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• Google Analytics 4 (GA4) vs Matomo

  7 avril 2022, par Erin

  Google announced that Universal Analytics’ days are numbered. Universal Analytics will be replaced by Google Analytics 4 (or GA4) on the 1st of July 2023. 

  If Google Analytics users want to compare year-on-year data, they have until July 2022 to get set up and start collecting data before the sun sets on Universal Analytics (or UA).

  But is upgrading to Google Analytics 4 the right move ? There’s a lot to consider, and many organisations are looking for an alternative to Google Analytics. So in this blog, we’ll compare GA4 to Matomo – the leading Google Analytics alternative. 

  In this blog, we’ll look at :

  What is Matomo ?

  Matomo is a powerful privacy-first web analytics platform that gives you 100% data ownership. First launched in 2007, Matomo is now the world’s leading open-source web analytics platform and is used by more than 1 million websites. 

  Matomo’s core values are based on ethical data collection and processing. Consistently more businesses and organisations from around the globe are adopting data-privacy-compliant web analytics solutions like Matomo. 

  Matomo offers both Cloud and On-Premise solutions (and a five-star rated WordPress plugin), making for an adaptable and flexible solution. 

  What is Google Analytics 4 ?

  Google Analytics 4 is the latest version of Google Analytics and represents a completely new approach to data-modelling than its predecessor, Universal Analytics. For an in-depth look at how GA4 and UA compare, check out this Google Analytics 4 vs Universal Analytics comparison. 

  Google Analytics 4 will soon be the only available version of analytics software from Google. So what’s the issue ? Surely, in 2022, Google makes it easy to migrate to their newest (and only) analytics platform ? Not quite.

  Google Analytics 4 vs Matomo

  Whilst the core purpose of GA4 and Matomo is similar (providing web analytics that help to optimise your website and grow your business), there are several key differences that organisations should consider before making the switch.

  Importing Historical Data from Universal Analytics

  Google Analytics 4

  Users assuming that historical data from Universal Analytics could be imported into Google Analytics 4 were faced with swift disappointment. Unfortunately, Google Analytics 4 does not have an option to import data from its predecessor, Universal Analytics. This means that businesses won’t be able to import and compare data from previous years.

  Matomo

  If you don’t want to start from scratch with your web analytics data, then Matomo is an ideal solution for data continuity. Matomo offers users the ability to import their historical Universal Analytics data. So you can keep all that valuable historical data you’ve collected over the years.

  

  User Interface

  Google Analytics 4

  GA4’s new user interface has been met with mixed reviews. Many claim that it’s overly complex and difficult to navigate. Some have even suggested that the tool has been designed specifically for enterprises with specialised analytics teams. 

  

  Matomo

  Matomo, on the other hand, is recognised for an easy to use interface, with a rating of 4.5 out of 5 stars for ease of use on Capterra. Matomo perfectly balances powerful features with a user-friendly interface so valuable insights are only a click away. There’s a reason why over 1 million websites are using Matomo. 

  

  Advanced Behavioural Analytics Features 

  Google Analytics 4

  While Google Analytics is undoubtedly robust in some areas (machine learning, for instance), what it really lacks is advanced behavioural analytics. Heatmaps, session recordings and other advanced tools can give you valuable insights into how users are engaging with your site. Well beyond pageviews and other metrics.

  Unfortunately, with this new generation of GA, Google still hasn’t introduced these features. So users have to manage subscriptions and tracking in third-party behavioural analytics tools like Hotjar or Lucky Orange, for example. This is inefficient, costly and time-consuming to manage. 

  

  Matomo 

  Meanwhile, Matomo is a one-stop shop for all of your web analytics needs. Not only do you get access to the metrics you’ve grown accustomed to with Universal Analytics, but you also get built-in behavioural analytics features like Heatmaps, Scroll Depth, Session Recordings and more. 

  Want to know if visitors are reaching your call to action at the bottom of the page ? Scroll Depth will answer that.

  Want to know why visitors aren’t clicking through to the next page ? Heatmaps will give you the insights you need.

  You get the picture – the full picture, that is. 

  

  Data Accuracy

  Google Analytics 4

  GA4 aims to make web and app analytics more privacy-centric by reducing the reliance on cookies to record certain events across platforms and devices. 

  However, when site and application visitors opt-out of cookie tracking, GA4 instead relies on machine learning to fill in the gaps. Data sampling could mean that your business is making business decisions based on inaccurate reports. 

  Matomo

  Data is the backbone of web analytics, so why make critical business decisions on sampled data ? With Matomo, you’re guaranteed 100% unsampled accurate data. So you can rest assured that any decisions you make are based on actual facts. 

  Compliance with Privacy Laws (GDPR, CCPA, etc.) 

  Google Analytics 4

  Google is making changes in an attempt to become compliant with privacy laws. However, even with GA4, users are still transferring data to the US. For this reason, both Austrian and French governments have ruled Google Analytics illegal under GDPR.

  The only possible workaround is “Privacy Shield 2.0”, but GDPR experts are still sceptical of this one. 

  Matomo

  If compliance with global privacy laws is a concern (and it should be), then Matomo is the clear winner here. 

  As an EU hosted web analytics tool, your data is stored in Europe, and no data is transferred to the US. On the other hand, if you choose to self-host, the data is stored in your country of choice.

  In addition, with cookieless tracking enabled, you can say goodbye to those pesky cookie consent screens. 

  Also, remember that under GDPR, and many other data privacy laws like CCPA and LGPD, end users have a legal right to access, amend and/or erase the personal data collected about them. 

  With Matomo you get 100% ownership of your web analytics data. This means that we don’t on-sell to third parties ; can’t claim ownership of the data ; and you can export your data at any time.

  

  Wrap up

  At the end of the day, the worst thing an organisation can do is nothing. Waiting until July 2023 to migrate to GA4 or another web analytics platform would be very disruptive and costly. Organisations need to consider their options now and start migrating in the next few months. 

  With all that said, moving to Google Analytics 4 could prove to be a costly and time-consuming operation. The global trend towards increased data privacy is a threat to platforms like Google Analytics which uses data for advertising and transfers data across borders.

  With Matomo, you get an easy to use all-in-one web analytics platform and keep your historical Universal Analytics data. Plus, you can future-proof your business by being compliant with global privacy laws and get access to advanced behavioural analytics features. 

  There’s a lot to weigh up here but fortunately, getting started with Matomo is easy. Try it free for 21-days (no credit card required) and see for yourself why over 1 million websites choose Matomo. 

  While this is the end of the road for Universal Analytics, it’s also an opportune time for organisations to find a better fit web analytics tool. 

  21 day free trial. No credit card required.

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  Choose your analytics subdomain
  

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  I hereby accept the terms and conditions and the data processing agreeement (DPA).

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  

  » Start improving your websites now

  

• Video encoding task not working with Django Celery Redis FFMPEG and GraphQL

  18 juin 2023, par phanio

  I'm having a hard time trying to understand how is this FFMPEG encoding works while using Django, Celery, Redis, GraphQL and Docker too.

  


  I have this video / courses platform project and want I'm trying to do using FFMPEG, Celery and Redis is to create different video resolutions so I can display them the way Youtube does inside the videoplayer ( the videoplayer is handled in frontend by Nextjs and Apollo Client ), now on the backend I've just learned that in order to use properly the FFMPEG to resize the oridinal video size, I need to use Celery and Redis to perform asyncronus tasks. I've found a few older posts here on stackoverflow and google, but is not quite enough info for someone who is using the ffmpeg and clery and redis for the first time ( I've started already step by step and created that example that adds two numbers together with celery, that works well ). Now I'm not sure what is wrong with my code, because first of all I'm not really sure where should I trigger the task from, I mean from which file, because at the end of the task I want to send the data through api using GrapQL Strawberry.

  


  This is what I've tried by now :

  


  So first things first my project structure looks like this

  


  - backend #root directory
 --- backend
    -- __init__.py
    -- celery.py
    -- settings.py
    -- urls.py
      etc..

 --- static
   -- videos

 --- video
   -- models.py
   -- schema.py
   -- tasks.py
   -- types.py
   etc..

 --- .env

 --- db.sqlite3

 --- docker-compose.yml

 --- Dockerfile

 --- manage.py

 --- requirements.txt


  


  here is my settings.py file :

  


  from pathlib import Path
import os

# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent

DEBUG = True

ALLOWED_HOSTS=["localhost", "0.0.0.0", "127.0.0.1"]

DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'


# Application definition

INSTALLED_APPS = [
    "corsheaders",
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',

    "strawberry.django",
    "video"
]

etc...

STATIC_URL = '/static/'
MEDIA_URL = '/videos/'

STATICFILES_DIRS = [
    BASE_DIR / 'static',
    # BASE_DIR / 'frontend/build/static',
]

MEDIA_ROOT = BASE_DIR / 'static/videos'

STATIC_ROOT = BASE_DIR / 'staticfiles'

STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage'

CORS_ALLOW_ALL_ORIGINS = True


CELERY_BEAT_SCHEDULER = 'django_celery_beat.schedulers:DatabaseScheduler'

# REDIS CACHE
CACHES = {
    "default": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": f"redis://127.0.0.1:6379/1",
        "OPTIONS": {
            "CLIENT_CLASS": "django_redis.client.DefaultClient",
        },
    }
}

# Docker
CELERY_BROKER_URL = os.environ.get("CELERY_BROKER", "redis://redis:6379/0")
CELERY_RESULT_BACKEND = os.environ.get("CELERY_BROKER", "redis://redis:6379/0")


  


  This is my main urls.py file :

  


  from django.contrib import admin
from django.conf import settings
from django.conf.urls.static import static
from django.urls import path
from django.urls.conf import include
from strawberry.django.views import GraphQLView

from video.schema import schema

urlpatterns = [
    path('admin/', admin.site.urls),
    path("graphql", GraphQLView.as_view(schema=schema)),
]

if settings.DEBUG:
    urlpatterns += static(settings.MEDIA_URL,
                          document_root=settings.MEDIA_ROOT)
    urlpatterns += static(settings.STATIC_URL,
                          document_root=settings.STATIC_ROOT)


  


  This is my celery.py file :

  


  from __future__ import absolute_import, unicode_literals
import os
from celery import Celery
from django.conf import settings

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')

backend = Celery('backend')

backend.config_from_object('django.conf:settings', namespace="CELERY")

backend.autodiscover_tasks()

@backend.task(bind=True)
def debug_task(self):
    print('Request: {0!r}'.format(self.request))


  


  This is my init.py file :

  


  from .celery import backend as celery_backend

__all__ = ('celery_backend',)


  


  This is my Dockerfile :

  


  FROM python:3
ENV PYTHONUNBUFFERED=1

WORKDIR /usr/src/backend

RUN apt-get -y update
RUN apt-get -y upgrade
RUN apt-get install -y ffmpeg

COPY requirements.txt ./
RUN pip install -r requirements.txt


  


  This is my docker-compose.yml file :

  


  version: "3.8"

services:
  django:
    build: .
    container_name: django
    command: python manage.py runserver 0.0.0.0:8000
    volumes:
      - .:/usr/src/backend/
    ports:
      - "8000:8000"
    environment:
      - DEBUG=1
      - DJANGO_ALLOWED_HOSTS=localhost 127.0.0.1 [::1]
      - CELERY_BROKER=redis://redis:6379/0
      - CELERY_BACKEND=redis://redis:6379/0
    depends_on:
      - pgdb
      - redis

  celery:
    build: .
    command: celery -A backend worker -l INFO
    volumes:
      - .:/usr/src/backend
    depends_on:
      - django
      - redis

  pgdb:
    image: postgres
    container_name: pgdb
    environment:
      - POSTGRES_DB=postgres
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
    volumes:
      - pgdata:/var/lib/postgresql/data/

  redis:
    image: "redis:alpine"

volumes:
  pgdata:


  


  And now inside my video app folder :

  


  My models.py file :

  


  

  • here I've created separated fields for all resolution sizes, from video_file_2k to video_file_144, I was thinking that maybe after the process of the encoding this will populate those fields..

  


  


  from django.db import models
from django.urls import reverse


class Video(models.Model):
    video_id = models.AutoField(primary_key=True, editable=False)
    slug = models.SlugField(max_length=255)
    title = models.CharField(max_length=150, blank=True, null=True)
    description = models.TextField(blank=True, null=True)
    video_file = models.FileField(null=False, blank=False)
    video_file_2k = models.FileField(null=True, blank=True)
    video_file_fullhd = models.FileField(null=True, blank=True)
    video_file_hd = models.FileField(null=True, blank=True)
    video_file_480 = models.FileField(null=True, blank=True)
    video_file_360 = models.FileField(null=True, blank=True)
    video_file_240 = models.FileField(null=True, blank=True)
    video_file_144 = models.FileField(null=True, blank=True)
    category = models.CharField(max_length=64, blank=False, null=False)
    created_at = models.DateTimeField(
        ("Created at"), auto_now_add=True, editable=False)
    updated_at = models.DateTimeField(("Updated at"), auto_now=True)

    class Meta:
        ordering = ("-created_at",)
        verbose_name = ("Video")
        verbose_name_plural = ("Videos")

    def get_absolute_url(self):
        return reverse("store:video_detail", args=[self.slug])

    def __str__(self):
        return self.title


  


  This is my schema.py file :

  


  import strawberry
from strawberry.file_uploads import Upload
from typing import List
from .types import VideoType
from .models import Video
from .tasks import task_video_encoding_1080p, task_video_encoding_720p


@strawberry.type
class Query:
    @strawberry.field
    def videos(self, category: str = None) -> List[VideoType]:
        if category:
            videos = Video.objects.filter(category=category)
            return videos
        return Video.objects.all()

    @strawberry.field
    def video(self, slug: str) -> VideoType:
        if slug == slug:
            video = Video.objects.get(slug=slug)
            return video

    @strawberry.field
    def video_by_id(self, video_id: int) -> VideoType:
        if video_id == video_id:
            video = Video.objects.get(pk=video_id)

          # Here I've tried to trigger my tasks, when I visited 0.0.0.0:8000/graphql url
          # and I was querying for a video by it's id , then I've got the error from celery 
            task_video_encoding_1080p.delay(video_id)
            task_video_encoding_720p.delay(video_id)

            return video


@strawberry.type
class Mutation:
    @strawberry.field
    def create_video(self, slug: str, title: str, description: str, video_file: Upload, video_file_2k: str, video_file_fullhd: str, video_file_hd: str, video_file_480: str, video_file_360: str, video_file_240: str, video_file_144: str, category: str) -> VideoType:

        video = Video(slug=slug, title=title, description=description,
                      video_file=video_file, video_file_2k=video_file_2k, video_file_fullhd=video_file_fullhd, video_file_hd=video_file_hd, video_file_480=video_file_480, video_file_360=video_file_360, video_file_240=video_file_240, video_file_144=video_file_144,category=category)
        
        video.save()
        return video

    @strawberry.field
    def update_video(self, video_id: int, slug: str, title: str, description: str, video_file: str, category: str) -> VideoType:
        video = Video.objects.get(video_id=video_id)
        video.slug = slug
        video.title = title
        video.description = description
        video.video_file = video_file
        video.category = category
        video.save()
        return video

    @strawberry.field
    def delete_video(self, video_id: int) -> bool:
        video = Video.objects.get(video_id=video_id)
        video.delete
        return True


schema = strawberry.Schema(query=Query, mutation=Mutation)


  


  This is my types.py file ( strawberry graphql related ) :

  


  import strawberry

from .models import Video


@strawberry.django.type(Video)
class VideoType:
    video_id: int
    slug: str
    title: str
    description: str
    video_file: str
    video_file_2k: str
    video_file_fullhd: str
    video_file_hd: str
    video_file_480: str
    video_file_360: str
    video_file_240: str
    video_file_144: str
    category: str


  


  And this is my tasks.py file :

  


  from __future__ import absolute_import, unicode_literals
import os, subprocess
from django.conf import settings
from django.core.exceptions import ValidationError
from celery import shared_task
from celery.utils.log import get_task_logger
from .models import Video
FFMPEG_PATH = os.environ["IMAGEIO_FFMPEG_EXE"] = "/opt/homebrew/Cellar/ffmpeg/6.0/bin/ffmpeg"

logger = get_task_logger(__name__)


# CELERY TASKS
@shared_task
def add(x,y):
    return x + y


@shared_task
def task_video_encoding_720p(video_id):
    logger.info('Video Processing started')
    try:
        video = Video.objects.get(video_id=video_id)
        input_file_path = video.video_file.path
        input_file_url = video.video_file.url
        input_file_name = video.video_file.name

        # get the filename (without extension)
        filename = os.path.basename(input_file_url)

        # path to the new file, change it according to where you want to put it
        output_file_name = os.path.join('videos', 'mp4', '{}.mp4'.format(filename))
        output_file_path = os.path.join(settings.MEDIA_ROOT, output_file_name)

        # 2-pass encoding
        for i in range(1):
           new_video_720p = subprocess.call([FFMPEG_PATH, '-i', input_file_path, '-s', '1280x720', '-vcodec', 'mpeg4', '-acodec', 'libvo_aacenc', '-b', '10000k', '-pass', i, '-r', '30', output_file_path])
        #    new_video_720p = subprocess.call([FFMPEG_PATH, '-i', input_file_path, '-s', '{}x{}'.format(height * 16/9, height), '-vcodec', 'mpeg4', '-acodec', 'libvo_aacenc', '-b', '10000k', '-pass', i, '-r', '30', output_file_path])

        if new_video_720p == 0:
            # save the new file in the database
            # video.video_file_hd.name = output_file_name
            video.save(update_fields=['video_file_hd'])
            logger.info('Video Processing Finished')
            return video

        else:
            logger.info('Proceesing Failed.') # Just for now

    except:
        raise ValidationError('Something went wrong')


@shared_task
# def task_video_encoding_1080p(video_id, height):
def task_video_encoding_1080p(video_id):
    logger.info('Video Processing started')
    try:
        video = Video.objects.get(video_id=video_id)
        input_file_path = video.video_file.url
        input_file_name = video.video_file.name

        # get the filename (without extension)
        filename = os.path.basename(input_file_path)

        # path to the new file, change it according to where you want to put it
        output_file_name = os.path.join('videos', 'mp4', '{}.mp4'.format(filename))
        output_file_path = os.path.join(settings.MEDIA_ROOT, output_file_name)

        for i in range(1):
            new_video_1080p = subprocess.call([FFMPEG_PATH, '-i', input_file_path, '-s', '1920x1080', '-vcodec', 'mpeg4', '-acodec', 'libvo_aacenc', '-b', '10000k', '-pass', i, '-r', '30', output_file_path])

        if new_video_1080p == 0:
            # save the new file in the database
            # video.video_file_hd.name = output_file_name
            video.save(update_fields=['video_file_fullhd'])
            logger.info('Video Processing Finished')
            return video
        else:
            logger.info('Proceesing Failed.') # Just for now

    except:
        raise ValidationError('Something went wrong')


  


  In my first attempt I wasn't triggering the tasks no where, then I've tried to trigger the task from the schema.py file from graphql inside the video_by_id, but there I've got this error :

  


  backend-celery-1  | django.core.exceptions.ValidationError: ['Something went wrong']
backend-celery-1  | [2023-06-18 16:38:52,859: ERROR/ForkPoolWorker-4] Task video.tasks.task_video_encoding_1080p[d33b1a42-5914-467c-ad5c-00565bc8be6f] raised unexpected: ValidationError(['Something went wrong'])
backend-celery-1  | Traceback (most recent call last):
backend-celery-1  |   File "/usr/src/backend/video/tasks.py", line 81, in task_video_encoding_1080p
backend-celery-1  |     new_video_1080p = subprocess.call([FFMPEG_PATH, '-i', input_file_path, '-s', '1920x1080', '-vcodec', 'mpeg4', '-acodec', 'libvo_aacenc', '-b', '10000k', '-pass', i, '-r', '30', output_file_path])
backend-celery-1  |                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
backend-celery-1  |   File "/usr/local/lib/python3.11/subprocess.py", line 389, in call
backend-celery-1  |     with Popen(*popenargs, **kwargs) as p:
backend-celery-1  |          ^^^^^^^^^^^^^^^^^^^^^^^^^^^
backend-celery-1  |   File "/usr/local/lib/python3.11/subprocess.py", line 1026, in __init__
backend-celery-1  |     self._execute_child(args, executable, preexec_fn, close_fds,
backend-celery-1  |   File "/usr/local/lib/python3.11/subprocess.py", line 1883, in _execute_child
backend-celery-1  |     self.pid = _fork_exec(
backend-celery-1  |                ^^^^^^^^^^^
backend-celery-1  | TypeError: expected str, bytes or os.PathLike object, not int
backend-celery-1  | 
backend-celery-1  | During handling of the above exception, another exception occurred:
backend-celery-1  | 
backend-celery-1  | Traceback (most recent call last):
backend-celery-1  |   File "/usr/local/lib/python3.11/site-packages/celery/app/trace.py", line 477, in trace_task
backend-celery-1  |     R = retval = fun(*args, **kwargs)
backend-celery-1  |                  ^^^^^^^^^^^^^^^^^^^^
backend-celery-1  |   File "/usr/local/lib/python3.11/site-packages/celery/app/trace.py", line 760, in __protected_call__
backend-celery-1  |     return self.run(*args, **kwargs)
backend-celery-1  |            ^^^^^^^^^^^^^^^^^^^^^^^^^
backend-celery-1  |   File "/usr/src/backend/video/tasks.py", line 93, in task_video_encoding_1080p
backend-celery-1  |     raise ValidationError('Something went wrong')
backend-celery-1  | django.core.exceptions.ValidationError: ['Something went wrong']


  


  If anyone has done this kind of project or something like this please any suggestion or help is much appreciated.

  


  Thank you in advance !

  


• Incrementality Testing : Quick-Start Guide (With Calculations)

  26 mars 2024, par Erin

  How do you know when a campaign is successful ? When you earn more revenue than last month ?

  Maybe.

  But how do you know how much of an impact a certain campaign or channel had on your sales ?

  With marketing attribution, you can determine credit for each sale.

  But if you want a deeper look, you need to understand the incremental impact of each channel and campaign.

  The way you do this ?

  Incrementality testing.

  In this guide, we break down what incrementality is, why it’s important and how to test it so you can double down on the activities driving the most growth.

  What is incrementality ?

  So, what exactly is incrementality ?

  Let’s say you just ran a marketing campaign for a new product. The launch was a success. Breakthrough numbers in your revenue. You used a variety of channels and activities to bring it all together.

  So, you launch a plan for next month’s campaign. But you don’t truly know what moved the needle.

  Did you just hit new highs because your audience is bigger ? And your brand is greater ?

  Or did the recent moves you made make a direct difference ?

  This is incrementality.

  

  Incrementality is growth directly attributed to marketing efforts beyond the overall impact of your brand. By measuring and conducting incrementality testing, you can clearly see how much of a difference each activity or channel truly impacted business growth. 

  What is incrementality testing ?

  Incrementality testing allows marketers to gauge the effectiveness of a marketing tactic or strategy. It tells you if a particular marketing activity had a positive, negative or neutral impact on your business. 

  It also tells you the overall impact it can have on your key performance indicators (KPIs). 

  The result ?

  You can pinpoint the highest-performing moves and incorporate them into your marketing workflows. You also discard marketing strategies with negligible, neutral or even negative impacts. 

  For example, let’s say you think a B2B LinkedIn ads campaign will help you reach your product launch goals. An incrementality test can tell you if the introduction of this campaign will help you get to the desired outcome.

  How incrementality testing works

  Before diving into your testing phase, you must clearly identify your KPIs.

  Here are the top KPIs you should be tracking on your website :

  • Ad impressions
  • Website visits
  • Leads
  • Sales

  The exact KPIs will depend on your marketing goals. You’re ready to move forward once you know your key performance indicators.

  Here’s how incrementality testing works step-by-step :

  1. Define a test and control group

  The first step is to define a test group and control group. 

  • A test group is a segment of your target audience that’s exposed to the marketing campaign. 
  • A control group is a segment that isn’t. 

  Keep in mind that both groups have similar demographics and other relevant characteristics. 

  2. Execute your campaign

  The second step is to run the marketing campaign on the test group. This can be a Facebook ad, LinkedIn ad or email marketing campaign.

  It all depends on your goals and your primary channels.

  3. Measure outcomes

  The third step is to measure the campaign’s impact based on your KPIs. 

  Let’s say a brand wants to see if a certain marketing move increases its leads. The test can tell them the number of email sign-ups with and without the campaign. 

  4. Compare results

  Next, compare the test group results with the control group. The difference in outcomes tells you the impact of that campaign. You can then use this difference to inform your future marketing strategies. 

  With Matomo, you can easily track results from campaigns — like conversions. 

  Our platform lets you quickly see what channels are getting the best results so you can gain insights into incrementality and optimise your strategy.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

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  Why it’s important to conduct incrementality tests

  The digital marketing industry is constantly changing. Marketers need to stay on their toes to keep up. Incrementality tests help you stay on track.

  For example, let’s say you’re selling laptops. You can increase your warranty period to three years to see the impact on sales. An incrementality test will tell you if this move will boost your sales (and by how much).

  Now, let’s dive into the reasons why you need to consistently conduct incrementality tests :

  Determine the right tactics for success

  Identifying the best action to grow your business is a challenge every marketer faces.

  The best way to identify marketing tactics is by conducting incrementality testing. These tactics are bound to work since data back them. As a result, you can optimise your marketing budget and maximise your ROIs. 

  It lets you run multiple tests to identify the most impactful strategy between :

  • An email marketing strategy
  • A social media strategy 
  • A PPC ad

  For instance, an incrementality test might suggest email marketing will be more cost-effective than an ad campaign. What you can do is :

  • Expose the test group to the email marketing campaign and then compare the results with the control group
  • Expose the test group to the ad campaign and then compare its results with the control group

  Then, you can calculate the difference in results between the two marketing campaigns. This lets you focus on the strategy with a better ROI or ROAS potential. 

  Accurate data

  Marketing data is powerful. But getting accurate data can be challenging. With incrementality testing, you get to know the true impact of a marketing campaign. 

  Plus, with this testing strategy, you don’t have to waste your marketing budget. 

  With Matomo, you get 100% accurate data on all website activities. 

  Unlike Google Analytics, Matomo doesn’t rely on inaccurate data sampling — limiting the amount of data analysed.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Get the most out of your marketing investment

  Every business owner wants to maximise their return on investment. The ROI you get mainly depends on the marketing strategy. 

  For instance, email marketing offers an ROI of about 40:1 with some sources even reporting as high as 72:1.

  Incrementality testing helps you make informed investment decisions. With it, you can pinpoint the tactics that are most likely to bring the highest return. You can then focus your resources on them. It also helps you stay away from low-performing strategies. 

  Increase revenue

  It’s safe to say that the goal behind every marketing effort is a revenue boost. The higher your revenue, the more profits you generate. However, for many marketers, it’s an uphill battle. 

  With incrementality testing, you can boost your revenue by focusing your efforts in the right direction. 

  Get more traffic

  Incrementality testing tells you if a particular strategy can help you drive more traffic. You can use it to get more high-quality leads to your website or landing pages and double down on high-traffic strategies to increase those leads.

  How to test incrementality

  

  Developing an implementation plan is crucial to generate accurate insights from an incrementality test. Incrementality testing is like running a science experience. You need to go through several stages. Each stage is important for generating accurate results. 

  Here’s how you test incrementality :

  Define your goals

  Get clarity on what you want to achieve with this campaign. Which KPIs do you want to test ? Is it the return on your overall investment (ROI), return on ad spend (ROAS) or something else ?

  Segment your audience

  Selecting the right audience segment is crucial to getting accurate insights with an incrementality test. Decide the demographics and psychographics of the audience you want to target. Then, divide this audience segment into two sub-parts :

  • Test group (people you’ll expose to the marketing campaign)
  • Control group (people who won’t be exposed to the campaign)

  These groups are a part of the larger segment. This means people in both groups will have similar attributes. 

  Launch the test at the right time

  Before the launch, decide on the length of the test. Ideally, it should be at least one week. Don’t run any other campaigns in this window, as it can interfere with the results. 

  Analyse the data and take action

  Once the campaign is over, measure the results from both groups. Compare the data to identify incremental lift in your selected KPIs. 

  Let’s say you want to see if this campaign can boost your sales. Check to see if the test group responded differently than the control group. If the sales equal your desired outcome, you have a winning strategy. 

  Not all incrementality tests result in a positive incremental lift ; Some can be neutral, indicating that the campaign didn’t have any effect. Some can even indicate a negative lift, which means your core group performed better than the test group. 

  Lastly, take action based on the test findings. 

  Incrementality test examples 

  You can use incrementality testing to identify gaps and growth opportunities in your strategy. 

  Here’s an example :

  Let’s say a company runs an incrementality test on a YouTube marketing strategy for sales. The results indicate that the ROI was only $0.10, as the company makes $1.10 for every $1.00 spent. This alarms the marketing department and helps them optimise the campaign for a higher ROI. 

  Here’s another practical example :

  Let’s say a retail business wanted to test the effectiveness of its ad campaign. So, the retailer optimises its ad campaign after conducting an incrementality test on a test and control group. As a result, they experienced a 34% incremental increase in sales.

  How to calculate incrementality in marketing

  Once you’ve aggregated the data, it’s time to calculate. There are two ways to calculate incrementality :

  Incremental profit 

  The first one is incremental profit. It tells you how much profit you can generate with a strategy (If any). With it, you get the actual value of a marketing campaign. 

  It’s calculated with the following formula :

  Test group profit – control group profit = incremental profit 

  For example, let’s say you’re exposing a test group to a paid ads campaign. And it generates a profit of $3,000. On the other hand, the control group generated a $2,000 profit. 

  In this case, your incremental profit will be $1,000 ($3,000 – $2,000). 

  However, if the paid ads campaign generates a $2,000 profit, the incremental profit would be zero. Essentially, you’re generating the same profit as before, which means the campaign doesn’t work. Similarly, a marketing strategy is no good if it generates lower profits than the control group. 

  Incremental lift

  Incremental lift measures the difference in the conversions you generate with each group. 

  Here’s the formula :

  (Test – Control)/Control x 100 = Lift

  So, let’s say the test group and control group generated 2,000 and 1,000 conversions, respectively. 

  The incremental lift you’ll get from this incrementality test would be :

  (2,000 – 1,000)/1,000 x 100 = 100

  This turns out to be a 100% incremental lift.

  How to track incrementality with Matomo

  Incrementality testing lets you use a practical approach to identify the best marketing path for your business.

  It helps you develop a hyper-focused approach that gives you access to accurate and practical data. 

  With these insights, you can confidently move forward to maximise your ROI since it helps you focus on high-performing tactics. 

  The result is more revenue and profit for your business. 

  Plus, all you need to do is identify your target audience, divide them into two groups and run your test. Then, the results will be compared to determine if the marketing strategy offers any value. 

  Conducting incrementality tests may take time and expertise. 

  But, thanks to Matomo, you can leverage accurate insights for your incrementality tests to ensure you make the right decisions to grow your business.

  See for yourself why over 1 million websites choose Matomo. Try it free for 21-days now. No credit card required.

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