Big Data

big data for customer targeting

How Big Data Helps in Smart Customer Targeting?


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Marketers have moved forward from traditional ways of marketing. With the advent of digital marketing, every step of a customer journey can be analyzed. And the accuracy level of customer engagement has improved with the help of predictive analysis, one of the big data techniques.

Methods of identifying the right target audience vary from one firm to another. However, big data analytics has made it easy yet time savvy.

Previously heavy research was used. The approach was to look into past performance and comparison of campaigns. Given the amount of content/data is produced daily, only the most efficient and well-crafted reaches out.

According to stats, only 5% of the 90% of the branded content garners attention. Rest goes unnoticed. What’s the reason behind? Consumers can consume only as much as they can. Demand stays the same whereas production is more.

All kinds of data produced and kept in record every year, collecting and analyzing such information will give business insight into what sells the best. Even such data can be used for making future predictions based on old customer behavior and patterns.

Big data can’t be used without structuring

Data without assembling is just a mass of unrelated information which can take years for comprehension. Here comes the big data and machine learning techniques implementation. Big data experts can help businesses structure data and use it for better insights.

Cut down data size, add filters

First step in smart targeting is determining what we need and exploring all sources of our user’s interest. In the second step comes cutting down data size and narrowing the search.

Narrow your options until you reach the exact proportion. For this, you can add a variety of filters. One basic could be time limit. If you just want results for last week, or even the last over, there must be a filter.

Analyze and develop a good understanding

Without stats, you may never know what your customer want. After filtering the right information, comes the part where we turn filtered information into manageable yet productive insights.

Advanced analytics in big data analytics help brands in many ways. Such as, business owners can look into associated words for known brands. Or we can see big influencers on Twitter, Facebook etc. making an impact for a certain brand....

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Top 3 Applications of Big Data for Supply Chain Management Operations


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Big data is no longer for marketing and sales only; it has enormous applications. Supply chain management is one of the sectors which could reap huge benefit from data science.

Traditional ways of supply chain management and Enterprise Resource Planning (ERP) have changed. Its quoted over Forbes that 80% of the manufacturers are handling their business network activities through big data and cloud-based technologies.

Rapid product lifecycles need speed and manufacturers relying on old Enterprise Resource Planning (ERP) are stupid. Reason? Lack of ‘forward-thinking’ approach. Companies are depending heavily on business models with rapid product launches and big data analytics.

Accuracy, speed and quality are the core parameters which is why businesses need big data integration into their processes. Here are top 3 applications of big data in supply chain management, based on 'how this industry can get maximum out of data analytics'.

Accuracy, Speed, and Real-Time Tracking

Real-time analytics offered by data helps companies to track deliveries of their products to end customers. Barcodes input processing, global positioning system devices, road network data, traffic sensor data, vehicle data etc. allows the logistics manager to optimize their delivery scheduling. Moreover, companies can automate delay notices to customers, give them real-time tracking delivery status updates.

Suppliers Network Management

A manufacturer could get a good sales margin if he uses mix of suppliers rather than producing the product with all the infrastructure. Big data facilitate in a way that it allows companies calculate mathematical models and forecast their margins which otherwise is a complex process.

Data can estimate additional costs because of the delivery speed from different suppliers, long-term contract cancellation costs, and even the supplier reliability. Further, data stats evaluate performance predictions of different suppliers.

Optimize Pricing Models

Consumer data extracted from internal and external sources used as a key for preparing pricing models. Predictive analytics which is one of the forms of big data could be used for determining or forecasting demand of the particular product at varying price points. Companies can use data for soft launches and checking the customer feedback.

Another useful application of big data is the sales forecast through pricing. Businesses can use dynamic pricing for increasing revenue in the phase of high market demand.

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