How do you leverage data and feedback to reduce innovation risk and failure and improve outcomes?

?? I believe defining your problem and goals is crucial to setting the right direction for innovation efforts. ?? Problem Identification Start by deeply understanding the issue at hand, ensuring efforts align with actual needs. ?? Clear Goals Establish specific, measurable objectives that will guide your innovation process and resource allocation effectively. ?? Data Utilization Leverage data from multiple sources to ensure your goals are realistic and aligned with market demands. ?? Defining problems and goals early on helps reduce innovation risks and ensures that resources are directed toward meaningful solutions.

To reduce the risk of failure of innovation projects while simultaneously generating valuable insights we use the combination of tech-enabled rapid innovation experiments at scale enriched with deep human-level insights. Thus we combine actionable quantitative data and have subject-matter-experts give us their subjective qualitative point-of-view of this data. We then add general market insights and forecasting to sketch a holistic picture of the risk-adjusted expected outcome for the innovation at hand. To do so we provide our partners a capability to combine proven innovation methodologies combined with GenAI, Performance Marketing and Data Analytics. By leveraging these technologies we are able to overcome common innovation fallacies.

In my experience attaining top quality costs a lot of time, which results that you are (too) late to market. So my tip is for your design think R&D process: “to fake it before you make it” among customers. Get their feedback on what quality means for them and which features are ‘need to have’. The earlier you connect customers to your innovation project the sooner you know you are on the right track.

Desde mi perspectiva, considerar la voz de los clientes en un proyecto de innovación bancaria es crucial porque garantiza que las soluciones se alineen con sus necesidades y expectativas reales. Su input proporciona información valiosa sobre sus problemas y preferencias, lo que ayuda a desarrollar productos y servicios más relevantes y efectivos. Ignorar esta perspectiva puede resultar en innovaciones que no resuelvan los problemas clave o que no sean bien recibidas, lo que afecta la adopción y el éxito del proyecto. Escuchar a los clientes asegura que la innovación tenga un impacto positivo y significativo.

The problem and the prompts are key unlocks to reducing risk of failure. I'm a huge fan of accelerated concept development: analyze markets, triangulate insights, aid ideation, conduct synthetic testing against personas, and enable rapid prototyping with text-to-image tools leveraging AI ????????????????????? ???????? ????-?????????????? ??????????: efficiently research + test new features and products. Utilize chatbots to conduct consumer research, get feedback, and gather hundreds of interviews. ? ?????????????????? ???????????????? ????????????????????: prioritize decisions + trade-offs using simulations to explore scenarios, gauge customer reactions, bridge gaps between interviews and actions, and predict market performance.

Design sprints can help prioritize and brainstorm ideas. We can get creative with techniques and making simple prototypes. We'll also use data and feedback to guide us - looking at sales trends, customer feedback, and listening to our athletes, store staff, and other insiders who know the problem well. By mixing creativity with smart data and feedback, we can focus on the best ideas to reach our goals and make products that really resonate with our audience.

Utilizing tools to streamline this process is beneficial. DirectSuggest, for instance, makes it simple for employees to make suggestions on any device surrounding company created categories with the assurance they automatically arrive to the proper decision-maker. Additionally, DirectSuggest facilitates collaboration through features like voting and commenting, enhancing the overall quality of innovative ideas that are brought up. Learn more at https://www.DirectSuggest.com

You generate ideas through conducting market research and analyzing customer data to identify real pain points. Ideas can be prioritized through pitching problems that directly address these pain points.

Utilize data analytics to identify trends, market gaps, and customer preferences. Solicit feedback from customers, employees, and industry experts to generate a diverse range of ideas. Prioritize ideas based on data-driven criteria such as market potential, feasibility, and alignment with strategic goals.

In ideation, I blend data insights with creative brainstorming. We start with market data and user feedback, then use mind mapping to explore these insights. The key step is 'idea-filtering', prioritizing based on feasibility, user alignment, and market impact. Instead of full-scale development, we employ early painted door tests. This approach presents concepts without building them, efficiently gauging user interest. It's a lean strategy that ensures we invest in products that our customers truly want.

It's imperative to introduce and test ideas in the real world. We can try out different methods like experiments, pilots, or MVPs to get our ideas in front of real customers. I find this step is often missed or only though small tests, instead of creating a viable test and learn iterative model which can help both accelerate and improve the long term value of innovations. A true test and learn model helps us see how we're doing, what's working, and what's not. Using it to tweak and improve based on what we learn, so we can keep moving forward and making better products that people love.

This is key: Experimenting with ideas in real-world settings is a crucial phase of innovation. It provides valuable insights that simply cannot be obtained through theoretical or internal assessments. Real customer interactions, reactions, and behaviors serve as a reality check for the desirability, viability & even feasibility of ideas.

It's so important always to be fully aware of what method answers which question. ?? HOW you should build a product: Clickdummy, MVP, survey, etc. ?? IF you should build a product: Painted door test, Pretotyping, Smoke test, etc.

Getting in a mindset of iteration is key. Do not fall in love with your product but rather the problem you’re solving. This will motivate you to continue to iterate the product.

Much data comes from experimentation. You see what works. You make tweaks. You compare iterations. Innovation is a tinkerer's delight. The right path is not always paved in light. You have to discover it inch by inch!!

Scaling is where I see many amazing innovations die, overpopulating innovation heaven and it can all be avoided! Moving from a successful small-scale project to a larger rollout is challenging. Without proper planning, execution, and adaptation informed by data and feedback, teams and organizations struggle to replicate success on a larger scale. Unforeseen challenges may arise, and strong leadership, strategy with clear expectations on contribution, collaboration, and resource allocation are essential for success. Therefore, careful planning, ongoing monitoring, and adaptation are crucial for effective delivery and meeting evolving customer needs.

There are many strategic activities to leverage innovation risks whilst improving outcomes on our way to scale and implement. Data-driven decision-making undeniably comes to our help. Also, virtuous feedback loops with customers are key. Running iterative pilot projects with clear KPIs and metrics allows us to fix, implement, scale, or reject small-scale programs. However, the most crucial thing is nurturing a learning mindset and, as a consequence, treating constraints and failures as opportunities.

It is also critically important to stop your ideas when your validation confirms that the solution does not align well with the problem we are trying to solve and leverage that data and feedback for the next solution.

The biggest part of implementation is often the behaviour change piece. Many good ideas don't work out because there hasn't been enough consideration of how people will adopt the idea in their context.

Creating a proof of concept with select focus groups will help you validate before scaling. Each time you want to scale ... start with a replication of the community through focus groups. There you can refine with the feedback and then you can launch.

Building a data-driven culture is key to using data and feedback effectively in your organization. It involves fostering a mindset where data is seen as a source of learning and improvement, rather than judgment or blame. This means creating systems and processes for collecting, analyzing, and sharing data and feedback across teams. Providing resources, tools, and skills to effectively use data and feedback is also crucial. By prioritizing a data-driven approach, organizations can make better-informed decisions and continuously improve their performance.

This is essential to build an innovative organisation. I recommend the concept of double-loop learning - moving from tacit to explicit learning practices.

Organizational change starts with the community's values. Instill a culture of data-driven decisions through standardized training for all employees to speak the same "language."

A mentor once told me feedback is a gift ... both to give and receive. Companies that have a culture of feedback are more likely to succeed. Feedback should be given mindfully with the intention of bettering the other person and the company.

A cultura orientada a dados é a principal parte do trabalho e muita gente negligencia por que ela é a famosa "agua e esgoto", n?o dá ibope na foto do endomarketing. Para ter feedback contínuo, analisar dados e tendencias no dia a dia para tomada de decis?es, prototipar rápido e aprender, é preciso ensinar as pessoas como fazer, tornar isso parte do cotidiano, e principalmente olhar para dados de maneira integrada. Poucas empresas integram seus dados, existem vários sistemas "puxadinhos"que pioram a vida das pessoas e a qualidade das analises. Investir em cultura, estrutura é a base para a estratégia ser implementada com maestria. Em outras palavras, se tratando de dados, todo mundo quer o gelo, mas ninguém quer encher a forminha.

You can boost your use of data and feedback by learning from those who've already mastered it. Seek out innovators, experts, or mentors who've successfully used data and feedback in their processes. Join communities or networks where you can share insights with others facing similar challenges. By tapping into their experience and expertise, you can improve your innovation process and reduce risk.

Study cases of successful and failed innovations to identify patterns and key takeaways that can inform your approach. 1. Choose Relevant Cases: Select studies that mirror your market or challenges. 2. Focus on Key Elements: Identify what led to success or failure, especially how data and feedback were used. 3. Examine Data Use: Note how data informed decisions and strategies. 4. Feedback Integration: Analyze how customer and market feedback shaped the innovation. 5. Assess Outcomes: Look at market acceptance and financial results. 6. Extract Lessons: Identify actionable insights and best practices. 7. Look Beyond Your Industry: Gain fresh perspectives from other sectors.

Consider leveraging innovation tools that harness data and feedback to mitigate innovation risks and enhance outcomes. A great example is DirectSuggest's award-winning employee suggestion box app, which streamlines the employee feedback process. Employees can easily be heard and their organizations can effectively leverage their collective knowledge to propel their mission forward and make the world a better place. Learn more at https://www.DirectSuggest.com

Debemos fundamentar nuestras decisiones en base a aquellas hipótesis que creemos que persiguen el éxito; para ello, tenemos que respaldarlas con información de calidad (cualitativa y cuantitativa) que nos acerque a tomar las mejores decisiones posibles. El mundo empresarial empieza a entender que, para sobrevivir en un entorno tan competitivo, cambiante e incierto, deben dejar de lado el azar y optimizar su toma de decisiones y entrega de valor.

I love data as much as anyone, but data is a story that can be told with bias. Our motto is: Data Influenced Experiment Driven Evidence-Based

Em última instancia, sempre pense nas pessoas. Como a sua inova??o vai resolver um problema ou melhorar a vida de alguém? Esta deveria ser a premissa central de qualquer processo de desenvolvimento da inova??o.

Ultimately, always think about people. How will your innovation solve a problem or improve someone’s life? This should be the central premise of any innovation development process.