The Power of Semi‑Supervised Learning in Sales and Marketing

What is semi-supervised machine learning?

Semi-supervised learning is a hybrid machine learning technique that uses a combination of labeled and unlabeled data.

This technique treats data points differently based on whether they have a label or not. If a data point is labeled, then the algorithm uses the data point to update the weights given to coefficients in, for example, a linear regression equation. If the data point is not labeled, then it seeks to minimize the differences captured in, for instance, k-means analysis.

The efficiency of semi-supervised learning can be improved by a collection of algorithms for active learning. Active learning algorithms need fewer queries to achieve high accuracy than random query selection. But for now, we will consider active learning as a form of unsupervised learning.

Here are the two most important takeaways about semi-supervised learning.

1. Semi-supervised machine learning treats labeled data the same way supervised learning does. It makes predictions and calculates weights for different variables.
2. Semi-supervised machine learning uses unlabeled data points to make the model more consistent. Unlabeled examples build on the progress made with labeled data.

Supervised vs semi-supervised vs unsupervised learning techniques

When it comes to knowing the difference between supervised, semi-supervised, and unsupervised learning, the biggest factor is the type of data each relies on.

Supervised machine learning uses labeled data to train its algorithms. Supervised learning is split into two main types: classification and regression. Classification algorithms could, for instance, assign prospects to a position in your sales funnel. Regression algorithms can identify cut-off values for a decision metric, such as granting credit, based on a combination of factors.

Supervised learning algorithms create a postdictive model that finds relationships between observations that tell humans things they didn't know about the data they have. But because labeling data is time-consuming and expensive, supervised machine learning algorithms often do not have all the data they need to reach their potential postdictive power.

Unsupervised learning finds relationships in unlabeled data. It can't necessarily tell you what your data is describing, but it can tell you which observations are similar. Because human inputs into a labeling process are not required, there is no shortage of data to feed into the algorithms.

But the sheer volume of data in an image can make processing so slow that the amount of useful information from the data is limited by the flood of data flowing into the system. As a result, the analysis may not achieve an adequate F1 score.

The F1 score is a computation of the ratio of true positives (observations that are categorized correctly) to the total of false positives (observations classified as something they weren't) plus false negatives (observations not classified as something they were).

As mentioned, semi-supervised learning does not require all the data to be labeled but can use unlabeled data as well. This characteristic makes semi-supervised learning a middle ground between supervised and unsupervised learning.

The basics of semi-supervised learning

Let's break down how semi-supervised learning works into 4 steps to help you understand some of its key components.

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.

Personalization in email marketing

Everybody uses semi-supervised machine learning in their email account every day. Spam filters operate with labeled data (the messages you have marked as spam) and unlabeled data (messages you probably will never see) to at least reduce the amount of spam mail in your mailbox.

The challenge of semi-supervised machine learning in email marketing is to "filter" your messages, so they are opened and acted upon. For this, semi-supervised machine learning can be extremely helpful.

• Limiting the amount of time you have to spend on A/B testing. With unsupervised machine learning, you can mix more elements into your emails to increase open rates, reader engagement, and click-throughs.
• Calibrating the timing of your emails. Wouldn't you like to know when your customers will have the time and inclination to read your emails? Semi-structured machine learning can identify the habits of email recipients to tell you when to send for best results.
• Promotion personalization. When you know what your customers want, you can sell it to them!

Predictive customer churn modeling

Semi-structured machine learning doesn't just help you make sales. Sometimes, it helps you salvage your customer relationships. Customer churn models are more accurate with less data with semi-structured machine learning.

Common challenges and limitations of semi-supervised learning

It is important to understand that semi-structured machine learning models perform better as they are trained and that their quality will depend on their training data. As such, this can lead to a few different limitations.

Limited labeled data and its impact on model performance

As mentioned, machine learning models are limited by the data they have. This means that a model with limited labeled data will be restricted to that information.

Ongoing maintenance required

Semi-structured machine learning is not a one-and-done model. It requires constant, knowledgeable maintenance. This means that machine learning will need to be a long-term investment and will require oversight as it runs.

Future outlook for semi-supervised learning

Semi-supervised and machine learning as a whole will continue to grow and become available through as-a-service offerings for businesses without the resources to build their own models.

As offerings become more accessible, businesses will be able to grasp machine learning as a tool to boost their services and customer experiences.