objectiv-basic-product-analytics

from datetime import datetime from modelhub import ModelHub from bach import display_sql_as_markdown

# instantiate the model hub and set the default time aggregation to daily modelhub = ModelHub(time_aggregation='%Y-%m-%d')

# add 'application', 'feature_nice_name' and 'root_location' as columns, so that we can use it for grouping etc later df['application'] = df.global_contexts.gc.application df['feature_nice_name'] = df.location_stack.ls.nice_name df['root_location'] = df.location_stack.ls.get_from_context_with_type_series(type='RootLocationContext', key='id')

# have a look at the data df.sort_values(['session_id', 'session_hit_number'], ascending=False).head()

dayobject

momentdatetime64[ns]

b1499fdf-8efa-4a06-a50e-502e9d864742

2022-07-26

2022-07-26T23:46:23.356000

828c22a9-0336-4848-bfa7-91d6e65e2a92

2022-07-26

2022-07-26T23:42:54.397000

c29bbd11-5eb7-4ed2-b46e-1d5f519d7e90

2022-07-26

2022-07-26T23:24:11.473000

b638d6f4-2d2c-44e6-a055-c80f1635a7a4

2022-07-26

2022-07-26T23:02:58.415000

70431e46-d1be-4f2b-9251-ff11efba8612

2022-07-26

2022-07-26T22:21:49.222000

# model hub: unique users, monthly monthly_users = modelhub.aggregate.unique_users(df, groupby=modelhub.time_agg(df, '%Y-%m')) monthly_users.sort_index(ascending=False).head()

# model hub: unique users, daily daily_users = modelhub.aggregate.unique_users(df) daily_users.sort_index(ascending=False).head(10)

# model hub: unique users, per main product section users_root = modelhub.aggregate.unique_users(df, groupby=['application', 'root_location']) users_root.sort_index(ascending=False).head(10)

retention_matrix = modelhub.aggregate.retention_matrix(df, time_period='monthly', percentage=True, display=True) retention_matrix.head()

_0float64

_1float64

2022-01

100

20.610687022900763

2022-02

100

4.21792618629174

2022-03

100

3.2653061224489797

2022-04

100

6.343283582089552

2022-05

100

3.4756097560975614

# calculate the first cohort cohorts = df[['user_id', 'moment']].groupby('user_id')['moment'].min().reset_index() cohorts = cohorts.rename(columns={'moment': 'first_cohort'}) # add first cohort of the users to our DataFrame df_with_cohorts = df.merge(cohorts, on='user_id')

# filter data where users belong to # 0 cohort cohort0_filter = (df_with_cohorts['first_cohort'] > datetime(2022, 2, 1)) & (df_with_cohorts['first_cohort'] < datetime(2022, 3, 1)) df_with_cohorts[cohort0_filter]['event_type'].value_counts().head()

# filter data where users belong to # 1 cohort (the problematic one) cohort1_filter = (df_with_cohorts['first_cohort'] > datetime(2022, 3, 1)) & (df_with_cohorts['first_cohort'] < datetime(2022, 4, 1)) df_with_cohorts[cohort1_filter]['event_type'].value_counts().head()

# model hub: duration, monthly average duration_monthly = modelhub.aggregate.session_duration(df, groupby=modelhub.time_agg(df, '%Y-%m')) duration_monthly.sort_index(ascending=False).head()

# model hub: duration, daily average duration_daily = modelhub.aggregate.session_duration(df) duration_daily.sort_index(ascending=False).head()

# model hub: duration, monthly average per root location duration_root_month = modelhub.aggregate.session_duration(df, groupby=['application', 'root_location', modelhub.time_agg(df, '%Y-%m')]).sort_index() duration_root_month.head(10)

# how is this time spent distributed? session_duration = modelhub.aggregate.session_duration(df, groupby='session_id', exclude_bounces=False) # Materialization is needed because the expression of the created series contains aggregated data, and it is not allowed to aggregate that. session_duration.materialize().quantile(q=[0.25, 0.50, 0.75]).head()

# by default we select only user actions top_product_features = modelhub.aggregate.top_product_features(df) top_product_features.head()

user_id_nuniqueint64

('objectiv-website', 'Pressable: after located at Root Location: home => Content: capture-data => Content: data-capture-workflow-before-after', 'PressEvent')

527

('objectiv-website', 'Pressable: after located at Root Location: home => Content: modeling => Content: modeling-workflow-before-after', 'PressEvent')

321

('objectiv-website', 'Link: about-us located at Root Location: home => Navigation: navbar-top', 'PressEvent')

318

('objectiv-website', 'Pressable: hamburger located at Root Location: home => Navigation: navbar-top', 'PressEvent')

296

('objectiv-website', 'Pressable: before located at Root Location: home => Content: capture-data => Content: data-capture-workflow-before-after', 'PressEvent')

292

# select only user actions, so stack_event_types must contain 'InteractiveEvent' interactive_events = df[df.stack_event_types.json.array_contains('InteractiveEvent')] top_interactions = modelhub.agg.unique_users(interactive_events, groupby=['application','root_location','feature_nice_name', 'event_type']) top_interactions = top_interactions.reset_index()

home_users = top_interactions[(top_interactions.application == 'objectiv-website') & (top_interactions.root_location == 'home')] home_users.sort_values('unique_users', ascending=False).head()

applicationobject

root_locationobject

objectiv-website

home

objectiv-website

home

objectiv-website

home

objectiv-website

home

objectiv-website

home

docs_users = top_interactions[top_interactions.application == 'objectiv-docs'] docs_users.sort_values('unique_users', ascending=False).head()

applicationobject

root_locationobject

objectiv-docs

home

objectiv-docs

home

objectiv-docs

home

objectiv-docs

tracking

objectiv-docs

modeling

df_acquisition = df.copy() # extract referrer and marketing contexts from the global contexts df_acquisition['referrer'] = df_acquisition.global_contexts.gc.get_from_context_with_type_series(type='HttpContext', key='referrer') df_acquisition['utm_source'] = df_acquisition.global_contexts.gc.get_from_context_with_type_series(type='MarketingContext', key='source') df_acquisition['utm_medium'] = df_acquisition.global_contexts.gc.get_from_context_with_type_series(type='MarketingContext', key='medium') df_acquisition['utm_campaign'] = df_acquisition.global_contexts.gc.get_from_context_with_type_series(type='MarketingContext', key='campaign') df_acquisition['utm_content'] = df_acquisition.global_contexts.gc.get_from_context_with_type_series(type='MarketingContext', key='content') df_acquisition['utm_term'] = df_acquisition.global_contexts.gc.get_from_context_with_type_series(type='MarketingContext', key='term')

# users by referrer modelhub.agg.unique_users(df_acquisition, groupby='referrer').sort_values(ascending=False).head()

# users by marketing campaign campaign_users = modelhub.agg.unique_users(df_acquisition, groupby=['utm_source', 'utm_medium', 'utm_campaign', 'utm_content', 'utm_term']) campaign_users = campaign_users.reset_index().dropna(axis=0, how='any', subset='utm_source') campaign_users.sort_values('utm_source', ascending=True).head()

utm_sourceobject

utm_mediumobject

google

cpc

google

cpc

google

cpc

google

cpc

google

cpc

# users by feature per campaign source & term users_feature_campaign = modelhub.agg.unique_users(df_acquisition[ df_acquisition.stack_event_types.json.array_contains('InteractiveEvent')], groupby=['utm_source', 'utm_term', 'feature_nice_name', 'event_type']) users_feature_campaign = users_feature_campaign.reset_index().dropna(axis=0, how='any', subset='utm_source') users_feature_campaign.sort_values(['utm_source', 'utm_term', 'unique_users'], ascending=[True, True, False]).head()

utm_sourceobject

utm_termobject

google

analytics modelling

google

analytics modelling

google

analytics modelling

google

analytics modelling

google

analytics sdk

# create a column that extracts all location stacks that lead to our github df_acquisition['github_press'] = df_acquisition.location_stack.json[{'id': 'objectiv-on-github', '_type': 'LinkContext'}:] df_acquisition.loc[df_acquisition.location_stack.json[{'id': 'github', '_type': 'LinkContext'}:]!=[],'github_press'] = df_acquisition.location_stack # define which events to use as conversion events modelhub.add_conversion_event(location_stack=df_acquisition.github_press, event_type='PressEvent', name='github_press')

# model hub: calculate conversions df_acquisition['is_conversion_event'] = modelhub.map.is_conversion_event(df_acquisition, 'github_press') conversions = modelhub.aggregate.unique_users(df_acquisition[df_acquisition.is_conversion_event]) conversions.to_frame().sort_index(ascending=False).head(10)

unique_usersint64

1 - 4

2022-07-26

2022-07-25

2022-07-24

2022-07-21

2022-07-20

2022-07-18

2022-07-17

2022-07-15

2022-07-14

2022-07-12

# calculate conversion rate conversion_rate = conversions / daily_users conversion_rate.sort_index(ascending=False).head(10)

# model hub: calculate conversions per marketing canpaign based on UTM data in MarketingContext campaign_conversions = modelhub.aggregate.unique_users(df_acquisition[df_acquisition.is_conversion_event], groupby=['utm_source', 'utm_medium', 'utm_campaign']) campaign_conversions.reset_index().dropna(axis=0, how='any', subset='utm_source').head()

utm_sourceobject

utm_mediumobject

google

cpc

hs_email

social

reactdigest

twitter

paid

conversion_locations = modelhub.agg.unique_users(df_acquisition[df_acquisition.is_conversion_event], groupby=['application', 'feature_nice_name', 'event_type']) # calling .to_frame() for nicer formatting conversion_locations.sort_values(ascending=False).to_frame().head()

unique_usersint64

('objectiv-website', 'Link: github located at Root Location: home => Navigation: navbar-top', 'PressEvent')

('objectiv-website', 'Link: github located at Root Location: home => Navigation: navbar-top => Overlay: hamburger-menu', 'PressEvent')

('objectiv-website', 'Link: objectiv-on-github located at Root Location: home => Content: the-stack', 'PressEvent')

('objectiv-website', 'Link: objectiv-on-github located at Root Location: blog => Content: post-meet-objectiv-open-source-product-analytics-designed-for-data-sc => Content: blog-post-try-objectiv', 'PressEvent')

('objectiv-website', 'Link: github located at Root Location: about => Navigation: navbar-top', 'PressEvent')

top_features_before_conversion = modelhub.agg.top_product_features_before_conversion(df_acquisition, name='github_press') top_features_before_conversion.head()

unique_usersint64

('objectiv-website', 'Pressable: hamburger located at Root Location: home => Navigation: navbar-top', 'PressEvent')

('objectiv-website', 'Pressable: after located at Root Location: home => Content: capture-data => Content: data-capture-workflow-before-after', 'PressEvent')

('objectiv-website', 'Pressable: after located at Root Location: home => Content: modeling => Content: modeling-workflow-before-after', 'PressEvent')

('objectiv-website', 'Pressable: before located at Root Location: home => Content: modeling => Content: modeling-workflow-before-after', 'PressEvent')

('objectiv-website', 'Pressable: before located at Root Location: home => Content: capture-data => Content: data-capture-workflow-before-after', 'PressEvent')

# label sessions with a conversion df_acquisition['converted_users'] = modelhub.map.conversions_counter(df_acquisition, name='github_press') >= 1 # label hits where at that point in time, there are 0 conversions in the session df_acquisition['zero_conversions_at_moment'] = modelhub.map.conversions_in_time(df_acquisition, 'github_press') == 0 # filter on above created labels converted_users = df_acquisition[(df_acquisition.converted_users & df_acquisition.zero_conversions_at_moment)] # how much time do users spend before they convert? modelhub.aggregate.session_duration(converted_users, groupby=None).to_frame().head()

session_durationtimedelta64[ns]

0 days 00:02:55.841644

# just one analysis as an example, this works for anything you do with Objectiv Bach display_sql_as_markdown(conversions)