As a trader, are you aware of the sales demand by the type of the products, the time period, and the region?
Are you fully informed about the inventory of each of your products at the moment and how much would you need to stock in the future? Or should you even think of restocking those products for the future?
Most of the distributors and traders sure wish they knew these things, more importantly, they wish they knew what their clients want and when they’d want it.
While many people still think that information like this is wishful thinking, it’s really not. At least not in this age and time where we can integrate robotic process automation and data analytics into our business process.
With this up and coming tech, not only can the trading businesses take the repetitive tasks off of the hands of their employees but they can also have access to big data that can give them insight into the future and lead them to make wiser decisions.
Soon enough every trading business would be using predictive sales analysis and RPA tools to increase client satisfaction and boost their sales. The days of traditional analytics are long gone, and predictive sales analytics is taking over.
No longer is it about the sales that happened, but it’s all going to be about, the sales that are going to happen!
How Sales Analytics Can Prevent Losses Caused By Poor Products
The best thing about sales analytics is that it is not a solution for after the poor products have done the damage. Rather, it is a preventative method that wouldn’t allow the poor products to exist, in the first place.
Here’s how sales analytics help decision-makers at trading businesses, make better decisions:
Prevents Hasty Sales and Availing ‘Good’ Deals
Jumping on board a sinking ship is not the wisest thing to do, but this is a lesson most of the inexperienced traders learn the hard way. However, with proper homework and research, even newbie traders can save themselves from the unintentional slip-up.
Most often than not, traders end up with poor products because they put their money on the losing side. When they see a product’s pricing being dropped, they stock up on it in hopes of increasing their profit margin. It looks like too good a deal to let it go.
Sales analytics prevent the traders from making this mistake by forecasting the future demands of the product and letting the traders know if it’s worth the investment. Some products might even be in high demand at the moment, and completely justifies the traders’ decisions, but the prices could have been dropped because of a long-term decision. Sales analytics looks at future trends, mines correlations, discovers hidden patterns, studies the sales of the substitute brand, and even examines minor complaints that have the potential to blow up and drives the traders’ decision based purely on facts and logic. A trader must only stock up on products that have the potential for sustained growth in the future, and predictive sale analytics allows the traders to see that.
Provides Visibility of the Point of Sales
Distributors lack the visibility that retailers have. They don’t know what is happening at the point of sales, they don’t know which product is being returned, and they don’t know the product that is being asked about the most and all other crucial things that happen at the point of sales.
They stay on the distribution side and end up missing the information that is generated at the demand side. Big data and RPA, on the other hand, generate meaningful analysis and provides visibility to the distributors on both, the sales side and the demand side.
With such a clear view of what is happening at the point of sales, distributors make better decisions regarding their inventory. It’s not even just about stocking up or not stocking up, but the algorithms allow them to decide exactly how much they need to stock up.
Allows Integrated Planning
Many distributors suffer at the hand of lack of proper communication and the different data each one of the departments has. Making guesses regarding the inventory also means every department head has a different idea.
When RPA is employed and paired with data analytics, it allows traders to establish an integrated system all across the business. All the department heads are looking at the same data, same results, and the same predictive analytics. When all the decision-makers are on the same page, making decisions from there is easy. There are less human errors and more accurate inventory decisions made by inventory managers.
Protects Against Potential Payment Failure
Anything is possible in business and this is not a farfetched reality that some clients will buy in bulk amount if they are on the brink of bankruptcy, especially if they have never bought in such bulk amount before.
Distributors cannot rush and place an order of a large shipment just because their client demanded it. They need to be very suspicious without being obvious and the best way to do it is via data analysis. Big data and analytics targets fulfillment rates, order accuracy and returns, in addition to product demands, trends and forecasts.
A manager needs to make a sound judgment based on the data before them and their intuition.
In such a fiercely competitive culture, one of the best ways for the distributors to stay ahead is to invest in data analytics. The voluminous amount of data they have with them alone is not significant unless they use that information to make decisions. Inventory decisions made with predictive sales analytics are strategic and have high chances of success, as opposed to making decisions completely blindsided. Decisions made by distributors with data analytics allows them to heighten their business printability and stay innovative.
Omnisys Solutions helps companies leverage the data they already have to get tangible dollar benefits. With a focus on business value, Omnisys helps its customers make the most of their technology investments and define the right roadmap for them.