
matching uniqueness of AIESEC are more likely accepted.
Motivation of candidates can give us a view on why they apply
vs. what we, as an organization, can provide to them. Big
shares of people rejected after RB can lead to a conclusion, that
we cannot meet their expectations.
Although, knowing market expectations now, we can emphasise
more the things we do provide as an organization. n=290 n=233
n=58 n=583n=20n=120
50. Charts - conversions
As previously mentioned, candidates with strong
personal goals & expectations are more likely to
be accepted.
You can have many more conclusions in this
part after consultancy with your TM - from my
experience I know that interviewers are taught to
look for people with certain skills/attitudes and
this might be the reason why charts and bars in
this part look the way they do.
In general, we are looking for people with
certain attitudes and skills and interviewers are
taught to look for these skills and attitudes. So,
to get further conclusions in this part it is
necessary to consult the results with TM.
51. Notifications
The data in the analysis may be slightly
different from data in Recruitment Report
released by NST Recruitment. Overally we got
1306 applications, and in the part with
customer analysis we analyzed 1304
applications.
Overall error is only 0,15%.
62. Conclusions
Poor Comms education about Content Marketing
(and generally their poor preparation) due to:
Term shift
Lack of structures to support Comms
Generally lack of support to Comms
Lack of proper planning before the campaign
Comms tracking by NST (lack of setting clear goals and expectations)
Promotion timeline (during exam sessions period)
63. Conclusions
We can predict (and as an effect. plan) the traffic we need to generate to
get planned number of applications. If you do not have results - you
need to improve the traffic on your channels, there is no other way to get
resulst! As there was no exception from this rule (and conversions can
confirm that) - you can plan it in advance (plan shares by your members,
posts on your local FP etc.) to generate traffic.
LCs online performance reflects overall performance.
I cannot believe that only 9% of people who clicked on Landing Page
applied for Future Leaders. I do not think there is something wrong with
the product, butmarket is changing. We should ask external environment
about what it needs - so we can provide it as an organization.
Drops in GC and GT reflect that too. Why don’t we ask them? :D
65. We achieved 98,5% of recruitment plans and 7 LCs achieved
more than 100% (Gdańsk, Lublin, Łódź, Olsztyn, Toruń, Warszawa
UW, Wrocław UE). Only LC Szczecin achieved less than 70% of what
they planned.
94%
109%
97%
70%
95%
123%
114%
105%
72%
80%
60%
115%
97%
163%
106%
80%
% of recruitment plans achieved
67. Allocation Results
per Future Leaders sub-program
94%
101% 101%
90%
92%
94%
96%
98%
100%
102%
Social Enter… Marketing Business Sales
Still we had the worst results in Social Entrepreneur
sub-program, but in Business Sales and Marketing we achieved more
than 100% of our plans
68. We allocated in AIESEC 889 members.
70% female 30% male.
LC Łódź & Olsztyn allocated less than 20% of men
Only LC Szczecin allocated almost equally of
women and men
63% 66% 71%
59%
77% 77%
78% 84%
70% 72,50%
48%
62%
73% 79%
70% 75%
37% 34% 29%
41%
23% 23%
12% 16%
30% 28%
52%
38%
27% 21%
30% 25%
Women Men
69. Recruitment and allocation
To recruit 675 people
(new members + returnees)
we needed 325 members.
Most of the members was allocated
to
Social Enterprenuer program
70. Only 7 LCs
haven’t had skip yet
( 43%)
Rest LCs noted
1-4 skips
till 25th March 2015
71. How Many people We Reallocate
5
36
7
17
24
11 12
8
26
2
7
18
23
24
16 17
5
36
7
15 15
9
11
8
26
2
5
16
14
23
15
5
Applicants Allocated members
9 LCs provided more than 10 TMP-TMP Experiences.
In every LC members want to repeat team experience.
72. Relation of new members to reallocation
in recruitment
11
42
65
16
78
61
30
13
41
30 25
39
52
78
52
38
5
35
7
15
15
9
11
8
24
2 5
20
17
23
14
5
New Members Reallocation
We can notice that we based most on new members during recruitment
73. How Many returnees we recruited
0 0 0 0 0 0 0
1
5
2
0 0 0 0 0 00 0 0 0 0 0 0
1 1
2
0 0 0 0 0 0
Applicants Allocated returnees
Only 4 LCs provided Integrated Experience.
We didn’t conduct recruitment process for people who were on GT and GC
74. 25%
75%
When did you allocate
people?
On LCC After interview
4 LCs in AIESEC
in Poland allocated
people on LCC !
It means that more OCPs went to LCC
prepared to their structure ( in 12 LCs)
19%
50%
25%
6%
How did you allocate people?
Final decision of VP TM and LCP
Final decision of VP TM
Final decision of EB
With engagement of OCPs
Only Warsaw SGH engaged
OCP to allocation. It means that
in most LCs TM had final
decidion about allocation
75. 35%
65%
How long was
Group Exercise?
30 min 30 min-60 min
23%
71%
6%
How long was
average interview?
Less than 45 min 45 min- 1h 1h -1,5h
In every
LC Group Exercise lastsed
less than one hour
Most of LCs conducted interviews
less than one hour !
(In 77% LCs RBs lasted less than 45min
and 45 min-1 hour)
It means that whole recruitment process was well-planned
and materials created by MC helped LCs running recruitment.
76. Preparation to Recruitment
16
4
2 2
Training Simulation Webinar Others
How did you prepare people to
recruitment process?
No of LCs which use each materials
11 10
15
3
12
2
What materials did you use
during recruitment process?
No of LCs which use each materials
Every LCs organized training for assessors and
Only 4 LCs tried to prepare them in practice way.
During recruitment in most of cases used materials prepared by MC
77. How many people RESignED AFTER EACH
ELEMENTS OF RECRiUTMENT PROCESS
0
10
20
30
40
50
60
70
Białystok
Gdańsk
KatowiceUE
Kielce
Kraków
Lublin
Łódź
Olsztyn
Poznań
Rzeszów
Szczecin
ToruńUMK
WarsawSGH
WarsawUW
WrocławUE
WrocławUT
After application
After Group Exercise
After RB
After LCC
78. What was the relations between people who resigned, were
rejested and were allocated
Biał Gd Kat Kie Kr Lub Łódz Olsz Poz Rze Szcz Tor SGH UW
Wro
UE
Wro
UT
Allocated 17 64 42 22 87 58 32 16 71 25 48 47 79 57 61 42
Resign 7 1 21 6 34 22 3 0 18 10 0 21 14 22 27 5
Rejected 9 53 5 7 91 14 13 5 68 14 14 37 63 60 28 39
On this scheme we can notice that number of applications and relations between
numbers of people who resigned, were rejected and were allocated.
