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How I Grew ThisAuthor: Branch
Dive into the dynamic world of digital marketing with Amanda Vandiver and Adam Landis in How I Grew This, where we invite leaders in the space to explore how they overcome industry challenges to achieve growth. Expect insightful conversations that uncover the secrets of our guests success in the ever-evolving landscape of digital marketing. Language: en Genres: Business, Management, Marketing Contact email: Get it Feed URL: Get it iTunes ID: Get it Trailer: |
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Why Your Mobile App Strategy Is Backwards — and What to Do Instead (with Matt Hudson)
Episode 117
Thursday, 5 February, 2026
What if your mobile app strategy was holding back your entire company's growth? In this episode, Amanda and Adam welcome back Matt Hudson, founder of BILDIT, to discuss why mobile-first thinking isn't just about technology—it's an organizational imperative. From breaking down the real ROI of app investment and the myth of channel cannibalization, to preparing your ecommerce business for AI discovery optimization, Matt shares hard-won lessons on aligning teams, personalizing customer experiences, and staying ahead of LLM-driven search trends. Whether you're scaling retail, launching a mobile strategy, or wrestling with how to compete in an AI-first world, this conversation cuts through the noise to deliver actionable insights that will reshape how you think about customer engagement across all channels.What You’ll Learn:How to determine if your ecommerce business actually needs a mobile appWhy organizational alignment across teams matters more than technologyThe critical difference between SEO and AI discovery optimizationHow to immediately implement AI-ready data on your site todayWhy React Native and cross-functional web-and-mobile teams accelerate app growthHow AI personalization works at scale using embeddings and vectorsEpisode Highlights:[00:05:35] The Five-Point Framework for Determining If Your Business Needs a Mobile App - Matt Hudson shares a strategic framework to help ecommerce businesses evaluate whether a mobile app investment makes sense for their company. The framework addresses a critical question many retailers face: with limited resources, is building an app worth the effort and cost? Rather than assuming all businesses need apps, Hudson identifies five specific criteria: having 50,000+ SKUs, operating physical stores, running loyalty programs, generating $100M+ in revenue, and understanding that app users are your most loyal customers—not necessarily younger demographics. For example, a retailer with nearby physical locations sees 50% higher app usage within a 25-mile radius, proving that apps convert loyal, high-value customers who trust the brand. This framework helps ecommerce leaders make data-driven decisions about mobile strategy instead of following industry trends blindly.[00:11:36] Organizational Alignment Over Technology: Why Mobile App Growth Requires Company-Wide Buy-In - Matt Hudson reveals that mobile app success depends far less on technical excellence and far more on getting every department—from stores to marketing to IT—genuinely invested in the app's growth. The challenge most retailers face is that mobile and web teams operate in silos, compete for attribution credit, and prioritize their own channel's metrics over total revenue. Hudson explains that when the marketing team sees improved ROAS (return on ad spend) from app traffic, and when stores actively promote downloads, the app grows exponentially; without this organizational alignment, even a perfect user experience fails. A key tactic is seating app and marketing teams physically next to each other and tying bonuses to overall company revenue rather than channel-specific metrics. This organizational shift removes the false notion of "cannibalization" and ensures every team pushes customers to their best experience—whether web or app.[00:21:19] Optimize for AI Discovery (AIO) Now or Lose 90% of Your Search Traffic - Matt Hudson warns that traffic from AI-powered discovery is already replacing traditional Google search, with click-through rates dropping from 15% to as low as 8% (or lower), and the trend will only accelerate. Unlike Google's SEO, which indexes everything and rewards backlinks, AI discovery prioritizes authoritative sources—Reddit, Quora, FAQs, podcasts, and trusted voices—and cares about giving correct answers, not just showing available links. Retailers must shift strategy immediately: stop relying on keyword rankings and start building authority through FAQ-formatted content, detailed product descriptions, JSON-LD schema markup, and getting mentioned in trusted communities where real people validate your answers. The good news is that unlike SEO, this content doesn't need to be visible to users—you can hide FAQs below product pages specifically for AI consumption. For any retailer serious about discoverability in the next 2–3 years, implementing AIO optimization today is non-negotiable for maintaining visibility.[00:32:38] Layer Personalized Customer Data into Product Pages Today So LLMs Reference It Now - Matt Hudson advises ecommerce companies to immediately begin adding customer-specific, personalized data to product pages—not just generic descriptions—so that large language models start consuming and referencing this information in recommendations today, rather than waiting for "perfect" AI features. LLMs use three-dimensional embeddings and vectors to predict which tokens (words) come next based on a user's entire conversation history; if you provide personalized data about who a product suits, the AI naturally generates personalized answers. For example, instead of just "Red Plaid Shirt," include FAQ-style answers like "Is this great for trendy ladies?" or "Will this fit someone with a small frame?" so the AI understands the product's full audience and context. The data doesn't need to be visible on the page—it can be hidden in structured markup or FAQ sections—but its presence trains the AI to make smarter, more personalized recommendations when customers ask about your products in ChatGPT or other LLMs. This practice compounds over time as models learn your data patterns and become more effective at positioning your products.Episode Resources:Matt Hudson on LinkedInBILDIT on LinkedInBILDIT WebsiteAmanda Vandiver on LinkedInAdam Landis on LinkedInBranch on LinkedInBranch WebsiteHow I Grew This on Apple PodcastsHow I Grew This on SpotifyHow I Grew This on Simplecast
