Customer intelligence and statistical data analysis

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Discover the three essential ingredients of statistical data analysis and the missing component to solve the customer equation.
Personalized offers, proactive customer service, detection of consumers who are at higher risk of churning... everything is possible with statistical data analysis applied to customer intelligence . Analysis and intelligence combine to give organizations the ability to refine their strategy, get the next steps right, attract more customers and boost the loyalty of existing ones.


What are the 3 key ingredients of customer intelligence?
To achieve any of these objectives, three ingredients are needed: data, tools and the necessary talent that will allow the application of the appropriate statistical techniques while keeping in mind the goals to be achieved. Here are the steps:

Data: According to IDC , the number of zettabytes of data overseas chinese in europe data will reach 40 by 2020.

Appropriate tools: these are none other than those that allow the business needs to be met, neither more nor less, in order to generate value and justify the investment in terms of ROI. Their selection must be complemented by the development of capabilities in the organization , since, if the information obtained is not managed and analyzed properly, it will be difficult to understand the client and act accordingly and in accordance with the knowledge acquired.

Talent : The growth rate for data science jobs continues to increase, with statistical analysis and data mining among the most sought-after skills (according to a survey by LinkedIn and a recent report by Adecco ). However, a study by McKinsey estimates that the US faces a shortage of up to 190,000 people with analytics experience to fill open data science roles, as well as a shortage of 1.5 million people for business analytics and management positions, where candidates are responsible for understanding and making data-driven decisions.
The better you understand the results of statistical data analysis, the more value you can extract from them. In addition to the quantification of things that statistics facilitates, you need to look for meaning in metrics, and yet there may still be one more ingredient missing from this equation.



MDM: How to create a single view of the customer, improve the customer experience thanks to the management of your master data


What is missing when statistical data analysis is not enough
Sometimes statistical data analysis may not be enough. As significant advances in calculation and analytical tools are taking place, it is becoming increasingly necessary to complement the knowledge obtained from the study of big data with that extracted from more direct person-to-person interactions with consumers. The proof is in the data itself:

- 85 percent of social media updates come from so-called "enthusiasts," but only 29 percent of a typical company's audience could be classified as this , with the remaining 71 percent falling into more moderate attitude ranges ( VisionCritical data ).