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Five reasons why BI tools aren't suitable for market research analysis

If you are frustrated with the platform you’re using to analyze and report on your market research data, you are not alone. Here are five “fails” that can be fixed with the right technology:

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If you are frustrated with the tools you’re using to analyze and report your market research data, you are not alone. In fact, a high percentage of market researchers are struggling daily with workarounds, becoming “hackers” to make their tools work for their needs. That’s because most data analysis and visualization tools are not built for the complexities of analyzing market research data. Forcing a tool that is not fit for purpose to accomplish the sophisticated levels of analysis that market research requires results in high frustration and poor quality.

Some agencies and brand teams are using technology, systems, or sequences of systems and processes that have been around a long time and likely a little outdated. Others are using modern systems, but that aren’t specifically architected for market research. For example, a BI tool might be optimized for large volumes of relatively simple datasets, but can’t cope with the inherent intricacies of advanced survey data analytics.

We need to use solutions that are made by market researchers, for market researchers, and for market research data.

Here are five “fails” that can be overcome with the right technology:

  • Your tool can’t handle the data input. Some generic tools are designed for aggregate and relational data, and so they can’t process respondent-level data or multiple data inputs. If you want to bring disparate data sources together, it must be done manually before uploading or using limited data preparation functionalities. This is time-consuming and error-prone. It is essential that we look at various data sources to get a holistic view of target audiences, potentially bringing together things like survey responses, online interactions, in-store shopping data, social media posts, qualitative interviews, and more. A solution like Harmoni can harmonize the data for you and create a single source of truth that automatically updates as new data is added.
  • Your tool doesn’t allow for specific applications. In market research, we like to look at data in a myriad of ways. When your tool doesn’t easily allow you to accomplish tasks like examining weighted data, significance testing, processing multi-response questions, or building metrics on the fly, you are at a serious disadvantage. For example, we all know it isn't easy to obtain samples representative of the general population. Generally, the data will under-represent some groups and over-represent others. If you want accurate insights, you need to weight the data. A platform that's architected for market research will do that inherently, but if you’re using a different type of application such as a BI tool, you will have to work really hard to apply that weighting.
  • Your tool isn’t user friendly. Sharing data among wider stakeholder groups is rising in importance. Data democratization is becoming a theme. But, when your tool only allows data experts with querying experience and data model knowledge to create meaningful analyses, many people are left out. You end up relying on just a few people who know how to do things like applying the correct function and feeding the tool the correct inputs. In contrast, Harmoni is easy for market researchers and agencies to learn, and no querying or data model knowledge is required. Because the platform is built for market research data, features like statistical functionalities are integrated without the user needing to know the appropriate test to apply and the calculation formula required to apply it.
  • Your tool makes sharing beautiful results difficult. Visualizing and sharing market research data among stakeholders is arguably the most important part of a researcher’s job. When source data needs to be manually imported, and reports are not designed for direct client or stakeholder interaction, you run into major inefficiencies. These extra steps in producing outputs from data consume extra time and increase the potential for errors. If you are sharing static files, like PDFs or PowerPoints that contain your insights, then people aren’t viewing the most up-to-date insights. When data visualization is a core part of your technology platform, you can easily ask questions of the research data and quickly display answers visually. With Harmoni, for example, reporting is organized as stories that are directly connected to the data. These stories can be shared in multiple consumable formats, including online interactive dashboards with the ability to dig deeper and filter by any available variable on the fly.
  • Your tool isn’t using automation to its fullest extent. Speed and accuracy are top demands in the market research ecosystem right now. Automation is the way to get there by completing tasks - such as bringing together multiple data sources - in minutes rather than days, meeting demands for quick turnarounds, and leaving you free to do your most valuable job of uncovering insights. For example, with Harmoni, all your data sources can be organized, harmonized, and structured to suit your needs. Your original data remains intact and retains label, meta, and time information. Every time you load more data, Harmoni remembers, then re-applies what you did previously. Finding insights can be a whole lot smarter with the right tech too. Instead of manually setting up endless crosstabs, with Harmoni’s Discover functionality, you can quickly see the differences or similarities among audience segments. Smart market researchers will adopt technology that creates value by balancing automation with human strengths and skills for the best results.

There’s no reason to let your technology choices fail you any longer. Harmoni has been built specifically for the complexity of processing, analyzing, reporting, and sharing market research data. No workarounds necessary, just speed and quality that’s made for market research.


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