The first thing we need to know is that “learning” is not memorizing. If we learn the book, we also remember it — to some degree, at least. However, if we place book’s PDF on computer’s hard drive, it will remember it, but never learn. For the machine, it will look like some chunks of whatever.

The computer will not even care because he is not programmed to do so.

Thus, machine learning means making computers to care about information. In case of marketing, it is caring about factors that determine ROI, ads effectiveness, best-converting prices, and target audiences — so care about profits marketers receive.

Today, Captain will explain why merely drawing charts and tables is not machine learning marketing, and how it is used for doubling, tripling and quadrupling profits by professionals.

How Computers Could Care More, and Why It Is Critical for Machine Learning Marketing?

We all know about Big Data — megabytes, gigabytes and even terabytes of data, which is crucial for analytics. Remarkably, even a couple of megabytes of unsorted and unorganized data can bring a human being to its knees. That is exactly the problem that marketers face today — General Metrics of Facebook Advertising campaigns already look like this.

If you add up details, it will transform into a kilometer-long table, that you will be unable to master. To reinforce the understanding, let us remind you how Google Analytics’ small report looks like — the thing that the majority of us see every day.

Can you already use this data to increase conversion rate and ROI to accomplish your marketing objectives? Probably, no. At the same time, computers are made to work with data tables, and they will be significantly better than humans in working with marketing data. Even your cell phone can browse through the table faster than you can even think about it.

What we need to computers to do is to find consistent patterns in marketing data, which is the first step of every analytic. For instance, if we found that males in Boston converse to Captain’s ads worse than any other audience segments, we would exclude them from the campaign and save money on CPM. If we revealed that kittens on the landing page increase sales more than penguins, penguins would be thrown out into the trash bin.

Finding and using data patterns and connections between variables is the primary task of marketing analytics, which takes 80% of average marketer’s work routine. If we could use computers for it, we would save this time for planning, creating ads and strategies — the most value-adding activity in marketing.

So, if we order the computer to find patterns, it is enough for machine-powered analytics, isn’t it? Unfortunately, data is much more troublesome, and that is why machine learning has been revolutionary in marketing.

What Is Wrong with Data Patterns and Why Machine Learning Marketing Analytics Crave Intelligence?

If the data patterns analysis was so easy, then data scientists would not be hired by Google and Facebook as quick as new iPhones are being sold out on preorder. Captain remembers many hilarious cases where mathematically-true patterns between variables appeared to be nonsensical.

For instance, let us take a computer, connect it to Wikipedia and allow extracting any data to find the cause of the global warming. The machine makes some noise with its fans, loads for a while and draws this graph.

Indeed, when we check pirates’ quantity and average Earth’s temperature by alone, they look like related — when there were many sea bandits, the temperature was lower. We may even retest these results because, with the emergence of Somali pirates, the global temperature decreased, which now fuels Pastafarian’s excitement.

However, it is entirely nonsensical — pirates have no means to affect temperature. The computer cannot get it, though. Without an understanding of data, all statistically significant patterns seem legit to software.

However, let us give a second chance to the computer and ask it to analyze the consequences of Internet Explorer failure in web browser market. Few seconds pass, and the computer alarms — critical coincidence is found!

Unless poor UI design drives people crazy and violent, these findings are nonsensical again. It appears that successful automated analytics requires the understanding of what is being analyzed, and what are its possible consequences.

By the way, the lack of machine understanding is the reason for why online marketing dashboards are useless. On dashboards, marketers dig through charts with global warming pirates and deadly Internet Explorers to find valuable insights — still wasting 80% of their time on data.

That is high time for data science to step in

Machine Learning and Marketing: How Data Science and Neural Networks Made Machine Intelligent (and Reluctant to Find Pirates)

The task of intelligence computer-assisted marketing is to use machine learning to make the computer understand the data and retrieve only valuable insights — ones that bring profit. In order to do that, data scientists have resolved next tasks.

• Machines learned to remember associations between different variables and evaluate them as positive and negative ones.
• Computers trained to differentiate valuable and useless correlations in data on the basis of learned structures, not only statistical significance.
• The software was programmed to remember all significant findings from first sets of data, then use them in next tasks to create even more advanced ones. Hence, computers became able to evolve and increase efficiency.

All these factors created marketing machine learning — systems that analyze users’ data to reveal hidden patterns, which are transformed into insights by learned success criteria. Moreover, machine learning analytics can use findings from one user’s data to make valuable suggestions to other users. As the time passes, intelligent systems accustom to the needs of users and find even more critical insights and recommendations.

It is a win-win situation for marketers, data scientists, and consumers.

• Marketers can now save 80% of their time in favor of creative activity and strategic planning.
• Consumers get improved and customized marketing offers.
• Data scientists retrieve enough funds to build even more advanced solutions.

Of course, questions arise. The Captain has prepared a brief FAQ — he always has an ace in the hole, you know.

What Machine Learning Can Do in Marketing? (Exactly)

You may already suspect that finding current patterns is very lame and superficial part of analytical activities. For you, Captain collected both basic and non-trivial functionality that data-trained systems may have.

• Prediction and forecasting. As far as artificial intelligence can be programmed to be aware of time flow, it soon finds patterns and sequences that may be triggered only in specific periods of time. Then, machines learn to determine factors that affect that period. It allows, for instance, predicting the level of demand on Black Friday to collect right enough goods in inventory.
• Ad performance evaluation. Together with image analysis systems, machines can evaluate the quality of ad’s creative part and say whether it will be engaging for consumer or not. Facebook uses such system to filter low-quality ads and increase user satisfaction.

• Bidding. Placing stakes on ads’ price is the very essence of Google and Facebook advertising systems. However, finding a median bid cost is not enough to make a cost-efficient strategy. Facebook Budget Optimization, together with other third-party projects, allows considering different factors that affect bid price and resulting conversion opportunities to find the best pricing strategy.
• Pricing. The price is an essential part of the marketing mix, and its adjustment is an itch for every business — but what about dynamic pricing? Some advanced machine learning systems can use consumer segmentation to set up price individually for every client. It allows boosting up sales while keeping profit margins within reasonable limits.

Every AI startup tries to add something new to this list. Machine learning can use everything that can be extracted from data for marketing purpose. The only limit is the proficiency of data scientists who stay behinds system’s back.

Why Did Not Machine Learning Marketing Emerge Earlier?

At the beginning of the XXI century, there were three barriers to machine learning for marketing and advertising. You are very familiar with them.

1. Data was non-digital and unsystematic at the time. In order to use them in machine learning, marketing required standardization and digitalization, which occurred as technology evolved and consumers moved online. Social media and search engines, which are the most popular marketing platforms, also produce digital data in an accessible format. Even offline software now uses cross-compatible industry standards.
2. Computing capacities were too scarce, and hardware and maintenance price were too high to afford it to anyone but big corporations. Now, cloud computing is affordable even for small businesses, hence there is plenty of cheap resources for machine learning and AI.

3. Internet bandwidth was low, which was not enough to send and receive gigabytes of data. Today, previously fantastic 100 Mbit/sec is quite a low speed, and there are literally no barriers for data transmission.

Thus, marketers of today are very lucky to live in the age where theoretical demand met technology offering. As far as the pace of innovations is accelerating, solutions for business and private individuals advance even further.

Who Use Machine Learning Marketing Analytics Today?

The answer “everyone” would not satisfy you for sure, but that is what exactly happens today. Without analytical informational systems, all corporations are shorthanded. Small and medium businesses, which have tasted forbidden fruit of machine learning, cannot imagine their operations without the aid of artificial intelligence.

Specifically, there is a loop cycle of machine learning, in which you and I participate too.

• Media corporations like Google and Facebook analyze consumer behavior and content’s semantic to optimize spending, serve best-appealing contents and manage advertisement to ensure best CPA. For example, Facebook’s new Budget Optimization Feature is explicitly based on machine learning — to adjust to target bids, FB needs 50 impressions for learning.
• Businesses analyze data from media corporations and user behavior on their websites to find the most cost-efficient audience segments, types of content, marketing strategies and marketing funnel structures.
• Consumers use machine learning to reach content and advertisements that fit best their interests, income, region and other targeting factors.

Generic machine learning marketing analytics picture. It never looks like that, you know.

Of course, the majority of profit is taken by those who generate and master data, which are big informational corporations like Google or Facebook. However, ignoring machine learning for businesses means losing profits and opportunities, because their competitors are likely to use AI and computer analytics. Indeed, about 75% of the most successful marketing teams used machine learning technologies in 2017.

How Is Captain Involved in Machine Learning?

Captain is an artificial intelligence superhero, so machine learning is his way to analyze gigabytes of data from successful and failure marketing cases.

• Captain improves his knowledge every day by analyzing the constant influx of new information from hundreds of his customers.
• Customers’ experience allows Captain to understand data not only in a marketing context but also in the context of the industry — online gaming, SaaS, eCommerce, governmental services and dozens of other branches.
• Captain also suggests strategies and hints from leading 50 marketing experts, which he verifies with real data. It is impossible to deceive Captain; don’t you even try to do that.

At the top of Captain’s learning ability is his artificial intelligence – the decision-making system, which he uses to evaluate and prioritize his insights. For example, once upon a time Captain discovered a GeoTrap for his client. It was a tricky issue in targeting, which flushed down $10,000 of the budget during first ten days of the campaign. Then, recommended adjustments saved over $20,000.



Captain learns to accommodate unique needs of every marketer and project, which empowers them to write as fancy experience-based blog posts as the Geotrap post. Actually, it is worth to train writing before AI changes strike the labor market. 30% of U.S. jobs are going to be replaced by AI by 2022, and 30% new positions will be created for those who operate artificial intelligence.

Thus, Captain Growth is going to become an employer in observable future. Learn how to learn with machine learning before it is used to learn about you!