The promise of using machine learning in marketing is this: Data, properly harnessed, can help marketers and advertisers better understand customers and automate finely tuned, targeted brand campaigns with unsurpassed consumer personalization.
Machine Learning in Marketing
To give you a better sense of how this promise is being kept, we rounded up 23 examples of machine learning in marketing.
23 Machine Learning in Marketing Examples
Uses of Machine Learning in Marketing
- Analyzing customer data.
- Recording and transcribing sales calls.
- Recommending products and services to customers.
- Mapping the social networks of leads.
- Mining and analyzing public social media posts.
1. JPMorgan Chase’s AI Copywriter
JPMorgan Chase equipped its marketing team with an AI copywriter that produces more customer-friendly content. The financial giant showcases a partnership with Persado, which uses machine learning and AI technology to write ad copy that catches consumers’ attention. By enlisting the capabilities of this software startup, JPMorgan Chase has enjoyed higher click rates with its revamped copywriting.
2. Gong’s Revenue Intelligence Platform
Gong employs artificial intelligence (including machine learning) to help B2B sales teams close more deals by automatically recording, transcribing and analyzing the content of all sales-oriented calls, web conferences and emails. Its revenue intelligence technology integrates well with platforms like Salesforce, Office 365 and Slack, creating a seamless experience for sales teams looking to refine their operations.
3. Netflix’s Recommender System
Netflix uses machine learning in its recommender system, as well as for research efforts and A/B testing. The company also uses machine learning to inform its decisions on which content to add to its streaming platform and to optimize production of original TV series and movies through its film studio.
4. IBM’s AI Assistant Watson
IBM offers a suite of AI tools that helps businesses streamline their marketing strategies and customer interactions. Its AI assistant Watson taps into the possibilities of machine learning to conduct audience analytics, personalize one-on-one conversations and connect with audiences through their preferred channels.
5. Bluecore’s One-on-One Campaign Tool
E-commerce companies can stay one step ahead of customers with Bluecore’s platform, which personalizes interactions for online shoppers. AI and machine learning guide one-on-one conversations and recommend products to customers across a range of channels. Predictive automated intelligence also gathers info on the ideal ways to reach out to customers, so teams can adapt marketing activities to audiences’ preferences.
6. Aerosolve From Airbnb
Airbnb hosts several open-source products, including Aerosolve, a machine learning library. The library is ideal for running algorithms with easily interpretable features like searching for keywords and filters for vacation rentals. Aerosolve is the same library that Airbnb uses to predict booking probability and suggest pricing to its hosts.
7. AI-Powered Customer Service Chat From Ada
Ada gives companies the ability to deliver consistent, high-quality customer service with a brand interaction platform that displays conversational AI features. Machine learning models enable Ada’s platform to analyze text in over 100 languages and then predict the needs of customers. With this proactive technology, businesses can reduce the time it takes to resolve issues and provide customers with the answers they’re looking for.
8. GumGum’s Contextual Intelligence Platform
As part of its partnership with Appen, GumGum has leveraged AI and machine learning to determine ideal web pages and digital spaces for posting ads. Verity serves as the company’s contextual intelligence platform, which scans videos, audio clips, images, text and other online elements. With this tool, businesses can place ads on web pages and platforms without accidentally associating their brands with irrelevant or controversial content.
9. Strong Analytics’s Platform for Building Machine Learning Applications
Strong Analytics makes it easier for marketers to develop personalized content and campaigns with a machine learning platform. A combination of AI, analytics and machine learning tools enables teams to compile data on customer behavior, predict future needs and tailor marketing efforts to meet those needs. In addition, marketers can rely on Strong Analytics’ platform to help them quickly deploy machine learning applications and streamline their operations.
10. Sales Automation Tools From People.ai
People.ai’s sales automation tools use machine learning algorithms, freeing up time for the sales and marketing professionals who use them. With the company’s data platform, teams are able to compile information from various interactions to determine who needs to be contacted to increase revenue streams moving forward. The company’s platform also sends out timely AI-driven alerts, so teams know which accounts to focus on to close deals.
11. Applecart’s Social Graph Platform
Applecart’s marketing platform locates promising leads — and their connections — leading to more efficient marketing campaigns. After collecting data from public sources, the company’s Social Graph Platform uses machine learning algorithms to determine professional and personal relationships for each individual. Companies can quickly map out the social networks of leads and tailor their content to these audiences.
12. Brandfolder’s Brand Intelligence Tool
Working behind the scenes, Brandfolder makes it easier for teams to find marketing assets with its Brand Intelligence platform. This platform uses AI, natural language processing and machine learning algorithms to tag creative assets, understand how they’re organized and recommend ways for classifying them. Teams and stakeholders can then save time and energy locating assets within a cleaner marketing ecosystem.
13. Real Estate Marketing Tools From Ylopo
A digital marketing technology platform for real estate agents, Ylopo incorporates a variety of ingredients — including social media marketing, targeted demographic and psychographic advertising, big data and AI — into its Total Digital Marketing Solution product. Its AI agent RAIYA blends natural language processing and machine learning to carry conversations with customers and guide them along their property search.
14. Drift’s Conversational Cloud Platform
Drift helps businesses forge bonds with customers and prospects through its AI-based platform. Equipped with machine learning, Drift’s Conversational Cloud platform engages with site visitors and potential buyers to answer questions, close deals and encourage future visits. Chatbots also record insights during each conversation, so companies can understand customer needs and better serve returning visitors.
15. Acquia’s Marketing Cloud Data Platform
Acquia provides marketers with the data they need to better understand audiences and tailor content to specific target groups. Machine learning features build models and classify customers based on certain behaviors, so marketing teams can form accurate insights and buyer personas. As a result, businesses can adapt their products to customers’ preferences and create unique marketing experiences.
16. Recommender Tools From Dynamic Yield
Dynamic Yield employs advanced machine learning to help marketers increase revenue through single-platform personalization, recommendations, automatic optimization and one-on-one messaging. Once businesses link their marketing campaigns with Dynamic Yield’s platform, they can sort through rich data sets and select content that caters to target audiences’ interests.
17. Affinitiv’s Atlas DX Platform
Affinitiv helps car dealerships and OEMs improve customer service and long-term loyalty with digital services. The company’s Atlas DX platform features rich demographic and behavioral data, so dealerships can match their marketing content to the interests of prospective and current customers. As a result, teams can determine the best channels through which to contact audiences and convert them into buyers.
18. Machine Learning Media Buying From Bliss Point Media
To optimize marketing campaigns, Bliss Point Media has designed an application that reveals where marketers should focus their attention. Machine learning tools compile real-time data, analyze it and determine sources that deliver the most revenue. Marketers can shape strategies around their target audiences while still diversifying their channels and outreach methods.
19. Generative AI Chatbots From Conversica
Built on an AI platform that blends machine learning with natural language processing and natural language generation, Conversica’s AI assistant automatically contacts, engages, qualifies and follows up with leads using natural two-way communication. It also fills in lead contact information, keeping a company’s CRM up to date.
20. Emotional Analysis Tools From Swayable
Swayable allows companies to gauge audience opinions through emotional analyses. A mix of machine learning and computer vision technology captures consumers’ reactions to products and tracks wider opinions after gathering data. Businesses use Swayable’s causal AI to sharpen their marketing campaigns right from the beginning.
21. Instapage’s AI-Generated Landing Page Copy
Instapage’s landing page builder helps marketers design their website with conversion in mind. It uses AI and machine learning to analyze ad campaigns and match the language to landing pages for more successful conversion rates. This AI-generated landing page content saves marketers time and money that would otherwise be spent on rewriting and A/B testing.
22. Dstillery’s Custom AI Audiences Tool
An applied data science company, Dstillery employs machine learning to produce actionable customer insights from its sprawling database of constantly updated online and offline behavioral profiles. Marketers use Dstillery’s Custom AI Audiences tool to build customer profiles with first-party data, pinpointing relevant leads.
23. DoorDash’s Historical Marketing Campaign Data
DoorDash is coming up with smarter ways to spend its marketing budget by employing machine learning to study previous campaigns. Machine learning algorithms review historical data to separate successful campaigns from less successful ones. With a clearer picture of what works and what doesn’t, DoorDash can adjust its marketing strategies and invest in content that resonates more with customers’ needs and interests.