Research on machine learning and artificial intelligence is advancing daily, with interesting implications for the economy. While many of the great innovations appear to be only in scientific fields, advances in the marketing discipline can also be seen. Below are some areas for marketers to explore to improve their processes.
Increased Accessibility to MLOp Processes
Open source machine learning frameworks have led to Machine Learning Operations (MLOps), a variation of devops influenced by deep learning that involve data modeling.
The concepts may seem too technical to some marketers, but growth has produced many tools and solutions that make it easy for non-tech marketers to help with the model workflow. For example, marketers unfamiliar with GitHub can still help capture model prep steps in GitHub projects, a project manager role in the GitHub repositories.
What This Means: Marketers should look for ways to use their business acumen without testing their skills or meddling in planning decisions that must lie with data engineers or managers.
Improved Capabilities in Integrated Analytics Solutions
Not all companies rely on open source solutions for analysis. Packaged solutions like Microsoft Power BI, Tableau, and Google Data Studio are also widely used to make data-related decisions. They generally have a friendly user interface that makes adding plugins and dependencies easy.
Microsoft, for example, offers a premium option in Power BI for access to Azure Cognitive Services, which introduces various algorithms such as sentiment analysis, speech recognition and image tagging into its workflows.
Dependency options can enrich existing data with data from various sources, which can be useful when planning machine learning models for training. The end result is a more sophisticated data preparation workflow that enables faster preparation for specialized data models.
What this means: Marketers should investigate how their tools combine data and simplify the data workflow. Look for solutions that offer the best integration for repetitive processes. Also expect more workflow integration options as some solutions add documentation or project management integration capabilities, to facilitate collaboration between teams.
Know the Fine Points of the Great Data Debates
Marketers need to be aware of the ongoing discussions and debates about the types of modeling data preparation techniques used in machine learning models. Many different frameworks and techniques have found new uses, so even seasoned data scientists are starting to get involved in the discussions.
Well known machine learning professor at Stanford University Andrew Ng wrote an interesting post about the development of data and code in model development last summer. It should be noted that code has been emphasized over data for years to model change.
What This Means: Marketers should add news about machine learning practices and concepts to their daily resources. These debates may seem strictly academic, but new approaches can have downstream consequences for ML business models. An example of such a debate is a wider awareness of the implications and use of facial recognition among people of color (which I have written about here).
Learn How to Apply Old Techniques for New Uses With Machine Learning
To support Professor Ng’s point of view, data scientists are discovering how older research concepts can be applied to new data sets that have real concerns, be it in business or in the life sciences. The data increasingly contain real functions such as geolocation. It opened up new possibilities for what can be done with data, from opening up access to data to coordinating joint projects between teams in multiple locations.
In the process, the data modeler learned to apply strictly academic concepts to real business applications. Feature engineering began to include more data based on product characteristics. A machine learning performance concept, the Shapley Value, has been around since the 1950s, with developers finding a new use for it through the amount of data that goes into customer approval processes such as homebuyer mortgage approval.
What This Means: Marketers should look for ways in which context is applied to data. Old techniques can lead to creative ideas on how machine learning solutions can be applied to business problems. Look for a variety of model concepts that focus on refining the results such as General Adversarial Networks (GAN), which make checks on how a model distinguishes between different types of images.
Keep the Supply Chain in Mind
This year’s holiday shopping season has been overshadowed by delays in supply chain deliveries; a wide variety of products, from consumer products to automobiles, are affected; In 2020, companies are quickly assembling their best machine learning processes to predict supply chain flow. It is likely that in 2022 the processes and functions available for predictive modeling will be refined.
What This Means: Marketers need to stay informed about the impact supply chain management has on the delivery of products and services. Use the knowledge to determine the timing of your marketing campaigns and messages.
Increased Exploration of Price Optimization
Partly due to supply chain issues, prices have also skyrocketed on several different products and merchandise, so interest in price optimization is expected to increase in 2022 to better match customer demand with potential income. it can be applied to price data to reveal correlations and potential optimization opportunities. This is especially useful when the price of the product is extremely volatile. It’s also useful for companies operating in industries with high churn rates. Basic risk models in optimization options to avoid the stirred client.
What This Means: Marketers should explore how to develop the best optimizations in their industry, building on techniques emerging from open source machine learning frameworks.
Machine Learning in Digital Marketing Media
A variety of digital ad analytics and solutions are now integrating machine learning algorithms as a platform function. In 2022, marketers will continue to rely on platforms that require some level of predictive analytics for bid optimization.
What This Means: Marketers need to be prepared to decide when and where to introduce human intervention into programmatic advertising campaigns. Some concerns have been raised in this area, but the new features will leave marketers wondering if the messages go too far in their digital marketing campaigns.