En este artículo aprenderemos:
When we talk about data analytics we refer to a wide range of tools and methodologies that convert raw data into valuable information for decision-making, without limitations of scope or applicability.
It involves tools capable not only of generating reports and dashboards based on historical data or what happens in real time, but it goes further.
Business data analytics includes sufficient intelligence to reach an explanatory, predictive and prescriptive level, offering an idea of the why, showing possible future scenarios and even underpinning solutions.
Data analytics optimizes operations
The only descriptive level of data analytics (Business Intelligence) will provide your business with all the information in real time on inventory stock breaking, reordering points, eventual delays, breakdowns in work centers or bottlenecks, all with in order to apply immediate corrections.
It is an input that makes it easier for manufacturing companies to optimize work centers and balance production lines.
With a history of claims or failures, it is even possible to forecast and anticipate a failure, for example, to apply preventive maintenance at appropriate times or implement equally proactive security measures.
More on data analytics and operations: 5 uses of data analysis techniques in manufacturing.
Data analytics optimizes marketing efforts
From various sources of information such as physical sales channels, online stores and social networks, companies can channel data regarding the consumption patterns of their customers.
With this, for example, more effective campaigns can be promoted in social networks and ecommerce in order to improve marketing positioning, behind is undifferentiated marketing.
Your business can use this valuable information to create complete profiles, segmenting the market effectively, redirecting and focusing marketing efforts, offering a dynamic and highly personalized experience.
All this results in a greater conversion of prospects with the consequent impact on income.
Data analytics encourages loyalty
Another pillar of data analytics to drive growth: service level and customer experience.
A customer satisfied with the level of service is a source of recurring income and a solid base of these is key to success, it is even one of the most effective sources of advertising.
Data analytics at its most basic level can provide sufficient information on the customer base, their degree of satisfaction with the level of service or after-sales and in specific cases of disagreement, it will allow your agents or customer service to be deployed with personalized attention.
Implementations even allow you to measure customer service staff performance, number of claims, tickets per agent, ratings, and comparisons.
All this remembered that knowing the customer’s expectation regarding the level of service is essential in its retention.
Data analytics for business growth
To conclude, everything that we have presented in this installment are pillars for growth and consolidation, via savings in time, money and efforts that free underutilized resources for more product purposes.
A company that departs from improvisation and mere beliefs, supporting its management in facts and data (Data Driven Business) gains competitive advantages and leverages its growth.
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