You’ve probably heard a lot about the buzz surrounding artificial intelligence (AI) and its applications in marketing. ChatGPT aside, AI encompasses a vast realm of technologies that have been evolving for years, presenting myriad opportunities for businesses. No longer the exclusive domain of tech giants, AI has practical applications for enterprises of all sizes, from small startups to major corporations.

Want to Use AI for Your Business? Contact Bigly Sales

What is AI in Marketing?

At its core, artificial intelligence aims to emulate human-like cognitive functions. We’re talking about a wide array of skills here, from voice recognition to machine learning and semantic search. Understanding AI is like diving into a sea of possibilities across various facets of your customer’s journey.

In our exclusive AI and Machine Learning briefings, we’ve identified 15 groundbreaking techniques that can redefine the marketing landscape. These techniques straddle the RACE framework — Reach, Act, Convert, Engage — to provide a comprehensive approach that guides your customer through every stage of the marketing funnel.

The ABCs of AI: Three Major Categories

While all of these methods fall under the AI umbrella, we categorize them into three distinct types:

• Machine Learning Techniques: Algorithms churn through historical data to produce propensity models, which predict future behaviors.

• Applied Propensity Models: Using these models to forecast events, like lead conversion probabilities.

• AI Applications: These are your conventional AI tasks, such as chat support and content generation.

Your Marketing Strategy, Supercharged with RACE

Using the RACE Framework, you can measure and fine-tune your marketing strategy. It spans the journey from Reach, Act, and Convert to Engage, empowering you to capitalize on customer insights and prioritize high-performing touchpoints.

Reach: The Starting Point

The “Reach” phase involves luring visitors through SEO, content marketing, and other inbound techniques. At this juncture, machine learning and applied propensity models come into play, enhancing user experience by providing personalized content and interaction.

1. AI-Generated Content

AI’s role in content creation is more nuanced than you might think. While it won’t pen an op-ed or deep-dive industry analysis, AI can excel in generating data-driven content. Take WordSmith, an AI writing program that churned out a staggering 1.5 billion pieces of content as far back as 2016. For sectors like finance, where quarterly reports are a staple, AI can be a game-changer.

2. Smart Content Curation

Personalized recommendations have never been easier, thanks to AI-powered content curation. Whether it’s suggesting complementary products or tailoring site messages, AI’s predictive capabilities can help sustain user engagement. If Netflix’s uncanny knack for suggesting binge-worthy shows has you hooked, you’ve got machine learning to thank.

3. Voice Search Mastery

Voice search isn’t something to develop in-house unless you’re Google, Amazon, or Apple. However, leveraging these tech giants’ voice search capabilities can position your brand to capture high-intent organic traffic.

4. Programmatic Media Buying

In light of recent controversies like Google’s brand safety scandal, the intelligence behind programmatic ads needs a serious reboot. AI can be that answer, effectively blacklisting questionable platforms and focusing on impactful placements.

5. Propensity Modeling

Understanding propensity models is essential in mastering AI applications. These models use historical data to predict future events, such as customer behaviors and buying tendencies. It’s the cornerstone of your machine learning efforts.

6. Predictive Analytics

Predictive analytics takes propensity models a notch higher. From forecasting conversion rates to estimating price points for conversion, predictive analytics offers granular insights into customer behaviors.

7. Lead Scoring

For businesses, particularly B2B enterprises, lead scoring can be the lynchpin of a successful sales strategy. Propensity models, fine-tuned by machine learning, enable sales teams to prioritize high-quality leads, thereby optimizing both time and effort. Discounts and sales can then be tailored to these leads for maximum impact.

8. AI-Driven Ad Targeting

One of the most influential applications of machine learning in digital marketing is ad targeting. This involves sifting through volumes of historical data to ascertain the best-performing ads for particular segments of your audience. The machine learning algorithms don’t just use basic metrics like age, gender, or location.

They analyze behavioral data, buying history, and much more, offering a far more nuanced approach to ad placement. However, let’s not forget the human element—creativity still lies in the domain of flesh and blood. But the strategic implementation of AI can turn cold traffic into warm leads, nudging them further down the sales funnel.

9. Dynamic Pricing:

Sales promotions and discounts are effective but can often come at the cost of reduced profit margins. Enter dynamic pricing. By using machine learning to analyze consumer behavior, you can send special offers only to those who are on the fence about making a purchase. This strategy keeps your profit margins robust, ensuring you don’t give away discounts to customers who would happily pay the full price.

10. Personalization

AI can also impact web and app personalization. By identifying a customer’s stage in the buyer’s journey, you can deliver tailored content right when they need it. Beginners to your site may benefit from educational content, while seasoned visitors might appreciate detailed product reviews or demos. This leads to higher engagement, better customer experience, and improved conversion rates.

11. Chatbots

Chatbots have democratized customer service. Open development platforms have simplified the process of creating a chatbot, so now you don’t need a massive budget or a team of developers. These virtual assistants can address consumer queries, finalize orders, and serve as a first line of customer support, effectively augmenting your customer service efforts.

12. Re-Targeting

AI’s capabilities extend into re-targeting efforts, using machine learning to predict which content will entice customers back to your site. This isn’t a one-size-fits-all approach; AI can customize your re-targeting ads to appeal to specific customer profiles, thus enhancing their effectiveness.

13. Predictive Customer Service

Your existing customers are gold. It’s more economical to retain them than to scout for new ones, particularly for subscription-based businesses. With predictive analytics, AI can evaluate which customers are most likely to unsubscribe and why. Armed with this information, you can then reach out proactively to prevent churn, offering incentives or resolutions that keep your customers loyal.

14. Marketing Automation:

Traditionally, marketing automation functions on a set of rules thought up by human marketers. Machine learning takes this a step further by analyzing billions of data points to determine the most effective timing and messaging for your campaigns. It removes the guesswork, enabling data-driven decisions that enhance your marketing ROI.

15. 1:1 Dynamic Emails

AI can transform your email marketing strategy by delivering highly personalized content to your subscribers. From predicting a customer’s likelihood to purchase specific products to tailoring the entire email based on real-time data, AI can make each communication feel like it was crafted just for the recipient.

Read more: 5 Steps to Develop a Generative AI Strategy

Conclusion

Embracing AI technologies in your marketing strategies offers a multitude of advantages that go far beyond traditional methods. From sophisticated ad targeting to intelligent customer service, AI helps in crafting personalized, effective, and data-driven campaigns that resonate with your target audience. Book a call today to discover how AI-driven strategies can help your business soar.