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

1. Collaborative tools
2. Persona development
3. Predictive analytics
4. Personalized marketing communications
5. Automated feedback loops

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:

1. Miro AI:? can help Product and Marketing teams collaborate more quickly together. For example, after a sticky notes brainstorm, someone needs to go collect similarly themed sticky notes into a group. Miro AI can do that for you.?
2. Lucid Collaborative AI: Lucid’s collaborative AI tools adds generative AI concepts mid-stream in ideation, effectively supercharging collaboration sessions

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?

1. Affinio: Specializing in understanding consumer behavior at scale, Affinio uses machine learning algorithms to uncover naturally-forming groups within a customer base. This can help in crafting highly segmented and focused personas.
2. MonkeyLearn: This company focuses on text analysis with AI. By scanning customer reviews, social media mentions, and more, MonkeyLearn can help you understand customer sentiment and needs, aiding in persona development.
3. Delve.ai: This platform specializes in generating customer personas through machine learning. It aggregates data from various sources such as surveys, customer interactions, and social media to build comprehensive personas. These personas help in creating more focused marketing campaigns and product enhancements.

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:?

1. Mixpanel: Mixpanel is another product analytics platform that offers predictive analytics features. Mixpanel's predictive analytics can be used to predict customer lifetime value, identify high-value users, and predict customer churn.
2. DataRobot: They offer an AI-driven platform that makes predictive analytics easy. Companies can use it to figure out future sales or customer behavior.
3. RapidMiner: This company focuses on making data science easy for everyone. Their tools can help predict all kinds of things, from customer satisfaction to what features people will want in a new product.

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':

1. Copywriting?Jasper: Content / copy creation engine. | ShortlyAI: A tool leveraging AI to assist writers in creating content. | SubtitleBee or Rev.com: They use a combination of AI and human expertise to translate.
2. Video?Lumen5: Converts text content into engaging video content. | Promo.com: An online video maker with ready-made video templates. | Vizard.com: Automates the development of long-form videos into shorter videos to use across the marketing and in product experience.?
3. WebsiteFramer.com - Create more personalized websites and content AI through faster web development processes.?
4. Media?Pencil: An AI-driven creative platform for producing video ads. | GumGum is an AI-powered advertising platform that uses computer vision to analyze images and videos for advertising purposes. | ?Pattern89: Uses AI to predict which creative assets will perform best.

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:?

1. UserVoice: UserVoice offers a platform for collecting and managing user feedback, helping businesses prioritize and act on customer suggestions.
2. Qualtrics: Qualtrics provides experience management software that allows companies to collect and analyze customer feedback to improve products and marketing efforts.
3. Zendesk: Zendesk provides customer service and support software that includes features for gathering and managing customer feedback.

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.?

1. DataRobot is an automated machine learning and predictive analytics platform that enables businesses to build and deploy machine learning models quickly and easily.?
2. HyperScience is an AI-powered document processing platform that uses machine learning algorithms to extract data from documents.
3. Luminoso is an NLP and sentiment analysis platform that uses AI to analyze and understand customer feedback, social media, and other text data.?
4. Clarifai is a visual recognition and analysis platform that uses deep learning algorithms to analyze and classify images and videos.

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.