In one of our Blog article, we had spoken about Segmenting and Classifying product Inventory and we would like to thank our readers for giving us the valuable feedback based on their business scenarios. And based on your feedbacks and our personal experience in implementing the FNS Segmentation, this time, we had decided to take a step back and explore something in pre FNS segmentation stages. Usually, it’s been observed that the database from where the report is fetched contains a collection of mixed data which includes data used for processing the software, data which contains the configuration values, transactional level data and so on. So, selecting the right kind of data and gathering it together to give a relevant output on which the next step (i.e. FNS Segmentation) can be applied plays an equal role in better demand forecasting.
Continuing our previous example, let’s say for demand forecasting for Mint Candies we had to choose all the data available in the backend. Under this condition, for e.g. fields like the name of a salesman who sold these candies and the vehicle information in which it got shipped will be an extra information which might not be useful in forecasting the sales for Candies. And also since every time the large chunk of data would be synced which will result in performance or slowness issues for fetching the data from the report. So, the first thing we need to take care is about selecting the exact useful data for processing reports.
The next feedback we had it from one of our existing client who had a common business scenario. Let’s understand it with our example, so now since our company has already three existing product i.e. Mint Candies, Bar Chocolates, Luxury Dark Chocolates. But to progress further, a new product has been launched in the middle of the financial year e.g. Jelly Beans. And by applying the same pattern and FNS Segmentation even Jelly beans had started showing its progress in sales. But, at the end of the financial year when we will evaluate all our products then the new product Jelly beans even after making a good sale, it would project low sales at the end of the financial year report. The reason behind this would be an introduction of the new product at mid of the year compared to the existing product. So, the next thing we need to take care is tracking the product from the date it has been introduced in the market or the warehouse.
Seasonality and Trend:
Moving further in analysis, we got to know that the product has to be tracked Season wise like during the times of festivals, regular days etc. and also based on customers taste certain products do well in one part of the country and at the same time doesn’t go well in the other part. So, we also need to take care that the products have to be tracked in a geographic way as well. There are many measures for tracking like at the Customer level, Market level, Shop wise and at times at Hub level as well. Also, if we expand our example horizon-wise for our industry like Chocolates, Soaps and Chips etc. then under this situation it will become necessary to track our products through their categories as well. So that the performance of each line of business can be tracked.
Last but not the least, it’s like a trend which now a day most of the industry is adopting. It’s about Segmentation of data, which ideally means dividing the customer into set of groups based on their buying pattern and lifestyle and then taking any business step by focussing on the certain group of customer. A simple example would be “A group of customer who buys Luxury Dark Chocolates frequently” can be treated as a Platinum group of Customer and to motivate them, more certain discounts or Value Added Services can be provided to them; to keep them engaged with the product sales. So, to continue or to improve product sales, we need to take care of Data Segmentation as well.
We hope our experiences would help in some way in optimizing or directing your business at any given point of time. Like always, we would like to conclude with; if you like any of our advice or suggestion or if you are looking forward to any of such implementation then you can mail us on firstname.lastname@example.org