longitudinality - het begrijpen van online consumentengedrag
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Longitudinality Het begrijpen van online consumentengedrag
Content marketing Experience, 2 oktober 2014
marnix bugel phd
2 2
3
Drs. Wiskunde & Informatica
en Phd Economie (RUG)
Voorheen Customer Analyst
ABN AMRO en partner VODW
Marketing
Managing en founding partner
MIcompany (since 2006)
Serving the board of leading
companies such as Achmea,
KPN, Bol.com and Postcode lottery
(Co) author of scientific articles
including article currently at 12th
position most cited list
Included on list best professionals
(Quote) and best marketers (Dutch
Marketing Magazine, 2nd place)
Short personal- an dcompnay introduction
*A skill that differentiates a company from its competitors
Marnix Bügel
Leading European agency on BIG DATA and
Commercial Analytics
YoY growth since start in 2006:
CAGR of 25%
Unique proposition focused on:
- sustainable growth through analytical
capability* building
- partner relationships with our clients
50 people on pay-roll:
- 85% of them studied Econometrics or
Mathematics
- Exceptional results in high school and
university (average high school score on
mathematics 9.4 (scale of 1 to 10); more
than half of employees graduated cum laude
once or twice)
4
(R)etail
Telecom
Insurance
Travel & Energy
Non-profit
Banking
1 2
Lotteries
3
Micompany serves leading companies across industries
1 2 3 100% online players (corporate) startups international assignments
5
Global media consumption per week Average hours per week
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
Cinema
1940 1920 1900
Analogue radio
Analogue TV
Outdoor
Digital TV
Digital radio
Internet
Wireless
Games
2020 2000 1980 1960
Source: Carat, World association of newspapers 2007-2008.
Two-way media
One-way media
Digital media take over the role of push media
6
0
5
10
15
20
25
30
35
Storage
capacity
Creation
2020 2018 2016 2014 2012 2010 2008 2006
Source: IDC publication – digital universe, are you ready 2010.
• Every two years the amount of
data doubles in the world.
• The Berkeley School of
Management forecasts that more
data will be created in the next
three years than in the previous
40,000 years.
Global data creation and storage capacity Zeta bytes
Data creation explodes in the world
7
type # bytes
1 bit = .125
1 byte = 1
1 kilobyte = 1,024
1 megabyte = 1,048,576
1 gigabyte = 1,073,741,824
1 terabyte = 1,099,511,627,776
1 petabyte = 1,125,899,906,842,624
1 exabyte = 1,152,921,504,606,846,976
1 zettabyte = 1,180,591,620,717,411,303,424
1 yottabyte = 1,208,925,819,614,629,174,706,176
1 xonabyte = 1,237,940,039,285,380,274,899,124,224
1 wekabyte = 1,267,650,600,228,229,401,496,703,205,376
1 vundabyte = 1,298,074,214,633,706,907,132,624,082,305,024
Wallmart collects more than 2,5 petabytes of data every hour from its customer transactions.
From bit to vundabyte
8
Large companies start to wonder what to do about this
8
9 9
10 Fact based marketing
10
11 11
+21%
Nieuwe premie
103,19
Oude premie
85,51
Bron: Bügel, 2010
Premieontwikkeling na prijsverhoging
12 Fact based marketing
12
13 13
Tevredenheid
Kwaliteit van alternatieven
Hoogte van investeringen
Relatiebinding Voortzet-/
beëindigingsgedrag
.845
-.500
.840
.528
Bron: Rusbult, 1983
Het investeringsmodel van rusbult
14 14
35
32
30
Totaal 97
Niet binnen 12 maanden
Niet binnen 3 maar wel binnen 12 maanden
Binnen 3 maanden 33% 33%
31% 64%
36% 100%
100% 100%
Snelheid van terugkeer Aantal vrouwen
Grootte %
Grootte cum %
Bron: Rusbult & Martz, 1995.
Het terugkeergedrag van mishandelde vrouwen
15 15
Determinant investeringsmodel
Grootte Ind %
Grootte Totaal %
Bron: Rusbult & Martz, 1995.
0,19
0,28
0,34
Investeringen
Alternatieven 0,45 0,11
Tevredenheid
Indirect
Direct
Het belang van factor
30%
12%
21%
30%
49%
21%
Het belang van factoren uit investeringsmodel op voortzet- beëindigingsgedrag
16 16
Totaal 1.386
Automotive 276
Mobiele Telcom 272
Supermarkten 277
Zorgverzekeraars 275
Bancair 286
Branche Steekproef grootte (N)
Bron: Bügel, 2010.
0,62
0,60
0,62
0,59
0,64
0,74
Verklarings- kracht (R2)
Investeringen 0,27**
Alternatieven 0,31**
Tevredenheid 0,42**
Determinant investerings- model Gewicht (beta)
** p < .01
investeringsmodel is in verschillende branches toepasbaar
17 17
Het verhogen van de tevredenheid
18 18
Het reduceren van de kwaliteit van alternatieven
19 19
Het verhogen van de investeringen in de relatie
20 20
Het effect van de hisociety
21 21
22 22
Er is meer dan ratio
23 23
Passion Commitment
Intimacy
Source: Sternberg, 1986.
Kamaraad-schappelijke liefde
Romantische liefde
Dwaze liefde
Liefde bestaat uit drie componenten
24 24
Le
vel
of
inti
macy
Duration of relationship
Successful relationship
Failed relationship
Level
of Intimacy
Le
vel
of
pa
ssio
n
Experienced
Level
Positive drive
Negative drive
Opponent processes
Duration of relationship
Source: Sternberg, 1986.
Liefde ontwikkelt zich gedurende de relatielevenscyclus
25 25
Tevredenheid
Kwaliteit van alternatieven
Hoogte van investeringen
Klantbinding (+)
(-)
(+)
Voortzet-/ beëindigingsgedrag
(+)
Klant- Intimiteit
(+)
Klantfase Bron: Bügel, 2010.
Het klantenbindingsmodel volgens bügel
26 26
0,37
0,210,19
0,28
0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
0,40
Several
years
marriage
Living
together
Courtship
0,09
In love
0,13
Get
acquinted
Ø 0,21
Divorce Marital
Grid
0,17
β reg De β voor intimiteit in het regressie model afs functie van de levensfase
2 Nummer van variabele affectie in regressiemodel
4 4 4 3 4 1
Intimiteit cruciaal aan begin en einde klantrelatie
27
de cohort analyse laat zien dat nieuwe gebruikers kwetsbaar zijn
27
148
July 159
June 186
May 247
December 129
November 134
October 136
September 142
August
Starters in May(424k)
Users with sessions
(*1000)
6,3 47
Month Sessions
Per day
Session
Index
10,6
9,3
10,1
10,0
8,1
9,3
9,3
77
90
92
93
96
99
105 58
61
62
66
71
85
119
Starters in June(233k)
Users with sessions
(*1000)
Sessions
Per day
Session
Index*
4,0
7,7
9,1
9,5
7,8
9,3
8,7
30
75
83
88
92
98
98 59
62
64
70
81
117
Starters in July(228k)
Users with sessions
(*1000)
Sessions
Per day
Session
Index*
3,8
8,9
9,5
8,2
9,1
9,0
37
81
88
97
97
103
28 28
29 29
30 30
31
The needs of customers differ between lifecycle phases
31
Custo
mer
phase
Org
anis
ational action
Orientation Selection Confirmation Standardi-
sation Extension Dispersion Seperation Payment
Seduce Offer
Introduce
Serve Extend Keep Save Collect
Customer life time
Dating
Redeem
Becoming customer
Becoming ex customer
32
Value is being built during the customer lifecycle
32
Custo
mer
phase
Org
anis
ational action
Orientation Selection Confirmation Standardi-
sation Extension Dispersion Seperation Payment
Seduce Offer
Introduce
Serve Extend Keep Save Collect
1
2
3
4
5
6
8
9
Customer life time
Customer life time value
Considertion
Acquisition
Welcome
Recurring costs & revenues
Cross- and upsell
Volume
mutation Win
back Debt
collection
Dating
Redeem
Churn
7
Becoming customer
Becoming ex customer
33
Longitudinality is key to capture value from data & analytics
33
34
Longitudinality is key to capture value from data & analytics
34
35 35
???
36 36
cadeautje
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