AI for Marketing: The Complete Guide for 2023

min read
AI for Marketing: The Complete Guide for 2023

Historically, many brand-based problems have stemmed from not being human enough. This issue is going to become more complex as people start talking to the smart microwaves and other devices Amazon and other tech companies plan to introduce. While artificial intelligence is here to help consumers, the future winners and losers of marketing will be determined by their AI skills.

According to Adobe, 47 percent of “digitally mature organizations” said they have a defined AI strategy. Also consider that per a recent Deloitte study, 70 percent of businesses rate their digital maturity as “early” or “developing.” Therefore, if you find yourself without a plan, you aren’t behind schedule, though now is the time to act.

A huge first step is to think about what problems AI can solve. A big one—though it may sound counterintuitive at first—is that AI can help your brand feel more real and more human. Here are a few problems it can solve to make that work.  In this post, we’ll cover the basics of AI in marketing and talk about some of the problems it can solve.

How Is Artificial Intelligence Used for Marketing? 

  • Conversation Intelligence: AI-powered conversation intelligence enables marketers to gain attribution for phone leads by analyzing and transcribing customer interactions. It captures valuable insights from these conversations, helping marketers understand customer preferences, pain points, and sentiment, ultimately guiding them to optimize marketing strategies and enhance customer experiences. 
  • Personalization: AI-driven personalization allows marketers to tailor content, offers, and product recommendations based on individual user behavior and preferences, leading to higher engagement and conversion rates. 
  • Predictive Analytics: AI in marketing facilitates predictive analytics, enabling businesses to anticipate customer behavior and preferences, thus optimizing their targeting efforts and improving campaign effectiveness. 
  • Chatbots and Virtual Assistants: AI-powered chatbots and virtual assistants enhance customer support and engagement by providing instant responses to inquiries, addressing common issues, and guiding users through the sales funnel. 
  • Content Generation: AI-generated content assists marketers in producing relevant and engaging content more efficiently, saving time and effort while ensuring consistency and quality.
  • Recommendation Engines: AI recommendation engines analyze customer data to offer personalized product or content recommendations, boosting cross-selling and upselling opportunities. 
  • Social Media Analysis: AI tools can analyze vast amounts of social media data, providing marketers with valuable insights into customer sentiment, trends, and brand perception to inform social media strategies.
  • Ad Targeting and Optimization: AI optimizes ad targeting by identifying relevant audiences and adjusting ad placements, leading to higher click-through rates and better return on ad spend.
  • A/B Testing and Optimization: AI automates A/B testing, allowing marketers to experiment with different variables and quickly identify the most effective strategies to maximize conversion rates. 
  • Customer Segmentation: AI aids in segmenting customers based on behavior, demographics, and preferences, enabling marketers to tailor campaigns for specific groups, leading to improved engagement and customer loyalty.

The Challenges of AI for Marketers

Implementing AI in marketing can bring substantial benefits, but it also presents certain challenges that marketers must address to ensure successful integration and utilization. Here are five key challenges: 

  • Data Quality and Availability: AI relies heavily on data to generate meaningful insights, and inadequate or poor-quality data can hinder AI's effectiveness. Marketers must ensure that the data used for AI analysis is accurate, relevant, and accessible. 
  • Integration with Existing Systems: Integrating AI-powered tools with existing marketing systems and technologies can be complex and time-consuming. Compatibility issues may arise, requiring careful planning and execution to ensure smooth integration. 
  • Cost and Resources: Adopting AI solutions can be costly, especially for smaller businesses with limited budgets. Additionally, hiring and training skilled personnel to manage and utilize AI technology may require a significant investment. 
  • Privacy and Security Concerns: AI often deals with sensitive customer data, raising concerns about data privacy and security. Marketers must take stringent measures to protect customer information and comply with data protection regulations. 
  • Understanding and Trust: Some marketers may be skeptical about AI's decision-making processes and may not fully comprehend how AI algorithms arrive at certain conclusions. Building trust in AI solutions and understanding their outputs is crucial for successful adoption. 

Overcoming these challenges involves careful planning, continuous monitoring, and a commitment to ongoing education and training for marketing teams. With the right strategies and dedication, marketers can harness the power of AI to overcome these obstacles and drive better marketing outcomes.

4 Common Problems AI Can Solve for Marketers

1. Acting overly transactional

Too often marketing is all about the sale in TV ads, on the radio, in print and in digital ads with retargeting. Even Amazon struggles with the challenge of being too transactional, and the ecommerce giant just began testing a system to make product recommendations more intuitive and helpful rather than simply cross-selling.

Seventy-two percent of business leaders are already terming AI as “a business advantage.”

Financial brands would seem at the highest risk of being too transactional, right? Not TD Ameritrade, which offers an investment bot called Alvi at no cost that allows customers to get personalized care by speaking into the system. AI-powered Alvi asks them personality-minded questions, like, “If you could write a letter to your 17-year-old self, what would you say?” The bot then breaks down the individual according to traits like openness, aggressiveness, neuroticism, conscientiousness and extraversion. Then it provides tailored, investment-based information in an on-demand fashion, an impossible service before AI. When permitted by customers, Alvi also mines their social media accounts for likes and dislikes and decides how to assist them even more intelligently.

On the other end of the consumer spectrum, Stitch Fix uses machine learning to show customers that their fashion interests—not sales—are always top-of-mind for the brand. The online shopping service has around 75 data scientists that employ AI to crunch 85 data points volunteered by customers, including their size or price preference as well as which parts of their bodies they want clothes to emphasize. The brand also has about 3,400 stylists who write notes to customers, addressing their specific situations based on data unearthed by AI.

2. Creating meaningful relationships

Whether data comes from a phone call, chat session, social media or mobile app, brands can use AI to regularly engage people in timely ways that are new. For instance, working with IBM Watson, Under Armour uses AI to power its app called Record, offering relevant training and lifestyle advice every day. If a male user wakes up after sleeping for just five hours, the app will tell him that the average body mass index is lowest among guys who sleep between seven and eight hours nightly based on the numerous men roughly his age in Under Armour’s database.

The AI also compares the users’ step activity and nutritional data against the larger Under Armour fitness community, helping them understand their level of performance while encouraging optimal fitness results. A brand helping customers become healthier is about as human as it can get.

3. Wasting budget

Brands have long wrestled with where to invest their ad budget. As marketing pioneer John Wanamaker famously said, “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.”

Marketers increasingly understand that AI can get around this ROI roadblock. Google recently revealed that ads improved upon by machine learning get 15 percent more clicks. For brands in insurance, telecom, automotive, financial and travel where paid search is typically expensive and the advertiser often wants to produce phone calls, it’s important to know if the people clicking through have real purchase intentions. One way to solve that conundrum is AI algorithms that determine the outcome of the call (purchase made, appointment booked, etc.) in real time, which is incredibly meaningful data to marketers.

This kind of intelligence saves advertisers significant budget not only with Google but also other digital platforms like Facebook, allowing them to reallocate spend to better targeting. It also saves customers from retargeting ads for a product they just bought.

4. Bolstering your career

Indeed, AI can solve three of marketing’s most persistent problems by using data to flesh out what’s missing from the equation. And if you believe AI is still a topic only for future consideration, think again. According to PwC,

Thanks to a wave of smart devices in the coming IoT era, companies that are ahead in the game will reap dividends. These new kinds of digital consumer experiences will also need to entail a human touch that will only be possible if informed by the data-processing abilities of AI.

So, the future is bright for marketers that overcome challenges with AI—as long as they learn to do it early enough in their careers. If they wait too long, they risk falling behind the pack as the big winners humanize brands one algorithm at a time.

Looking Ahead: 4 Future Trends for AI in Marketing

1. Emotional AI Marketing: AI will evolve to understand and analyze human emotions better, enabling marketers to create emotionally resonant content and personalized experiences, thereby deepening customer engagement and brand loyalty. 

2. AI-Driven Hyper-Personalization: AI will enhance hyper-personalization efforts by processing vast customer data, enabling marketers to deliver highly tailored content and offers in real-time across multiple channels, optimizing customer experiences. 

3. AI for Ethical Marketing: AI will be utilized to monitor and ensure ethical marketing practices, identifying and preventing potentially misleading or harmful content, thereby enhancing brand trust and reputation. 

4. AI in Augmented Reality (AR) Marketing: AI and AR will merge, providing marketers with innovative ways to deliver interactive and immersive experiences, allowing customers to visualize products and experiences in real-world settings, leading to increased conversion rates. 

As AI continues to advance, these trends will reshape the marketing landscape, driving innovation and efficiency while offering exciting opportunities for brands to connect with their audiences in meaningful ways.

Additional Resources

Want to learn more about how leading marketers use Invoca’s AI to improve the brand experience? Check out these resources:

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