Market Research’s Post

When it comes to helping consulting firms quickly adapt to changing client needs and market conditions, rapid research beats time-consuming and expensive traditional methods to the punch. Read how rapid research takes care of non-core tasks, leaving time and resources for consulting businesses to drive better recommendations. https://lnkd.in/dUY_9_3u #Consulting #RapidResearch #Innovation #ClientNeeds #MarketConditions

Planning how to organise data isn't the most glamorous step in research, but it's an absolutely vital one. Victoria Atkinson has six tips from experts on how to keep your results in order.

In the dynamic landscape of #business, information is the key to success. Every decision, every move is backed by data. But the question arises: which type of data holds the most power. #Quantitative or #Qualitative? Both have their place in the arsenal of a successful business. Read this #blog to know Why does your business need qualitative and quantitative research?- https://lnkd.in/gsEn_2tC #QuantitativeResearch #QualitativeResearch #BusinessGrowth #MRX #MarketResearch #InsightsOpinion 

When it comes to helping consulting firms quickly adapt to changing client needs and market conditions, rapid research beats time-consuming and expensive traditional methods to the punch. Read how rapid research takes care of non-core tasks, leaving time and resources for consulting businesses to drive better recommendations. https://lnkd.in/dPDseTaY #Consulting #RapidResearch #Innovation #ClientNeeds #MarketConditions Sankar Paul | Arpita Mazumder | Mallika Kakra Vohra | Dipayan Mukherjee | Ishika Mukherjee

As policy-making is a complex process, it's important to break it down into specific steps. Here are the key stages involved: 1. Data Collection: Gather relevant information to understand the issue, assess needs, and identify potential solutions. This involves activities such as surveys, interviews, literature reviews, and other methods to collect quantitative and qualitative data. 2. Data Analysis: Analyze the collected data to derive meaningful insights and inform the policy-making process. This involves statistical analysis, thematic coding, and interpretation to identify patterns, trends, and potential policy implications. 3. Policy Formulation: Develop specific policy proposals based on the insights gained from data analysis. This involves drafting policy documents, considering alternative options, and incorporating stakeholder input. 4. Pilot Project: Test the feasibility and effectiveness of the proposed policy on a smaller scale before full implementation. This involves implementing the policy in a limited scope or specific area to assess its impact and identify potential challenges. 5. Finalization of Policy: Refine and finalize the policy proposal for consideration and adoption. This involves incorporating feedback, addressing concerns, and preparing the policy for approval. 6. Post Policy Analysis: Evaluate the implemented policy's effectiveness and impact after it has been put into action. This involves monitoring outcomes, collecting post-implementation data, and conducting a comprehensive analysis to inform future adjustments or improvements. By following these steps, policymakers can ensure that their policies are evidence-based, effective, and well-informed. #policy #datanalysis #policymaking

𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗶𝗻𝘁𝗼 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: 𝗔 𝗧𝗵𝗿𝗲𝗲-𝗦𝘁𝗲𝗽 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 In the realm of complex social, political, economic, environmental, and private sector challenges, understanding and intervening effectively requires more than just good intentions. It demands a superior strategy that navigates through the intricate web of factors influencing outcomes. 𝘏𝘦𝘳𝘦’𝘴 𝘩𝘰𝘸 𝘸𝘦 𝘥𝘰 𝘪𝘵: ■ 𝗦𝘁𝗲𝗽 𝟭: 𝗖𝗮𝗽𝘁𝘂𝗿𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 The first step is about embracing complexity, not shying away from it. By modeling the entangled network of factors within systems, from stakeholder insights to other relevant variables, we uncover the full landscape of factors that influence the system. This isn't just about gathering data; it's about mapping out the deep, intertwined relationships that define the system. Our workshops and stakeholder engagements are testament to how diverse perspectives can illuminate the multifaceted nature of focal interventions. ■ 𝗦𝘁𝗲𝗽 𝟮: 𝗠𝗮𝗸𝗲 𝗦𝗲𝗻𝘀𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗨𝘀𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 With the complexity laid bare, the next step is to make sense of it. This is where cutting-edge technology and computation come into play. By leveraging custom algorithms and data science, we distill the tangled information into coherent insights. This process goes beyond traditional analysis, employing tools that can handle the dynamic and non-linear characteristics of complex systems to identify key leverage points—those factors that hold the greatest potential for impact. ■ 𝗦𝘁𝗲𝗽 𝟯: 𝗗𝗶𝘀𝘁𝗶𝗹𝗹 𝘁𝗵𝗲 𝗙𝗮𝗰𝘁𝗼𝗿𝘀 𝗧𝗵𝗮𝘁 𝗗𝗿𝗶𝘃𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 - 𝗦𝗧𝗥𝗔𝗧𝗘𝗚𝗬 The final step from analysis to action is about strategy—identifying and focusing on the leverage points that will drive meaningful change. This isn't about small tweaks but about finding those few, critical interventions that can significantly alter the system's trajectory. Our approach isolates these factors, transforming a dense network of possibilities into a clear roadmap for intervention. This clarity enables policymakers, stakeholders, and teams to direct resources and efforts where they are most effective, ensuring that every action taken is a strategic step towards the desired outcome. ✱ ✱ The journey from capturing complexity to distilling actionable strategies is not only about more rigorous methods; it’s about creating a future where decisions are informed, impactful, and sustainable. By navigating through the complexity with a blend of technology and strategic insight, we're not just solving problems—we're unlocking new possibilities that empower organizations to thrive in an uncertain and ever-evolving landscape. #Strategy #Innovation #DataScience #Management #DecisionMaking

To stay ahead of new trends, identify risks and opportunities, and gain a competitive advantage, you need the ability to conduct deeper market analysis that goes beyond simply consuming information but that helps you make smarter decisions and build better strategies. Deep market analysis is a key business process for organizations in various sectors: - Financial services and investors use it to monitor trends, build smart investment strategies, and optimize portfolio performance. - Consulting firms use it to provide confident, accurate, data-driven solutions for clients. - Corporate teams use it to maintain ongoing market and competitive intelligence. We will guide you through the process of conducting a deep market analysis, including introducing the tools and technologies that will enable you to perform this analysis most effectively. ⬇ https://lnkd.in/ekYU89Pb

𝗪𝗲'𝘃𝗲 𝗯𝗲𝗲𝗻 𝘁𝗮𝗸𝗶𝗻𝗴 𝗮 𝗹𝗼𝗼𝗸 𝗯𝗮𝗰𝗸 𝗮𝘁 𝘀𝗼𝗺𝗲 𝗼𝗳 𝗼𝘂𝗿 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲𝘀 𝗮𝗻𝗱 𝗵𝗮𝘃𝗲 𝗴𝗶𝘃𝗲𝗻 𝘁𝗵𝗲𝗺 𝗮 𝗳𝗿𝗲𝘀𝗵 𝗻𝗲𝘄 𝗹𝗼𝗼𝗸 𝘁𝗼 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲𝗺 𝗳𝗲𝗲𝗹 𝗲𝘃𝗲𝗻 𝗺𝗼𝗿𝗲 𝘀𝗽𝗲𝗰𝗶𝗮𝗹! First published in 2018, 𝗖𝗿𝗼𝘀𝘀-𝗖𝗲𝗻𝘁𝗿𝗲 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 remains extremely relevant, and this Special Issue contains a wealth of valuable information from centres far and wide including Canada, UK, and Australia. 𝗦𝗼, 𝗶𝗳 𝘆𝗼𝘂 𝗺𝗶𝘀𝘀𝗲𝗱 𝘁𝗵𝗲𝘀𝗲 𝗮𝗿𝘁𝗶𝗰𝗹𝗲𝘀 𝗳𝗶𝗿𝘀𝘁 𝘁𝗶𝗺𝗲 𝗮𝗿𝗼𝘂𝗻𝗱 𝗼𝗿 𝗳𝗮𝗻𝗰𝘆 𝗮 𝗿𝗲𝗳𝗿𝗲𝘀𝗵, 𝘁𝗵𝗲𝗻 𝘄𝗵𝘆 𝗻𝗼𝘁 𝗮𝗱𝗱 𝘁𝗵𝗶𝘀 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗿𝗲𝗮𝗱𝗶𝗻𝗴 𝗹𝗶𝘀𝘁. Being able to work effectively across different centres or jurisdictions is increasingly important to open up new research opportunities, but there can be considerable challenges to address. This special issue includes a variety of articles on outcome-based research and on practical, methodological developments to overcome the hurdles and enable data to be used more effectively. Some articles include a combination of both. 𝗢𝘂𝘁𝗰𝗼𝗺𝗲-𝗯𝗮𝘀𝗲𝗱 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 Integrating data from multiple sources can allow questions to be addressed that would otherwise require prohibitive effort and cost, or not be possible at all. Such research studies can add value by enabling, for example: larger sample sizes for greater statistical power; the creation of richer cohorts; and cross-jurisdictional comparisons. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗶𝘀𝘀𝘂𝗲𝘀 𝗶𝗻 𝗰𝗿𝗼𝘀𝘀-𝗰𝗲𝗻𝘁𝗿𝗲 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 Cross-centre data-intensive working holds great potential to promote and enable research. There is an array of practical issues that may impact on this goal, and these may require innovative solutions. Some examples are: creating technologies and data sharing platforms; developing methodological solutions; overcoming challenges inherent in the data; encountering cultural issues; and navigating regulatory & governance frameworks. Take a look Inside this Special Issue: https://lnkd.in/etQRaQ9q

Help AIIM understand the state of information management practice by taking this survey before May 1st. It only takes 20-25 minutes and all participants get a complimentary copy of their research report. https://buff.ly/3VzmzPy

Exploring & Discovering 💯The article underscores the critical need for self-assessment in understanding one's systems, information flows, and data. It introduces the concept of "Exploring and Discovering" as a valuable tool in the fast-paced 21st century business environment. The text emphasizes the importance of knowledge and the need to continually explore, discover, and share insights to remain competitive and responsive to client demands.💯 www.plutoanalytics.com