AI (artificial intelligence) marketing uses various technologies and methodologies to optimize automated decision making based on data collection, analysis, and observation of audience data or current trends (economic, social, etc.) that may affect marketing efforts.
It’s clear that AI holds a vital role in helping marketers connect with consumers. The following are some use cases for marketers that are implementing AI Marketing Technology.
1. Recommend highly targeted content to users in real time
AI tools use data and customer profiles to learn how to best communicate with customers, then serve them personalized messages at the right time without intervention from marketing team members, ensuring maximum efficiency.
Spotify, for example, has tens of millions of users listening to music every minute of the day, and has to accumulate a mountain of customer data.
This information is used to train algorithms that estimate relevant insights both from content on the platform and from online conversations about music and artists, as well as from customer data.
And use this to enhance the user experience providing them highly targeted content.
2. Adapt audience targeting based on behavior and lookalike analysis
Let’s take a look at Google. Google’s AI algorithms use machine learning to watch what we search when we search it, and the data we input surrounding those pursuits. This means that Google is not only gathering the words we type in, but also the searches we perform directly after, websites we visit following our query, and what we do with the information we get as a result.
With the information amassed, Google can then use predictive analytics to process the data and predict behaviors based on our previous search and buying history. These predictions are then used to display ads to us based on our specific personalities. Machine learning picks up on these personalities and then categorizes them further into audience clusters called “lookalikes” or types of people with similar traits and/or habits.
With these audiences, we create personalized ads based on people’s tastes, preferences and searches.
3. Measure return on investment (ROI) by channel, campaign and overall
An important part of any marketing team is the ability to measure campaign success and establish baselines that can serve as a reference for future efforts.
Return on investment, or ROI, refers to the amount of money you generate after making an investment in something.
Calculating the Return on Investment (ROI) on data science, machine learning, or AI projects is often critical to secure resources.
Zigatta implements Salesforce CRM, which allows different departments to collaborate seamlessly and promotes intelligent decision making by showing a clear picture of departmental performance. This results in business processes being standardized and simplified. Promising a significant return on investment (ROI).
Selecting the right metrics to measure ROI is critical for determining the actions that are most impactful to converting customers to engage with the brand, purchase products or services, and ultimately become brand advocates.
4. Discover insights in top performing content and campaigns
Customer insights serve the purpose of building the most robust relationship with a customer and serving up relevant product and service recommendations.
The consumer insights can also enhance your overall marketing strategy by providing a personalized brand experience that focuses on what your customers truly value.
Netflix uses AI to provide personalized recommendations to its users based on what they like to watch. Classifying content by genre, time period, mood, etc, but also, and most importantly, by popularity and success. If any type of movie, documentary, or series obtains popularity, it will be considered “top performing” and Netflix will recommend it even further.
What Netflix actually understood well is that there are lots of different niches that people like to watch, and if they produce content for those niches, they will have a ready audience for those bits of content.
Netflix has done a great job of applying AI, data science, and machine learning using a product-based approach that focuses on business needs first, then AI solution next.
5. Create data-driven content
Understanding your customers’ intention is a powerful way to make better decisions, reduce waste (related to time, energy, and budgets), and create better performing ads. You already have the data and the ecosystem. All you need is the right AI platform to deliver insights straight into your workflow.
Starbucks captures its prospects’ data through the use of AI in their Rewards Program and Mobile App. In fact, Starbucks has successfully built its relationships with customers through this Rewards Program. Through this AI-powered tool, Starbucks acquires your buying history and gives you recommendations they know you will enjoy after considering the date of purchase, the time of order, and the location.
6. Predict winning creative (e.g. digital ads, landing pages, CTAs) before launch without A/B testing
Marketers can now predict what creative will work for their ads, before campaigns begin.
Thanks to this you can reduce the time and cost of discovery by leveraging your data to gain predictions and insights into what consumers want and why.
EHL Group wanted to see if they could determine their best advertising creative, before campaigns began. Using artificial intelligence to predict their ad creative performance, was able to drive conversions at lower costs.
7. Forecast campaign results based on predictive analysis.
Predictive analytics seeks, through statistical analysis, to predict future events by studying past events.
A great example of this is Power BI, which helps organizations collect, manage and analyze data from a variety of sources through an easy-to-use interface.
Zigatta designed a set of reports and dashboards with Power BI to present KPIs visualizations for customer engagement.
8. Deliver individualized content experiences across channels
Personalizing customer experiences involves tailoring the content we offer to the preferences and needs of each individual customer to ensure engaging interactions.
Thanks to the implementation of personalization strategies, when customers visit your company, you will be able to provide a unique and personalized service.
At Zigatta we use the Salesforce tool, where dynamic content blocks for personalization, predictive content powered by Einstein and pre-defined event triggers, marketers can automate relevant messages throughout the customer journey.
9. Choose keywords and topic clusters for content optimization
Keywords are more than just the words or phrases used by a user when performing a specific search on the Internet, a good selection of keywords will help you attract more qualified traffic and increase your online sales.
An example of the importance of choosing keywords and topic groups for content optimization is when we carry out an SEO strategy, because it is based on choosing the right keywords. The success or failure of an is based first and foremost on choosing the right keywords.
10. Optimize website content for search engines
Search engine optimization, or SEO, is incredibly important for marketers.
When you optimize your web pages you’re making your website more visible to people who are using search engines.
People use search engines to find answers or solutions to their questions and search engines serve up the most relevant content they can find.
If you don’t have the high-quality content that search engines want, your SEO rankings and readership are likely to be low.
Marketing that incorporates Artificial Intelligence is based on the use of the latest technologies for the benefit of consumers and improving the customer journey.
Artificial intelligence and machine learning are improving the performance and productivity of companies by automating processes. We will be able to use customer data to capture their attention and solve their needs, as well as increase your revenue and synchronize your marketing and sales teams.
Zigatta uses Artificial Intelligence and Machine Learning to provide sufficient resources that allow automating operational processes, data analytics, and results in less time and determine if it’s actually driving results at the end of the day.