Big data driving value for marketing

As driving value and engaging customers more effectively is the center of Big Data, marketers must understand the implication of it in Marketing and realize that they can have that elusive 360-degree view of their customers. Converting this data into useful information allows business to deliver the right message, through the right channel, at the right time, to the right customer, because data drives exceptional insights and those insights drives better interactions (Arthur, 2013).

But first, it is important to know how to analyse the data to extract real value from it, the key to do that is to understand the different types of data analysis. Descriptive analysis is the simplest of all and with the most functional uses on business nowadays (Bhardwaj, 2019), it mines historical data to find similar patterns and correlations between outcomes, for example Google Analytics, a tool that shows how keywords positions have changed in the past, but it does not tell why that happened, that comes to be explained by diagnostic analysis, this type of analysis help to locate the root cause of an issue, to do that, the algorithms combine owned proprietary data and outside information to understand what happened and find a way to fix it (Karapalidis, 2018).

However, what is changing the analytics landscape is predictive and prescriptive analysis, the first one use the past data to predict future outcomes with a degree of probability, while the second takes the past information and uses it to straightforward future activities to obtain optimal or near optimal results (Minelli and Chambers and Dhiraj, 2013). In other words, predictive analysis makes business operations more efficient by cutting costs down, by optimizing marketing campaigns, and by promoting cross-sell opportunities, this type of analysis can also help companies engage, retain and maintain their most valued customers. On the other hand, prescriptive analysis help companies to answer the question “what should we do to reach the desired outcome?”, it takes into the insights of the past analyses to detect the best way to solve a problem or make a decision (Bhardwaj, 2019).

In conclusion, data mining business grows 10 percent a year as the quantity of data generated is booming (Mushtaq and Kanth, 2015), as well as more and more professionals are educated in the field, it will be seem higher number of companies joining the data-driven realm (Bhardwaj, 2019).

REFERENCES

Arthur, L., 2013. Big data marketing: engage your customers more effectively and drive value. John Wiley & Sons.

Minelli, M., Chambers, M. and Dhiraj, A., 2013. Big data, big analytics: emerging business intelligence and analytic trends for today's businesses (Vol. 578). John Wiley & Sons.

Mushtaq, A. and Kanth, H., 2015. Data mining for marketing. International Journal on Recent and Innovation Trends in Computing and Communication, 3(3), pp.985-991.

Bhardwaj, S. (2019) ‘Data Analysis and its Types’, Medium. Available at: https://medium.com/analytics-vidhya/data-analysis-and-its-types-88d001a9ea5a (Accessed: 12 Fev 202­­0).

Karapalidis, G. (2019) ‘Data Analysis and its Types’, Business 2 Community. Available at: https://www.business2community.com/big-data/data-science-for-marketers-part-2-descriptive-v-diagnostic-analytics-02144223 (Accessed: 12 Fev 202­­0).