The digital return on investment is usually measured by a company using macroscopic indicators that are classically derived: sales, lead generation, brand awareness, cost reduction, advertising income.
Although this approach is perfectly valid, the fragmentation of consumers' everyday experiences and their interaction in a continuous but also a discontinuous fashion, coupled with a wide variety of corporate channels, can mean it is somewhat limited with regard to both the time horizon (short term) and the snapshot it gives us of digital business.
To continue with the metaphor, this approach gives us a close-up shot, when what we really want these days is a wide-angle view.
A company's instances of interaction with its own customers and also the sources of data available to the business are now countless and enable the possibility of prompt measurement, thus enriching data assets. These assets, under certain conditions and thanks to specific, large-scale technologies, can indeed be deemed big data.
These days, chains of stores are able to measure their own transactions at the point of sale (POS Measurement Protocol), and these sales data can then be compiled with those from e-commerce and supplemented with data collected in store (digital totems, Bluetooth beacons, etc.), resulting in an expanded database that enables us to get that wide-angle picture we mentioned above.
The IOT (Internet Of Things) now allows us to create a huge variety of "data-enabled" objects, with applications ranging from telemedicine to home automation, and from fashion to the environment.
For these businesses, it is a short hop from "data-enabled" to the "data-driven" approach characteristic of a strategy that relies fundamentally on clean, unambiguous and meaningful data.
What these digital data – and in a specific way big data – allow us to do is to fine-tune the indicators that are used for measuring the return on investment. The development of predictive models then ensures this return continues over time, since they become a supporting pillar of the company's own digital business.
The modern technologies of machine learning and deep learning allow us to predict the relationship that the company's own customers maintain in various moments of contact with ever greater precision and also to increase the efficacy of the very technology that lies behind it all.
In this varied and dynamic context, we make use of analytics as a kind of architecture that goes from a macro to a microscopic level and vastly increases the number of indicators available to the company – all the more so if the digital data are supplemented with legacy data (CRM) and those of third parties. Not only do they change the analysis tools and the methodologies available for corporate evaluations, but digital data (and by extension big data) also make it possible to shift the return on investment away from a quantitative plane and towards a more qualitative plane, thereby offering a broader view over the long term.
There is an important change of perspective here: Thanks to the new analytics technologies, the data – which typically come from a quantitative context largely defined by specific company processes – contribute to depicting the course of business in its entirety and with a long-term vision that is continually updated and remains responsive to changes in the market and among customers. Concepts such as "user engagement", "conversion propensity" and "user journey", which are possible thanks to analytics, contribute to a new data-driven approach for the evaluation of a company's results.