How insufficient data quality affects your conversion rate
Low data quality is a problem that affects many organizations, even though they don’t necessarily notice it. It is, however, a problem that is fixable and optimized data quality can in turn contribute to an increased revenue and an improved customer journey. This article will elaborate the importance of sufficient data quality and after that, it is described how you can improve the quality of your data.
What is data quality?
Data has become an increasingly important asset for nowadays businesses. When customers order in your store, they probably leave their personal details and with this information, you can reach them in the future. But then there’s another type of data: product data. Product data provides information about products, like product brand, model number, color, size, etc. Within this type of data, there are some dimensions that indicate to what extent a product data set is of high quality. These dimensions will be described below.
- Accuracy: data is accurate when it correctly reflects information of the product.
- Relevance: in some data sets, there is irrelevant information. Such information negatively influences your data quality.
- Time: all product data in your data set should be up to date. Dated information has a negative effect on your product data.
- Consistency: ideally, data is always accessible for the entire organization: that means that your data needs to be accessed from multiple data locations. In order to achieve this, you want your data to be consistent.
- Complete: many product data sets appear to be incomplete, which has a negative effect on your data quality. All the product data that you need, should be present.
Why is qualitative data important?
Because data is a relatively new phenomenon, not everyone knows the possibilities with it. Especially for larger businesses, data provides opportunities to increase your conversion rates and revenue, and it also opens doors to an improved customer journey. For all these doors to open, however, there is a need for data that is accurate, relevant, and complete, i.e., qualitative. Unfortunately, this is not always the case when data is received from suppliers. This data is often unstructured, contains inconsistencies and doublures, and some data values are highly unlikely. To turn this unstructured data into usable data with structure in it, some improvements will need to be made. However, the process of manually correcting entire data sets is extremely time-consuming and goes along with many unnecessary costs. This process gets even worse for e-commerce retailers and wholesalers with over 1,000 products in their assortment.
How to improve data quality?
Manually improving your data may be feasible for smaller organizations, but organizations with many products have trouble coping with this process. Luckily, for these companies, there is a possibility to automate the process of improving data quality. Certain software tools are able to use a range of AI-techniques to improve the availability and quality of your product data. Such smart tools aim to find inconsistencies, anomalies, doublures, and unlikely data values within your data set. PowerImprove.ai is such a smart tool: you simply need to upload an Excel-file with product data into the tool. After that, just sit back while the algorithm does all the hard work for you. When the algorithm is finished with improving your data, you’re able to manually validate all of the improved features and values in order to be certain that the data is fully optimized. Even though the algorithms work without any problems, human validation is always advisable and takes way less time than manually improving data.
Still not convinced?
There are multiple multinational organizations that have used PowerImprove.ai successfully. If you want to read about their experiences, click here!