4 Ways Machine Learning Can Level Up Your Agency’s Client Reporting

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    As your marketing agency battles to win (and keep) clients in today’s competitive environment, finding a way to maintain an edge over your competition is a constant fight. One way to sharpen your agency’s value proposition is by leveling-up in an area that most agencies choose to put little effort into: client reporting.

    However, creating detailed and insightful reports can be time-consuming, especially as your client roster expands. Luckily, we come to you with a potential productivity-booster: machine learning.

    Machine learning can be used to automate many of the tedious and time-consuming aspects of client reporting, allowing your team to focus on solving your clients’ biggest marketing challenges.

    Before we dive into how you can leverage machine learning to your advantage, let’s explore what it is.

    What is Machine Learning (ML)?

    Machine learning is a subset of artificial intelligence that allows computers to learn from data and make predictions or decisions without being explicitly programmed to do so.

    ai vs ml vs deep learning

    ML algorithms use statistical techniques to analyze and identify patterns in data, and then use these patterns to make predictions or decisions without being explicitly programmed.

    Machine Learning vs. Artificial Intelligence

    The term machine learning is often used interchangeably with artificial intelligence (AI). However, as we mentioned, they are two distinct concepts (ML being a subfield of AI).

    Artificial Intelligence refers to the broader concept of creating machines that can perform tasks that would typically require human intelligence, such as understanding natural language, recognizing objects, making decisions, and solving problems. AI systems aim to simulate human intelligence and reasoning capabilities.

    4 Ways to Leverage Machine Learning In Your Client Reporting

    Now that we have a better idea of what ML means, let’s establish 4 ways you can use ML to level up your agency’s client reporting:

    1. Automated Data Collection & Cleaning
    2. Predictive Analytics
    3. Customizable Dashboards
    4. Natural Language Processing

    1. Automated Data Collection & Cleaning

    One of the biggest challenges of client reporting is collecting and cleaning the necessary data. This often involves pulling data from various sources, such as GA4, social media platforms, and email marketing software. Manually collecting and cleaning this data can take hours, especially when working with multiple clients.

    Machine learning can automate and optimize the data collection and cleaning process by integrating with various data sources and using algorithms to clean and organize the data. This can save your team hours of work each week, allowing them to focus on analyzing the data and creating valuable insights for clients.

    2. Predictive Analytics

    Machine learning algorithms can also be used to make predictions about future trends and behaviors based on past data. For example, predictive analytics can be used to forecast website traffic, social media engagement, and email open rates.

    By incorporating predictive analytics into your client reporting, your team can provide clients with valuable insights into upcoming trends and behaviors, allowing them to make informed decisions about their marketing strategies.

    3. Customizable Dashboards

    Dashboards are an important aspect of client reporting, as they allow clients to view their marketing performance metrics quickly and easily. However, creating customized dashboards for each client can be time-consuming and tedious.

    Machine learning can be used to create customizable dashboards that automatically update with real-time data. These dashboards can be tailored to each client’s specific needs and preferences, allowing them to view the metrics that are most important to them.

    4. Natural Language Processing

    Finally, natural language processing (NLP) can be a major time-saver in data analysis. Thinking generally, NLP can be leveraged in two ways in data analysis:

    • (1) interpreting and summarizing qualitative data
    • (2) simplifying the query-generation process.

    By using NLP algorithms, your team can quickly identify trends and insights from data, providing valuable insights to clients without the normal legwork.

    For example, NLP can be used to identify common themes and sentiment in customer reviews, allowing clients to make data-driven decisions about product development and customer service.

    What Does It Take to Implement Machine Learning in Your Reporting?

    So, what are some practical steps you can take to start using machine learning in your marketing analytics? Here are a few ideas to get you started:

    Start With A Clear Problem Statement

    Before you dive into machine learning, it’s important to have a clear understanding of what problem you’re trying to solve.

    What specific business challenge are you trying to address? What data do you need to analyze? What outcomes are you hoping to achieve?

    By starting with a clear problem statement, you can ensure that your machine learning efforts are focused and effective.

    Identify The Right Data Sources

    Once you have a clear problem statement, the next step is to identify the right data sources. This may involve gathering data from a variety of different sources, such as CRM systems, social media platforms, and website analytics tools. It’s important to ensure that the data is accurate, complete, and relevant to the problem you’re trying to solve.

    Choose The Right Machine Learning Algorithms

    With your data in hand, the next step is to choose the right algorithms to analyze it. There are a wide variety of machine learning algorithms to choose from, each with its own strengths and weaknesses.

    Some common algorithms used in marketing analytics include decision trees, random forests, logistic regression, and neural networks. The choice of algorithm will depend on the specific problem you’re trying to solve and the characteristics of your data.

    Test And Iterate

    Machine learning is an iterative process. It’s unlikely that you’ll get everything right on the first try, and that’s okay. It’s important to test your models, evaluate their performance, and iterate on your approach.

    By continually refining your models and incorporating new data, you can improve their accuracy and predictive power over time.

    Monitor And Measure Results

    It’s important to monitor and measure the results of your machine learning efforts. Are your predictions accurate? Are your marketing campaigns delivering the expected results? By closely monitoring your results, you can identify areas for improvement and make data-driven decisions to optimize your marketing strategies.

    Select The Right Technology Stack

    Outside of understanding which algorithms to leverage, selecting the right tools is the most difficult (and confusing) part of implementing machine learning in your reporting.

    There’s an abundance of choice in business intelligence and data analytics platforms on the market that can help you build a reporting workflow that leverages ML. As you build your reporting tech stack, you should consider:

    1. How easily does the platform integrate with your data sources? What is the cost (and timeline) to integrate to new data sources as you add clients?
    2. Can you quickly make use of your data using low- and no-code transformations, merges, fusions, and more?
    3. Does the platform give you the flexibility to add your own ML algorithms and garner the insights you’re looking to showcase?
    4. Does the platform allow you to quickly and easily deploy fully customized dashboards & reports on a per-client basis?
    5. Is the platform built from the ground up to facilitate a smooth collaboration and sharing experience with your clients?

    Be aware that many tools on the market perform well at tackling one or two of these challenges, but few perform well in all 5 areas

    What to Expect by Adding Machine Learning to Your Client Reporting Workflow

    So, what exactly can you gain by adding machine learning to your agency’s client reporting workflow?

    While no one could promise you’ll suddenly see a 10x increase in ROI, here are some outcomes we think you can achieve with ML:

    1. Increased productivity in your client reporting workflow
    2. Cleaner and more unified data
    3. Deeper understanding of trends & patterns
    4. Forward looking insights
    5. More consistent qualitative data analysis
    6. Position your agency as truly data-driven.

    These 5 outcomes can help keep your marketing agency competitive in the consistently challenging agency environment, showcasing clear and actionable client reporting as an essential part of your value proposition. While you could achieve these outcomes through manual data manipulation, machine learning can automate your data workflows and unlock deeper insights for your clients without breaking the bank.

    Remember that while machine learning can automate certain aspects of client reporting, human expertise is still crucial for interpretation, strategy development, and decision-making. Use machine learning as a powerful tool to augment your capabilities and provide deeper insights to your clients – they’ll thank you for it!