1. Data labeling
First, and one of the most important components, is the data. As mentioned, semi-supervised learning uses labeled and unlabeled data to train its algorithms.
With semi-supervised learning, you will first add labels to some of the data. This will give you a foundation to build on, which will be important for the following steps.
2. Model training
Now that you have labeled data, you must teach your algorithm what to do with it and what outcomes are expected before adding any unlabeled data to the mix.
3. Integrating unlabeled data
Once your model is trained on the labeled data, you can add in the unlabeled data. Because this machine learning technique can use both types of data, this lets you reduce costs compared to supervised learning because you are expanding your dataset through the addition of unlabeled.
4. Model evaluation and refinement
Machine learning requires evaluation and changes to ensure that the model you have created is accurate. Training is continuous progress, so you expect to have to make adjustments to your algorithm.
The advantages of semi-supervised learning in sales and marketing
Machine learning in marketing can help you improve your lead scoring for enhanced personalization to help you identify your target audience, manage your audience, and reduce customer churn.
Semi-structured machine learning is also useful for a variety of data analysis goals common to digital marketing operations.
Improved customer segmentation
In studies of semi-supervised machine learning as a tool for improving customer segmentation, the technique is often described as a feed-forward neural network trained by a backpropagation algorithm.
What that means is that when you don't have complete information on your prospects, a semi-supervised machine learning program can backfill data so you don't have to collect, enter, and verify it.
Why is this important?
You have probably come across the Pareto Principle. Applied to a business, this would mean that 20% of your customers generate 80% of your profits. Or you may be familiar with an idea developed by two marketing researchers named Reichfeld and Teal called The Loyalty Effect. A 5% increase in customer loyalty can result in a 20 to 95% increase in your profits.
There is a tradeoff between the cost of data collection and the benefits of improved customer segmentation with supervised learning programs, but the cost of data collection is much lower with semi-supervised learning programs.
Enhanced lead scoring and qualification
Salespeople want to be able to predict which leads will close with a sale. Especially in B2B sales, the data to score and qualify leads probably already exists in your web tracking and email analytics from Mailchimp, plus your CRM database records.
Some lead scoring and qualification can be done with supervised machine learning. For instance, your Mailchimp analytics data will tell you the number of emails opened. You can mine your sales data to compute the correlation between the number of emails opened and sales conversions.
Similarly, you could compute a statistic correlating the number of visits to your website with the probability of closing a sale with your Mailchimp web tracking data. But probably, you would find that not all visits to web pages are the same. Semi-supervised machine learning can identify the on-page analytics that add to the predictive power of your lead scoring and qualification model.
Increased targeted advertising effectiveness
Targeted advertising is oriented toward audiences that share certain characteristics, depending on the product being promoted. These characteristics can be demographic, psychographic, or past patterns of buying decisions.
Once the target audience is identified, then the advertising is property-targeted, placed on a particular page of a chosen website, or behaviorally targeted, displayed after a prospect performs a certain behavior online.
The effectiveness of targeted advertising is limited by the amount of data the seller has on prospective customers. Semi-structured machine learning works with the data on hand. It creates a model of buyer behavior based on a mix of labeled and unlabeled data.
It creates pseudo-labels to make further predictions and refines the model as sales data comes in. As the model fills in more and more of the gaps in data collection, it offers a better and better understanding of the ideal buyer persona.