This article aims to analyze how it is now possible to treat the informational deluge in terms of marketing.
With the explosion of structured and unstructured data from websites and social networks, marketing and CRM experience have made a disruption for a paradigm shift. This result of a new theory of CRM and segmentation that gives birth to a new CRM which can be called the "Big CRM data".
The supposed main objective of the "Big CRM data" is to enjoy the incredible wealth that represent conversational exchanges on Facebook, Twitter, Foursquare and other social networks to be able to collect, analyze, cross , categorize data, so it will then be possible to address customer and prospects of a company rather than usual behavioral criteria. It would be about building a new segmentation that can qualify for attitudinal, and so far emanate opinions, comments, desires, judgments, values, tastes, preferences, dislikes, criticisms, demands, expectations, desires ... This includes all statements made on canvas or on social networks, which is a background noise a speech seemingly meaningless, meaning, or direction. This is an indication that trend, once aggregated and analyzed semantically, becomes intelligible and operable to decipher what is being said about a brand, product or service.
But "Big CRM data" can be more than that. It is far beyond the simple "trend analysis" or "sentiment analysis", it is beyond the scope of the "e-reputation". In fact, many companies use more or less sophistication and relevance of the "e-reputation," but do not use the data once purged and analyzed semantically. They merely read it, but do not do anything in terms of post-exploitation semantic analysis: again, the value of treating such information lies in the ability to store data, to categorize by type of content conveyed messages (critical product, requests for new services ...) and create a group of micro-segments, the latter being stored as new cohorts of clients.
The information categorized as attitudinal can be crossed with customer data stored in the databases of the enterprise CRM to create new distinctions among clients and prospects and enrich the existing segmentation. Cohort analysis will then answer the questions that may arise in their business portfolio of products and services on their position in relation to the competition and it may lead to reconsideration of all or part of the commercial offering, depending on the degree of dissatisfaction and wishes expressed by a micro-segments to an existing product or a new ... The expected number of occurrences at the time of categorization is crucial, it is about observing a trend, based on dozens of identical clients in terms of message content. Communication activities targeted by ultra-micro-segment and media used can be implemented to better address the needs identified.
The critical step could start by exploring data in the light of assumptions and objectives clearly defined and used as a guideline throughout the process that is specific to the industry, banking or distribution.
Large banks, such as major retail chains also face the versatility of their clients become "ubiquitous" with technology becoming more prevalent. As for retail banking distribution that has often important commercial networks, the quality of the relationship and customer intimacy are gradually degraded and dissolved with the advent of the Internet, mobile networks and social. The time has come to implement new ways of working to win back lost customers, limiting the "customer attrition," and increase the rate of loyal customers, while preserving its share market in a highly competitive and sluggish. That is where "customer intelligence" in the service of business can offer a fantastic opportunity for these industries.
Businesses can analyze the perception on perceived products and services prospects and / or customers brand from the aforementioned sectors and compare the perception of products and services by collecting all the transcripts, all opinions left on blogs, forums and other social networks on the company and its competitors to make possible a "clustering" Attitudinal based on semantic analysis of sentiment ("sentiment analysis" and "opinion mining"). This analysis is the building of a "clustering" Attitudinal complementary to existing behavioral segmentation process in a company (the traditional segmentation is primarily sociotypage and behavioral). These new data may come and enrich the CRM business and give birth to a so-forth segmentation "augmented".
We might even consider cross identifiable information with personally identifiable information contained in the CRM business information directly and thus the existing segmentation, optimize, revise, the amend, transform ... These steps are intended to improve the depth and reduce the granularity of customer knowledge to strengthen actions targeting marketing and sales. For it is in knowing what actually think its prospects and customers that can meet their needs in a more cost-effective to the digital era!
Thus, real plan of campaign advertizing and personalized marketing can be made to win new customers by tailoring products and services portfolio of the bank actor or distribution corrected through attitudinal analyzes. Tools and methods are now ripe and ready to be tested, it remains to convince decision makers...