Customers Make Brands, Not Marketing Agencies

With over 25 years in Marketing, much of my attention has been on brand creation. I firmly believe that customers make brands, not marketing agencies. Leveraging the voice of the customer (VOC) should be the top priority for any CMO and is my main focus in my new role as CMO for AI platform supplier Vespa.ai. Fortunately, it's easier now than ever, with customers readily sharing their experiences on review sites like Gartner Peer Insights and social media—for example, Emerging vector databases: A comprehensive introduction on the Capital One website.

Validation from users is critical to influencing prospects. Eventually, enterprises will use AI to evaluate and select products by matching user requirements with product capabilities. VOC will become a critical input in this process, alongside other technical sources like user manuals. AI buyers will ignore marketing content to eliminate bias, making it even more crucial for marketing teams to prioritize authentic customer feedback.

A Rush to Engage Enterprise Buyers

My first campaign for Vespa aimed to build awareness in the enterprise space. I partnered with EM360 for a podcast, Vespa.ai: Generative AI needs more than a Vector Database, and the results have been astounding, especially from e-commerce managers. This success has created a nice-to-have problem: we're now scrambling to create engaging nurture content. Thankfully, the blog Scaling Recommenders systems with Vespa by Farfetch came to the rescue.

Farfetch, part of South Korean e-commerce giant Coupang, is a global platform for the luxury fashion industry, connecting consumers from 190 countries with luxury merchandise from over 1,200 brands, offering a unique shopping experience. The company operates through its digital marketplace, physical retail stores like Browns and Stadium Goods, and offers e-commerce and technology solutions for luxury retailers. Farfetch serves over 4 million active customers through its e-commerce platform.

Killer use cases for Vespa are personalization and recommendation engines, which are critical in e-commerce to enhance customer experience and drive business growth. These engines tailor shopping experiences based on individual preferences, improving customer satisfaction and making the shopping process more efficient and engaging.

AI significantly enhances these systems by analyzing customer data to identify patterns and preferences. AI uses machine learning algorithms and other data sources to deliver accurate product recommendations, improving the shopping experience and boosting sales. By continuously learning from user interactions, AI adapts to evolving customer preferences, ensuring relevance. Additionally, AI-driven personalization optimizes marketing strategies by targeting customers individually, leading to more efficient marketing spend and higher ROI.?

Developing and maintaining sophisticated recommendation engines requires significant technical expertise and execution resources, as well as continuous updates to adapt to changing customer preferences. As a resource-intensive technology, AI is expensive, often forcing retailers to compromise engine sophistication to lower costs.

Voice of the Customer, not Voice of the Vendor!

In their blog, Scaling Recommenders systems with Vespa Farfetch calls out the following benefits of Vespa:

• Efficient Data Handling: Vespa rapidly and effectively processes any scale data sets to make personalized recommendations. Data can be structured, unstructured, and vector using Vespa’s vector database–recognized as a Leader and Forward Mover in the GigaOm Sonar Report for Vector Databases.

• Advanced Ranking: Vespa uses multiple methods to evaluate and rank how well products match customer preferences, ensuring the most relevant items are suggested.

• Keyword Search: Vespa finds products based on loosely defined keywords, making it easier for customers to find what they want.

• Learning to Rank (LTR): Vespa has built-in capabilities to continuously learn and improve how products are ranked based on customer interactions and feedback.
• Customized Filtering: Vespa is adjusted to include or exclude certain products based on specific criteria, ensuring recommendations are tailored to specific marketing goals.
• High Performance: Vespa is optimized to run efficiently, ensuring quick responses and smooth user experiences.

• High Performance: The engine is optimized to run efficiently, ensuring quick responses smooth user experiences.

And Finally, the Product Plug!

Vespa offers a versatile collaborative platform for building AI use cases, including recommendation, personalization, conversational AI, and enterprise search. Delivered as a cloud service with predictable pricing, Vespa is managed by experienced AI engineers who advise on AI execution and is supported by a vibrant community of thousands of AI professionals, ensuring ongoing development and sharing of industry best practices.

The strategic use of personalization and recommendation engines is essential for staying competitive. Vespa addresses these challenges through a cost-effective AI platform, allowing e-commerce Managers to enhance customer engagement, drive sales, and optimize marketing efforts, ultimately building long-term customer loyalty.?

But don’t take my word for it: read what some of our customers say!