Data is considered as the most valuable resource. How can companies use their data to increase sales, get insights and improve overall efficiency?

Now companies are using data science to make there data to speak. They are also using AI to predict there sales or targets. So overall companies are now using their data to improve their sales and efficiency.

Data has indeed been the most valuable resource; in order to increase efficiency or the goal set by the company models need to be built to analyze the data and get insights. For example, to increase sale the company could analyze the sale trends series over the past to determine which segment dominate drive the company sales and focus on it or build customized offers to increase the market share. On other hand, which segment the company weak to analyze the problems and find solution if possible.

Analyze the data  thorugh KPIs, the KPI indicator is normally shows the performance of the company. according to the  performance, identify the provision of the improvement area. Compare the company performance with other known bentch marker company and tune accordingly.

In order to increase sales, nowadays the machine learning contributing alot. For example the Day today activities on Electronic devices is providing the enough data to understand the exact interest of an any individuals. Normally this will be provide us data like1) Number times searched in web,2) Qualtity3) price etc.

This will provide a very good feed back about our products

Companies need to realize the fact that only product features is no more hot cake to sell more and more products. Marketing efforts and customers interest and intentions should be well mined in order to sell goods. Each data point is important to be captured no matter what. Data for each stage of customer journey should be captured and analyzed to get insights and improve efficiency of product selling.

Data is a powerfull tool to predict customer requirements and needs, as an example Walmart used data science to understand the habits of it's customers, if a female customer buys pregnancy books, waistband extenders, essential pregancy vitamins...etc. the machine learning algorithm will predict that this woman is pregnant and will send her emails with items related to pregnancy.

I used data science to analyze a large number of invoices in order to detect inconsistencies, I found one item being sold with large quantities but the price so low it barely covers the break even point, when I inquired the reason for this they had no justified reason so I advised to increase the price by a tiny percent in order to raise profit by few hundred thousand dollars.

Historical data is asset for the company and can be used for forecasting and making offer. and market share analysis. 

Data interpretaion has been a very strong tool for analysing fututre, it will give information  about trends and help the managemant to take proper decision as per the demand of the market.

Nowadays companies make use of data science and machine learning to tell stories from their data and identify trends that will help them respond to changes quickly. 

I think that most of companies do not really do research and work on available tools where the scientific research is so advanced, espcially in the last few years that most of companies lost the link with the state-of-the-art.For my point of view, research is a necessity. Companies should invest in reasearch, but not necessary to have their own research labs, but for example, creating links with available research labs and universities, financing for example PhD, who will do research for the companies problematics and PhDs are the best to analyse and use data so they can develop excellent and very efficient tools for analysis. It's a win-win.

Data is an useful resource to understand the current status of sales which can in turn help us to compare it with our overheard. That's what most business owners would like to know. Whether my sales is covering my expenses. On top of that useful insight we can explore the shortcomings and flaws by drilling down the sales data by region,salesman,sales office etc. Upon getting the insight of shortcomings, the sales manager can work on that particular attribute to increase the sales. 

Bottom line:- Data is critical to the business owners of sales industry. They are in downfall if they fail to analyze data