New functionality added to Harmoni allows users to automatically cross-reference layers of survey data, streamlining the analysis and visualization of multi-level (hierarchical) survey data.
The complexity surrounding consumer behavior seems to be increasing on a daily basis. Decision-making processes are not linear, and people experience multiple touchpoints with products, services and brands. As marketers and market researchers, we must examine the context that is driving people’s choices and behaviors. Keeping up is harder than ever, with data coming from all directions. How does it all fit together?
To better understand their audience behaviors, motivations, and why they do what they do, many brands present survey questionnaires that are somewhat complex. For example, let’s consider a survey that includes questions about people’s feelings, as well as questions about events in which those people have been involved. The data from that survey can be presented in two or more levels. One level represents the sentiment and other characteristics of the respondents (people), and the other level represents the events (such as consumption occasions).
When data is collected in this way, it is possible to look at all the relationships between the characteristics of the people (such as age, occupation, gender, and other demographics) and the characteristics of their “events” (such as when and where they consumed a drink, what brand it was, etc). Being able to layer information and compare relationships between layers of data is essential when seeking a deeper understanding of consumer motivations.
There are several solutions that can bring various sources of data together for analysis, such as “stacked” databases. But, really understanding and cross-referencing the data has traditionally required a lot of manual data preparation. A few of the pitfalls traditionally encountered in this type of analysis include:
Manual manipulation of the datafiles, leaving the data open to human error
Inaccessibility of the data by general research teams, requiring the intervention of the advanced research team or vendor
Difficulty in interpreting data and calculating percentages
Manual replication of data across the levels, risking another layer of possible human error and potential for misinterpretation
Lack of flexibility in meeting stakeholder needs, when the information needed was not already stacked and processed
The bottom line is that we just simply don’t have time (and can’t afford the possibility of errors) to analyze multi-level data using traditional, manual-heavy methods. Many technology-driven solutions that solve a handful of these inefficiencies still don’t intrinsically work with a market researcher’s unique needs in mind, and analysis can still prove cumbersome.
Harmoni was built by market researchers who know just how important this kind of analysis is to understand an audience and their behaviors. With it, you have an elegant and streamlined solution for important multi-level survey analysis, which improves your workflow in several key ways:
Immediately connect and cross the levels (such as stacked and unstacked data)
No need to manually replicate the data within the levels
Easy to switch between levels, cross-reference them against one another and automatically get the most relevant results (such as average number of occasions per respondent or number of drinks per occasion)
No latency, no database downloads to remember, and no need for tracking versions
Leverage the data easily without the need for scripting
As new data arrives, see the impact of the new wave immediately in the multi-level analysis and reports.
When analyzing multi-level data, the difference between various views can sometimes be overwhelming and challenging because it is so complex. To add to the fun, you must also ensure that the insights you are uncovering will correctly and thoroughly answer the business questions at hand. Harmoni’s user-friendly approach makes these kinds of analyses intuitive.
Harmoni offers the perfect solution for undertaking multi-level survey data analysis. It is ideal for researchers who are:
Collecting consumer diary data and survey questionnaire data for CPG or FMPG brands
Seeking to understand occasion-driven behavior using multi-occasion behavioral and segmentation data
Looking to drive business decisions with the five Ws (and H) marketing method - everything from what consumers are doing to why and how they are doing them
Dealing with brand or product profiling studies or normed databases
Gaining a deeper understanding of your customer just got easier!