How To Design Workflows In Marketing Based On AI And Machine Learning

How To Design Workflows  In Marketing Based On AI And Machine Learning

Automation is a crucial trend in inbound marketing. Given the amount of traffic and data we handle today, it is essential to have automated processes that help us manage everything and not miss any opportunity. But the processes can become very complex, and traditional automation tools fall short. This is where machine learning and artificial intelligence come into play, so let’s see how we can use them to organize your workflows better.

Why Do You Need Marketing Workflows?

A workflow is a series of steps to follow to manage a project or launch a marketing campaign. The workflow defines the sequence of actions that are carried out to carry out the task. Using workflows helps us organize the various tasks of inbound marketing: reviews, approvals, results from monitoring, priority changes. To see it more clearly, we are going to review very common workflows in day-to-day marketing.

How To Automate Your Inbound Marketing Workflows With AI

The Unstructured Data Problem

Automating processes is difficult because many workflows require human intervention. The reason for this is that they use what is known as “unstructured data.” The unstructured data can not quickly be introduced into a database as it does not fit specific formats or sequences. By not having an easily identifiable structure, they are more challenging to search, manage and analyze.

For example, a list of songs, singers, and music genres might be considered structured data, but the songs themselves would be unstructured. Other prevalent examples in inbound marketing would be emails, presentations, texts, or images. Working with unstructured data is a challenge for automating inbound marketing since it cannot be integrated into existing information systems. Therefore, traditional automation solutions cannot process it.

To overcome this obstacle, the answer is to incorporate machine learning into unstructured processes, to add a cognitive element that helps make sense of the information. This is done using technologies such as:

  • Computer Vision: This technology identifies, classifies, and processes images or objects in pictures and videos. For example, it can be used to identify diseases in photos, classify PDF attachments or organize the inventory of an online store.
  • Natural Language Processing: It is used to understand, categorize or prioritize texts. For example, it can create chatbots, prioritize customer service requests, or do sentiment analysis.
  • Optical Character Recognition: This technology is capable of recognizing printed or handwritten characters within digital images of documents. It is used to extract information.
  • Sound Processing: It is used to identify, classify and process audio files or ambient sounds. For example, we can use it to tag music, search for songs, or implement voice controls. Therefore, these technologies help us create and train machine learning models that enable us to automatically decipher our unstructured data and incorporate it into our processes.
  • Also Read: The Circle Of Digital Marketing

How To Incorporate Cognitive Automation Solutions Into Your Workflows

Depending on your company’s situation, there are various solutions to incorporate cognitive automation and be able to interpret unstructured data. It is not necessary to rethink the entire structure of your company from top to bottom, but you can look for the tool that suits you. These are six options:

  • Self-Service Tools: Self-service workflow cognitive automation platforms empower you to manage multi-phase processes that use unstructured data. These are low-code solutions that allow you to create personalized workflows with machine learning algorithms through an easy-to-use interface. Compared to other automation solutions, self-service tools are affordable. With its ease of use, this allows you to do experiments and implement the results quickly.
  • Machine Learning Solutions: These platforms allow you to create machine learning models from scratch with little or no programming knowledge. They are handy for giving structure to unstructured data, but they do not have workflow creation tools, so they do not serve to automate processes from start to finish.
  • Automation Solutions With Specialized Artificial Intelligence: These tools are focused on a specific sector. This means that they can manage complex processes with unstructured data, but their application area is limited. Another aspect to consider is that they tend to have high prices due to the high cost of preparing the data necessary to train the algorithms.
  • Smart RPA: Classic robotic process automation (RPA) has some limitations when it comes to processing unstructured data. But as new machine learning technologies are developed, traditional RPA solutions are incorporating artificial intelligence capabilities to improve process automation. This type of solution is especially suitable for medium and large companies that have specialized RPA developers.
  • AI Consulting: Large companies that have to automate complex processes with unstructured data can turn to specialized AI consultancies to find the best solution. Typically, these consultants develop bespoke innovative automation solutions or suggest vendors that are best suited to specific customer needs.
  • Experts In Artificial Intelligence On Staff: Finally, the most complex cases’ definitive solution is to incorporate intelligent workflow automation within the company’s workforce. Companies with large IT departments are likely to have the resources to manage intelligent automation “in-house,” perhaps by hiring additional programmers or reassigning roles within the team. This allows you to design complex and personalized solutions, but the problem is finding and hiring skilled enough people for it.


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