29
February
2016

Predictive Analytics in 2016: Application in Retail Big Data

Big and small and medium enterprises (SME) in 2016 are using Retail Analytics to drive sales and provide an excellent customer support service to its patrons. This need increase with the growing size of the super competitive market and customer's access to unlimited data sources. So, now the prime target of the market is to identify the customer needs and customize product accordingly.

 

Handheld devices are facilitating the retailers to know information about their customers. The more is the data, the more will be the overall view of the purchase habits of the customer. Following these, the retailers are developing a market strategy which is positively boosting the buying behavior of the customers. All of this insight if interpreted correctly, can be used to make a more informed business decision using predictive analysis software.
 

predictive analytics in 2016

 

  • Need Identification

 

A company must understand that all big data are not the useful ones, and it is not your data that will provide an answer to your present business condition. So in this scenario, it is the predictive analytics that a company must look for. With the right analysis of data, we get required information which results in greater business transparency and business improvements. While computing data strategy for your business, you must extract the same on the basis of the traditional rules and the algorithm.

Data scientists play a vital role in today’s corporate world, but business line executives should not have to rely on them to run analytics and make the inferences that are the basis for decisions.

 

  • Head for a C-Level Data and Predictive Analytics Genius.

With big data analytics rapid transformation and straining information structures, corporations and governments need executive horsepower or top-management muscle behind the data initiatives. Subsequently, a C-level officer who comes from both the supply chain and analytics framework must have the charge to lead model analytic centers.

In order to enjoy a successful business, analysts with deep data experience must have a clear strategy with defined initiatives to achieve successful business results. A forward-thinking analytics strategy thus must take place at the business unit level. If you are wondering why, the reasons are – First of all, priorities will differ by business unit; the treatment of data in one business unit may have little utility in another one. And secondly, management priorities have to reinforce functional level goals with some definite targets and metrics. So, for the perfect business prediction, a C-level executive who can manage working with business line managers but champion analytics in the C-suite is a necessity.

 

  • Market Trends: Use of Analytics tools

 

Predictive Analytics also can be used to gain an understanding of the extensive market environment and where it’s directed in order to take advantage of possibilities, and help to drive supply and other marketing decisions. In fact, most of the retailers are of the opinion that predictive analytics is originally about exploiting an opportunity.

 

Factoring in conditions like weather forecasts to predict demand for seasonal goods, as well as building behavioral patterns of consumer demographics to anticipate what channels they normally want to use, can inform retailers insight into what they should be doing to gain a competitive edge in their respective industry. By using sales and behavioral data along with information on the current market conditions, retailers can accurately project demand for anything from mango shake to a mobile optimized website.

 

  • Big Data was size optimization.

Demand for different garment sizes, colors, types can vary dramatically across geographical areas, and sometimes it depends upon the overall climate of the place throughout the year. It is true that the merchants were aware of the trend of the specific geographical location, but they could not help the supply as the data volume was so large that it require a ton of employees for a perfect prediction.

But today, with the help of the big data analytics, each store enters inventory and sales numbers of each location and the retail big data analytics crunches the data to give an optimal size configuration result.

 

  • Give Your Data Time-Critical Situational Awareness.

In a case of most of the organizations, data are pulled from scattered source which are then processed to get the business intelligence reports. With the help of the BI tools, improvement points can be identified.

Business houses need to make changes in the real time as well as within the constraints posed by the increasingly distributed nature of modern sets of data.

Ongoing supply chain management is concerned with multi-dimensional data including temporal, like the internet, speech-video data and geospatial elements (location tracking, natural calamity, and external body invasion) as well. Thus, it can save the operator from the immediate danger and unpredicted damage following a loss.

 

  • Risk Mitigation

 

Gap prediction is necessary for the supply chain management in order to minimize risk. Retail Analytics system can identify the unusual trend in the operation and pinpoint the sick zone in real time. This allows the retailers to control their production, reduce loss and stay ahead of their competitors.

 

Predictive Analytics showers innumerable benefits and transforming the retail world. Getting accessed to data and performing a detailed statistical analytics is helping the retail giants in their analytical experience.

With the development of predictive analytics, technology is becoming more user-friendly and the role of data interpretation is penetrating into every aspect of the business lines and generating a positive outcome.

The real value and business insight is easily recognizable with the clubbing of educating employees and application of predictive analytics.

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