Simple AI Marketing Campaigns You Can Start Using Right Now

Marketing benefits the most from artificial intelligence integration in a company. Marketing’s basic tasks include understanding customer needs, matching them to products and services, and asking customers to buy. AI marketing campaigns could improve each of these areas. 

In fact, a 2020 global survey of early adopters of AI by Deloitte revealed that three out of the top five objectives for using AI relate to marketing. That is, to improve present products and services, develop new products and services, and build good customer relations.

However, deciding how to use AI to improve your marketing approach can be overwhelming. This article is here to guide you.

AI in Marketing: A Game-Changer for Campaigns

Many companies are successfully using AI marketing campaigns, ranging from more narrow use cases like programmatic ad placement to broader applications like enhancing prediction specificity, such as sales forecasts. AI also enhances human work in structured tasks such as customer service. In 2023, there was a 250% increase in AI adoption, and the global AI market was worth US $279 billion.

AI equips chatbots for full-fledged marketing, including lead generation, customer support, and cross-selling. From an inbound call analytical perspective, it adds AI integration to marketing. 

AI adoption supports all phases of the customer journey, serving ads during the “consideration” phase, guiding searches, and connecting customers with human agents through chat, video or co-browsing when necessary. (Chatbots or AI systems improved the response times by 92% for companies implementing it, and 83% said it made it easier to respond to customer requests.)

Based on detailed data, such as geolocation, AI personalises offers and increases sales by decreasing cart abandonment. For example, bots can show messages like “Trisha, Neha, and nine others bought this bed sheet today,” increasing conversion rates by a factor of five.

In fact, post-sale, AI-enabled agents manage customer interactions around the clock, handling changing service volumes as needed. When a customer asks a simple question, they answer. But when a question is complex, they escalate, sometimes analysing the customer’s tone to see if they can guide the response or invoke a supervisor to deliver a better outcome.

Easy-to-Implement AI Strategies for Your Next Campaign

Here’s a list of effective AI marketing strategies for you that could give you a headstart:

Contextual Personalisation Powered by End-to-End AI to Increase Profits

Marketers widely use A/B testing, yet the biggest flaw of this approach is that it assumes the best option only because the winning variant is declared the best option. Traditional A/B testing can be limiting, as it often only considers the most successful variant, overlooking the preferences of smaller groups. 

If, for example, 60% of customers prefer the “Buy One, Get One Free (BOGO)” deal and 40% prefer a flat 20% discount, an A/B test would suggest prioritising BOGO at the expense of the latter’s needs.

Comparatively, AI-powered contextual personalisation uses customer data metrics like purchase history and browsing behaviour to create tailor-made experiences. By doing so, you ensure that consumers who prefer discounts receive applicable promotions while those who prefer BOGO are shown what they like. 

Sending Offer Most Likely to Resonate with The Customer

Data Privacy: Traditional personalisation methods use single algorithm-level offerings, which limits contextualisation. AI marketing campaigns use context, such as past purchases and browsing activities, to understand each buyer. This means businesses can present the best variant to each customer instead of the one that most people find appealing. 

As a result, discount-seeking customers will still be shown discounts, while BOGO-seeking customers will also see those kinds of promotions. This process increases tailored experiences and conversion rates.

IKEA does it so well, leveraging AI to provide personalised product suggestions driven by a shopper’s activities on the brand site. For example, a customer browsing living room furniture might receive AI-generated recommendations for matching rugs or lamp options during their session. 

Ad Targeting with AI

With the phase-out of third-party cookies looming, marketers must seek strategies to learn about what people want and serve relevant ads instead. Data can be utilised to empower campaign performance, and conversion APIs are the most effective solution for them.

Whereas cookies only offer a narrow view of data, a conversion API can enable marketers to use zero-party and first-party information, much of which they’ve collected in-house. By layering this data upon ad platforms, marketers have more accurate segmentation data, leading to best-in-class targeted and effective advertising campaigns.

Look at Starbucks. The coffee giant creates refinement ads with conversion APIs and AI. It does this via strategic targeting, utilising customer purchasing patterns to reach people who are most likely to purchase premium beverages or engage with new product launches. This ensures a highly accurate return on ad spend, producing higher conversion rates.

Automated Product Recommendations Based on AI to Boost Average Order Value (AOV)

Consumers want personalised recommendations based on their needs today. Studies show that 78% of consumers say they’re more likely to purchase again from a brand that gives them AI personalised experiences by better understanding their preferences. Therefore, an eCommerce brand needs to eavesdrop on what the customer might be seeking. It’s done (in a legal manner) by suggesting products based on their browsing history (what the user has seen) and information about the previous purchases and user preferences. Just like the famous beauty brand Sephora does it.

Sephora uses its AI chatbot to make recommendations based on a shopper’s browsing and purchase history. A customer browsing skincare products might receive personalised suggestions for serums or moisturisers, resulting in larger basket sizes or higher AOV and a superior shopping experience.

AI-Originated Personal Content Recommendations for “Complete Your Look”

Brands creating artificial intelligence advertising campaigns can incentivise with something like, “Get 15% off your next purchase with these pearl-studded jeans.” It’s a shopping accelerator that personalises the experience, getting consumers there and spending more.

Zara and H&M use AI-based “complete the look” suggestions by studying customer purchases and prompting additional products; for example, if users buy a blazer, both stores also suggest matching trousers, earrings, shoes and a bag that goes well, and a belt, too, maybe. 

This strategy has lured customers into shopping more. Studies show that these strategies can increase sales by 10–30% for retailers, putting a stamp on their effectiveness in improving customer engagement and product sales.

Tips for Running Effective AI Marketing Campaigns

Here are some of the best practices that you should think of when creating AI-powered campaigns:

Start with Something Small and Build Up

It is wise to start small, as this will give you the freedom to experiment with the new AI marketing technology without overextending your human resources. Take it slow to keep this process risk-free, juggling some small aspects and then expanding your approach when you are more confident about the results your strategy can produce. 

That way, your marketing team can learn new things that help you scale it right later. Instead of a broad spectrum, zoom in on one area, e.g. customer segmentation or predictive analytics, to which AI can add immediate value.

Keep Checks and Make Improvements on an Ongoing Basis

It is important to continue reviewing the performance of your AI-enabled campaign and adjusting strategies based on real-time data. This will allow campaigns to be fine-tuned and remain effective for the long term. You should also use AI analytics tools to monitor key performance indicators (KPIs) and spot areas for improvement.

You must keep making these observations and revising or fine-tuning the results. By reviewing your programmes regularly, you can anticipate the changing mood of the market, placing your campaigns at the right time to last. 

Data Quality Comes First

Currently, AI systems mostly use data to deliver insights of any kind, predict trends, and even personalise customer experiences. Your aim should be consistency, accuracy, and relevance in the quality of information that drives these technologies. Ensuring data quality forms a solid foundation for gaining accurate, relevant, and actionable insights from AI that will propel your marketing strategy.

So, you start by thoroughly reviewing your current data sources and fixing discrepancies or other problems. You then implement strict data management procedures and maintain the high standard of accuracy and dependability that they currently have. 

In addition, you should be completely transparent with your customers about how you use their data to gain their trust and meet privacy standards.

Conclusion

AI has totally transformed and personalised marketing, making it easier to understand customers to suit their preferences and create deeper levels of engagement. At every point in the customer’s journey, from targeted ads to automatic product recommendations, AI can improve the overall performance of a campaign and inject more value into customer interaction. 

However, AI’s full potential can surface once such businesses take responsibility for maximum data accuracy, make long-term investments, and never stop refining marketing strategies. Want to supercharge your marketing efforts? You can take help from Engage 360 for personalised and effective customer engagement.