Introduction
E-commerce and subscription businesses are in a tough spot (actually everyone is). They need to keep up with changing customer tastes and demands for personalized experiences. Marketing is key to getting new customers and keeping them coming back, but it can also be a drain on resources if not done efficiently. Marketing teams constantly have to experiment with different channels, solve attribution problems (which actually makes no sense, it all about A/B testing) and so on. It's hard and you can't just keep hiring people to manage every channel and every function for that.
The Marketing Landscape in E-commerce and Subscriptions
E-commerce and subscription models rely on frequent purchases, customer retention, and personalized brand experiences. With so many digital channels available, marketers are have to optimize campaigns and deliver consistent messaging across multiple touchpoints, often with limited resources. According to a McKinsey reports (1, 2, 3, 4) companies that use data-driven personalization can increase revenue by 5-15% and marketing-spend efficiency by 10-30%. However, many organizations are not taking full advantage of these possibilities due to operational and strategic inefficiencies meaning they are too busy with their current KPI and often there is no leader in the organization who will drive AI adoption.
Common Marketing Inefficiencies
- Manual Reporting and Analysis: Many organizations still rely on manual data aggregation from spreadsheets, emails, and legacy systems. This can take up a lot of time and resources, and can lead to inaccurate reporting. And that's just part of the problem, connecting these data points is a separate challenge.
- Outdated Lead Generation Tactics: Many companies still depend on broad-stroke approaches like mass email blasts, poorly targeted ads, or cold calling. Sending billions of emails is expensive and just bad practices.
- Scattershot Personalization Efforts: Personalization is often touted as a marketing must-have, but actual implementations often focus on superficial tactics rather than leveraging user behavior, purchase history, or real-time engagement data. You need to have experts data people in your team who will cut sales BS and will ask tough questions.
- Siloed Customer Data Management: Data silos are pervasive, existing across CRM systems, email marketing tools, social media dashboards, and e-commerce platforms. This can lead to inconsistent messaging and difficulty tracking the customer journey holistically. From what I see companies work with 10-12 systems on average to do what they need to do. And in each of them data model is different.
- Inconsistent Branding and Messaging: As companies expand and launch new products or services, maintaining a uniform brand voice becomes increasingly challenging. Without centralized guidelines and checks, marketing materials can drift off-brand.
AI Solutions for Marketing
- AI for Campaign Management and Optimization: Modern advertising platforms like Google Ads and Facebook Ads use machine learning to auto-optimize bids in real time, adjusting audiences and budgets to maximize outcomes. Third-party solutions, such as Persado or Phrasee similarly apply AI to create, test, and refine ad creative for higher engagement.
- Predictive Analytics for Lead Generation: Tools like HubSpots predictive lead scoring and Salesforce Einstein analyze user behavior, campaign interactions, and historical purchase data to forecast which leads are most likely to convert.
- Customer Data Platforms (CDPs) and Unified Profiles: A Customer Data Platform (CDP) centralizes data from multiple sources to create a single, real-time customer profile.
- Brand Consistency and Dynamic Content Generation: Tools that leverage Natural Language Processing (NLP) and image recognition can review marketing assets for brand compliance.
- Chatbots and Conversational Marketing: AI-powered chatbots serve customers around the clock, answering queries and guiding them through purchase decisions.
Examples
- Starbucks AI-Driven Recommendations: Starbucks leverages AI in its loyalty program and mobile app to deliver personalized offers based on purchase history and location. (1, 2)
- Dollar Shave Clubs Marketing Automation: Dollar Shave Club uses AI-driven automation to segment customers by shaving habits, preferences, and purchase frequency.(1, 2)
Future Outlook & Best Practices
- Trends to Watch: Generative AI in Marketing, Voice and Conversational Commerce, and Ethical Data Handling.
- Best Practices for Adopting AI in Marketing: Start Small and Scale, Invest in Data Infrastructure, Cross-Functional Alignment, Monitor and Measure, and Maintain Human Oversight.
Conclusion
If adoption if AI is not on your roadmap for this year don't expect that your CPA will go down or that your team will magically overreach their KPIs. This is a very crowded space so eat or be eaten. My recommendations for next steps:
- Start collecting data from different channels in one place in automated mode
- Start using AI to analyze trends and highlight issues
- Remove manual operations in your teams
- Start delivering consistent message from ads to CX interactions
