As your marketing agency battles to win (and keep) clients in today’s challenging 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 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.
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 ML In Your Client Reporting
Now that we have a better idea of what ML means, let’s establish 3 ways you can use ML to level up your agency’s client reporting:
- Automated Data Collection & Cleaning
- Predictive Analytics
- Customizable Dashboards
- Natural Language Processing
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 Google Analytics, 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 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.
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.
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.
Natural Language Processing
Finally, natural language processing (NLP) can be used to analyze and interpret text data, such as social media posts and customer reviews. By using NLP algorithms, your team can quickly identify trends and insights from text data, providing valuable insights to clients.
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.