In-article:

The future of customer relations will depend on well-used data


Today, data management is too often perceived as an optional expense item, and therefore postponed until later, once the problems become impactful. This can have serious economic repercussions: according to Gartner’s 2020 analysis of data quality management solutions, more than 25% of the largest companies’ critical data is in error, to the point that the average cost of poor data quality data could amount to €11 million per year for organisations.

However, if it is well mastered, in particular via behavior analysis, it can significantly improve the customer experience and business performance, and thus constitutes a real source of value.

The art of data…

The CRM is the key component of the architecture enabling the implementation of customer data quality management processes. For the most complex cases, in particular with the presence of numerous and heterogeneous data sources, it is associated with an MDM. The objective is to achieve unique, up-to-date, correctly formatted and as complete data as possible.

Non-quality, whether inaccuracy or errors in the data, non-compliance with the regulations in force can lead purely and simply to the loss of a customer.

It is also responsible for a significant drop in productivity if the computer processing process is not optimal or if the recorded data does not bring any benefit. For example, Harvard Business Review estimated in 2017 that a task carried out with erroneous data incurred a cost 100 times greater than that of a task carried out using initially verified and correct data.

Quality data reveals how well a company knows its customers. It allows him to contact them, interact with them and have a 360 view of their history. It is therefore above all useful data. It is not a question of aiming for perfection because this approach can paralyze the establishment of an efficient data quality management process. Data must adapt to business and not become an end in itself.

We must also be aware that the system can cause difficulties and be prepared for them. Indeed, any management rule has potential side effects, in particular because of the risk of “false positives”, typically merging two duplicates which actually correspond to two distinct customers. The challenge is therefore to know what are the risks that a company is prepared to tolerate in relation to the benefits that it will derive from them.

It is first necessary to define the priorities by analyzing the more or less problematic points to understand the levers to activate. Moreover, the more the quality approach is integrated into operational habits, the lower the risks associated with post-processing will be. Also, their awareness and training are essential factors for successful data management.

…Keystone of predictive marketing

Thanks to behavioral data, it is now possible to anticipate purchasing behavior, customer needs and appetites, as well as sales trends: this is predictive marketing. But for there to be predictive marketing, it is important to begin by building up a relevant memory base by implementing a strategy for collecting and consolidating behavioral data (for example the types of purchases or the pages consulted relating to A brand).

Injecting this information into tools that are now more easily accessible, because they have gradually become more democratic, in particular thanks to AI, makes it possible to generate predictions. Salesforce conveys to its customers the conviction that they can do predictive marketing without necessarily calling on the skills of a data scientist, as long as they are equipped with the tools in line with the environment of their CRM.

However, for the approach to be effective, the usable data must be sufficiently comprehensive. Thus, the consumer who likes to be guided and aware of the latest news from a brand will appreciate that his expectations are precisely targeted. For its part, the company will benefit from a range of additional services to offer to its customers.

For example, a bank or an insurance company will be able to predict a family’s needs and present them with a range of solutions they may need in advance (opening an account, insurance contract, etc.). The company will also be able to prevent the loss of customers and carry out re-engagement actions.





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