Spring-cleaning for Product Data

Spring-cleaning for Product Data: How to cleanse Product Data

Clean product data is qualitative product data. And quality has a direct impact on sales. This was confirmed by a study conducted by Forrester in 2019. The Forrester report “Why Marketers Cant Ignore Data Quality” makes it unmistakably clear that success in marketing depends on qualitative product data. You should therefore determine the quality of your product data during your “product data spring clean” and identify and correct inaccurate or irrelevant data. You can do this with the following criteria:

1. Completeness

Missing or inaccurate product data can lead to misunderstandings and undermine customer confidence. For this reason, you should check whether all the required product data is available and, crucially, actually accessible. So first check whether you have provided all the expected attributes – not only from the perspective of your customers, but also from the perspective of the respective sales channels.

2. Correctness

Online business thrives on speed. Only up-to-date product data is convincing. Therefore, attach great importance to current product data, because only current data is also correct data. Consequently, check your data sources to ensure that they are up to date so that you can always obtain and offer up-to-date product data. In addition, this topicality gives you decisive insights into what is happening and at the same time opens up quick and well-founded options for action.

3. Consistency

Contradictions prevent purchases in online business. After all, there is no personal salesperson who could sort out the contradictions. Your customer relies on the online data you provide. For this reason, you should make sure that your product data is coherent. And across all channels, platforms and campaigns. For example, what is advertised on social media should be presented consistently and without contradiction in your own online store. In short: information and sales channels should work hand in hand.

4. Conformity

Today, there is more than just one sales channel. The potential for sales is significantly higher and is also readily utilized. After all, the reach and trustworthiness of the e-commerce top dogs should not be underestimated. But despite the abundance, there are no data standards here. Each channel has its own specifications, which means that each sales channel requires different formats. Therefore, check your channels regularly to ensure that your product data is converted correctly and therefore displayed in a channel-specific way. This is the only way you can be compliant everywhere and offer your customers a seamless experience across all sales channels.

5. Uniqueness

Data maintenance and preparation is time-consuming. Depending on the size of the company, different teams also work on the preparation of the product data. A common database is therefore essential in order to avoid duplicates and thus work effectively. Only if there is one data source can cleansing and updates to product data be carried out quickly and in a targeted manner. Instead of using Excel spreadsheets or different systems, a centralized system should be used. PIM systems are the right tool when it comes to product data. They represent a single source of truth and are able to obtain the required data from other sources and merge it centrally. So instead of processing your product data multiple times in different systems, you should think about introducing a PIM system if you don’t already have one in place. This is because adjustments in the PIM system are generalist and have an automatic effect in all sales channels.

6. Integrity

Your product data consists of master data, advertising texts and supporting visual content (e.g. images, videos, documents, etc.). A coherent image can only be created in the online channel if all the data harmonize with each other. You should therefore ensure that your product data is correctly linked. Nothing is worse for a customer when they search for a product and the product text displayed does not match the images. Product images in particular are simply the door opener. They are looked at first, before the product texts are considered. Clean interfaces between the systems that manage your product data and your image materials that are provided are therefore a must. They have to work so that there are no errors in the product presentation. Image materials and other visual content are therefore managed in so-called DAM systems, which are the ideal complement to PIM systems and work seamlessly with each other.

7. Transparency

If your product data is transparent, you can also clean it up very easily. So take a close look at your data sources and see if you can easily identify the sources and track the data. Especially when working with wholesalers and manufacturers, you are dependent on the provision of their product data. Here it should be important to actually receive the data that you need for marketing. Modern PIM systems are equipped in such a way that you can specify which information you need from the respective supplier. With the correct product data provided directly, it is then immediately possible to move on to marketing. There is no need to check the data received, revise it or make a new request if it is incomplete.

8. Processes and Tools

Product data can best be cleansed if the appropriate tools are available and a clear procedure is in place. It’s similar to spring-cleaning. If there are no cleaning utensils and you don’t know where to start, then the project is doomed to failure. For this reason, you should shed light on your processes. Has the process of product data preparation and enrichment been considered? Are the responsibilities and systems involved known? Are there reviews and approval processes? Which tools provide support? It must also be mentioned here that you cannot do without a PIM system at this point at the latest. The PIM is not only a tool for doing things, it also maps workflows that create clear processes. Only if there is a systematic, tool-supported approach can product data shine brightly in all channels. So don’t be afraid to support your product data quality with quality-oriented processes and technologies.