Machine learning and marketing automation: How to make it work

80% of marketing automation users saw an increase in the number of leads using marketing automation software - Invespcro

Marketing automation is today helping marketers optimize repetitive tasks, minimize errors, predict outcomes, reduce costs, and generate more leads. Both B2B and B2C companies are using automation technologies in marketing to maximize efficiencies and reduce overhead. Machine learning is one of the most effective tools of marketing automation and has already penetrated into every marketing sphere from content creation and SEO to advertising and HR marketing.

So what benefits does ML hold in marketing? What are the main use cases of ML in marketing? Here, you will learn the answers to these and more questions and discover how to get machine learning and marketing automation software work for you.

Machine learning and marketing automation: stats, challenges, and benefits

According to Mordor Intelligence, the marketing automation software market was estimated at $6.87B in 2020 and is forecasted to be $19.66B in 2026, growing at a CAGR of 19.2% from 2021 to 2026. The marketing technology landscape is versatile and is now represented by a total of 8,000 martech solutions. 

This is a significant increase since 2019. 1 in 5 of the solutions on this year’s martech landscape weren’t there last year. The fastest growing category in the martech technology landscape is data. No wonder, data solutions are on the rise, because marketing specialists put a particular emphasis on AI & ML. Data and ML are interdependent. The ability to derive insights from data is the key to enabling success through AI & ML. As the latest survey by Econsultancy claims, the majority of marketers anticipate that data, analytics, AI & ML would be of high priority for their organisation in the year ahead.

Customer demand for personalisation will only drive the adoption of machine learning and marketing automation software. However, it is a false assumption that AI & ML will eliminate marketing jobs. In fact, AI and ML will create more jobs in marketing, not less. According to the Econsultancy survey, 42% of respondents “somewhat” agree with the statement while 32% of respondents “somewhat” disagree. More and more companies will have to upskill their marketing personnel and look for people with AI/ML skillset. 

Despite the rapid growth of the marketing automation software market and marketers’ interest in automation technology, implementing AI & ML in marketing is not an easy task. Marketing specialists report that lack of budget, lack of skills, and poorly integrated data & systems are the main obstacles to adopting AI and ML.

Organizations risk investing in automation technology without being 100% sure about the outcomes. However, the benefits of machine learning and marketing automation outweigh all the risks and challenges:

• Increasing customer satisfaction and retention  
• Improving lead nurturing process 
• Shortening sales lifecycle
• Increasing customer LTV
• Automating routine marketing tasks
• Optimizing costs
• Creating personalized customer experience
• Predicting customer needs and behaviours
• Forecasting outcomes of marketing campaigns
• Improving CTR and conversion rates
• Generating greater ROI
• Making more informed decisions regarding products, services, websites, and marketing campaigns

There are many players on the market that specialize in machine learning and marketing automation: HubSpot, Salesforce.com Inc., Zoho Corporation, Oracle (NetSuite Inc.), etc. They all incorporate AI and ML into their solutions to help marketers make well-informed decisions and boost their campaigns.  

Popular use cases of machine learning and marketing automation

1. Marketing analytics

There are four types of data analytics that marketers use: descriptive, diagnostic, predictive, and prescriptive. In today’s fast-paced world, traditional analytics based on descriptive and diagnostic analysis is not enough. To achieve success and outrun competitors, marketers need to forecast outcomes and take immediate actions to avoid any untoward incident in the future. With the help of predictive and prescriptive analytics powered by ML, CMOs have real-time visibility into their marketing campaigns and can assess their efficiency, see possible improvements, and act on data. 

2. Content marketing

Machine learning and marketing automation help content marketers reduce manual work, improve customer experience, and boost the performance of marketing campaigns. With ML, marketing specialists can:

• Improve content with data-based recommendations
• Automatically classify content with tags
• Recognize the content in images and videos
• Create personalized messages for different types of audiences
• Automate repetitive tasks: social media posting, keyword research, email sending.
• Auto-optimize marketing messages and emails
• Auto-generate different types of content for simple stories

3. AI-enhanced advertising

In CPC, marketers use AI & ML to ensure that the ad is shown to a more targeted audience, increasing the effectiveness of campaigns and optimizing the advertising budget. The top ML use cases in CPC are:

• Smart bidding
• Micro-moment targeting
• Responsive ads
• Performance analysis
• Dynamic search ads
• Price optimization
• Account management

4. SEO

SEO is changing rapidly. It is no longer simply about keywords. Keyword stuffing doesn’t work anymore. For your content to rank high, you need to create a positive user experience. It is important to understand the idea behind the user’s query, its intent, and offer the most relevant answers to it. ML and AI help marketers optimize content for user intent and create a more personalized user experience on your website. SEO specialists leverage machine learning and marketing automation to:

• Look for relevant variations of top keywords
• Analyze and optimize title tags, meta descriptions, headers, paragraph headers, etc.
• Improve image/video/voice search
• Improve link building
• Optimize featured snippets
• Conduct website audits
• Fix duplicate content issues
• Predict pages rankings, etc.

5. Account-based marketing 

In B2B marketing, account-based marketing (ABM) is a key driver to increase sales. With the help of machine learning and marketing automation, companies can optimize their budget, streamline account prioritization by identifying the accounts that are most likely to convert. Moreover, ML helps project the best times for sales outreach. As contacts move along the buyer’s journey, it is important to approach the customer at the right time with the right message before it gets outdated. Also, thanks to ML, marketers can predict the lead behaviour and tailor their messages depending on the existing data they have about the lead.

6. Sales

Machine learning and marketing automation help standardize and streamline many sales activities: writing emails, scheduling calls, entering data, researching leads, creating reports, etc. Further on, machine learning helps process the data, prioritize sales activities, and optimize sales processes. Predictive analytics integrated into CRMs helps identify leads with a high probability to convert. Prescriptive analytics gives recommendations to salespeople of which products, services, and bundles to offer at which price. With the help of chatbots, sales specialists improve their communication with leads by giving quick answers about pricing, product features, or contract terms. 

7. Dynamic websites

To improve conversion rates on websites, marketers are increasingly applying AI and ML to create dynamic websites tailored to the specific type of audience. Depending on the need of the users, their interests, and background, the branding message, the content tone and voice, and even the color selection for the website design are different. Product range, services, and pricing can also be adjusted to suit a certain type of users.This helps create a more personalized user experience and lower bounce rates by offering only relevant info to the users. 

8. HR and brand marketing

Machine learning and marketing automation help brands target the right people with the right content to increase awareness. In brand marketing, ML helps gauge brand sentiment by analyzing mentions of their business in search and on social media. Also, it identifies gaps and areas of opportunities in the positioning in comparison to competitors. In HR marketing, ML helps attract and retain talents. Thanks to AI and ML, it is possible to develop personalized marketing campaigns and build the right plans for employees to learn and grow within the company.

How to implement ML in marketing

Understand and assess your marketing activities

Before implementing machine learning in marketing, you should evaluate your marketing operations:

• Analyze your marketing channels and their performance
• Assess the tools you use
• Measure the effectiveness of your marketing campaigns: click-through rates; engagement rates, conversion rates, etc.
• Analyze your traffic and leads
• Evaluate your the Return on Investment (ROI)
• Compare your strategy to competitors

Set your goals

To figure out under what conditions machine learning would be beneficial in marketing, you need to estimate TCO and the profitability you will gain in the short term and in the long run.

Also, it is essential to prepare a rigorous plan defining your goals and requirements needed to reach them. To eliminate ambiguity, it is necessary to align your machine learning KPIs with business KPIs. In other words, you should define your business problem in ML terms. For example, if your need to boost your sales, optimize marketing budget, increase market reach, you can apply ML to send personalized product recommendations, automate social media posting, speed up content creation, etc.

Ensure the effective ML engineering process

If the company decides to utilize machine learning for marketing analytics, they either need to develop custom ML algorithms and models or make use of third-party machine-learning solutions. When using third-party solutions, it is critical to integrate them right and ensure the data sets are clean and preprocessed properly. On the other hand, developing a ML solution from scratch is also the challenging task. To succeed with machine learning  and marketing automation, you need to ensure a seamless ML engineering process. Here at N-iX, we can support you in aligning your teams, technology, and business processes. Our specialists will help you:

• Set up a multifunctional team of professionals with expertise in data science, DevOps, BI, Python, Java, QA, data analytics, etc.
• Shape a business problem statement.
• Establish the right success metrics.
• Choose the right tech stack.
• Prepare your data: focus on data quality and quantity. 
• Run ETL procedures (extract, transform, and load).
• Develop, train, test, and optimize models.
• Deploy and retrain models.
• Monitor model performance.
• Integrate machine learning into existing and new marketing solutions.
• Make use of lucid visualization of the insights. 

Wrap-up

Machine learning is the essential element of the modern marketer’s strategy. It helps whenever marketers need to find customer behavior patterns, tailor content into targeted messaging, segment audiences into target groups, automatically insert tailored messaging into campaigns, etc. Thanks to machine learning and marketing automation, companies can optimize costs, improve performance of marketing campaigns, and generate more leads. 

If you want to implement machine learning in your marketing department, contact our experts and they will help you generate value from your machine learning initiatives right from day one.