How machine learning can help marketers today

Over the next 3 years, both Artificial Intelligence (AI) and Machine Learning (ML) are set to create between $1.4 Trillion to $2.6 Trillion worth of value by solving marketing and sales problems. With these sort of figures behind them, it’s more important than ever to take your first step in leveling up your machine learning knowledge.

Even on a smaller level,  once you’re able to use machine learning to analyze your business' big data and make data-driven decisions based on it,  the benefits your business will see, plus time and resources saved will have you saying, “why didn’t I do this years ago?”

Before we get started, one of the most important points to make is that AI and ML are different, meaning they can not be used interchangeably. While they are closely connected, they are simply not the same. In fact machine learning is considered a subset of AI. Now with that out of the way, it’s time to dive into machine learning in marketing. 

What is machine learning?

Let’s start off with a test. Are you able to count all the shapes in the image below in five seconds?

It’s ok if you weren’t able to, it means you're only human. This is just a simple example of how machine learning works. It can label and analyze a great number of data sets extremely quickly.

Machine learning algorithms are designed to analyze data and discover patterns that people wouldn’t otherwise be able to find by themselves — in contrast, rule-based decision systems follow a specific set of instructions known by the developers in advance. 

Essentially, machine learning allows computers to learn and adapt without being explicitly programmed to do so. It also leverages the massive power and objectivity of machines to see things in big data that humans are unable to see — and it can help marketers in many ways.

What is artificial intelligence? 

Artificial intelligence is the field of computer science which gives a computer system the capabilities to mimic cognitive functions, for instance learning and problem solving. 

By using math and logic, the computer system can simulate the reasoning that humans use to learn from information and make decisions. 

Machine learning vs artificial intelligence marketing?

Artificial intelligence in marketing will use both offline and online customer data alongside AI technologies (including machine learning) to analyze information, predict audience behavior based on actions or societal and economic trends, and automate marketing campaigns, customer support responses, digital ad buying, lead scoring, and that’s just the beginning. 

Machine learning in marketing is the data collection and analysis used to help make the forecasts for the AI. The algorithms used by machine learning, work to study the data provided to then be able to perform actions, resulting in better outcomes. It helps marketers be able to optimize and improve the customer journey by making data-driven decisions, and  ultimately improve customer experiences.

10 jaw dropping machine learning statistics

1. The machine learning and deep learning U.S market will be worth $80 million in 2025. (Statista)

2. Early adopters of machine learning saw a 47% improvement of their sales and marketing efforts. (Deloitte)

3. There are close to 100,000 jobs worldwide requiring machine learning. Nearly half of them are based in the U.S. (Forbes)

4. Netflix saves up $1 billion per year due to machine learning. (Lighthouse Labs)

5. Of the surveyed customers, 62% do not have a problem in sending their usage data to an artificial intelligence platform to improve machine learning. Resulting in an improvement of customer experience. (Salesforce)

6. 48% of businesses use machine learning, data analysis, and artificial intelligence tools to maintain data accuracy. (O’Reilly)

7. Google's deep machine learning technology claims its 99% accuracy rate is more effective than human pathologists at detecting metastatic breast cancer. (Google AI blog)

8. Machine learning allows Oxford University's artificial intelligence system to read lips at a 93% accuracy level, making the system more accurate than human lip readers. (BBC)

9. The expected value of the global machine learning market by 2027, is estimated to be $117.19 billion.  A CAGR of 39.2% during the forecast period (Fortune Business Insights, 2020).

10. 10 million products – the number of products consumers can avail via one-day shipping thanks to Amazon’s use of AI and machine learning at fulfillment centers (Feedvisor, 2019).

How machine learning can help marketers today

Big data is serious business. And without machine learning, it will be increasingly difficult for today’s marketers to compile, absorb, and analyze the vast streams of data coming from multiple sources, let alone predict what marketing message will work for each customer.

Today, the best companies are using machine learning to understand, anticipate, and act on the problems their customers are trying to solve — and they are doing it faster and with more clarity than their competitors.

According to Forbes,50% of businesses aim to spend more on AI and machine learning. With Gartner adding that customer satisfaction is expected to grow by 25% in organizations who use AI. 

These organizations understand that having the insight to personalize content while qualifying leads to close more efficiently is largely thanks to machine learning-based programs capable of learning what’s most effective for each lead. Essentially, machine learning is taking personalized marketing, marketing automation, lead scoring, and sales forecasting to a whole new level.

Here are a few more ways machine learning can help marketers:

• Customer segmentation: learning customer segmentation models can be extremely effective at extracting small, homogenous customer groups with similar behaviors and preferences;
  

• Customer churn prediction: by discovering patterns in the data generated by many customers who churned in the past, churn prediction machine learning forecasting can accurately predict which current customers are at a high risk of churning. This allows marketers to engage in proactive churn prevention, an important way to increase revenues;
  

• Customer lifetime value (CLV) forecasting: identifying your CLV will enable you to segment your customers more effectively, measure the future value of your business, and predict growth more accurately.
  

Machine learning best practices for marketers

The idea of getting acquainted with machine learning sounds scary, but it doesn’t have to be that way. Today, machine learning is everywhere in the marketing technology landscape — and there’s no shortage of opportunities to test machine learning-powered tools and technology.

To get you started, we’ve outlined 3 machine learning  best practices to set your business up for success:

1. Set clear goals

First things first. Whether you’re building your own machine learning model, incorporating elements of machine learning  into your best-of-breed marketing stack, or setting up Smart Bidding in Google Ads, it is crucial that you’re clear about the aim you’re trying to solve. Is your goal to decrease the cost per conversion? Or is it to increase conversions, no matter the cost?

It doesn’t matter what your goal is; what really matters in this context is that it will naturally dictate the machine learning solution you’ll use, so don’t treat this step too lightly. More importantly, make this the first thing you do.

2. Both data quality and quantity are equally important

Machine learning requires data to learn from. The more data you can feed your machine learning solution, the better it will perform. If there’s not enough data, it is very likely to underperform. Poor quality or non-specific data are also contributing factors to underperforming machine learning solutions; there’s not much point having lots of data at your fingertips if the data sets are inaccurate, inconsistent, and incomplete.

3. Be patient

Like most complex systems, machine learning needs time to learn especially if you’ll be feeding large volumes of data. We also suggest resisting the urge to make changes too often — every time you make a change, it may take some time for the machine learning system to readjust as they re-learn. Patience is key — and so is adopting a long-term approach.

5 ways to use machine learning in marketing

Machine learning is not just for large companies and data scientists. Your average marketer, working in a smaller company (yes, even in a startup) can employ the same machine learning tactics used by the likes of Netflix and Amazon. There’s no need to build your own machine learning algorithm from scratch either. Instead, integrate cloud apps that have in-built machine learning capabilities into your marketing strategy.

To get the ball rolling we have listed the three ways marketer can use machine learning without starting from scratch.

1. Improved  customer support

There is still a strong human element to customer support, however according to Statista, 57% of organizations used artificial intelligence and future predictions created by machine learning to improve the customer experience and support. 

Chatbots are a great way to answer frequently asked questions, however they can be restrictive when it comes to specialized support. This is where software like Zendesk comes in. Zendesk’s Answer Bot empowers customers to solve their issues by themselves, no matter the time of day, or night.  This provides support staff the ability to solve the hard-to-answer customer questions. 

Zendesk has incorporated machine learning into its platform to organize and identify all the high and low performing help center articles. These ‘Content Cues’ keep the most relevant articles at the top, and alert agents to the articles which need to be updated.

A machine learning algorithm has also been used to read hundreds of signals from customer interactions in order to predict customer satisfaction. From the moment the client ticket enters the Zendesk queue, factors like text description, number of replies, and total wait time are all included to calculate a predicted satisfaction score between 1 and 100.

This rating helps customer service staff to prioritize tickets, see patterns forming, or trigger downstream integrations based on data-driven analysis.

2.Customize Facebook advertising

Facebook is already capturing a vast amount of data from its users. When creating a Facebook ad to showcase your latest products and promotions, customer data will help you personalize your campaigns. 

But you don’t have to sort through this customer data all by yourself. Facebook has a sophisticated machine learning algorithm that will help you leverage data and improve the performance of your ad campaigns.

We’re already seeing the effects of Facebook advertising and some ads are so targeted that users often question if Facebook’s algorithm is reading their mind.

When creating a campaign on Facebook, marketers can target ads to specific groups of users, who have traits and buying behavior that align with their best customers. By selecting a campaign objective, the Facebook algorithm can determine the best audience to place the ad in front of. You can also set a conversion objective so your ads are delivered to people that are most likely to complete a conversion, i.e., make a purchase.

By integrating Facebook ads into your marketing strategy, you can reach an audience that sits outside your organic reach.

3. Better content 

Even the creativity of content can be backed up by machine learning analyzing the data. It’s very likely that you are already using machine learning for your content, through the process of A/B testing (and if you’re not, then it’s time to start)

A/B tests are available for marketers to use to discover what content sits better with their audience, from the best email subject line, to better Facebook ad imagery, to the time your emails are sent.

This type of machine learning use in marketing is incredibly valuable when it comes to understanding the kind of messaging that performs best with  your audience segments. The feedback from each A/B test you conduct is another step into creating more targeting content, leading to higher engagement with your users. 

4. Boost personalization

Machine learning can process a huge amount of data from information collected by different inputs. Whether it’s information from your sales team, or customer service, or online patterns, machine learning can process data from various sources  and evaluate customer interactions in real-time. 

Meaning you, as a marketer, can gain far superior insights and learnings from each individual customer and tweak the messaging or promotion they receive to gain better results. This helps you achieve true 1:1 personalization with your customers, a step above using segmentation or micro-segmentation. Using a platform like Ortto, will help you simply and easily unify this customer data.

The best example to show the benefits of machine learning is Netflix. In case you missed the jaw dropping stats above, Netflix saves up $1 billion per year due to machine learning. 

Netflix is the leader in personalization in the way of recommendations. If you have somehow never made your way on to the Netflix platform, here’s how it works:Your homepage is made up of TV shows or films similar or theme matching to what you have watched previously. Needless to say, there’s no human out there cueing up your recommendations, everything you see here is thanks to machine learning.

Netflix drives a huge 80% of viewers’ activity from its personalized recommendations. The platform filters over 3,000 titles at a time using 1,300 recommendation clusters taken from the preferences of the user.  This Non-Recurring Expense (NRE) is approximately saving the streaming giant over $1 billion annually.

5. Data-driven decisions with CRM systems

The amount of customer data collected by CRM systems daily is huge. So much so that extracting insights can be daunting for any marketer. If companies want to make data-driven decisions, they’ll need to rely on machine learning.

Integrating a CRM system like Pipedrive or Salesforce into your marketing strategy allows you to track, maintain, and segment your customer data. A machine learning algorithm works underneath it all to transform your customer’s data into actionable insights.

 Salesforce uses machine learning to generate predictive lead scoring, capture interactions between users, and sift through information in a contact’s email and calendar accounts. All this information helps you understand who your customers are and what they want.

Integrated CRM solutions are instrumental in helping marketers analyze data and gain insight into the behavior, status, and position of their contacts within the customer journey.

With machine learning, marketers can watch their CRM work in the background while on the forefront they can put energy into making smarter marketing decisions. This is made easy with Ortto. All your CRM data can be combined with product and website data, to give you an even more comprehensive view of your customer. 

Power up your marketing with machine learning

Big data and technology have already transformed the way businesses communicate with their customers. More importantly, the future of digital marketing is closely aligned with AI and machine learning-based marketing.

Given that a large number of big corporations are already benefiting from AI and machine learning and many SMBs are extensively exploring a similar path,  the time is now to integrate it into your marketing plan. The sooner your machines are learning, the better the outcome.