Transcript
Rubens Zimbres Fev/2016 1
Rubens Zimbres Fev/2016 2 All images, graphics, analysis and models presented here were developed by me
Rubens Zimbres Fev/2016 3
Rubens Zimbres Fev/2016 4
I was CEO of Doux from 2010 to 2013. The clinic has a yearly revenue of 2.5 million BRL. In 2010 the business has a debt of 1 million BRL and monthly revenue of 64 thousand BRL. I fired 6 out of 12 employees, hired new ones and did a quality research with clients. I analyzed results of the research and the database (750,000 records) regarding demographic data, profitability, purchases patterns, cost, time spent in procedures and sales. I opted for a value strategy, high service quality, moderate cost control, investment in digital strategies and fostering of word of mouth advertising. In the first years profit increased 100% and lots of patients returned to the business. Then I increased price by 8%, defined goals for variable remuneration and more flexible payment options. Most part of profit was reinvested in the business. I increased salary of employees by 20% and goals were achieved in 70% of time. Most employees received 2 or 3 additional salaries due to variable remuneration. They also got healthcare and their satisfaction ncreased quality offered to the clients. After 2.5 years, net profit increased 239%, financial leverage decreased 36%, ROI increased 206%, ROE 95%, EBITDA 98.5% and monthly revenue became 180 thousand BRL (almost tripled).
Rubens Zimbres Fev/2016 5
Tracking tools: -‐ Google Adwords -‐ Google Webmasters Tools -‐ Google Analytics -‐ Google Search -‐ Woorank -‐ Locaweb Control Panel -‐ Internal reports -‐ Database data
Locaweb Graphics
Daily Visits to Doux Dermatology Website
Rubens Zimbres
START
Rubens Zimbres Fev/2016 6
REVENUE
PROFIT
Rubens Zimbres Fev/2016 7
Rubens Zimbres Fev/2016 8
GOAL
Rubens Zimbres Fev/2016 9 Tableau database
Rubens Zimbres Fev/2016 10
Rubens Zimbres Fev/2016 11
Rubens Zimbres Fev/2016 12
Rubens Zimbres Fev/2016 13
Rubens Zimbres Fev/2016 14
Rubens Zimbres Fev/2016 15
Rubens Zimbres Fev/2016 16
Rubens Zimbres Fev/2016 17
Rubens Zimbres Fev/2016 18
Rubens Zimbres Fev/2016 19
Rubens Zimbres Fev/2016 20
Rubens Zimbres Fev/2016 21
Rubens Zimbres Fev/2016 22
Rubens Zimbres Fev/2016 23
Rubens Zimbres Fev/2016 24
Rubens Zimbres Fev/2016 25
Rubens Zimbres Fev/2016 26
Rubens Zimbres Fev/2016 27
Rubens Zimbres Fev/2016 28
Rubens Zimbres Fev/2016 29
Rubens Zimbres Fev/2016 30
Rubens Zimbres Fev/2016 31
Rubens Zimbres Fev/2016 32
Rubens Zimbres Fev/2016 33
Rubens Zimbres Fev/2016 34
Rubens Zimbres Fev/2016 35
Rubens Zimbres Fev/2016 36
Rubens Zimbres Fev/2016 37
Rubens Zimbres Fev/2016 38
Rubens Zimbres Fev/2016 39
Rubens Zimbres Fev/2016 40
Rubens Zimbres Fev/2016 41
Rubens Zimbres Fev/2016 42
Rubens Zimbres Fev/2016 43
Rubens Zimbres Fev/2016 44
Rubens Zimbres Fev/2016 45
Rubens Zimbres Fev/2016 46
Rubens Zimbres Fev/2016 47
Rubens Zimbres Fev/2016 48
Rubens Zimbres Fev/2016 49
Rubens Zimbres Fev/2016 50
Rubens Zimbres Fev/2016 51
Rubens Zimbres Fev/2016 52
Rubens Zimbres Fev/2016 53
Rubens Zimbres Fev/2016 54
Rubens Zimbres Fev/2016 55
Rubens Zimbres Fev/2016 56
Rubens Zimbres Fev/2016 57
Rubens Zimbres Fev/2016 58
Rubens Zimbres Fev/2016 59
Rubens Zimbres Fev/2016 60
Rubens Zimbres Fev/2016 61
Rubens Zimbres Fev/2016 62
Rubens Zimbres Fev/2016 63
Rubens Zimbres Fev/2016 64
Rubens Zimbres Fev/2016 65
Rubens Zimbres Fev/2016 66
Rubens Zimbres Fev/2016 67
Rubens Zimbres Fev/2016 68
Rubens Zimbres Fev/2016 69
Rubens Zimbres Fev/2016 70
Rubens Zimbres Fev/2016 71
Rubens Zimbres Fev/2016 72
Rubens Zimbres Fev/2016 73
Rubens Zimbres Fev/2016 74
Rubens Zimbres Fev/2016 75
Rubens Zimbres Fev/2016 76
Rubens Zimbres Fev/2016 77
Rubens Zimbres Fev/2016 78
Rubens Zimbres Fev/2016 79
Rubens Zimbres Fev/2016 80
Rubens Zimbres Fev/2016 81
Rubens Zimbres Fev/2016 82
Rubens Zimbres Fev/2016 83
Rubens Zimbres Fev/2016 84
Rubens Zimbres Fev/2016 85
Rubens Zimbres Fev/2016 86
Rubens Zimbres Fev/2016 87
Rubens Zimbres Fev/2016 88
Rubens Zimbres Fev/2016 89
Rubens Zimbres Fev/2016 90
Rubens Zimbres Fev/2016 91
Rubens Zimbres Fev/2016 92
Rubens Zimbres Fev/2016 93
Rubens Zimbres Fev/2016 94
Rubens Zimbres Fev/2016 95
Rubens Zimbres Fev/2016 96
Rubens Zimbres Fev/2016 97
Rubens Zimbres Fev/2016 98
Rubens Zimbres Fev/2016 99
Rubens Zimbres Fev/2016 100
Rubens Zimbres Fev/2016 101
Rubens Zimbres Fev/2016 102
Rubens Zimbres Fev/2016 103
Rubens Zimbres Fev/2016 104
Rubens Zimbres Fev/2016 105
Sales
Profit
Rubens Zimbres Fev/2016 106
Rubens Zimbres Fev/2016 107
Rubens Zimbres Fev/2016 108
Rubens Zimbres Fev/2016 109
Rubens Zimbres Fev/2016 110
Rubens Zimbres Fev/2016 111
Rubens Zimbres Fev/2016 112
Rubens Zimbres Fev/2016 113
Rubens Zimbres Fev/2016 114
Rubens Zimbres Fev/2016 115
Rubens Zimbres Fev/2016 116
Rubens Zimbres Fev/2016 117
Rubens Zimbres Fev/2016 118
Rubens Zimbres Fev/2016 119
Rubens Zimbres Fev/2016 120
Rubens Zimbres Fev/2016 121
Rubens Zimbres Fev/2016 122
Rubens Zimbres Fev/2016 123
Rubens Zimbres Fev/2016 124
Rubens Zimbres Fev/2016 125
Below I present a model that simulates population dynamics, where we can see number of consumers in each class (satisfaction levels, diferente colors) and their evolution over time (weeks). The model uses artificial intelligence with autonomous choices of partners (unsupervised learning) and complex behavior.
Tempo t
Tempo t+23
Consumidores
Estados
Rubens Zimbres Fev/2016 126
Rubens Zimbres Fev/2016 127
Geolocation of sales in the states
Profit/Loss Analysis
Rubens Zimbres Fev/2016 128
Patent of the Social Network code
I also develop social network models using artificial intelligence associated to supervised and unsupervised machine learning, to identify clusters and relevant customers in a community. This way, marketing efforts can be easily directed.
Rubens Zimbres Fev/2016 129
Rubens Zimbres Fev/2016 130
Clusters inside community 1
Community 1 (dark red)
This model was conceived through interactions among clients. We can see the spatial configuration of the social network (social distance), connections, positioning of people of interest (red circles), communities (diferente colors), clusters inside a coommunity, people of interest’s connections and simulate future interactions following a pattern of interaction.
Consumer of interest
Rubens Zimbres Fev/2016 131
Social Network of 5.000 consumers
Communities in different colors
Consumer of interest (red
dot)
Rubens Zimbres Fev/2016 132
Rubens Zimbres Fev/2016 133
Rubens Zimbres Fev/2016 134
Rubens Zimbres Fev/2016 135
Pattern 1 Pattern 2
Line of pattern GOAL
Rubens Zimbres Fev/2016 136
Rubens Zimbres Fev/2016 137
80% of Big data is unstructured. This is the great challenge of businesses in the analysis, given that academic literature lacks a reliable way to code and analyze this type of data. I developed an algorithm that identifies keywords, the reasoning of the text and the subject and I have the research skills to develop a new way to code and analyze unstructured data via qualitative analysis.
Rubens Zimbres Fev/2016 138
Rubens Zimbres Fev/2016 139
Rubens Zimbres Fev/2016 140
Rubens Zimbres Fev/2016 141
Rubens Zimbres Fev/2016 142
Rubens Zimbres Fev/2016 143
Rubens Zimbres Fev/2016 144
Rubens Zimbres Fev/2016 145
Rubens Zimbres Fev/2016 146
RUBENS ZIMBRES
rubens.zimbres@eclipso.de (11) 96750-‐6962 (11) 3022-‐6755
top related