"Know your channels", a wise man once said. Get actionable insights on your channel performance by building custom attribution models using Google Analytics 4 data in BigQuery. This end-to-end tutorial will show you how to prepare the GA4 data and build rule based and data driven (Markov) models.

Build a Data Driven Attribution Model using Google Analytics 4, BigQuery and Python

 **The *dbt* (data build tool) framework has gained much popularity over the last few years. It's an excellent tool to transform your data, but also helps with setting up a data warehouse workflow (orchestration, dependencies, environments, documentation etc). This tutorial will provide you with an example *dbt* project repository, using the Google Analytics 4 BigQuery dataset, and walks you through the most important parts of the setup, so you can bootstrap your own project quickly.**

- A starter **dbt project example**, using BigQuery as a data warehouse backend.
- Using the Google Analytics 4 event data export in BigQuery.
- **Transforming Google Analytic 4 event level data** to sessions.
- Create a user mapping table and grouping session data to customer profiles.
- Create the base for **marketing reports**, like a channel performance report.
- Tips regarding **project structure**, **incremental models** and workflow.

## What is dbt?

I would recommend you check out the [dbt introduction articles](https://docs.getdbt.com/docs/introduction) first, but for starters;

*dbt (data build tool) enables analytics engineers to transform data in their warehouses by simply writing select statements. dbt handles turning these select statements into tables and views. dbt does the T in ELT (Extract, Load, Transform) processes – it doesn’t extract or load data, but it’s extremely good at transforming data that’s already loaded into your warehouse.*

These are the most important features;

- **Orchestration**: *dbt* automatically detects dependencies (lineage) between models, generates a dependency graph (DAG) and can executes SQL (models) in the correct order.
- **Deployment**: Support for development and production environments, track history using the *dbt* repository, forces you to use a code repository basically.
- **Testing & integration**: *dbt* enables you to set up test easily (missing data, data formats etc) and performs integration tests.
- **Flexibility**: Easily recreate or backfills tables.
- **Documentation**: *dbt* automatically generates documentation / data catalog
- **DRY**: Reusable chunks of code in the for of macros

![dbt-overview-datawarehouse.PNG](https://static.stacktonic.com/medium_dbt_overview_datawarehouse_dd4909e027.PNG)

And above all, *dbt* doesn't really have a steep learning curve.

## 1. The example project

> **Update Augusts 2022**: Recently, an open-source [dbt-ga4](https://github.com/Velir/dbt-ga4) package was released by [Adam Ribaudo](https://twitter.com/adamribaudo) and others. The dbt package transforms the GA4 dataset into report-ready dimensional models. This article still helps you to get to know dbt using the GA4 dataset, but we don't provide all the transformations the package does. So. check this one out!

First of all, the example project **can be found [here on GitHub](https://github.com/stacktonic-com/stacktonic-dbt-example-project)**. I recommend you check out the *dbt* documentation first and understand the concepts of *dbt* in general. This project can help to master (and how to apply) the concepts. Besides you can use this project to bootstrap your project.

The project has the following characteristics:

** Data warehouse **

The example project uses Google BigQuery as a data warehouse backend.

**Sources**

The projects transforms the default [Google Analytics 4 BigQuery exports](https://support.google.com/analytics/answer/7029846?hl=en&ref_topic=9359001). Google Analytics exports to [date sharded tables](#4-ga4-source-tables-and-incrementality) (and this is something that you can't change currently).

** Transformations **

The project will transform the raw GA4 data into the following models:

- A staging model of the Google Analytics 4 data source (where you can rename and recast your data sources into a consistent format).
- Intermediate model to aggregate event level GA4 data to sessions (in an incremental table, so the complete GA4 dataset doesn't have to be transformed completely on every run). 
- Intermediate model to build a user mapping table out of all the available user identifiers in GA4 (based on [this article](https://stacktonic.com/article/create-a-user-mapping-table-based-on-the-google-analytics-4-big-query-dataset))
- Create a core user (customer) model, aggregating online behavioral data to user (customer) level. Can be used to build a [360 customer view](https://stacktonic.com/article/enrich-a-single-customer-view-with-google-analytics-4-big-query-data) for example)
- Create the base for marketing reports (channel performance report on day level).

## 2. Prerequisites

You can configure the example project to use your own settings and data warehouse (BigQuery). For that, you have te setup at least the following:

-  Created a Google Cloud Project and enabled the Google Analytics 4 exports. The project "as is" is configured to use the [Google Analytics 4 example dataset](https://support.google.com/analytics/answer/10937659?hl=en#zippy=%2Cin-this-article).
- [Installed *dbt*](https://docs.getdbt.com/dbt-cli/installation) or use the cloud hosted version of *dbt* (free for 1 developer seat, benefit that is has a built-in / dedicated IDE). 
- I've used dbt Cloud, were you can configure the BigQuery dbt profile, but you could also [set this up manually](https://docs.getdbt.com/reference/warehouse-profiles/bigquery-profile)
- Check and adjust the settings in the following files: `dbt_project.yml` and `.dbt/profiles.yml` (especially if your not using dbt Cloud)

## 3. Project structure

You're free to define your own structure, but I tried to keep the proposed setup by *dbt* in mind, so there is some logical structure you project stays manageable when it grows over time. The following articles regarding style and project structure are a good read:

- [dbt best practices](https://docs.getdbt.com/docs/guides/best-practices)
- [How dbt structures their projects](https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355)

In the example project;

- Intermediate models are placed within a subfolder of the most relevant mart (you could create a separate top-level folder for intermediate tables as well)
- Intermediate tables and views are placed into a dedicated BigQuery dataset
- The `schema` and `dataset` parameters are interchangeable. Since BigQuery uses the last term, `dataset` is used throughout the project. 

**Folder structure**

![dbt-folder-structure.PNG](https://static.stacktonic.com/dbt_folder_structure_87fc340ced.PNG)

**BigQuery Datasets**

*dbt* will create and populate multiple datasets within BigQuery. Next to adding some structure to your project, splitting up your models enables you (more easily) to hide certain datasets for all users in your organization.

| Dataset | Description |
| --- | --- |
| `<prefix>_staging` | Staging data models (renaming / recasting source tables so sources are in a consistent format) | 
| `<prefix>_core` | Core data warehouse models |
| `<prefix>_marketing` | Marketing data warehouse models |
| `<prefix>_int` | All intermediate tables (e.g. joining and grouping different sources). Placed in a separate dataset so you can hide these tables from certain the user(groups) |
| `<prefix>_utils` | Dataset holding utilities, like (reusable) User Defined Functions (UDFs) |

*The development environment will create the same datasets, but with a development prefix*

![dbt-bigquery-structure.PNG](https://static.stacktonic.com/dbt_bigquery_structure_bbb1d91e10.PNG)

*dbt BigQuery dataset structure*

## 4. GA4 source tables and incrementality

The GA4 tables are date sharded (`events_YYYYMMDD`) instead of partitioned. In terms of performance (and costs), it's important to query these tables correctly.

> Google [recommends](https://cloud.google.com/bigquery/docs/partitioned-tables#dt_partition_shard) using partitioned tables instead of sharded tables (like Google the Google Analytics 4 tables). So it strange that that Google Analytics 4 uses date sharding instead of partitioning.

If you use a date column to select incremental amounts of data (and not using the `_table_suffix` pseudo column), every time you select an incremental amount data, it will query all the date sharded tables of the Google Analytics 4 exports. Not that of a big problem when you don't have a lot of data, but it is a problem (in terms of cost and performance) when handling large datasets. 

Some solutions;

1. Ideally, partitioned tables are to way to go. However we have no control over this data source, so you could materialize the date sharded tables in a date (day) partitioned table yourself. However, since the the Google Analytics 4 dataset can be rather large, it will generate extra costs because your duplicating the dataset, so it might not be the logical choice in some situations.
2. Filter on `_table_suffix` instead of a date column (see [this article](https://docs.getdbt.com/reference/resource-properties/identifier)).
3. Create a macro to insert `execution_date` into the models alias parameter ([article from The Telegraph team](https://medium.com/the-telegraph-engineering/dbt-a-new-way-to-handle-data-transformation-at-the-telegraph-868ce3964eb4))

In our project, we've selected option #2, so we don't have to duplicate our data. If there is any better method (now or in the future) please let me know and I will update the project. Example method below or see [this file](https://github.com/stacktonic-com/stacktonic-dbt-example-project/blob/master/models/marts/core/intermediate/int_events_sessions__grouped.sql) in the example project:

*In the example below, the `table_suffix` column refers to the BigQuery `_table_suffix` pseudo column*

```sql
 {{
  config(
    materialized = 'incremental',
    partition_by = {'field': 'session_start_at', 'data_type': 'timestamp'},
    incremental_strategy = 'insert_overwrite',
    tags=['incremental']
  )
}}

-- google analytics 4 events
with events as (
    
    select 
        * 
    from {{ ref('stg_google_analytics__events') }}

    
    {% if var('execution_date') != 'notset' %}
        
        where
            -- specific date set by variable (using the _table_suffix_ pseudo column for performance)
            table_suffix = '{{ var('execution_date') }}'

    {% elif is_incremental() %}

        where
            -- incremental data (using the _table_suffix_ pseudo column for performance)
            (table_suffix between format_date('%Y%m%d', date(_dbt_max_partition))
                and format_date('%Y%m%d', date_sub(current_date(), interval 1 day)))

    {% endif %}
)
```

## 5. Execution & scheduling

You can use the following commands to run all or specific models.

``` bash
# Run all models
dbt run

# Run a specific model
dbt run --select int_events_sessions__grouped

# Run models with a specific tag
dbt run --select tag:incremental

# Run all models and rebuild tables
dbt run --full-refresh

# Run all models for a specific date -> replaces (date) partitions for incremental models, for example the incremental session models build on top the GA event exports
dbt run --vars '{execution_date: 20211025}'

```

The relation between the models (lineage):

![dbt-google-analytics-4-lineage.PNG](https://static.stacktonic.com/medium_dbt_google_analytics_4_lineage_88526232af.PNG)

** Scheduling **

You can run dbt both locally as in dbt Cloud. This last options has a built-in scheduler to run your *dbt* commands. Another good option is to use Apache Airflow to schedule *dbt* model execution. Benefit is that you have more insights into model execution, tracks history and you can (re)run specific models with ease. You can check out the following articles:

- Astronomer https://www.astronomer.io/blog/airflow-dbt-1
- https://towardsdatascience.com/creating-an-environment-with-airflow-and-dbt-on-aws-part-3-2789f35adb5d
- https://analyticsmayhem.com/dbt/schedule-dbt-models-with-apache-airflow/

## 6. Other features

Lastly, the example project has some other small features and capabilities.

- **Profiles**: Configuration for a development and production environment (`.dbt/profiles.yml`).
- **Model configuration**: Default model configuration (e.g. dataset / suffixes / folders) in [`dbt_profile.yml`](https://github.com/stacktonic-com/stacktonic-dbt-example-project/blob/master/dbt_project.yml)
- **Documentation**: All fields in the final dimension and fact tables are documented (within the `core.yml` and `marketing.yml` files) 
- **Variables**: `start_date` can be used to define the start date of your data and `execution_date` can be used to rebuild or backfill (incremental) models for a specific date (and overwrite the old partitions). `dataset_udf` is used to configure the BigQuery dataset where the UDFs (macros) are stored.
- **Macros**: There are several macros available in the project. Within the `/macros/udf` folder there are several macros that create user defined functions (UDFs), such as a channel grouping function. The `create_udfs.sql` macro runs all the UDF macros when the model is run (so the UDFs are up-to-date).
- **Tags**: Incremental models are tagged with `incremental`, daily incremental tables with `daily`.
- **Testing**: [Freshness settings](https://github.com/stacktonic-com/stacktonic-dbt-example-project/blob/master/models/staging/google_analytics/src_google_analytics.yml) for the GA4 source table is setup and for the fact and dimensions tables, [checks](https://github.com/stacktonic-com/stacktonic-dbt-example-project/blob/master/models/marts/core/core.yml) on unique identifiers are build in.

## Closing thoughts

The goal of this example projects is to bootstrap your project quickly and show you some of dbt's features how to apply them in your project. If you have any suggestions and improvements, please let me know!

The dbt (data build tool) framework has gained much popularity over the last few years. It&#x27;s an excellent tool to transform your data, but also helps with setting up a data warehouse workflow (orchestration, dependencies, environments, documentation etc). This tutorial will provide you with an example dbt project repository, using the Google Analytics 4 BigQuery dataset, and walks you through the most important parts of the setup, so you can bootstrap your own project quickly.

Google Analytics, BigQuery & dbt: A dbt example project

**Make your life easier and define custom Channel Groupings in a reusable function (UDF) when working with Google Analytics 4 data in BigQuery. The channel grouping definition based on GA4 parameters *source*, *medium* and *campaign* can be reused with ease, without duplicating all the channel grouping logic in every query (win!)**

- [The function](#example-1---default-channel-grouping-ua) is saved into BigQuery as a **persistent** User Defined Function (UDF)
- The channel grouping logic can be edited in one place and changes will directly affect all the queries where the UDF function is used.
- Examples provided for both the **Universal Analytics** as the **Google Analytics 4** default channel grouping definitions, which you can **customize to your needs**.

> **Update 2021-11-17**: Google released a new Default Channel Grouping definition for Google Analytics 4. I've tried to capture this new GA4 definition [in a BigQuery equivalent](#example-2---default-channel-grouping-ga4). 

## Defining Channel Groupings

Channel groupings are a logical combination of acquisition parameters (like `source`, `medium`, `campaign`) to classify your traffic into channels. In Google Analytics, the parameters mentioned are populated based on URL parameters (like `?utm_x=...`) or the *auto tagging* link between GA and Google Ads or Campaign. More information about channel groupings [in the Google documentation](https://support.google.com/analytics/answer/6010097?hl=en#zippy=%2Cin-this-article).

![default-channel-grouping-paths.PNG](https://static.stacktonic.com/small_default_channel_grouping_paths_60d90a9cf2.PNG)

The definitions of the channels should depend on your specific business needs. Next to the *Default Channel Grouping* definition of Google Analytics, you can create your own custom channel grouping(s). As mentioned, mappings can be made based on dimensions `source`, `medium`, `campaign`, but you can add more parameters to the function easily. 

In the [first SQL example](#example-1---default-channel-grouping) below, we've provided a generic definition which looks a lot like the Default Channel Grouping of Google Universal Analytics. 

- See [this page](https://support.google.com/analytics/answer/3297892?hl=en&ref_topic=6010089) for the Default Channel Grouping definition within the GA interface. 
- Google Analytics 4 does not provide the option (yet) to create custom channels groupings in the interface. Universal Analytics does, but it doesn't export these groupings to BigQuery (and if they did, you can't alter historical data). 
- The [Google Analytics 4 Default Channel Grouping definition](https://support.google.com/analytics/answer/9756891?hl=en) differs from Universal Analytics.
- The functions below can be used for both the Google Analytics 4 and Universal Analytics 360 exports.

> We highly recommend to customize your channel grouping based on your business needs. Therefore it's important to get your UTM tagging in order, so you can add more depth in to your mappings.

To illustrate, you could create the following additional rules in your groupings:

- Paid search: branded vs. non-branded campaigns (based on the `campaign` parameter for example)
- Social: organic social vs. paid social (make sure you ?utm= tag your social media campaigns accordingly)
- Email: Transactional emails vs. marketing related emails

You could also throw the `traffic source` into the mix. This can be useful when you want to slim down all the unique "source / medium" combinations. Of course, you can **create multiple functions** for **different levels of depth**.

## The BigQuery functions

Let's talk action. You can run the SQL function below directly in the BigQuery interface and it will automatically create the function within the dataset your specified in the `CREATE OR REPLACE FUNCTION`. It will show up in the tree on the left side of your screen;

![user-defined-function-channel-grouping.PNG](https://static.stacktonic.com/user_defined_function_channel_grouping_1061a47b1a.PNG)

### Example 1 - Default Channel Grouping UA

This is the BigQuery equivalent of the Google Universal Analytics default channel grouping definition.

*[View code on GitHub](https://gist.github.com/krisjan-oldekamp/bad52a06062b09729434ab65091b5393)*
```sql
create or replace function `<your-project>.<your-dataset>.channel_grouping`(tsource string, medium string, campaign string) as (
    case
        when (tsource = '(direct)' or tsource is null) 
            and (regexp_contains(medium, r'^($not set$|$none$)$') or medium is null) 
            then 'direct'
        when regexp_contains(medium, r'^(social|social_advertising|social-advertising|social_network|social-network|social_media|social-media)$') 
            then 'social'
        when regexp_contains(medium, r'^(email|mail)$') 
            then 'email'
        when regexp_contains(medium, r'^(affiliate|affiliates)$') 
            then 'affiliate'
        when regexp_contains(medium, r'^(cpc|ppc|paidsearch)$') 
            then 'search_paid'
        when regexp_contains(medium, r'^(display|cpm|banner)$') 
            then 'display'
        when medium = 'organic'
            then 'search_organic'
        when medium = 'referral'
            then 'referral'
        else '(other)'
    end
);
```

### Example 2 - Default Channel Grouping GA4

**2021-11-17**: Google recently [updated the default channel grouping definition](https://support.google.com/analytics/answer/9756891?hl=en) of Google Analytics 4, so I've replaced the old advanced example with the GA4 example, since there is a lot of overlap (the GA4 definition has mush more dept than the Universal Analytics one). 

> Be aware that not all the rules applied in GA4 can be used in BigQuery since we do not have the exact definitions of rules like "Source matches a list of social sites". Please let me know if you have any suggestions to further tweak the function.

*[View code on GitHub](https://gist.github.com/krisjan-oldekamp/bad52a06062b09729434ab65091b5393)*
``` sql
create or replace function `<your-project>.<your-dataset>.channel_grouping`(tsource string, medium string, campaign string) as (
    case
        when (tsource = 'direct' or tsource is null) 
            and (regexp_contains(medium, r'^($not set$|$none$)$') or medium is null) 
            then 'direct'
        when regexp_contains(campaign, r'^(.*shop.*)$') 
            and regexp_contains(medium, r'^(.*cp.*|ppc|paid.*)$') 
            then 'shopping_paid'
        when regexp_contains(tsource, r'^(google|bing)$') 
            and regexp_contains(medium, r'^(.*cp.*|ppc|paid.*)$') 
            then 'search_paid'
        when regexp_contains(tsource, r'^(twitter|facebook|fb|instagram|ig|linkedin|pinterest)$')
            and regexp_contains(medium, r'^(.*cp.*|ppc|paid.*|social_paid)$') 
            then 'social_paid'
        when regexp_contains(tsource, r'^(youtube)$')
            and regexp_contains(medium, r'^(.*cp.*|ppc|paid.*)$') 
            then 'video_paid'
        when regexp_contains(medium, r'^(display|banner|expandable|interstitial|cpm)$') 
            then 'display'
        when regexp_contains(medium, r'^(.*cp.*|ppc|paid.*)$') 
            then 'other_paid'
        when regexp_contains(medium, r'^(.*shop.*)$') 
            then 'shopping_organic'
        when regexp_contains(tsource, r'^.*(twitter|t\.co|facebook|instagram|linkedin|lnkd\.in|pinterest).*') 
            or regexp_contains(medium, r'^(social|social_advertising|social-advertising|social_network|social-network|social_media|social-media|sm|social-unpaid|social_unpaid)$') 
            then 'social_organic'
        when regexp_contains(medium, r'^(.*video.*)$') 
            then 'video_organic'
        when regexp_contains(tsource, r'^(google|bing|yahoo|baidu|duckduckgo|yandex|ask)$') 
            or medium = 'organic'
            then 'search_organic'
        when regexp_contains(tsource, r'^(email|mail|e-mail|e_mail|e mail|mail\.google\.com)$') 
            or regexp_contains(medium, r'^(email|mail|e-mail|e_mail|e mail)$') 
            then 'email'
        when regexp_contains(medium, r'^(affiliate|affiliates)$') 
            then 'affiliate'
        when medium = 'referral'
            then 'referral'
        when medium = 'audio' 
            then 'audio'
        when medium = 'sms'
            then 'sms'
        when ends_with(medium, 'push')
            or regexp_contains(medium, r'.*(mobile|notification).*') 
            then 'mobile_push'
        else '(other)'
    end
);
```

## How to use and test the function

When the function is saved, you can use the function this way:

``` sql
select
    <your-dataset>.channel_grouping(source, medium, campaign) aschannel_grouping
    -- or
    `<your-project>.<your-dataset>.channel_grouping`(source, medium, campaign) as channel_grouping
from
     <your-table>
```

Make sure you test your function when you are creating the grouping rules. You could use the following query to check what traffic parameters are mapped to which channel:

> Google Analytics 4 currently has some quirks and unknowns to deal with. Related to traffic acquisition parameters, sessions are not reset when a user is switching from channel within an active session.  Next to that it's not clear how GA4 is generating / attributing the *Traffic Acquisition* reports in the interface. Also, Google Analytics 4 uses conversion modelling to attribute conversions (due to tracking mechanisms such as Apple ITP). So currently it's impossible to align your BigQuery data with these interface reports. However, this is not a problem when defining your channels.

```sql
with
-- select session in last 30 days
sessions as (
    select
        user_pseudo_id as ga_client_id,
        concat(user_pseudo_id,'.',(select cast(value.int_value as string) from unnest(event_params) where key = 'ga_session_id')) as session_id, -- combine user_pseudo_id and session_id for a unique session-id
        timestamp_micros(min(event_timestamp)) as session_start,
        array_agg(
            if(event_name in('page_view','user_engagement','scroll'), struct(
                event_timestamp,
                lower((select value.string_value from unnest(event_params) where key = 'source')) as source,
                lower((select value.string_value from unnest(event_params) where key = 'medium')) as medium,
                lower((select value.string_value from unnest(event_params) where key = 'campaign')) as campaign,
                (select value.int_value from unnest(event_params) where key = 'entrances') as is_entrance,
                (select value.int_value from unnest(event_params) where key = 'ignore_referrer') as ignore_referrer
            ), null) 
        ignore nulls) as channels_in_session,
        countif(event_name = 'purchase') as conversions,
        sum(ecommerce.purchase_revenue) as conversion_value
    from
        `<your-project>.analytics_<your-dataset>.events_*`
    where
        _table_suffix between 
            format_date('%Y%m%d', date_sub(current_date(), interval 30 day))
            and format_date('%Y%m%d', date_sub(current_date(), interval 1 day))
    group by
        user_pseudo_id,
        session_id
),
-- get first campaign parameters from session and aggegrated metrics
traffic_acquisition as (
    select
        (select t.source from unnest(channels_in_session) as t where t.ignore_referrer is null order by t.event_timestamp asc limit 1) as source,
        (select t.medium from unnest(channels_in_session) as t where t.ignore_referrer is null order by t.event_timestamp asc limit 1) as medium,
        count(distinct session_id) as sessions,
        sum(conversions) as conversions,
        ifnull(sum(conversion_value), 0) as conversion_value
    from
        sessions
    group by
        1, 2
)

-- map source / medium to channel grouping using a user defined function (ignore campaign)
select
    *,
    <your-dataset>.channel_grouping(source, medium, null) as channel_grouping
from
    traffic_acquisition
```

![channel-grouping-test.PNG](https://static.stacktonic.com/channel_grouping_test_b4792e476b.PNG)
*Check your rules.*

## Some last tips

- Create a dedicated dataset for your UDF functions
- When you rerun the SQL, the UDF function will update.
- Create different channel grouping functions, based on the needs (using different levels of granularity for example)

*For more information on UDFs within BigQuery, see the [Google Documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_function_statement).*

Make your life easier and define custom Channel Groupings in a reusable function (UDF) when working with Google Analytics 4 data in BigQuery. The channel grouping definition based on GA4 parameters source, medium and campaign can be reused with ease, without duplicating all the channel grouping logic in every query (win!).

Google Analytics 4 & BigQuery: Create custom Channel Groupings in a reusable SQL function

Cloud (data) platforms such as AWS, Google Cloud or Azure enable you to build a marketing data platform that doesn't only ingest and join data, but can also produce actionable insights and activate data in other tools and platforms. 

Building a cloud Marketing Data Platform

Google Analytics 4 is more than just a measurement and reporting tool. The event data exports to Google BigQuery (available in the free version) and the GA4 API brings a ton of new possibilities. Think of advanced analysis, joining data with other sources, building prediction models or integrate with other systems.

Google Analytics 4 Advanced

Increased privacy concerns, legislation and browser updates affecting (third-) party cookies is resulting in a new era where users regain control over their data and organizations should rethink their customer and marketing strategy. This will impact a lot of area's such as data collection, advertising, first-party data strategy, legal and UX. 

Privacy, cookies & first-party data

I specialize in Marketing Technology & Analytics. The digital world has always interested me, I've started writing code at age 12. My education background is in business and marketing. The combination of those two caught my interest quickly and throughout my career I mainly focused on projects that lie at the intersection of marketing, technology and data. 

After 8 years at a digital agency (Merkle Netherlands), ending up as Marketing Technology lead, I decided to fuel my entrepreneurial ambition and started as an independent, freelance consultant under the flag of Stacktonic.

## Stacktonic - Background

Stacktonic started as a platform to share knowledge in the form of hands-on marketing technology, data and analytics related tutorials. Continuing doing that, Stacktonic is also offering professional services & consulting in these areas as well. As a freelance consultant or in collaboration with other independent consultants in my network.

## Getting stuff done

Leveraging business, digital marketing and technical experience, I can assist on strategic level but are also fully able to turn strategy into action. I can lead or assist projects and teams, from ideation to execution, in a hands-on manner. Talking the same language as management, marketers and developers I can operate efficiently and maximize results.


Krisjan Oldekamp

Marketing Technology & Analytics consultant.

Stacktonic - Background

Getting stuff done

Interested?