Your POS data plays a significant role in your retail business. It allows you access to inside information about your shoppers and, essentially, gives you a successful path to follow. Of course, it’s more than that. This data also forms the foundation for any future analysis you plan on doing around your business. And without it, you’ll be lost.
In part one of this two-part series, we focused on a general overview of how you can gain more value from your POS Data. In this, the second and final part, we’re digging a little deeper, looking specifically at how DotActiv not only stores the data but also what it does with it and what that means for you.
How is POS data stored in DotActiv?
Before we take a look at POS data and how it’s stored in DotActiv, it’s important to first point out that this type of data is stored by retailers at a transactional level. That means that it’s data recorded from transactions - your POS system.
DotActiv doesn’t use transactional data. Why? The short answer is that it’s just too much data and is simply not necessary to get the right result.
Instead, DotActiv looks at rolled-up data. Rolled up data is essentially data that is grouped together into manageable clusters such as by week or by calendar month. Before sending any POS data through, it’s essential that it is rolled up by any of these periods.
The data is then ready to send. And there are various formats that you can send it in.
There is Microsoft Excel (the least preferred method), CSV Files or you can even send it directly to the SQL Staging table. From there, and depending on the format in which it is received, it’ll either be scripted into staging tables or it will already be in the staging table. From there, it’s all about setting up the Data Import Utility.
DotActiv’s data import utility is a dedicated application that imports data from SQL staging tables into DotActiv’s data platform. It also monitors the staging tables, which is done through a flat column that shows which rows have or haven’t been processed. This is done automatically. Upon finding any unprocessed records, it simply picks up the top ones and processes them into a database. And it keeps churning through these staging tables until it has completed its work.
Different types of data feeds
As for how the data import utility manages to work so efficiently, it comes down to the fact that the data is split into a few different feeds, namely Market, Product and Fact.
The reason for working this way is simple: it doesn’t help to repeat all the store and product information thousands of times when you are just looking for the Fact. Plus, in splitting the data feeds, you’re reducing the load and machine time required on the servers.
This dimension will always look at your key market detail, which is your primary key. This dimension includes Store Detail (Store Name, Store Code, Retailer Name, Store Format), Cluster Detail (Cluster Name, Cluster Size, Cluster Type), and Geographic Location (Country, Province, Region Code, Suburb, Street).
This dimension will always look at your key product detail, which is your primary key. This dimension includes Item Detail (Barcode, Brand, Product Description), Dimensions (Height, Width, Depth), and Hierarchy (Supergroup A, Supergroup B, Category, Sub-Category).
This dimension contains data that can’t be altered. For example, your sales and units. It’s also worth mentioning that while both Market and Product are flexible, Fact is not. It includes Retail (Sales, Units, % Sales, % Units), Indicators (Core Range, Ranging Indicators, Buyers Indicators), and Stock (Stock Value at Cost, Minimum Stock, Lead Time, Case in DC).
Also, it’s worth noting that certain feeds are received daily. In that case, it’s simply a matter of constantly overwriting the data each time it is received. For example, let’s say that we are in January and it’s currently the 3rd. Every day we receive a stock file and at the end of every week, we receive the total sales.
As the data hits the staging table, the import utility picks up that there is new data that is unprocessed, and so begins processing again until there is nothing left.
How does your POS data become more valuable once it's in DotActiv?
1. It becomes accessible
For one, your data, once in DotActiv, becomes that much easier to clean, classify and cluster. And it can be done on the fly which is a major benefit
One example of this is the importance for the user (anyone using the DotActiv application) to ensure that the product classifications are clean and complete. That’s simply because most of the POS data environments don’t have completed and accurate product classifications.
2. You can combine retail space data with product performance data
In the first part of this series, we mentioned that combining your POS data with the data around your retail space can lead to a better understanding of what is going on at a store level. In DotActiv’s application, you can go even further by combining your retail space data with individual product performance data.
Since store space is one of the retailer’s biggest expenses the value derived from combining these data sets shouldn’t be underestimated.
Once you’ve cleaned and classified your data and pulled it into an assortment/ranging plan, you can start making any ranging decisions before exporting them to your planogram so that you can begin space planning.
Once your data is in a planogram, you have access to the Highlights feature found in the application. With it, you can look at your days of supply, your percentage profits and more. Based on that, you can make better decisions around your space allocation, which will ultimately help you make more sales, increase your profit margins, and reduce out of stocks.
3. Connect to other data sources for better context
If you want to get true value from your POS data, you need to ensure that you can connect to other data sources. The DotActiv data platform is unique in that you can add multiple custom fields to the database. There is a bank of standard popular fields but in the event that you wanted to bring a unique data set into DotActiv then you can do just that. There are four steps to do this:
- Add your custom field in DotActiv
- Add your custom field to the staging table
- Script the desired data into the staging table
- Update the mappings in the data import utility
4. Use a retail analytics tool to build custom reports
The power of data can’t be underestimated. We’ve noted that time and again in articles across our Category Management blog. If you have multiple data sets stored in DotActiv, as we have explained above, then this is the part where you get to bring that data to life with custom visualisations. This can be done in the DotActiv software.
So, as an example, think about the power you would gain from being able to visualise your space planning, POS, digital advertising, inventory management and loyalty card data directly in your category management software. The possibilities are endless.
5. Use resulting data to drive other retail systems
When you combine certain data you are able to gain access to data that you otherwise wouldn’t have been able to. Take days of supply for example. To understand how many days it will take for a product to require replenishment on the shelf you would need to have retail space data and product sales data. This is just one example of data that can become available by combining multiple data sources.
The resulting data can be used to add value to other retail systems. In many of our integrated environments, DotActiv drives and adds value to inventory replenishment engines.
DotActiv was built using point of sale data as a starting point for category management. If you want to get more value from your data then DotActiv is for you. For more information, book a custom exploratory consultation here or visit our online store here.