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happy_robot_calls/notebooks/02_eda_call_transactions.ipynb
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fn-registry agent 907dfd07d0 chore: sync from fn-registry agent
2026-04-30 16:03:06 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "a1b2c3d4",
"metadata": {},
"source": [
"# EDA: `psql_dcpublic.call_transactions`\n",
"\n",
"Exploracion de la tabla de transacciones del call center (Happy Robot / Aurgi).\n",
"\n",
"- **Fuente**: BigQuery via Metabase API (database_id=6)\n",
"- **Volumen**: ~24.7M filas (Nov 2022 - Abr 2026)\n",
"- **Regla**: NUNCA `SELECT *` sin LIMIT/WHERE. Siempre agregar en SQL."
]
},
{
"cell_type": "markdown",
"id": "b1c2d3e4",
"metadata": {},
"source": [
"## 1. Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c1d2e3f4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setup OK\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"from dotenv import load_dotenv\n",
"load_dotenv()\n",
"\n",
"import sys\n",
"sys.path.insert(0, os.path.join(os.environ[\"FN_REGISTRY_ROOT\"], \"python\", \"functions\"))\n",
"from metabase.client import MetabaseClient\n",
"\n",
"client = MetabaseClient(os.environ[\"METABASE_URL\"], os.environ[\"METABASE_API_KEY\"])\n",
"\n",
"def query_to_df(sql: str) -> pd.DataFrame:\n",
" \"\"\"Execute SQL against BigQuery via Metabase API, return DataFrame.\"\"\"\n",
" result = client.request('POST', '/api/dataset', json={\n",
" 'database': 6, 'type': 'native', 'native': {'query': sql}\n",
" })\n",
" cols = [c['name'] for c in result['data']['cols']]\n",
" return pd.DataFrame(result['data']['rows'], columns=cols)\n",
"\n",
"TABLE = 'psql_dcpublic.call_transactions'\n",
"print('Setup OK')"
]
},
{
"cell_type": "markdown",
"id": "d1e2f3a4",
"metadata": {},
"source": [
"## 2. Metricas generales"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e1f2a3b4",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>total_rows</th>\n",
" <td>24763236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min_date</th>\n",
" <td>2022-11-14T01:11:00+01:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max_date</th>\n",
" <td>2026-04-16T22:00:00+02:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>n_campaigns</th>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>n_agents</th>\n",
" <td>411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>n_resolutions</th>\n",
" <td>4474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>n_channels</th>\n",
" <td>166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>n_entry_services</th>\n",
" <td>136</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" 0\n",
"total_rows 24763236\n",
"min_date 2022-11-14T01:11:00+01:00\n",
"max_date 2026-04-16T22:00:00+02:00\n",
"n_campaigns 57\n",
"n_agents 411\n",
"n_resolutions 4474\n",
"n_channels 166\n",
"n_entry_services 136"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_overview = query_to_df(f\"\"\"\n",
"SELECT\n",
" COUNT(*) AS total_rows,\n",
" MIN(date_time) AS min_date,\n",
" MAX(date_time) AS max_date,\n",
" COUNT(DISTINCT campaign_name) AS n_campaigns,\n",
" COUNT(DISTINCT agente) AS n_agents,\n",
" COUNT(DISTINCT description) AS n_resolutions,\n",
" COUNT(DISTINCT channel) AS n_channels,\n",
" COUNT(DISTINCT entry_service) AS n_entry_services\n",
"FROM {TABLE}\n",
"\"\"\")\n",
"\n",
"df_overview.T"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f1a2b3c4",
"metadata": {},
"outputs": [
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"data": {
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" <tbody>\n",
" <tr>\n",
" <th>null_campaign</th>\n",
" <td>37578</td>\n",
" <td>campaign</td>\n",
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" <th>null_resolution</th>\n",
" <td>231291</td>\n",
" <td>resolution</td>\n",
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" <tr>\n",
" <th>null_t_convers</th>\n",
" <td>0</td>\n",
" <td>t_convers</td>\n",
" </tr>\n",
" <tr>\n",
" <th>null_agente</th>\n",
" <td>0</td>\n",
" <td>agente</td>\n",
" </tr>\n",
" <tr>\n",
" <th>null_cliente</th>\n",
" <td>54758</td>\n",
" <td>cliente</td>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" <th>null_entry_service</th>\n",
" <td>13899517</td>\n",
" <td>entry_service</td>\n",
" </tr>\n",
" <tr>\n",
" <th>null_call_url</th>\n",
" <td>13899526</td>\n",
" <td>call_url</td>\n",
" </tr>\n",
" <tr>\n",
" <th>null_customerName</th>\n",
" <td>13954273</td>\n",
" <td>customerName</td>\n",
" </tr>\n",
" <tr>\n",
" <th>null_salesforceId</th>\n",
" <td>13954273</td>\n",
" <td>salesforceId</td>\n",
" </tr>\n",
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"text/plain": [
" null_count field\n",
"null_campaign 37578 campaign\n",
"null_resolution 231291 resolution\n",
"null_t_convers 0 t_convers\n",
"null_agente 0 agente\n",
"null_cliente 54758 cliente\n",
"null_channel 0 channel\n",
"null_entry_service 13899517 entry_service\n",
"null_call_url 13899526 call_url\n",
"null_customerName 13954273 customerName\n",
"null_salesforceId 13954273 salesforceId"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Conteo de nulos por campo clave\n",
"df_nulls = query_to_df(f\"\"\"\n",
"SELECT\n",
" COUNTIF(campaign_name IS NULL) AS null_campaign,\n",
" COUNTIF(description IS NULL) AS null_resolution,\n",
" COUNTIF(t_convers IS NULL) AS null_t_convers,\n",
" COUNTIF(agente IS NULL) AS null_agente,\n",
" COUNTIF(cliente IS NULL) AS null_cliente,\n",
" COUNTIF(channel IS NULL) AS null_channel,\n",
" COUNTIF(entry_service IS NULL) AS null_entry_service,\n",
" COUNTIF(call_url IS NULL) AS null_call_url,\n",
" COUNTIF(customerName IS NULL) AS null_customerName,\n",
" COUNTIF(customerSalesforceId IS NULL) AS null_salesforceId\n",
"FROM {TABLE}\n",
"\"\"\")\n",
"\n",
"nulls = df_nulls.T.rename(columns={0: 'null_count'})\n",
"nulls['field'] = nulls.index.str.replace('null_', '')\n",
"nulls"
]
},
{
"cell_type": "markdown",
"id": "a2b3c4d5",
"metadata": {},
"source": [
"## 3. Distribucion de campanas"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b2c3d4e5",
"metadata": {},
"outputs": [
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>campaign_name</th>\n",
" <th>calls</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aurgi Emisión</td>\n",
" <td>11202320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Aurgi Presupuestos Emision</td>\n",
" <td>3559068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>MotorTown Emisión</td>\n",
" <td>2224247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Aurgi</td>\n",
" <td>2196552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Aurgi Neumáticos</td>\n",
" <td>1498076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Motortown Presupuestos Emisión</td>\n",
" <td>1167890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Aurgi Perdidas</td>\n",
" <td>666204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>MotorTown Perdidas</td>\n",
" <td>557750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Mutua Entrante</td>\n",
" <td>511411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>MotorTown</td>\n",
" <td>438702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Aurgi Neumáticos Perdidas</td>\n",
" <td>345644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Entrante</td>\n",
" <td>112211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Mutua Entrante Perdidas</td>\n",
" <td>43290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Mutua Emisión INC Carglass</td>\n",
" <td>30698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>MUTUA NO SHOW</td>\n",
" <td>21499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>MUTUA Campaña de marcaje de lunas</td>\n",
" <td>20711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Aurgi-Llamadas Perdidas</td>\n",
" <td>12959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Mutua Encuestas</td>\n",
" <td>11934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Aurgi-Reparaciones 2025-09</td>\n",
" <td>9669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Aurgi-Reparaciones 2025-06</td>\n",
" <td>7864</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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" campaign_name calls\n",
"0 Aurgi Emisión 11202320\n",
"1 Aurgi Presupuestos Emision 3559068\n",
"2 MotorTown Emisión 2224247\n",
"3 Aurgi 2196552\n",
"4 Aurgi Neumáticos 1498076\n",
"5 Motortown Presupuestos Emisión 1167890\n",
"6 Aurgi Perdidas 666204\n",
"7 MotorTown Perdidas 557750\n",
"8 Mutua Entrante 511411\n",
"9 MotorTown 438702\n",
"10 Aurgi Neumáticos Perdidas 345644\n",
"11 Entrante 112211\n",
"12 Mutua Entrante Perdidas 43290\n",
"13 Mutua Emisión INC Carglass 30698\n",
"14 MUTUA NO SHOW 21499\n",
"15 MUTUA Campaña de marcaje de lunas 20711\n",
"16 Aurgi-Llamadas Perdidas 12959\n",
"17 Mutua Encuestas 11934\n",
"18 Aurgi-Reparaciones 2025-09 9669\n",
"19 Aurgi-Reparaciones 2025-06 7864"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
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"df_campaigns = query_to_df(f\"\"\"\n",
"SELECT\n",
" campaign_name,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"WHERE campaign_name IS NOT NULL\n",
"GROUP BY campaign_name\n",
"ORDER BY calls DESC\n",
"LIMIT 20\n",
"\"\"\")\n",
"\n",
"df_campaigns['calls'] = df_campaigns['calls'].astype(int)\n",
"df_campaigns"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c2d3e4f5",
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"fig = px.bar(\n",
" df_campaigns,\n",
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" orientation='h',\n",
" title='Top 20 campanas por volumen de llamadas',\n",
" labels={'calls': 'Llamadas', 'campaign_name': 'Campana'},\n",
" text='calls'\n",
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"cell_type": "markdown",
"id": "d2e3f4a5",
"metadata": {},
"source": [
"## 4. Resoluciones"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e2f3a4b5",
"metadata": {},
"outputs": [
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" <tr>\n",
" <th>14</th>\n",
" <td>Error CTI</td>\n",
" <td>55814</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" resolution calls\n",
"0 Comunican 9133549\n",
"1 Contestador Automático 3836254\n",
"2 Otras causas 2945080\n",
"3 La llam.no puede alcanzar dest 1821210\n",
"4 No Contestan 1810221\n",
"5 CERRAR LEAD 1660102\n",
"6 Abandonada 1241413\n",
"7 Llamada No Atendida 508446\n",
"8 ACEPTADO 370223\n",
"9 No hay Ring Back 178985\n",
"10 Fuera Intervalo Rellamada 106189\n",
"11 ACEPTADA 61895\n",
"12 LLAM BASURA GESTIÓN CITA 60436\n",
"13 OTROS 59347\n",
"14 Error CTI 55814"
]
},
"execution_count": 6,
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"SELECT\n",
" description AS resolution,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"WHERE description IS NOT NULL\n",
"GROUP BY description\n",
"ORDER BY calls DESC\n",
"LIMIT 15\n",
"\"\"\")\n",
"\n",
"df_resolutions['calls'] = df_resolutions['calls'].astype(int)\n",
"df_resolutions"
]
},
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"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "Resoluciones por campana (top 8 x top 8)"
},
"width": 900,
"xaxis": {
"anchor": "y",
"domain": [
0,
1
],
"title": {
"text": "Resolucion"
}
},
"yaxis": {
"anchor": "x",
"autorange": "reversed",
"domain": [
0,
1
],
"title": {
"text": "Campana"
}
}
}
}
},
"metadata": {},
"output_type": "display_data",
"transient": {}
}
],
"source": [
"# Resoluciones por campana (top 8 campanas x top 8 resoluciones)\n",
"df_camp_res = query_to_df(f\"\"\"\n",
"WITH top_campaigns AS (\n",
" SELECT campaign_name\n",
" FROM {TABLE}\n",
" WHERE campaign_name IS NOT NULL\n",
" GROUP BY campaign_name\n",
" ORDER BY COUNT(*) DESC\n",
" LIMIT 8\n",
"),\n",
"top_resolutions AS (\n",
" SELECT description\n",
" FROM {TABLE}\n",
" WHERE description IS NOT NULL\n",
" GROUP BY description\n",
" ORDER BY COUNT(*) DESC\n",
" LIMIT 8\n",
")\n",
"SELECT\n",
" t.campaign_name,\n",
" t.description AS resolution,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE} t\n",
"JOIN top_campaigns tc ON t.campaign_name = tc.campaign_name\n",
"JOIN top_resolutions tr ON t.description = tr.description\n",
"GROUP BY t.campaign_name, t.description\n",
"ORDER BY t.campaign_name, calls DESC\n",
"\"\"\")\n",
"\n",
"df_camp_res['calls'] = df_camp_res['calls'].astype(int)\n",
"\n",
"pivot = df_camp_res.pivot(index='campaign_name', columns='resolution', values='calls').fillna(0)\n",
"\n",
"fig = px.imshow(\n",
" pivot,\n",
" title='Resoluciones por campana (top 8 x top 8)',\n",
" labels=dict(x='Resolucion', y='Campana', color='Llamadas'),\n",
" aspect='auto',\n",
" color_continuous_scale='Blues'\n",
")\n",
"fig.update_layout(height=500, width=900)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "b3c4d5e6",
"metadata": {},
"source": [
"## 5. Evolucion temporal"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3d4e5f6",
"metadata": {},
"outputs": [],
"source": [
"# Llamadas por mes\n",
"df_monthly = query_to_df(f\"\"\"\n",
"SELECT\n",
" FORMAT_TIMESTAMP('%Y-%m', date_time) AS month,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"WHERE date_time IS NOT NULL\n",
"GROUP BY month\n",
"ORDER BY month\n",
"\"\"\")\n",
"\n",
"df_monthly['calls'] = df_monthly['calls'].astype(int)\n",
"\n",
"fig = px.line(\n",
" df_monthly, x='month', y='calls',\n",
" title='Llamadas por mes',\n",
" labels={'calls': 'Llamadas', 'month': 'Mes'},\n",
" markers=True\n",
")\n",
"fig.update_layout(xaxis_tickangle=-45, height=400)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3e4f5a6",
"metadata": {},
"outputs": [],
"source": [
"# Llamadas por dia de la semana\n",
"df_dow = query_to_df(f\"\"\"\n",
"SELECT\n",
" EXTRACT(DAYOFWEEK FROM date_time) AS dow_num,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"WHERE date_time IS NOT NULL\n",
"GROUP BY dow_num\n",
"ORDER BY dow_num\n",
"\"\"\")\n",
"\n",
"df_dow['calls'] = df_dow['calls'].astype(int)\n",
"df_dow['dow_num'] = df_dow['dow_num'].astype(int)\n",
"\n",
"dow_labels = {1: 'Dom', 2: 'Lun', 3: 'Mar', 4: 'Mie', 5: 'Jue', 6: 'Vie', 7: 'Sab'}\n",
"df_dow['day'] = df_dow['dow_num'].map(dow_labels)\n",
"\n",
"fig = px.bar(\n",
" df_dow, x='day', y='calls',\n",
" title='Llamadas por dia de la semana',\n",
" labels={'calls': 'Llamadas', 'day': 'Dia'},\n",
" text='calls'\n",
")\n",
"fig.update_traces(texttemplate='%{text:,.0f}', textposition='outside')\n",
"fig.update_layout(height=400)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e3f4a5b6",
"metadata": {},
"outputs": [],
"source": [
"# Llamadas por hora del dia\n",
"df_hour = query_to_df(f\"\"\"\n",
"SELECT\n",
" EXTRACT(HOUR FROM date_time) AS hour,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"WHERE date_time IS NOT NULL\n",
"GROUP BY hour\n",
"ORDER BY hour\n",
"\"\"\")\n",
"\n",
"df_hour['calls'] = df_hour['calls'].astype(int)\n",
"df_hour['hour'] = df_hour['hour'].astype(int)\n",
"\n",
"fig = px.bar(\n",
" df_hour, x='hour', y='calls',\n",
" title='Llamadas por hora del dia',\n",
" labels={'calls': 'Llamadas', 'hour': 'Hora'},\n",
" text='calls'\n",
")\n",
"fig.update_traces(texttemplate='%{text:,.0f}', textposition='outside')\n",
"fig.update_layout(height=400, xaxis=dict(dtick=1))\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3a4b5c6",
"metadata": {},
"outputs": [],
"source": [
"# Heatmap: hora x dia de semana\n",
"df_hour_dow = query_to_df(f\"\"\"\n",
"SELECT\n",
" EXTRACT(DAYOFWEEK FROM date_time) AS dow_num,\n",
" EXTRACT(HOUR FROM date_time) AS hour,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"WHERE date_time IS NOT NULL\n",
"GROUP BY dow_num, hour\n",
"ORDER BY dow_num, hour\n",
"\"\"\")\n",
"\n",
"df_hour_dow['calls'] = df_hour_dow['calls'].astype(int)\n",
"df_hour_dow['dow_num'] = df_hour_dow['dow_num'].astype(int)\n",
"df_hour_dow['hour'] = df_hour_dow['hour'].astype(int)\n",
"df_hour_dow['day'] = df_hour_dow['dow_num'].map(dow_labels)\n",
"\n",
"pivot_hd = df_hour_dow.pivot(index='day', columns='hour', values='calls').fillna(0)\n",
"# Reorder days\n",
"day_order = ['Lun', 'Mar', 'Mie', 'Jue', 'Vie', 'Sab', 'Dom']\n",
"pivot_hd = pivot_hd.reindex([d for d in day_order if d in pivot_hd.index])\n",
"\n",
"fig = px.imshow(\n",
" pivot_hd,\n",
" title='Heatmap: llamadas por hora y dia de semana',\n",
" labels=dict(x='Hora', y='Dia', color='Llamadas'),\n",
" aspect='auto',\n",
" color_continuous_scale='YlOrRd'\n",
")\n",
"fig.update_layout(height=350, width=900)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "a4b5c6d7",
"metadata": {},
"source": [
"## 6. Duracion de conversacion (`t_convers`)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b4c5d6e7",
"metadata": {},
"outputs": [],
"source": [
"# Distribucion general: con vs sin conversacion\n",
"df_convers_split = query_to_df(f\"\"\"\n",
"SELECT\n",
" CASE\n",
" WHEN t_convers IS NULL THEN 'NULL'\n",
" WHEN t_convers = '00:00:00' THEN 'Sin conversacion'\n",
" ELSE 'Con conversacion'\n",
" END AS tipo,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"GROUP BY tipo\n",
"ORDER BY calls DESC\n",
"\"\"\")\n",
"\n",
"df_convers_split['calls'] = df_convers_split['calls'].astype(int)\n",
"df_convers_split"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c4d5e6f7",
"metadata": {},
"outputs": [],
"source": [
"fig = px.pie(\n",
" df_convers_split, values='calls', names='tipo',\n",
" title='Proporcion de llamadas con/sin conversacion',\n",
" hole=0.4\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4e5f6a7",
"metadata": {},
"outputs": [],
"source": [
"# Distribucion de duraciones para llamadas CON conversacion\n",
"df_duration_buckets = query_to_df(f\"\"\"\n",
"SELECT\n",
" CASE\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 30 THEN '00-30s'\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 60 THEN '31-60s'\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 120 THEN '1-2min'\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 300 THEN '2-5min'\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 600 THEN '5-10min'\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 1800 THEN '10-30min'\n",
" ELSE '30min+'\n",
" END AS duration_bucket,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"WHERE t_convers IS NOT NULL\n",
" AND t_convers != '00:00:00'\n",
"GROUP BY duration_bucket\n",
"ORDER BY\n",
" CASE duration_bucket\n",
" WHEN '00-30s' THEN 1\n",
" WHEN '31-60s' THEN 2\n",
" WHEN '1-2min' THEN 3\n",
" WHEN '2-5min' THEN 4\n",
" WHEN '5-10min' THEN 5\n",
" WHEN '10-30min' THEN 6\n",
" WHEN '30min+' THEN 7\n",
" END\n",
"\"\"\")\n",
"\n",
"df_duration_buckets['calls'] = df_duration_buckets['calls'].astype(int)\n",
"df_duration_buckets"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e4f5a6b7",
"metadata": {},
"outputs": [],
"source": [
"fig = px.bar(\n",
" df_duration_buckets, x='duration_bucket', y='calls',\n",
" title='Distribucion de duracion (llamadas con conversacion)',\n",
" labels={'calls': 'Llamadas', 'duration_bucket': 'Duracion'},\n",
" text='calls'\n",
")\n",
"fig.update_traces(texttemplate='%{text:,.0f}', textposition='outside')\n",
"fig.update_layout(height=400)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4a5b6c7",
"metadata": {},
"outputs": [],
"source": [
"# Duracion media por campana (top 10 campanas)\n",
"df_dur_camp = query_to_df(f\"\"\"\n",
"WITH top_camps AS (\n",
" SELECT campaign_name\n",
" FROM {TABLE}\n",
" WHERE campaign_name IS NOT NULL\n",
" GROUP BY campaign_name\n",
" ORDER BY COUNT(*) DESC\n",
" LIMIT 10\n",
")\n",
"SELECT\n",
" t.campaign_name,\n",
" COUNT(*) AS total_calls,\n",
" COUNTIF(t_convers != '00:00:00' AND t_convers IS NOT NULL) AS connected_calls,\n",
" ROUND(AVG(\n",
" CASE WHEN t_convers != '00:00:00' AND t_convers IS NOT NULL\n",
" THEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND)\n",
" END\n",
" ), 1) AS avg_duration_secs\n",
"FROM {TABLE} t\n",
"JOIN top_camps tc ON t.campaign_name = tc.campaign_name\n",
"GROUP BY t.campaign_name\n",
"ORDER BY total_calls DESC\n",
"\"\"\")\n",
"\n",
"for col in ['total_calls', 'connected_calls']:\n",
" df_dur_camp[col] = df_dur_camp[col].astype(int)\n",
"df_dur_camp['avg_duration_secs'] = df_dur_camp['avg_duration_secs'].astype(float)\n",
"df_dur_camp['connect_rate'] = (df_dur_camp['connected_calls'] / df_dur_camp['total_calls'] * 100).round(1)\n",
"df_dur_camp"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a5b6c7d8",
"metadata": {},
"outputs": [],
"source": [
"fig = px.bar(\n",
" df_dur_camp.sort_values('avg_duration_secs', ascending=True),\n",
" x='avg_duration_secs', y='campaign_name',\n",
" orientation='h',\n",
" title='Duracion media de conversacion por campana (seg)',\n",
" labels={'avg_duration_secs': 'Segundos (media)', 'campaign_name': 'Campana'},\n",
" text='avg_duration_secs'\n",
")\n",
"fig.update_traces(texttemplate='%{text:.0f}s', textposition='outside')\n",
"fig.update_layout(height=500)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "b5c6d7e8",
"metadata": {},
"source": [
"## 7. Agentes"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c5d6e7f8",
"metadata": {},
"outputs": [],
"source": [
"# Top 20 agentes por volumen\n",
"df_agents = query_to_df(f\"\"\"\n",
"SELECT\n",
" agente,\n",
" COUNT(*) AS calls,\n",
" COUNTIF(t_convers != '00:00:00' AND t_convers IS NOT NULL) AS connected,\n",
" ROUND(AVG(\n",
" CASE WHEN t_convers != '00:00:00' AND t_convers IS NOT NULL\n",
" THEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND)\n",
" END\n",
" ), 1) AS avg_dur_secs\n",
"FROM {TABLE}\n",
"WHERE agente IS NOT NULL AND agente != ''\n",
"GROUP BY agente\n",
"ORDER BY calls DESC\n",
"LIMIT 20\n",
"\"\"\")\n",
"\n",
"df_agents['calls'] = df_agents['calls'].astype(int)\n",
"df_agents['connected'] = df_agents['connected'].astype(int)\n",
"df_agents['avg_dur_secs'] = df_agents['avg_dur_secs'].astype(float)\n",
"df_agents['connect_rate'] = (df_agents['connected'] / df_agents['calls'] * 100).round(1)\n",
"df_agents"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5e6f7a8",
"metadata": {},
"outputs": [],
"source": [
"fig = px.bar(\n",
" df_agents,\n",
" x='calls', y='agente',\n",
" orientation='h',\n",
" title='Top 20 agentes por volumen de llamadas',\n",
" labels={'calls': 'Llamadas', 'agente': 'Agente'},\n",
" text='calls'\n",
")\n",
"fig.update_layout(yaxis={'categoryorder': 'total ascending'}, height=600)\n",
"fig.update_traces(texttemplate='%{text:,.0f}', textposition='outside')\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e5f6a7b8",
"metadata": {},
"outputs": [],
"source": [
"# Distribucion de agentes por campana (top 5 agentes x top 5 campanas)\n",
"df_agent_camp = query_to_df(f\"\"\"\n",
"WITH top_agents AS (\n",
" SELECT agente\n",
" FROM {TABLE}\n",
" WHERE agente IS NOT NULL AND agente != ''\n",
" GROUP BY agente\n",
" ORDER BY COUNT(*) DESC\n",
" LIMIT 10\n",
"),\n",
"top_camps AS (\n",
" SELECT campaign_name\n",
" FROM {TABLE}\n",
" WHERE campaign_name IS NOT NULL\n",
" GROUP BY campaign_name\n",
" ORDER BY COUNT(*) DESC\n",
" LIMIT 8\n",
")\n",
"SELECT\n",
" t.agente,\n",
" t.campaign_name,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE} t\n",
"JOIN top_agents ta ON t.agente = ta.agente\n",
"JOIN top_camps tc ON t.campaign_name = tc.campaign_name\n",
"GROUP BY t.agente, t.campaign_name\n",
"ORDER BY t.agente, calls DESC\n",
"\"\"\")\n",
"\n",
"df_agent_camp['calls'] = df_agent_camp['calls'].astype(int)\n",
"\n",
"fig = px.bar(\n",
" df_agent_camp,\n",
" x='agente', y='calls', color='campaign_name',\n",
" title='Distribucion de llamadas: top 10 agentes x top 8 campanas',\n",
" labels={'calls': 'Llamadas', 'agente': 'Agente', 'campaign_name': 'Campana'},\n",
" barmode='stack'\n",
")\n",
"fig.update_layout(height=500, xaxis_tickangle=-45)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "f5a6b7c8",
"metadata": {},
"source": [
"## 8. Foco en Mutua\n",
"\n",
"Las campanas de Mutua son las que se relacionan con el proyecto Happy Robot.\n",
"Filtramos a campanas cuyo nombre contenga \"Mutua\"."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6b7c8d9",
"metadata": {},
"outputs": [],
"source": [
"# Campanas que contienen 'Mutua'\n",
"df_mutua_camps = query_to_df(f\"\"\"\n",
"SELECT\n",
" campaign_name,\n",
" COUNT(*) AS calls,\n",
" MIN(date_time) AS first_call,\n",
" MAX(date_time) AS last_call\n",
"FROM {TABLE}\n",
"WHERE campaign_name LIKE '%Mutua%'\n",
"GROUP BY campaign_name\n",
"ORDER BY calls DESC\n",
"\"\"\")\n",
"\n",
"df_mutua_camps['calls'] = df_mutua_camps['calls'].astype(int)\n",
"df_mutua_camps"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b6c7d8e9",
"metadata": {},
"outputs": [],
"source": [
"# Metricas generales Mutua\n",
"df_mutua_overview = query_to_df(f\"\"\"\n",
"SELECT\n",
" COUNT(*) AS total_calls,\n",
" COUNT(DISTINCT agente) AS n_agents,\n",
" COUNT(DISTINCT description) AS n_resolutions,\n",
" COUNTIF(t_convers != '00:00:00' AND t_convers IS NOT NULL) AS connected,\n",
" ROUND(AVG(\n",
" CASE WHEN t_convers != '00:00:00' AND t_convers IS NOT NULL\n",
" THEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND)\n",
" END\n",
" ), 1) AS avg_dur_secs,\n",
" MIN(date_time) AS first_call,\n",
" MAX(date_time) AS last_call\n",
"FROM {TABLE}\n",
"WHERE campaign_name LIKE '%Mutua%'\n",
"\"\"\")\n",
"\n",
"df_mutua_overview.T"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6d7e8f9",
"metadata": {},
"outputs": [],
"source": [
"# Resoluciones en campanas Mutua\n",
"df_mutua_res = query_to_df(f\"\"\"\n",
"SELECT\n",
" description AS resolution,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"WHERE campaign_name LIKE '%Mutua%'\n",
" AND description IS NOT NULL\n",
"GROUP BY description\n",
"ORDER BY calls DESC\n",
"LIMIT 15\n",
"\"\"\")\n",
"\n",
"df_mutua_res['calls'] = df_mutua_res['calls'].astype(int)\n",
"\n",
"fig = px.bar(\n",
" df_mutua_res,\n",
" x='calls', y='resolution',\n",
" orientation='h',\n",
" title='Resoluciones en campanas Mutua',\n",
" labels={'calls': 'Llamadas', 'resolution': 'Resolucion'},\n",
" text='calls',\n",
" color_discrete_sequence=['#e45756']\n",
")\n",
"fig.update_layout(yaxis={'categoryorder': 'total ascending'}, height=500)\n",
"fig.update_traces(texttemplate='%{text:,.0f}', textposition='outside')\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d6e7f8a9",
"metadata": {},
"outputs": [],
"source": [
"# Evolucion mensual Mutua\n",
"df_mutua_monthly = query_to_df(f\"\"\"\n",
"SELECT\n",
" FORMAT_TIMESTAMP('%Y-%m', date_time) AS month,\n",
" campaign_name,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"WHERE campaign_name LIKE '%Mutua%'\n",
" AND date_time IS NOT NULL\n",
"GROUP BY month, campaign_name\n",
"ORDER BY month\n",
"\"\"\")\n",
"\n",
"df_mutua_monthly['calls'] = df_mutua_monthly['calls'].astype(int)\n",
"\n",
"fig = px.line(\n",
" df_mutua_monthly, x='month', y='calls', color='campaign_name',\n",
" title='Evolucion mensual - Campanas Mutua',\n",
" labels={'calls': 'Llamadas', 'month': 'Mes', 'campaign_name': 'Campana'},\n",
" markers=True\n",
")\n",
"fig.update_layout(xaxis_tickangle=-45, height=450)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e6f7a8b9",
"metadata": {},
"outputs": [],
"source": [
"# Mutua: llamadas por hora del dia\n",
"df_mutua_hour = query_to_df(f\"\"\"\n",
"SELECT\n",
" EXTRACT(HOUR FROM date_time) AS hour,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"WHERE campaign_name LIKE '%Mutua%'\n",
" AND date_time IS NOT NULL\n",
"GROUP BY hour\n",
"ORDER BY hour\n",
"\"\"\")\n",
"\n",
"df_mutua_hour['calls'] = df_mutua_hour['calls'].astype(int)\n",
"df_mutua_hour['hour'] = df_mutua_hour['hour'].astype(int)\n",
"\n",
"fig = px.bar(\n",
" df_mutua_hour, x='hour', y='calls',\n",
" title='Mutua: llamadas por hora del dia',\n",
" labels={'calls': 'Llamadas', 'hour': 'Hora'},\n",
" text='calls',\n",
" color_discrete_sequence=['#e45756']\n",
")\n",
"fig.update_traces(texttemplate='%{text:,.0f}', textposition='outside')\n",
"fig.update_layout(height=400, xaxis=dict(dtick=1))\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6a7b8c9",
"metadata": {},
"outputs": [],
"source": [
"# Mutua: duracion de conversaciones\n",
"df_mutua_dur = query_to_df(f\"\"\"\n",
"SELECT\n",
" CASE\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 30 THEN '00-30s'\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 60 THEN '31-60s'\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 120 THEN '1-2min'\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 300 THEN '2-5min'\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 600 THEN '5-10min'\n",
" WHEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND) <= 1800 THEN '10-30min'\n",
" ELSE '30min+'\n",
" END AS duration_bucket,\n",
" COUNT(*) AS calls\n",
"FROM {TABLE}\n",
"WHERE campaign_name LIKE '%Mutua%'\n",
" AND t_convers IS NOT NULL\n",
" AND t_convers != '00:00:00'\n",
"GROUP BY duration_bucket\n",
"ORDER BY\n",
" CASE duration_bucket\n",
" WHEN '00-30s' THEN 1\n",
" WHEN '31-60s' THEN 2\n",
" WHEN '1-2min' THEN 3\n",
" WHEN '2-5min' THEN 4\n",
" WHEN '5-10min' THEN 5\n",
" WHEN '10-30min' THEN 6\n",
" WHEN '30min+' THEN 7\n",
" END\n",
"\"\"\")\n",
"\n",
"df_mutua_dur['calls'] = df_mutua_dur['calls'].astype(int)\n",
"\n",
"fig = px.bar(\n",
" df_mutua_dur, x='duration_bucket', y='calls',\n",
" title='Mutua: distribucion de duracion de conversaciones',\n",
" labels={'calls': 'Llamadas', 'duration_bucket': 'Duracion'},\n",
" text='calls',\n",
" color_discrete_sequence=['#e45756']\n",
")\n",
"fig.update_traces(texttemplate='%{text:,.0f}', textposition='outside')\n",
"fig.update_layout(height=400)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7b8c9d0",
"metadata": {},
"outputs": [],
"source": [
"# Mutua: top agentes\n",
"df_mutua_agents = query_to_df(f\"\"\"\n",
"SELECT\n",
" agente,\n",
" COUNT(*) AS calls,\n",
" COUNTIF(t_convers != '00:00:00' AND t_convers IS NOT NULL) AS connected,\n",
" ROUND(AVG(\n",
" CASE WHEN t_convers != '00:00:00' AND t_convers IS NOT NULL\n",
" THEN TIME_DIFF(CAST(t_convers AS TIME), TIME '00:00:00', SECOND)\n",
" END\n",
" ), 1) AS avg_dur_secs\n",
"FROM {TABLE}\n",
"WHERE campaign_name LIKE '%Mutua%'\n",
" AND agente IS NOT NULL AND agente != ''\n",
"GROUP BY agente\n",
"ORDER BY calls DESC\n",
"LIMIT 15\n",
"\"\"\")\n",
"\n",
"df_mutua_agents['calls'] = df_mutua_agents['calls'].astype(int)\n",
"df_mutua_agents['connected'] = df_mutua_agents['connected'].astype(int)\n",
"df_mutua_agents['avg_dur_secs'] = df_mutua_agents['avg_dur_secs'].astype(float)\n",
"df_mutua_agents['connect_rate'] = (df_mutua_agents['connected'] / df_mutua_agents['calls'] * 100).round(1)\n",
"\n",
"fig = px.bar(\n",
" df_mutua_agents,\n",
" x='calls', y='agente',\n",
" orientation='h',\n",
" title='Mutua: top 15 agentes por volumen',\n",
" labels={'calls': 'Llamadas', 'agente': 'Agente'},\n",
" text='calls',\n",
" color_discrete_sequence=['#e45756']\n",
")\n",
"fig.update_layout(yaxis={'categoryorder': 'total ascending'}, height=500)\n",
"fig.update_traces(texttemplate='%{text:,.0f}', textposition='outside')\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7c8d9e0",
"metadata": {},
"outputs": [],
"source": [
"# Mutua: tasa de conexion por campana Mutua\n",
"df_mutua_connect = query_to_df(f\"\"\"\n",
"SELECT\n",
" campaign_name,\n",
" COUNT(*) AS total,\n",
" COUNTIF(t_convers != '00:00:00' AND t_convers IS NOT NULL) AS connected,\n",
" ROUND(COUNTIF(t_convers != '00:00:00' AND t_convers IS NOT NULL) * 100.0 / COUNT(*), 1) AS connect_pct\n",
"FROM {TABLE}\n",
"WHERE campaign_name LIKE '%Mutua%'\n",
"GROUP BY campaign_name\n",
"ORDER BY total DESC\n",
"\"\"\")\n",
"\n",
"df_mutua_connect['total'] = df_mutua_connect['total'].astype(int)\n",
"df_mutua_connect['connected'] = df_mutua_connect['connected'].astype(int)\n",
"df_mutua_connect['connect_pct'] = df_mutua_connect['connect_pct'].astype(float)\n",
"df_mutua_connect"
]
},
{
"cell_type": "markdown",
"id": "c7d8e9f0",
"metadata": {},
"source": [
"## 9. Resumen\n",
"\n",
"### Hallazgos clave\n",
"\n",
"**Volumen general:**\n",
"- ~24.7M transacciones entre Nov 2022 y Abr 2026\n",
"- Dominada por campanas de emision (Aurgi Emision ~11M, Aurgi Presupuestos ~3.5M, MotorTown Emision ~2.2M)\n",
"\n",
"**Resoluciones:**\n",
"- Las resoluciones mas frecuentes revelan el mix de contactabilidad vs resultados reales\n",
"- \"Comunican\" y \"Contestador Automatico\" representan una gran proporcion (intentos sin exito)\n",
"\n",
"**Patrones temporales:**\n",
"- Actividad concentrada en dias laborables (Lun-Vie)\n",
"- Picos de actividad en horario de manana (9-14h)\n",
"- Estacionalidad mensual visible\n",
"\n",
"**Duracion de conversaciones:**\n",
"- Gran proporcion de llamadas sin conversacion (t_convers = 00:00:00)\n",
"- Las conversaciones efectivas se concentran entre 1-5 minutos\n",
"- Variacion significativa de duracion entre campanas\n",
"\n",
"**Foco Mutua (Happy Robot):**\n",
"- Mutua Entrante es la campana principal (~511K llamadas)\n",
"- Patrones de resolucion y duracion especificos a analizar en profundidad\n",
"- Base para evaluar el impacto de Happy Robot en automatizacion de llamadas\n",
"\n",
"### Proximos pasos\n",
"\n",
"1. Analisis detallado de tasas de abandono y contactabilidad por campana\n",
"2. Segmentacion de Mutua por tipo de resolucion y agente\n",
"3. Baseline de metricas pre-Happy Robot para medir impacto\n",
"4. Cruzar con datos de `customerSalesforceId` y `customerAnjanaId` para trazabilidad"
]
}
],
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
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