The Challenge of AI and Marketing Data

Customer data

Let’s reflect a bit on the so-called AI revolution in marketing.

Companies are sitting on an almost endless amount of customer data. Most marketing strategies are already based on data. However, with AI, we can do better.

Before getting into AI, you need to ask yourself one thing:
Which questions do you want to be answered by AI-related methods and tools?

Customers or prospects are creating more and more data which is great for marketing. Today, we rely on marketing analysts to understand the data and discover insights. However, given the amount of data generated, this approach doesn’t scale, that’s why we need AI.

Data control

With GDPR impacting Marketing, data is at the forefront. While these changes make businesses accountable for their data, how they manage that data can ultimately lead to even more siloed, disconnected operations. As a consequence, marketers need to take control of their data.

Do you control your data?

The first challenge is about data ownership. For instance, are you the owner of your data generated on social media? A data analysis of your entire marketing ecosystem should be done. If you do control most of the data, I recommend you to make sure this data is not in silos.

At this moment, you probably need strong data governance to optimize the entire data + AI process.

Workflow integration

Another important aspect of AI: People

Whenever AI is mentioned, we tend to think about algorithms. However, I often see people underestimating the work-flow integration.

It’s really important to understand how marketers work, and it is great if your company has a cloud-based software on a browser, so you can actually track how people use your software and make it better for them. The goal is to make sure that the information created will benefit your teams. In order to achieve this, you need an easy-to-use and scalable interface that can deliver the right information to your marketing team.

Are you ready for an AI solution?

Here are a few identified problems that a business must actually look out for in marketing analytics.

  • Improper data interpretation

This comes as the number one reason for the failure of marketing analytics. Data is only good as the analyzer and therefore interpretation of data is of vital importance. Misinterpretation of facts or preconceived opinions can lead to mistakes in decision-making ruining the whole process in a marketing strategy.

  • Data quality

The vast majority of data fails to meet the basic standards needed to properly train a predictive model (ML). The quality demands of machine learning are steep, and bad data can impact the entire process (in the data used to train the predictive model and second in the data used to make future decisions). To compensate, data scientists have to “clean” the data before training the predictive model. It’s the problem data scientists complain about most.

  • Data Silos

Data Silo: repository of fixed data that remains under the control of one department and is isolated from the rest of the organization

It is necessary for all the information being captured to be collected in a central repository. Many organizations continue to have silos of data being stored in the capture system but they are not integrated together.

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You can either create a central repository in a structured environment such as a marketing database or in an unstructured environment such as a data lake. Many organizations are starting to rely on data lakes to enable the capture of unstructured data.

Data Lake: a centralized repository that allows you to store all your structured and unstructured data at any scale

An aspect of AI technologies that is often overlooked, is that AI requires good data to perform effectively.

AI & Marketing?

Artificial intelligence and data are being put to good use in marketing in a number of different ways. The use case that is the most familiar to people is recommendations. From product recommendations on Amazon to movie recommendations on Netflix, these are all being driven by AI algorithms.

AI will provide your company with a holistic view of marketing. This makes the integration of any new data extremely fast, as compared to earlier. This also means marketers can start reporting on their newest campaigns immediately.

Some of the marketing disciplines that AI can perform or assist with are:

HIAI

My personal opinion is that analytics-focused practitioners should be thinking of HIAI–Human Intelligence, Artificial Intelligence–as a hybrid approach.

I think there’s great potential in taking an HIAI approach in marketing analytics. Indeed, there’s a lot of human knowledge–learned over time through experience that we cannot just ignore.

AI uses large arrays of data to identify valuable patterns

In a data-driven world, marketing teams can be overwhelmed with a number of different data sources and marketing software services. Companies are seeking to integrate insights across all these tools in a way that is less time-consuming and costs less.

Through analytics, companies can leverage AI to identify, to find out how a specific can be relevant to consumers. By doing so, it will become easier and faster to determine the marketing approach.

Source

AI is a synonym of data… a lot of it. these massive data sets required are difficult to obtain for most companies, and labeling remains a challenge. Most current AI models are trained through “supervised learning”, which requires humans to label and categorize the underlying data.

Supervised learning: the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.

However, new techniques are emerging to overcome these data issues, such as reinforcement learning, generative adversarial networks, and transfer learning.

Having the right data governance, data quality and data infrastructure are really important before considering an AI solution.

Original. Reposted with permission.


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Opinions expressed by contributors are their own.

About Alexandre Gonfalonieri

Contributor Consultant at CKS Consulting | Writer

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