A single data record does not tell you anything; it only makes sense in a chain of additional information. A name and an address, for instance, may be interesting, but only if they are used in the context of an offer, an order, or a purchase order. And even more so, when payments are posted to individual transactions. This is, of course, only a small example. Data only appear conclusive if this form of correlation is understood.
You always need a summarizing signal to tell you how to evaluate any given status. This has to be quickly apparent and understandable because only then will the whole become conclusive and thus correct. However, this is already the preliminary stage for our next step.
Conclusiveness refers to the processing of individual operations. This is also a form of analysis where information is summarized and forwarded. Just think, by way of example, of the netted position lists per customer, the total annual revenue, etc. displayed directly in the customer mask.
The actual interpretation, however, happens in the statistics and reports. Here, large amounts of data are consolidated, grouped, and displayed depending on their meaning. The data are only good when the result of these processes is both sufficient and understandable.
It is not easy to distinguish the individual parameters, since everything is so closely related. Analyses alone are helpful and necessary, but here they are an integral part of further processing.
A system like Odoo is a rather extensive data kraken that does not only help to transfer data within departments, to control the subsequent necessary steps, and, ultimately, to make them transparent, but also to process them for sales and marketing.
In both cases, knowledge of the customer base is essential. Let us find out why.
Good customer, bad customer – how much attention do I give to whom? Who has priority? How much does the customer owe me? These are essential questions in sales. If a customer calls, and I already have the relevant data, I save on ”postage,“ as it were, and do not need to chase him up by phone.
Here, things become more complicated because marketing requires correspondingly larger amounts of data for a very specific purpose, such as newsletters, campaigns, or trade fairs. Consequently, both customer profiles and transactions are selected according to various criteria. This process a) necessitates complex queries, and b) has to be filtered additionally at different points.
In this case, the data can only be called good if large amounts of information have been processed, and the campaign has been successful.