4 reasons why you should use big data for sales prospecting

With the continuous progress in data science, there is a huge opportunity for sales managers to achieve new insights on how to increase sales, have more satisfied customers who return again and again.

But, one obstacle many organizations face is learning how to convert ‘big data’ into use-able data. This is why the availability of data is not enough, knowing how to use it is crucial.  Businesses need to learn to extract actionable data and use it in the right way to increase sales prospects.

According to a recent research by IBM, only 5% businesses in the UK are using big data to their advantage, and almost 70% of the businesses are only operating within the first two stages i.e. educating and exploring, while missing out on engaging and executing. Another eye-opening statistic states that 41% of businesses lack an understanding of how to use big data effectively, and this is why they choose not to engage with it.

This is why, you need to learn how to use big data efficiently in order to increase the effectiveness of your marketing and sales teams. The process starts with collecting and consolidating data. Sales leaders also need to learn tools that will help them transform the data available into intelligent sales strategies.

With more than 41% of the business lacking analysis and understanding, it is crucial to learn how to analyze and translate big data, so that the bottom line reflects that.  Here are 4 reasons that will show you how big data can take your sales to the next level;

One of the greatest benefits of analyzing big data is that it can help you gain invaluable insights about how  users feel about your product or service. By identifying the frequently bought services or products, you can link big data with social data to help you understand any barriers present in increasing overall sales. This can help you tap new markets, gain access to a bigger target audience and thus provide your business with new leads.

By using the results of social data intelligently, businesses can increase their prospects of ‘social selling‘. Marketing teams can offer their ideal customer profile brand new content of high quality,  thus taking audience engagement to a whole new level.

Data that is obtained from social networks can be used in ‘recommender systems‘. One of the best examples of a recommender system is Amazon which provides a customized homepage to each user according to their profile and interaction history. This can help generate repeat sales, hence increasing overall sales by leaps and bounds.

This is only possible if   record of repeated sales is kept for each user in the database.  With so many effective sales tools in the market, and available online it has become really easy to review sales report. Marketers don’t need to overburden themselves with updating and maintaining complex spreadsheets anymore.