How Cash-Strapped Brands Can Leverage AI to Revolutionize Customer Interactions and Boost Profits
The landscape of consumer engagement with brands has been transformed significantly by the integration of advanced artificial intelligence (AI) technologies. Generative and predictive AI are at the forefront of this revolution, offering consumer brands innovative ways to interact with customers more meaningfully, enhance the bond between consumer and brand, speed growth, and drive profitability.
This white paper explores how these AI technologies are being employed by brands to enhance customer experience, optimize marketing strategies, and streamline operations. It also offers solutions for companies with cash constraints, for whom AI presents both an opportunity and a challenge. How do they keep up in the face of rapidly evolving technology, which takes time and investment to implement, when their human capital and capital resources are putting out the day-to-day fires of a business that is teetering on the edge?
Hyper-Personalization at Scale through Generative AI
Generative AI has made significant strides in creating content that profoundly resonates with consumers. By leveraging models trained on vast datasets, brands are now capable of generating personalized marketing materials, including emails and product descriptions, as well as video and music content tailored to individual preferences.
For example, consumer brands are increasingly using generative AI to create customized product descriptions that appeal to a shopper’s style preferences and past purchase behavior. AI analyzes customer data to understand style preferences and generates descriptions that highlight aspects of products most likely to appeal to the individual consumer. Brands like Levi’s utilize AI to serve targeted web experiences to customers based on real-time shopping behavior. [1] Ralph Lauren uses generative music AI to create branded soundscapes tailored to their target audiences. [2]
These approaches not only increase customer engagement and attachment, but also boost sales conversion rates, average order value, and customer lifetime value.
Enhancing Customer Insights and Decision Making Using Predictive AI
Predictive AI takes historical data and uses it to forecast future outcomes, helping brands make more informed decisions. This can range from predicting market trends and customer purchasing behaviors to identifying potential new product opportunities.
Retail giants such as Amazon [3] and Walmart [4], as well as Reliance Retail [5], India’s largest retailer, are employing predictive analytics to optimize inventory management, reducing overstock and understock situations, saving millions in lost revenues and operational costs. Inventory management through AI helps locate the product where it is most needed while lowering overall inventory expense and increasing customer satisfaction. Predictive AI is also used to tailor promotions and discounts to individual customers, enhancing the effectiveness of marketing campaigns and improving customer satisfaction. With the aid of AI, brands have the opportunity to create sample marketing campaigns tailored to customer profiles on which they can test marketing campaigns, rather than A/B testing on actual consumers themselves. Based on the results of testing with the AI-generated profiles, brands can then quickly implement the most effective marketing strategies directly with their customers, ultimately boosting campaign effectiveness and associated revenues.
AI is Enhancing In-Store Experiences
Brick-and-mortar retailers are using AI to bridge the gap between online and in-person shopping experiences, moving well beyond buy-online-pick-up-in-store (BOPIS) engagement. For example, smart mirrors use AI to recommend products based on the items a customer is trying on, providing options for sizes, colors, and similar styles available in-store. Utilizing radio frequency identification (RFID) scanning technology rather than cameras in order to protect the privacy of consumers in their dressing rooms, the scanner senses items that the customer brought into the fitting room and makes recommendations accordingly. [6] This combination of the physical and digital worlds, which some now refer to as “phygital selling,” links elements of the omnichannel shopping experience. Other, initial examples of phygital tools include using a cell phone camera so a customer can view how a pair of eyeglasses look on their face to allowing customers to virtually place a desired piece of furniture directly in their physical room.
Leveraging AI in the Face of Resource Constraints: Improving Efficiencies, then Expanding to Marketing
It is reasonable to expect that cash-constrained companies may think they will be left even further behind because they do not have the resources to investigate and implement AI tools. But this is not the case. How can cash-constrained companies unlock the potential that AI has to offer such that the opportunities offered by AI are more than simply a pipe dream?
Over time, it may be the case that the most robust AI tools will be based on a company’s closed data sets and accordingly, companies will need to invest in tools – home grown or licensed – that create curated data sets from which to query. For now, though, even cash-constrained companies can responsibly leverage free tools for certain functions.
Two such functions can be optimized with relative ease – rote, repeatable tasks and consumer marketing. AI is not a panacea, but cash-constrained companies may find that developing a small task force and identifying a handful of potential use cases for AI could alleviate some of their resource constraints. Over time, the tools themselves may become assets – against which an asset-based lender may be willing to provide liquidity as part of the brand’s package of intangible assets – and will lead to greater liquidity. For example, a retailer can apply AI to the reams of their available sales data to more completely understand which product traits drive sales and which traits allow for price elasticity. As the tools become more robust and capable, retail and consumer brands will be able to gain greater insights from the data. AI tools can also be instrumental in assisting with copyrighting for marketing including assisting in developing campaigns.
Further, applying predictive AI to historical sales data will lead to more accurate forecasting, giving companies and lenders alike more confidence in budgets and sales targets. Regardless, even if current liquidity issues limit a company’s AI expenditures, any business can benefit from decisions based on a data-driven approach. Applying even basic statistical analysis can shine a light on trends and opportunities that can drive strategic choices.
Challenges and Ethical Considerations
While the benefits of using generative and predictive AI are substantial, brands face challenges, including data privacy concerns, the potential for biased algorithms, and the need for continuous model training to adapt to changing consumer behaviors.
Ethical considerations must be at the forefront of AI deployment. Brands need to ensure that AI systems are transparent, fair, and respect user privacy. Consumers are increasingly aware of these issues, and their trust depends on how responsibly brands use AI technologies.
These challenges highlight that the optimal solution still requires human input, creativity, and decision making. The best path forward leverages humans plus technology, rather than one over the other.
A Path Forward
The widespread adoption of generative and predictive AI by consumer brands is in its infant stages. These technologies not only enhance the customer experience but also streamline operations and boost profitability. Cash-constrained brands may initially shy away from the opportunity out of fear of that they do not have the resources – cash or bandwidth – to adopt an AI-first mindset. We urge them to play the long game as it relates to AI, while leveraging short-term low-cost AI solutions. While AI will not solve all of their problems, implementing it responsibly may provide near-term liquidity relief, boost profitability and increase its balance sheet assets. The future of consumer engagement is here, and it is powered by artificial intelligence.
[AI assisted in the development of this article]
[1] https://www.marketingscoop.com/ai/generative-ai-in-marketing/
[2] https://www.marketingscoop.com/ai/generative-ai-in-marketing/
[3] https://flow.space/blog/ai-in-supply-chain/
[4] https://aiexpert.network/case-study-walmarts-ai-enhanced-supply-chain-operations/
[5] https://www.indiaretailing.com/2023/09/26/5-use-cases-of-ai-in-retail/
[6] https://retailsystems.org/future-retail-and-smart-mirrors/
