You would expect a PR firm to tell you that you need a brand narrative—and to help you establish the storylines that highlight differentiation and catapult media relations campaigns and other brand-wide initiatives.
You might not expect your PR firm to tell you that your narrative must be told internally first to ignite the passion in employees before it’s used with the media, industry analysts and others.
Most companies struggle with understanding the ROI of crafting a compelling narrative that brings their business strategy to life. It requires a deep understanding of the company’s starting position, its aspirations and capabilities, those of its competitors, the needs of its customers and the dynamics of the market.
The journey doesn’t end on paper: It is only the starting point for engaging those who will implement it and those who will write about it. A company strategy should motivate people and be easily remembered and communicated. External communication firms are often asked to tell a story that hasn’t been told well internally, creating missed opportunities to unify stakeholders and messaging.
Most internal narrative storytelling falls flat or never gets the airlift needed! Look no further than this MIT Sloan School survey of over 4,000 managers that found only 28% could correctly list three of their firm’s top strategic priorities, let alone know their role in achieving them.
Writers, conversations and research matter
AI is fine for idea generation and competitive research, but it won’t be the magic wand companies wave to generate their business stories. You have to question whether you want to put your business strategy document into any generative AI platform, because once it’s absorbed, it’s no longer yours.
I thought I’d see if anyone was drafting copy around how they successfully used AI to support brand narrative work. The primary content is still all coming from AI companies prompting us to use them. Companies are tapping into AI for internal and external communications and marketing purposes to generate listicles or blogs for the content engine, find out what AI has to say about their company or CEO, dive into research and even ask AI to draft topics relevant to key customer profiles.
Our writers have AI and machine learning tools for a variety of use cases: conducting market or company-specific research to evaluate customer sentiment, competitive analysis, content marketing applications and best practices and much more. These include SEMRush, Relative Insight, ChatGPT, Cision’s platform, customer behavior and social media listening tools. At the end of the day, the role of the great writer remains firm. They prompt company leaders to create stories that support the vision and narrative in a way that people can remember and repeat.
This is why we have a proven process for helping companies turn their strategy into key brand narrative assets driven by humans:
- Mission, vision and values with meaning that are easily understood and acted upon (what this looks like in a sales conversation, how we solve a customer problem, how we galvanize employees, etc.).
- A CEO’s internal messaging platform for telling brand stories and showing how they come to life each quarter through moments, anecdotes and data.
- A company story to share with reporters, analysts and investors that is elevated by employees, internal subject matter experts, customer and partner activities, case studies, articles, studies and growth.
- Corporate messaging—the definition of the company’s purpose and goals to be used by all consistently.
- A great company story can be nimble and expand with new examples over time, but leaders need to own getting that spark and encourage others to imagine with you.
SO HOW CAN WE USE AI WISELY?
Predictive analytics: AI can analyze vast amounts of data to predict future customer behavior. It uses algorithms and machine learning to process data from sources like past purchases, browsing history and social media activity. This helps brands tailor offerings and marketing strategies to their target audience.
Content and social media analysis: AI tools can scan and analyze social media conversations and mentions to gauge public sentiment about a brand. This allows brands to react quickly to negative feedback and capitalize on positive sentiments. Similarly, we can use AI to crawl sites and determine which subjects are getting the most attention and, of course, benefit from a company having a distinct opinion.
Personalization engines: By analyzing customer data, these engines provide personalized experiences on websites, in email and through digital ads. This enhances customer engagement and loyalty. Remember, this still requires a strong writer and editor as a project manager!
Chatbots: These can provide 24/7 customer service, answer queries, provide product recommendations and assist with transactions. They learn from interactions to provide more accurate and helpful responses, improving the overall customer experience over time.
Several brands have successfully harnessed AI to boost brand awareness:
Amazon—Personalization and recommendation engines: Amazon uses AI to power its recommendation system, which suggests products to customers based on their browsing and purchasing history. This strategy has significantly contributed to Amazon’s customer engagement and sales.
Spotify and Netflix—Content analysis and curation: Both Spotify and Netflix use AI to analyze listening and viewing habits and create personalized recommendations. This approach has successfully engaged users by introducing them to new music, podcasts, shows and movies tailored to their tastes, thereby increasing user engagement and time spent on the platforms. Their algorithms consider viewing and listening history, search queries and ratings. This personalized experience has increased customer satisfaction and has been instrumental in retaining subscribers.
Nike—Product design and customer experience: Nike has integrated AI into its product design and customer experience. For example, the Nike Fit app uses AI to scan users’ feet to recommend the best-fitting shoe size. This innovative use of AI enhances the customer experience and helps reduce returns due to sizing issues.
Coca-Cola—Marketing and consumer insights: Coca-Cola has used AI for various marketing campaigns to collect consumer insights. By analyzing data from social media and other sources, Coca-Cola has been able to tailor marketing efforts, leading to more effective and targeted campaigns.
AI CONCERNS TO WATCH
Using AI in brand marketing offers benefits but also raises ethical implications and challenges that must be acknowledged:
Data privacy: As Deloitte’s “Technology Trust Ethics Report” underscores, the most significant ethical concern is the privacy and security of consumer data. Data collection, storage and analysis pose risks if not managed correctly. Brands must ensure compliance with data protection laws like GDPR and seek explicit consent from consumers for data collection and usage. Breaches or misuse of data can lead to loss of trust and potential legal consequences.
Data quality: The effectiveness of AI in marketing depends heavily on the quality of the data fed into these systems. Poor quality, outdated or irrelevant data can lead to inaccurate predictions and ineffective marketing strategies. Brands must continuously ensure that the data they collect and use is relevant, accurate and up-to-date, which can be a significant challenge given the dynamic nature of consumer behavior.
Bias and fairness: AI systems learn from the data they analyze, and if this data contains biases, the AI’s outputs will also be biased. This can lead to unfair or unethical targeting or exclusion of certain groups. For example, if an AI system is trained on data that predominantly represents a specific demographic, it may not perform as well for other demographics. Brands need to work to mitigate these biases in their AI systems.
Transparency and explainability: Brands should strive to make their AI-driven marketing strategies as transparent and explainable as possible, including disclosing the use of AI in marketing campaigns and how consumer data is being used to shape these campaigns.
Depersonalization of customer experience: AI carries a risk of depersonalization, where interactions feel too automated or generic. Brands need to strike a balance between automation and human touch to ensure that marketing strategies do not alienate customers.
AI systems are not infallible and can make mistakes or overlook nuances that human marketers would catch. A blend of AI and human judgment is essential. Doing so ensures compliance with regulations and ethical standards, helps maintain customer trust and builds a sustainable brand reputation.
A source I’m excited to share is the MIT CIO Generative AI report, which shows how the advent of broader AI accessibility has opened up exciting new possibilities across a variety of business functions. What’s most interesting is seeing the functions that are indicating they will be adopting AI the fastest: finance and of course IT.
The possibilities are vast!
