Most of us intuitively know that bringing together data from multiple sources or taking a multi-faceted approach to answering a research question usually yields more impactful results than employing a narrow and focused approach. There is, of course, a place for discrete research projects that can be conducted in isolation and addressed adequately by fielding a single survey, for example. But for researchers, especially those focused on CX, crafting a research program that integrates relevant data sources holistically is increasingly critical to creating real business impact.
This said, many more traditionally-minded market researchers, those steeped in the notion that changing “tried and true” approaches is asking for trouble and deterred by the still-too-common reality of siloed research departments, resist change. But their numbers are dwindling – and that’s a good thing. Through talking with clients and staying close to industry conversations, I’ve observed that openness to refashioning and improving methodologies and programs is increasing. Survey data remains central – and rightfully so. And by connecting survey data with other data sets, it becomes more powerful. Three trends are making data integration increasingly important increased speed to insights, questions about data accuracy, and technological efficiency.
Nod your head if you have been asked this year to deliver data or insights faster than years prior? Now imagine everyone reading this article nodding their head along with you. Utilizing data that you own or can get from partners that doesn’t require direct engagement with research participants can supplement and make survey research more efficient. When conducting research with your own customers, it is essential to make sure you are using your internal data to target the right audiences. What you know about them from CRM or transaction data, for example, should be leveraged to reach exactly who you want to engage with. Additionally, diving into your data to fully understand what you already know (or should know) prevents you from asking people questions they don’t really need to answer, wasting both your time and theirs. When conducting competitive or non-customer work with a panel, external data can be utilized for similar benefits. Panel providers profile their members on numerous relevant attributes (e.g., financial status, employment, brands/products used, hobbies and interests) and can link them to various third party datasets – or even your own datasets – in order to target people effectively and make surveys more efficient. Asking what you need when you need it and as concisely as possible helps jumpstart speed to insights.
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Being fast is great, but only if the data you are getting fast is also good. Accurate data lies at the heart of any research initiative. When working with clients on how to improve their research programs, one of the most common topics of conversation is why they are asking certain questions in the fashion they are. Often the answer is “because that is how we’ve always done it” – or some variation on that theme. Much of the time asking people in a survey about their experiences is completely reasonable and a fruitful way of understanding your customers. However, there are also many cases where researchers ask simply out of habit and ask questions that can’t be answered well. Take a few seconds and ask yourself how well you could answer questions about a financial transaction you made three months ago, the details of an insurance policy you haven’t thought about in years, or nuances about what drove purchase decisions on routine shopping trips. It’s likely that in many cases data you own on your customers or that can be linked to research participants can answer these types of questions – and answer them well. As much as possible, let observational or already collected data answer the “what” and spend your time engaging with people to understand the “why”.
One of the reasons that data has historically lived in silos and research teams engaging in seemingly related projects never spoke to each other is that bringing together data from various sources with different structures was hard. While it can still take some work, we live in a world where plenty of tools exist to efficiently merge data, grapple with data of different formats, and present merged data in a way that makes sense. The nightmares of manually sending, merging, and fighting with big nasty data files, while still a reality sometimes, are quickly being replaced with sweet dreams of APIs, cloud-based analytical tools, and intuitive data visualizations. Researchers are well served to keep abreast of relevant technology and the ever-growing suite of tools that make their lives better by making data integration faster and more accurate. CX practitioners need not be shy because they aren’t tech experts or data scientists, but rather should explore how they can bring their research chops to bear on bringing data to life and telling stories that truly impact their business.
Thinking about how to leverage external data in combination with survey data, Research Now SSI and MaritzCX partnered to improve the research program of one of their clients. The client wanted to conduct a competitive study of their industry across five countries by understanding the experiences of people employed at certain companies. One of the challenges, something fairly common in B2B research, was to engage with people who had the right on the ground knowledge of their business and industry but also could accurately portray high level and operational detail about their companies. Screening in B2B research is absolutely critical and we knew we could find a better way. We integrated data from a trusted partner who specializes in business information dynamically. Company information was able to be dynamically integrated into the survey, which allowed us to validate in real time that we were reaching the right people at the right companies. This, in and of itself, was a win, but perhaps the biggest benefit to the client was appending twelve distinct variables with company information focused on areas like size and revenue to the survey data. We were confident we reached the right people. We didn’t have to ask questions that people potentially couldn’t answer accurately. And we reduced the length of the survey. Most important, the client was confident in the data collected from five thousand industry employees and successfully utilized this data to drive key initiatives.
There are myriad use cases for integrating data, and this is a pretty simple and straightforward one. The point in sharing it here is to remind you that while you can and should think big about how your programs can bring more data together, it is also worth thinking about how each piece of your program can be improved through integrating relevant data into your CX studies.