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When thinking about data collection, it’s important to think about your points at which you are going to collect robust elements of data. And if you do that, you need to think about what ways and methodologies you’re going to use to be able to do that collection.
We talked about the difference between qualitative versus quantitative data, and this is very important when you’re collecting and analyzing, because they both give you different sides of the coin.
One of the core concepts when thinking about what type of data you collect is “triangulation.” And triangulation effectively means using multiple data sources as a means through which to collect both qualitative and quantitative data, and when put together, they add to the richness of it all their own.
So thinking about the data collection process is very important. You need to think about what customers you’re targeting, what kind of questions you’re asking those customers, and when are you asking them. It may be the same customer, but at a point where they’re disgruntled they will give you a very different response to when they’re actually very happy and satisfied with your organization.
So you need to get a really good cross-section across a number of different dimensions in order to make that data analysis and collection meaningful to what you are trying to really achieve and the insights you’re trying to get on that.
Let’s think about some of the recent or current trends when thinking about data collection. There’s a number of different ways to basically do data collection, and a lot of the time, some of the more conventional ways seem a little bit stuffy and non-responsive from a customer perspective. It feels like the rule of sending a survey through the post and expecting someone to fill that out both accurately is probably in the past. Some of the most common things we see is people visiting websites, for example, and you’ll be asked a short survey on the back-end of it to see how your experience is.
One of the key concepts around data collection is the point at which you do so. So for example, if you have an experience and then a company asks you for feedback five or six days before, it could be very, very different from when they may have asked you to do that at the outset of that process. So think about what time is the most relevant time to collect that data.
Let’s now start to think about data collection and thinking specifically about some of the key considerations we need to make when collecting data.
Ritchie Mehta has had an eight-year corporate career with a number of leading organizations such as HSBC, RBS, and Direct Line Group. He then went on setting up a number of businesses.
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ABOUT THIS DIGITAL MARKETING MODULE
This module dives deep into data and analytics – two critical facets of digital marketing and digital strategy. It begins with topics on data cleansing and preparation, the different types of data, the differences between data, information, and knowledge, and data management systems. It covers best practices on collecting and processing data, big data, machine learning, open and private data, data uploads, and data storage. The module concludes with topics on data-driven decision making, artificial intelligence, data visualization and reporting, and the key topic of data protection.
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