I'm a packaging scientist and we are building Run Insights - a ...
...“Grammarly for packaging design” in the $1.2T packaging market. It’s an industry still running on outdated tools, and now is the perfect time to bring AI in. Our AI shows brands exactly which parts of their packaging consumers will remember, using neuroscience (eye-tracking, memory recall) to eliminate guesswork and save them from $20K+ trial-and-error cycles.
People love using similar products but resist paying. You’ll need to either find who will pay or create additional value that’s worth paying for.
Should You Build It?
Build but think about differentiation and monetization.
Your are here
You're entering the packaging design market with Run Insights, aiming to be the 'Grammarly' for packaging. This is a good place to be as there are 3 other similar products in this space. Given the number of similar products, you should be aware of the competition and focus on differentiation. The engagement in this category is high, with an average of 57 comments per product, indicating strong interest in these types of solutions. Since you're in the 'Freemium' category, the challenge will be convincing users to pay after they get hooked on the free version. You'll need to figure out what premium features will truly entice users to upgrade.
Recommendations
- Given the high engagement in the packaging design space, prioritize creating a compelling free version that delivers immediate value to packaging scientists and brands. Focus on the core 'Grammarly' functionality: identifying key areas of packaging that resonate with consumers based on neuroscience. Make sure users immediately see the value, but also feel they need more to unlock the full potential.
- Since you're in the Freemium category, identify early adopters who derive significant value from the free features. Understanding their workflows and pain points will reveal opportunities for premium features they'd gladly pay for. Consider conducting user interviews and surveys to gather insights on their needs and willingness to pay for specific enhancements.
- Based on feedback from similar products like Packify.ai and Pietra, focus on user experience. Many users criticized the UI and chat functionality for being slow. Invest in a smooth, intuitive interface and fast AI processing to differentiate Run Insights from competitors. Consider adding in UX features that make you stand apart.
- Explore team-based pricing models. Large brands may be willing to pay for Run Insights to ensure consistent and effective packaging design across their product lines. Offer collaborative features and reporting tools that appeal to teams, increasing the value proposition and justifying a higher price point.
- Offer personalized support or consulting services as a premium add-on. Some brands may need help interpreting the AI insights or integrating them into their design processes. Providing expert guidance can create a valuable revenue stream and differentiate Run Insights from purely software-based solutions. This can be a great upsell.
- Test different pricing strategies with smaller customer groups. Experiment with freemium models with feature limitations, usage limits, or time-based trials. Analyze user behavior and conversion rates to determine the optimal pricing structure for Run Insights. Create a solid testing strategy so your choices are driven by science.
- Differentiate yourself from competitors like Spring by Sourceful by focusing on the scientific rigor behind your AI insights. Emphasize the use of neuroscience (eye-tracking, memory recall) to validate the accuracy and effectiveness of your recommendations. Provide data-driven reports and visualizations to build trust and credibility with brands.
- Address the criticisms leveled at similar AI design tools by focusing on manufacturing feasibility. Ensure that Run Insights considers real-world manufacturing constraints and generates designs that are practical and cost-effective to produce. Include features that allow users to specify manufacturing requirements and validate designs against those constraints.
Questions
- Given that Run Insights uses neuroscience to inform packaging design, how will you ensure that the AI models are continuously updated with the latest research and consumer behavior data to maintain accuracy and relevance?
- Considering the criticisms of similar products regarding UI/UX and slow AI processing, what specific strategies will you implement to ensure Run Insights offers a seamless and responsive user experience, especially when dealing with large packaging design files?
- Given the freemium model, what key performance indicators (KPIs) will you track to measure the success of your freemium tier in driving user engagement and conversion to paid plans, and how will you use this data to optimize your product and pricing strategy?
Your are here
You're entering the packaging design market with Run Insights, aiming to be the 'Grammarly' for packaging. This is a good place to be as there are 3 other similar products in this space. Given the number of similar products, you should be aware of the competition and focus on differentiation. The engagement in this category is high, with an average of 57 comments per product, indicating strong interest in these types of solutions. Since you're in the 'Freemium' category, the challenge will be convincing users to pay after they get hooked on the free version. You'll need to figure out what premium features will truly entice users to upgrade.
Recommendations
- Given the high engagement in the packaging design space, prioritize creating a compelling free version that delivers immediate value to packaging scientists and brands. Focus on the core 'Grammarly' functionality: identifying key areas of packaging that resonate with consumers based on neuroscience. Make sure users immediately see the value, but also feel they need more to unlock the full potential.
- Since you're in the Freemium category, identify early adopters who derive significant value from the free features. Understanding their workflows and pain points will reveal opportunities for premium features they'd gladly pay for. Consider conducting user interviews and surveys to gather insights on their needs and willingness to pay for specific enhancements.
- Based on feedback from similar products like Packify.ai and Pietra, focus on user experience. Many users criticized the UI and chat functionality for being slow. Invest in a smooth, intuitive interface and fast AI processing to differentiate Run Insights from competitors. Consider adding in UX features that make you stand apart.
- Explore team-based pricing models. Large brands may be willing to pay for Run Insights to ensure consistent and effective packaging design across their product lines. Offer collaborative features and reporting tools that appeal to teams, increasing the value proposition and justifying a higher price point.
- Offer personalized support or consulting services as a premium add-on. Some brands may need help interpreting the AI insights or integrating them into their design processes. Providing expert guidance can create a valuable revenue stream and differentiate Run Insights from purely software-based solutions. This can be a great upsell.
- Test different pricing strategies with smaller customer groups. Experiment with freemium models with feature limitations, usage limits, or time-based trials. Analyze user behavior and conversion rates to determine the optimal pricing structure for Run Insights. Create a solid testing strategy so your choices are driven by science.
- Differentiate yourself from competitors like Spring by Sourceful by focusing on the scientific rigor behind your AI insights. Emphasize the use of neuroscience (eye-tracking, memory recall) to validate the accuracy and effectiveness of your recommendations. Provide data-driven reports and visualizations to build trust and credibility with brands.
- Address the criticisms leveled at similar AI design tools by focusing on manufacturing feasibility. Ensure that Run Insights considers real-world manufacturing constraints and generates designs that are practical and cost-effective to produce. Include features that allow users to specify manufacturing requirements and validate designs against those constraints.
Questions
- Given that Run Insights uses neuroscience to inform packaging design, how will you ensure that the AI models are continuously updated with the latest research and consumer behavior data to maintain accuracy and relevance?
- Considering the criticisms of similar products regarding UI/UX and slow AI processing, what specific strategies will you implement to ensure Run Insights offers a seamless and responsive user experience, especially when dealing with large packaging design files?
- Given the freemium model, what key performance indicators (KPIs) will you track to measure the success of your freemium tier in driving user engagement and conversion to paid plans, and how will you use this data to optimize your product and pricing strategy?
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Confidence: Medium
- Number of similar products: 3
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Engagement: High
- Average number of comments: 57
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Net use signal: 26.2%
- Positive use signal: 26.2%
- Negative use signal: 0.0%
- Net buy signal: 0.0%
- Positive buy signal: 0.0%
- Negative buy signal: 0.0%
Help
This chart summarizes all the similar products we found for your idea in a single plot.
The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments.
The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.
The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category.
The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.
The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.
Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.
