How to Measure the Value of LinkedIn Ads with Zero Clicks

· 10 min read

Table of Contents

    BrightonSEO Presentation


    Though advertisers can already directly see a good proportion of value from their adverts via engagement or form fills, it's likely they are missing out on at least a reasonable amount of the impact.

    What if we could measure whether advert impressions lead to website visits or even sales - without the need for clicks?

    This guide will outline how - using the LinkedIn API, fuzzy matching and IP look up services.

    The Value of LinkedIn for Advertisers

    LinkedIn has made a strong case for itself in the advertising domain.

    A significant 40% of B2B advertisers affirm that they source quality leads from LinkedIn - likely from 'Lead Gen Form' ad formats, for example.

    However, the full potential of these LinkedIn ads remains unknown for many, given that the average click-through rate to websites stands at a mere 0.08%.

    The Quest to Connect LinkedIn Activity with Sales

    The underlying question for many advertisers is: How can we determine if our LinkedIn activity is having an affect on sales?

    LinkedIn's user interface does provide some insights on ad engagement and views:

    Example of the LinkedIn UI data breakdown

    However, this barely scratches the surface:

    • It doesn't give you all organizations that have seen your ads - only a selection
    • It doesn't give you creative/advert level data

    Most businesses yearn for deeper, ad-level insights and a comprehensive view of all organizations that saw an advert or campaign.

    Getting Started with the LinkedIn API is the First Step

    The LinkedIn API gives you a lot more. To harness the true potential of LinkedIn ads, integration with the LinkedIn API is essential.

    What you need to do to get started:

    Once you have access, you'll need to extract that data. Here's some starter code for getting up and running with extracting data the LinkedIn API:

    import requests
    import pandas as pd
    # Set the API endpoint URL
    url = ""
    # Set the API headers
    headers = {
        'Authorization': '' #Add your token here
    params = {
            'q': 'analytics',
            'accounts[0]': 'urn:li:sponsoredAccount:', #Add your account ID here
            '': '01',
            'dateRange.start.month': '02',
            'dateRange.start.year': '2023',
            '': '01',
            'dateRange.end.month': '02',
            'dateRange.end.year': '2023',
            'pivot': 'MEMBER_COMPANY',
            'timeGranularity': 'DAILY',
            'fields': 'pivotValues,dateRange,clicks,impressions,actionClicks,adUnitClicks,costInLocalCurrency,costInUsd,externalWebsiteConversions,videoStarts,videoViews,videoCompletions,totalEngagements,videoMidpointCompletions,reactions,follows,comments,approximateUniqueImpressions,registrations'
    response = requests.get(url, headers=headers, params=params)
    table = response.json()
    data = table['elements']
    df = pd.DataFrame(data)

    Alternatively, you can run this in Google Colab.

    This is the rough format of the data you'll receive:

    Format of the LinkedIn data you receive via the API

    Two Pioneering Methods to Track LinkedIn's Impact

    1. Analyzing the Impact on Sales Only: This method exclusively examines the influence of LinkedIn activity on sales.
    2. Analyzing the Impact on Website Behavior: This strategy delves deeper by studying the effect of LinkedIn activity on website interactions.

    1. Analyzing the Impact on Sales Only

    To derive meaningful insights, businesses need to merge their CRM data with LinkedIn data. This can be achieved using fuzzy matching algorithms. Such algorithms can help businesses discern patterns, such as whether video posts are more likely to lead to conversions.

    Harnessing Data from CRM Platforms

    Firstly you need to extract sales data from your CRM - such as Salesforce. You only need the basic information, such as business name, whether they are client and the date they became a client.

    Fuzzy Matching CRM Data to LinkedIn API data

    Once you have the data from your CRM, you can fuzzy match the names from the CRM system to the data you extracted via the LinkedIn API, allowing you to augment with all of the LinkedIn data points you have extracted (e.g. impressions):

    Process for fuzzy matching CRM and Linkedin API business names.

    Here's some code for a fuzzy matching algorithm as a starter:

    import pandas as pd
    from fuzzywuzzy import fuzz
    # Sample data with famous company names
    data = {
        'Column_A': ['Apple Inc.', 'Google LLC', 'Microsoft Corp.', 'Facebook, Inc.', ' Inc.'],
        'Column_B': ['Apple', 'Gogle', 'Microsft', 'Facebok Inc.', 'Amazone']
    df = pd.DataFrame(data)
    # Fuzzy match columns and get the ratio
    df['Match_Score'] = df.apply(lambda row: fuzz.ratio(row['Column_A'], row['Column_B']), axis=1)

    Alternatively, you can open this in Google Colab.


    By default, LinkedIn will give you an organization ID instead of the organization name when you extract data using the API. In order to fuzzy match the organization names to the names contained within your CRM system, you will need to lookup the organization ID using the organizationsLookup endpoint.

    Getting the Data into the Right Format

    Once you have the businesses that are your current clients and the LinkedIn data associated with that business (via the fuzzy match), you need to ensure the data is in the right format so that you can start to assess what the most important features for driving sales via LinkedIn. You probably want to answer questions such as:

    • Which is the best ad format for driving sales?
    • Which is the best performing campaign?
    • Which is the best performing campaign group?

    Here's the format you need - the business, a column for each 'feature' that you want so assess with the value for the associated business. And crucially, whether the business went on to become a sale or not:

    Example data format required for machine learning algorithm.

    As you can see, you could have columns for:

    • Number of impressions via video/image/text ads
    • Number of impressions for each campaign
    • Number of impressions for campaign groups

    As long as it is a value associated with each business (Client ID in the image above).

    Analyzing the Results

    We're now going to train a machine learning model to take the data as an input and learn which features (for example, impressions by ad format) best predict whether a business will become a client.

    How machine learning can help you understand data with feature importance

    You can just use ChatGPT's Code Interpreter to guide you through this. Here's an example prompt. Ensure you upload your data in the correct format outlined above:

    Example prompt for using XGBoost and feature importance to analyze your data.

    If you don't have access to Code Interpreter, here's some starter code for training an XGBoost model:

    # Import necessary libraries
    import xgboost as xgb
    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score
    # Load the iris dataset - EXAMPLE, REPLACE WITH YOUR DATASET
    iris = load_iris()
    X =
    y =
    # Split the dataset into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    # Convert data into DMatrix format
    dtrain = xgb.DMatrix(X_train, label=y_train)
    dtest = xgb.DMatrix(X_test, label=y_test)
    # Set model parameters
    param = {
        'max_depth': 3,
        'eta': 0.3,
        'objective': 'multi:softprob',
        'num_class': 3
    # Train the model
    num_round = 20
    bst = xgb.train(param, dtrain, num_round)
    # Predict the classes
    preds = bst.predict(dtest)
    best_preds = [int(p.argmax()) for p in preds]
    # Calculate accuracy
    acc = accuracy_score(y_test, best_preds)
    print(f"Accuracy: {acc:.2f}")

    More about Feature Importance

    Feature importance and feature engineering are really important topics for ensuring your model performs well. There are a number of different methods to assess feature importance, including using different permutations of features and different values for the assessment.

    Some important points to note:

    • You want to assess a similar number of organizations that did not become a sale, to the number that did become a sale (say 500 each).
    • As part of your process with Code Interpreter (and included in the starter code above), you should assess the accuracy of the model. Looking at important features of a model that is less than 95% accurate isn't ideal.
    • You can train various models (see other example of supervised machine learning models here) and then use the averages of the feature importance values to try and provide a robust method to get around a lack of data - but this is still only available when you have reasonable accuracy (above 85% for each model).

    2. Analyzing the Impact on Website Behavior

    The less sales data you have, the less accurate your model will be.

    Graph showing data volume vs machine learning model accuracy.

    Therefore, one answer to still gain insight into the performance of your LinkedIn ads is to expand the amount of data. What if we can use micro conversions further up the funnel instead of final sales - and assess the impact of LinkedIn ad impressions on those instead?

    Conversion funnel example - there are more micro conversions than sales

    The way to do this is to lookup the IP addresses of visitors to your websites and fuzzy match those to your organization-level LinkedIn data instead.

    Diagram showing how to fuzzy match LinkedIn data to website visitors.

    A number of services can help you do this:

    This means you can see who has been on your website from the accounts you are targeting on LinkedIn, and then determine if they have behaved in a way that shows interest in your product.

    Using Website Behavior Segments as Micro Conversions

    Behavior segments on a website can act as invaluable micro-conversions - particularly when assessing your LinkedIdIn activity.

    If you are able to identify key behavioural segments on your website that map to potential job titles on LinkedIn, then you can infer whether your job title targeting has been successful, based on the behaviour of the users that came to your website from that account.

    For example, if you have a technical product and you target developers on LinkedIn, then users from that account spend a lot of time in your product, you know you've reached and got your message to the right people.

    Here's a presentation I did at MeasureFest about how to cluster your website users based on their behaviour:

    Want to Learn More about Clustering?

    Clustering is an unsupervised machine learning technique that finds patterns in your data and can be really useful for audience segmentation. There are lots of different algorithms to use. We have created a full guide with walkthrough and code examples.

    The Advantages of Event Data in GA4

    GA4 (Google Analytics 4) has actually made this analysis a lot easier, by being events' focused. This means that for each user, you can get individual data points about their behaviour, such as URLs they've visited.

    This analysis is much easier if you connect your Google Analytics 4 account to BigQuery:

    Image shoing how to connect your Google Analytics 4 account to BigQuery.

    You should do this as soon as possible, as the data does not backdate.

    Correlating Time Proximity of Impressions and Website Visits

    You can also perform a correlation analysis between ad impressions and website visits. Did ad impressions for an account happen in close proximity to when the website visits from that account occurred?

    Code Interpreter can help again here, if you need:

    Using ChatGPT's Code Interpreter for correlation analysis.

    This can help in further discerning the impact of LinkedIn ads on website interactions and micro conversions.

    How you can take this further

    The insights derived from LinkedIn can be instrumental in shaping strategies for other channels. You can take your learning about the kinds of content that perform well on LinkedIn into your organic search strategy, for example.

    You can also add extra channels when training your supervised machine learning model:

    Data for training a machine learning model to predict sales with extra columns for other channels.


    LinkedIn ads can offer much more value than businesses might initially realize. By setting up the LinkedIn API and tapping into the power of tools like GA4, businesses can unearth deep insights that go beyond mere clicks. By using platforms like ChatGPT's Code Interpreter to walk you through some of the technical aspects, you can really unlock the potential for LinkedIn ad analytics.

    Richard Lawrence

    About Richard Lawrence

    Constantly looking to evolve and learn, I have have studied in areas as diverse as Philosophy, International Marketing and Data Science. I've been within the tech space, including SEO and development, since 2008.
    Copyright © 2024 evolvingDev. All rights reserved.