A Multi or a Omni-Channel Retail business tend to focus on one business unit most strongly. It is tempting and natural to focus on the unit that brings the highest turnover. However we see that many business opportunities are foreseen when doing so.
In fashion, we oftentimes see that sourcing is generally done for the stationary stores. It is assumed that the customers that buy online are the same ones as offline.
Yet we have come to believe that this is not necessarily the case. Next to that, the customer behavior is generally different between online and offline stores. It is generally important to develop a sourcing strategy that takes both businesses into consideration.
Omni-Channel data management is very difficult. Especially for businesses that have both brick and mortar stores as well as online webshops. The challenge is usually that customer and transactional data is not stored centrally. This can be due to the fact that the offline stores were there first and online followed just “recently”.
However, we have come to know that there is a lot of optimization potential for multi-channel businesses that combine the insights of both business units.
By using cross-functional sources of information, we are able to identify hidden potentials. These could range from short term sourcing quick wins to long term marketing budget allocation potentials. At the basis of it all are a variety of sources of information. The source of this data could be: transactional data, sourcing data as well as customer data.
Why strategic data collection is so important
We can help you understand which sets of data are relevant to your long term business success and show you how you can combine the insights from the different business areas. This will help your stay on top of your business developments even better.
Creating an in-depth understanding of your customer base is one thing. It is another to provide your company with the data-basis it needs to read and discover trends. These trends can be as concrete as product trends, insights on size developments per brand (returns optimization) or understanding who shops when and where. These insights can help you plan your resources better and use your budgets efficient and effectively.
The way we go about data cleansing is that we first analyse and consolidate the status quo. See if there are any quick-wins that can be made. After that we determine the most important long term KPIs and drivers for your business. These can be inventory risk, sales insights, customer management information. We show you how you can use your data to support you with your daily challenges.
How to clean the data from your fashion retail business
However, before we can start to create fancy Tableau dashboards we generally need to clean up the data at hand. Data cleansing might sound somewhat abstract, but it is a necessary process that provides you with a solid data basis that can be worked with.
This means that we look at how customer, sales and sourcing (purchasing and inventory) information is gathered from a PROCESS point of view.
- It is important to understand WHO contributes to the data collection.
- HOW and WHERE the data is (ideally centrally) saved.
- WHAT is currently done with the information that is available.
- WHICH challenges and opportunities lay ahead.
The long term goal is to create a data-basis that is strong enough for you to support you in your daily strategic decisions. These decisions can both be management decisions or daily operations. Once the data has been cleaned we create management dashboards that will help you in the long run. The correct visualization of your data will help you understand the dependencies between your business units, customer base and inventory fluctuations.
Creating a good dashboard takes the industry and business needs into consideration. We believe that our expertise in (high) fashion and retail will help you find the insights that you need and support your mid-long term goals and business success.
Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.
Data cleansing case study and insights
Each individual retail business is a little different. We know that there is not one perfect one-size fits all solution. Hence, we focus on the methodology and strategic steps we need to reach our goals. It does not matter whether you work with Advarics, BüroWare or some sort of SAP-system.
If you would like to find out more about our data optimization techniques please do write us. We can provide you with more insights and fashion / retail business case studies. These will show you how you can use your data in the best possible manner. We believe that data cleansing should be a top priority in any retail business.
Data-driven decision making should not only be something that is done by marketeers. We believe that by combining the insights of various parts of your retail business, you can take better and qualified management decisions. Whether you would like to cut cost, optimize your buying decisions or want to know how to spend your marketing budget most effectively – it all starts with a “clean” set of data.
