How AI Enhances the Partnership Between Product and Marketing
By:
David Novick. CMO with multiple exits. Co-founder of DarnDigital.com.
Connie Kwan. CPO, Investor, ex-Atlassian, ex-Microsoft. https://www.productmaestro.com
AI is reshaping the collaboration between product and marketing teams. It is driving improvements and efficiencies that lead to an improved, unified customer experience. In this blog post, we'll explore how AI is transforming this collaboration, making it more effective, informed, and customer-centric.
Taking Product and Marketing Collaboration to the Next Level
Traditionally, product and marketing teams have worked in tandem to bring offerings to market. Product teams create solutions that address customer needs, while marketing teams devise strategies to promote these solutions. While this collaboration has yielded successful outcomes, the integration of AI takes it to a new level.
AI Driven enhancements at the intersection of product management and marketing include
We will touch on each of these and provide examples. In future posts we can do deeper dives.
1. Collaborative Tools
Collaborative AI tools help to streamline processes between teams, but also foster a unified understanding, bridging the gap between ideation, implementation, and market engagement. By harnessing the capabilities of collaborative AI, product and marketing professionals can effectively navigate the complexities of consumer behavior, market trends, and technological advancements, ensuring that both their innovations and campaigns resonate deeply with their target audience. Here are few tools in the Collaborative space:
2. AI Driven Persona Development
Imagine you're part of a team that's trying to sell a new kind of shoe. One way to get people to buy it is to understand who those people are and what they like. This is called persona development and is often the starting point for collaboration between product and marketing.
Teams have always been making educated guesses about who might like their product. AI makes this much more accurate. AI helps us understand patterns or trends. For instance, by analyzing collected data, AI can show us that kids between 10 and 12 like bright-colored shoes and like to watch short, funny videos about them. With this info, teams can make products and ads that are just right for these kids. This was done more manually before and can be performed more quickly and accurately with AI.
Here are examples of companies pursuing persona development using AI?
3. Predictive Analytics for Product and Marketing
When a team is trying to make or sell something, like those kids shoes, they need a plan. In the old days, these plans were mostly based on what happened in the past. Now, thanks to AI and predictive analytics, we can make smarter plans based on what's likely to happen in the future. So how does predictive analytics work?
Known data, from past buying habits, for example, are collected, cleansed, then used to train a predictive model. Due to the availability of models, it is now possible to use pre-trained models, from other retailers for example. The trained model then takes in new data and provides a prediction. So, for example, we can forecast the customer lifetime value based on their usage and interactions in the app, number of integrations turned on, and % of their employees with an account in our app.?
Here are examples of companies that provide predictive analytics using AI:?
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4. Personalization at Scale
Imagine visiting a coffee shop where the barista remembers your favorite drink and offers it as soon as you walk in, making you feel special and saving you time. That's similar to how personalization works in Marketing and Product.?
Tailored marketing messages, like a barista recognizing your drink preference, boosts brand engagement. But generating enough variations in messaging to target every customer segment has been difficult and time consuming, that is, until generative AI.
On the flip side, a product experience that intuits your preferences feels smooth, like effortlessly paying in a mobile app with your saved credit card.
Historically, though, syncing up the 'coffee preferences' in Marketing with those in Product felt like a café with two baristas who never talked to each other. Customers are segmented into large groups rather than micro groups, and interactions can feel canned and impersonal.??
Enter the magic of low-cost, generative AI tools. Marketing and Product teams can now coordinate AI input parameters to create “sets” of content personalized at scale, by segment, and even micro segments; allowing teams to create a myriad of seamless and uniquely personalized experiences across each user’s journey.?
And while there are certainly still privacy issues to work through here, there are emerging tools that help each stage of the personalization process. Here are some AI tools making a splash in personalizing your 'coffee experience':
5. Automated Feedback Loops:?
Automated feedback loops enable effective product management and marketing.?
Consider a mobile app that incorporates automated user feedback mechanisms. When users encounter issues or suggest improvements within the app, the system collects and analyzes this feedback in real-time. High-priority bug fixes bubble up to the top. Desirable features inform the future roadmap.? By crunching through this data using AI, rapid updates and improvements can be made.?
Simultaneously, the marketing team can use this information to align their messaging with the app's product strengths and optimize promotional materials. This synergy between product management and marketing leads to a more customer-centric approach, improved user satisfaction, and enhanced user acquisition efforts.
Automating the feedback loop shortens the learning cycle that is critical for continuous product market fit. There are two types of tools offering feedback loop automation. The first are tools that collect feedback and are already adding AI. These include:?
The second are tools that enable fast deployment of your own AI enabled feedback loop. Since most companies have their own bespoke feedback pipeline, of which the above tool might just be a part of the puzzle, building your own feedback loop might be necessary. For this we’ve seen the following tools.?
Overcoming Challenges and Embracing Opportunities
While AI offers immense potential, it's essential to acknowledge and address challenges such as data privacy, bias, and ethics. Collaboration between product and marketing should involve ensuring AI systems are transparent, accountable, and aligned with the organization's values.
In conclusion, the integration of AI transforms the collaboration between product and marketing, making it more efficient, data-driven, and customer-focused. By leveraging AI's capabilities, organizations can create a synergy between these two critical functions, resulting in products that resonate with customers and marketing strategies that engage and inspire. As AI continues to evolve, the collaboration between product and marketing stands to become even more powerful, driving innovation and success in the digital age.