How To Make Smart Use Of Data To Improve Textile Market Research?
Data is the backbone of modern manufacturing. Having more data means you can assess and respond to dynamic market trends (ideally in real time). That said, more is not always better. What’s the point of having terabytes of data if you cannot separate the wheat from the chaff?! Put another way: quantity of data can never substitute for good quality data.
This is why data intelligence is a crucial tool in an entrepreneur’s toolkit. Without data intelligence, you might as well be shooting blind at fish in a barrel!
What is Data Intelligence?
Techopedia defines data intelligence as “the analysis of various forms of data. . . [such] that it can be used by companies to expand their services or investments.” Data intelligence uses data from various sources: this includes consumer data mining, online analytics, business metrics etc.
It is important not to confuse ‘data intelligence’ with the related concept of ‘business intelligence.’ While the two terms have some overlap, data intelligence is more focused on data analysis. Business intelligence on the other hand is more focused on organizing data (specifically from business processes) to generate insights.
The potential benefits of data intelligence can be applied to all aspects of a business. However, it is most useful when it comes to operational efficiency, process innovations, and environmental impact.
In the case of the fashion world, we find data intelligence to be particularly useful for textile manufacturers. This is because the textile industry is subject to unpredictable market fluctuations caused by shifting consumer behaviors. That said, all fashion brands and retailers benefit from data intelligence as they can respond-in a timely and effective manner- to dynamic consumer trends.
Deploying Data Intelligence to Improve Textile Market Research
Conduct Primary As Well as Secondary Research
Data collected for data intelligence purposes must be from a broad variety of sources. Therefore, data collection requires both primary and secondary research. Primary research includes the information gathered by a company via surveys, interviews, and social media monitoring. Secondary research, on the other hand, comes from the already published data (e.g., statistics, articles and blogs).
Analyze the Market
Gathering primary and secondary data is just the first step. This data must be processed and analyzed to generate insightful information. A business can focus its research on the entire market or a specific segment of the same.
Market research is used to deploy the correct market strategy as well as build an effective business model. Other uses include understanding the behavioral response of your customers, assessing rival operations etc.
Refer to the Data History
Data intelligence is not a one-time deal. Data collection needs to take place over a period of time. This allows a business to have multiple data points (i.e., a comprehensive data history). These data points (ideally stored in the cloud) can be referenced when needed.
Market research-bolstered by a comprehensive data history-allows a business to do many things. For example, a business can assess what market strategies succeeded and which ones failed. Likewise, such research can be used to identify breakthroughs to longstanding pain points.
Prioritize Key Elements: Create a Database Hierarchy
Not all data is created equal. Businesses can waste valuable time trawling through vast bodies of data. Therefore, any good database should be organized with data hierarchies in mind. In other words, the important sections should be more accessible.
For example, a database hierarchy can prioritize information related to production, distribution, raw material prices etc. over machine and labour performances. This may help a business save time and resources.
Be an E-Commerce Smart Business
Smart businesses thrive on e-commerce platforms. Why? Because these businesses know how to use data intelligence to gain the market advantage.
A business should look for opportunities to integrate data intelligence systems into their online operations. Examples include collecting data on customer attitudes and digital preferences. Another example is deploying a virtual customer service platform to conduct primary research.