Data management is the root of Data Marketing , a strategy that considers consumer behavior, preferences and interactions to produce more effective and profitable campaigns.
With the application of Artificial Intelligence (AI) and Machine Learning (ML), Data Marketing shows results that significantly boost campaigns and we talk more about this throughout this text.
Keep reading!
The Data Marketing Revolution with AI and ML
AI and ML innovate data marketing by providing the precise use of large volumes of data in an agile way, with highly efficient and profitable insights.
Below are three actions with which AI and ML are revolutionizing Data Marketing.
Automation of marketing actions
By efficiently automating many routine marketing actions, AI and ML reduce the manual workload and allow teams to focus on strategic and creative activities.
In advertising campaigns, technologies process data on consumer behavior, online interactions, market trends, and historical campaign performance to set optimal ad bids, select the most effective content, and determine the best times to display advertising.
Email blasts yield impressive results in intelligent automation based on user actions. For example, when a customer signs up for a webinar, AI can determine when to send a sequence of emails at specific times with advance event information, timely reminders, and personalized follow-ups.
If there are ignored emails or emails with low interest, ML algorithms "notice" the problem and develop dynamic adjustments, with resending messages with more attractive subjects and alternative content, significantly increasing the chances of engagement.
Another marketing action that is significantly improved with the use of AI and ML is lead management.
AI can analyze website visits, content downloads, email interactions, and demographics and assign a qualification score. This can help determine which leads are most likely to convert, allowing sales teams to focus on the most promising ones.
With ML, leads are automatically classified as “hot,” “warm,” or “cold” based on past behaviors and engagements, making it easier to identify leads ready to convert.
Audience segmentation
By analyzing browsing behaviors, product preferences, personal interests, purchase history, and previous interactions with the brand, AI and ML technologies point out patterns and trends that would be difficult to detect manually, allowing for extremely detailed and accurate segmentations.
Using vast amounts of data, AI can identify consumers who frequently browse certain products and are more likely to purchase at specific times of the day or week. This allows companies to create highly targeted campaigns to maximize impact at the most opportune moments.
Additionally, ML algorithms analyze purchasing and viewing patterns to accurately predict what a customer is most likely to purchase.
This allows you to identify a recent purchase and make suggestions for related products, increasing cross-selling opportunities. Predictions are continually refined and adjusted based on new data, making personalization more efficient and relevant on an ongoing basis.
ROI Optimization
ROI (Return on Investment) is one of the key success metrics for any marketing campaign. With the use of AI and ML, strategies aimed at improving this indicator have advanced considerably.
Omnichannel management is a clear example of how these technologies can positively impact ROI. The application of AI allows you to create fluid and personalized experiences, with integrated and coherent messages across all communication channels.
A customer who visits a store’s website and views a specific product but does not complete the purchase may receive a personalized email encouraging them to complete the purchase. In addition, they may see dynamic ads on their social networks that reinforce their interest in the product. If the customer visits a physical store, sales associates, armed with information about their online journey, can offer more personalized service, increasing the chances of conversion.
Innovative tools with AI and ML
Data Marketing professionals have at their disposal a wide range of innovative tools that combine AI and ML, providing a significant competitive advantage in the market.
We highlight five fundamental options for Data Marketing.
Google Analytics 4 (GA4)
Google Analytics 4 integrates AI and ML capabilities to provide deep insights into user behavior and optimize marketing strategies.
Highlights
Insights such as a customer’s likelihood to purchase or churn help predict behaviors and create more targeted campaigns.
Intelligent and automated reports created with AI that allow you to identify optimization opportunities.
Analyzing user behavior across platforms and devices through ML, with a holistic view of customer interactions.
HubSpot
HubSpot is a marketing automation platform that integrates AI and ML to improve lead [https://dbtodata.com/uk-whatsapp]uk number for whatsapp[/url] management, customer nurturing, and email campaign personalization.
Highlights
Using ML to automatically score leads based on interactions, website behavior, and demographics, helping sales teams focus on the most promising leads.
AI applied to personalizing email sending, based on user actions such as website visits or filling out forms, which results in greater engagement.
Actionable AI-generated campaign performance insights and optimization recommendations to improve results.
Salesforce Marketing Cloud (Einstein AI)
Salesforce Marketing Cloud is a digital marketing tool that uses Einstein AI, a built-in AI engine that powers personalized campaigns and automation.
Highlights
ML in verifying customer behavior, offering prediction of interactions.
Automation and personalization of campaigns across multiple channels (email, SMS, social media) to provide a consistent and personalized customer experience.
AI in sentiment analysis in social interactions, allowing adjustments to communication strategies in real time.
Marketo Engage (Adobe Experience Cloud)
Marketo Engage, powered by Adobe Experience Cloud, powers marketing automation with AI and ML to enable advanced audience segmentation and marketing campaigns.
ML automatically classifies and scores leads, identifying those most likely to convert to optimize sales and marketing efforts.
AI develops highly targeted and personalized campaigns based on customer behavior, demographic profile and interaction history.
AI checks campaign performance and automatically adjusts marketing strategies to maximize ROI.
Dynamic Yield
Dynamic Yield focuses on AI-powered personalization to optimize the customer experience across websites, mobile apps, and other digital interfaces.
Highlights
AI adjusts content, offers, and product recommendations based on real-time user behavior, increasing engagement and conversions.
ML performs A/B and multivariate testing to quickly identify which content elements work best for different audience segments.
Automation and personalization of website layout and promotional campaigns based on behavioral data, maximizing relevance for each visitor.
AI and ML Use Cases in Marketing Campaigns
Magazine Luiza: connection with the consumer
Magazine Luiza, one of the largest retail chains in Brazil, uses AI and ML to establish an emotional connection with consumers, considering the different stages of their journey, from product selection to finalizing the purchase and after-sales.
This makes item recommendations based on the customer's purchase history, offers personalized promotions and discounts and adapts communication to each profile.
Based on browsing behavior, purchase history, and user preferences, AI capabilities offer personalized product recommendations in real-time.
With ML, the company identifies and segments its customers and sends targeted offers via email and push notifications, optimizing engagement and conversion rates.
Lacoste: programmatic advertising
The French clothing and accessories brand had a challenge: to take advantage of the 2016 summer season to boost its sales in France, the UK and Germany. To achieve this goal, the company’s marketers decided to adopt a targeted programmatic advertising approach.
Using available consumer data, the team turned to Artificial Intelligence (AI) and Machine Learning (ML) tools to create precise segmentations of their target audiences. Based on these insights, they applied targeting and retargeting strategies, exploring a variety of positioning options and creative formats.
With a significant budget, the team rigorously tested different banner formats, media placements, and daily ad budget allocation strategies. Through continuous tweaking, refining, and optimizing campaigns, they were able to maximize the return on each ad served.
As a result, the company achieved nearly 20 million brand impressions and recorded 2,290 sales in target markets.
Itaú Unibanco: new internal marketing strategy
To improve its internal marketing strategies, Itaú Unibanco uses an advanced digital marketing management tool that allows you to create, automate and manage campaigns across multiple communication channels.
With over 92,000 employees in Brazil and around 5,000 in international units, the bank faces the challenge of communicating efficiently and in a personalized manner. The adoption of the new tool brought a significant gain: the delivery time for internal communications was reduced from 3 hours to less than 1 hour.
Using AI technologies, Itaú personalizes internal communications based on the profile, behavior and preferences of each employee. Through data analysis, the tool recommends the most relevant content for different groups of employees, such as personalized newsletters, updates on specific benefits and announcements tailored to the needs of each department or function.
In turn, machine learning (ML) enables more precise and effective segmentation of employees, identifying behavioral patterns and creating segmented groups based on factors such as length of service, location, department, and engagement with previous communications, among others. This ensures that messages are targeted in a more relevant and strategic way, increasing engagement and the effectiveness of internal campaigns.
Prepare for the future of Data Marketing
The evolution of Data Marketing, driven by AI and ML, signals a future in which companies will be fully empowered to identify customer wants and needs, optimizing their strategies and minimizing the risk of unsuccessful marketing campaigns.
MATH offers robust solutions to prepare your company for the future of Data Marketing. Our team, which has helped large companies achieve impressive results, is ready to do the same for yours.