Why Marketers Need Automated Machine Learning

We can imagine that marketing before computers was primarily based on intuition. Sure, managers had ledgers that allowed them to see sales lift from advertising campaigns, but without insights about which customers were responding to which programs. (We’ll spare you another recitation of the John Wanamaker quote.) High-value customers were known through personal relationships, but marketing and selling costs could not be tracked very well by individual or account. Manual accounting tabulation meant slow reporting. Successful marketers were those who could “feel” what worked based on this restricted set of information.

How far we’ve come.
Here we are, with widespread use of CRM and marketing automation systems tracking business with phenomenal granularity. We know more about consumer behaviors and customer lifetime value that was imaginable only a few years ago. Advertising is measured and viewed instantly with explicit response tracking and social media profiling. Segmentation is fast approaching single-member entities for targeting and personalization. We can see statistics in reports in near real-time. Filters, consolidations, charts, and dashboards provide an exquisite view using the rear-view mirror of history.

Yet successful marketers are still those who can “feel” what works. Contemporary marketing systems are heavy on the past and light on prediction.

Faced with hundreds or thousands of tags and attributes, conventional methods are ill-suited to deciphering trends and motivations and applying them to prospect and customer databases to produce real predictions. It is just too difficult to code Boolean-logic programs to produce predictive analytics in a timely manner. The promise of accurate marketing attribution to guide the future remains largely unfulfilled.

Human intuition is still the primary way marketers transform data insights into actions.

Until now, that is…
Automated Machine learning (AutoML) is a revolution in predictive accuracy and speed that delivers what marketers need. AutoML is a technique that enables systems to provide answers without explicit programming. Using artificial intelligence (AI) algorithms, AutoML learns from training data sets such as won-lost or lead conversion reports. This “supervised learning” is doing exactly what smart people would do if they had months to work at it with a team of analysts and programmers. It just happens in minutes with no development project. When models are applied to full prospect and customer databases, startlingly precise predictions are generated.

It isn’t magic. AutoML is just automation of AI computational breakthroughs that can find the patterns and associations that have always been present in your data. AutoML gives marketers their first, real tool for augmenting intuition with data science. Finally, we have ways to apply imagination with confidence. AutoML frees marketers to do what they do best; what they really want to do in place of system wrangling. Marketers are free to market.

  • Predict which customers are likely to churn so you can target them with retention programs.
  • Rank offers by predicted likelihood to convert for each prospect and send the best.
  • Forecast which leads will produce deals the highest customer lifetime value.
  • Identify cross-sell/up-sell opportunities that will boost revenue.
  • Offer account based marketing content that actually accelerates customer journeys.

For the first time, predictions that you’ve always sought are accessible without coding. Your world just changed.  Find a way to jump into the revolution. Ask Squark for a free AutoML assessment.

Weak AI

The current state of AI, which does single tasks like playing games recognize images, or predicting outcomes. This is as opposed to Strong AI, also known as Artificial General Intelligence (AGI), which could do anything that humans do.

Explainable AI (XAI)

AI that reveals to human users how it arrived at its conclusions.

Unsupervised Learning

Learning is unsupervised when AI algorithms are given unlabeled data and must make sense of it without any instruction. Such machines “teach themselves” what result to produce. The algorithm looks for structure in the training data, like finding which examples are similar to each other and grouping them into clusters.

Unsupervised Learning is used for clustering, association, anomaly detection, and recommendation engines.

Transfer Learning

This method tries to take training data used for one thing and reused it for a new set of tasks, without having to retrain the system from scratch.

Supervised Learning

Learning from data sets containing labels or known outcomes, where the algorithms build models based on the patterns in that “training” data. The resulting models are generalized and can be applied to new, never-before-seen data. Supervised Learning is used for classification and regression problems.

Training Data contains labels for data columns (features) and known outcomes for the columns (features) to be predicted. Known outcomes may included classifications of two (binary) or more (multi-class) possibilities. Known outcomes that are scalar values (numbers) are used for regression predictions such as forecasts.

When the machine learning process is completed, the Machine Learning system uses models built from Training Data to add predicted values to the Production Data. Production data with the appended prediction values are output as Predictions data sets.

Reinforcement Learning

Leaning from unlabeled data based on reward-punishment feedback with successive tries at stochastic (random) solutions to problems. Reinforcement Learning is useful when there are rules, but no pre-defined methods to approach problems, such as in games or autonomous navigation.

Predictive Analytics

Statistical techniques gathered from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.

Machine Learning

“Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed.” This definition, often attributed to computer pioneer Arthur L. Samuel, is actually a paraphrase of his work from a 1959 paper, “Some Studies in Machine Learning Using the Game of Checkers” in IBM Journal of Research and Development.

This notion that computers could learn from data and outcomes does hold up as a useful description of Machine Learning today. Samuel correctly predicted, “Programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort.”