The third industrial revolution is that of cognitive capitalism, that of knowledge. Value creation involves the production of knowledge, a cumulative, non-exclusive and non-rival product, enriched each day by new information. This growth is accelerated by the multiplication of sources of data creation (computing, telephony, metadata …), the explosion of storage capacities, the progress of software solutions and IoT (Internet of Things), we fully entered the era of Big Data.
Beyond digital marketing
Today, the reality has gone beyond fiction: data mining technologies already impact all sectors (agricultural, industrial and tertiary), sectors and trades. Spontaneously, predictive solutions are often thought to be used in the context of digital marketing. Technological tools make it possible to analyze the situation, the context and the behaviour of millions of consumers in real time, to predict their reactions and thus to propose, in anticipation, offers and personalized messages.
Beyond the marketing issues, the exploitation of the data also makes it possible to envisage new commercial opportunities, to significantly reduce the risks of error and breakdown, to optimize the productive tool and the supply chain.The “business” applications take shape, and the uses of predictive analytics are growing rapidly. Here are three examples to understand the range of possibilities available.
The low-cost designer furniture website Made.com, founded in 2010 by two young French people, uses data to anticipate sales, supplies and thus optimize inventory. Since its creation, Made.com has massively collected data on the behaviour of Internet users on their site. By analyzing the first navigations on the page of a new product, the Made.com teams anticipate future purchases and thus adjust their own orders with the designers. Thanks to this predictive model, nearly half of the products arriving in their warehouses already have an acquirer. Analytics is at the heart of commercial management and the pure player supply chain.
Perfect reverse logistics
Zalando, a major player in online ready-to-wear in Europe, was one of the first pure players in online sales to offer, as of 2008, the free return of products within 100 days – more than 50% of products sold in Europe are now returned. A strategy focused on customer satisfaction but at significant cost for the German e-commerce company. The challenge for Zalando is therefore to innovate and optimize the supply chain, especially the reverse logistics or logistics of returns. Exploitation and data analysis allow Zalando to predict the geographical area in which the returned product is most likely to be resold and thus determine the returns platform closest to the future customer potential. The product is thus more quickly put back in the marketing and distribution circuit.
Reduce the risk of rupture
Many companies are now able to offer their retail customers a predictive solution that analyzes, in addition to simple product information, all sales data, stocks, consumer opinions, social networks, advertising, weather data, etc. The predictive models seek to define consumer buying behaviour in store to optimize supply, inventory management, and product placement in store and so on, all in order to avoid the risk of breakage. A key: increased turnover, lower costs and higher commercial margins.
Five new areas of development
There are many opportunities for the development of new data mining solutions. Five major areas of development are currently underway:
- Greater understanding of consumer behaviour and optimization of the customer experience by exploiting so-called unstructured data: photos, blogs, articles, reviews and comments.
- Optimization of production processes and the supply chain.
- Taking into account an increased diversity of data: emails, photos, videos, files, comments on social networks, GPS signals, banking transactions, sounds, voice messages …
- A better valuation of the data (quantity and quality of the data).
- New uses offered by geolocation, mobile terminals and IoT.
The ecosystem of the data feeds each day a little more so of the variety of the data of the Internet, the social Web and the connected objects, reinforcing in return their volume, velocity, veracity and value. Data offers real opportunities for companies, but also for all, because the environment, health and social sectors will benefit from the contributions of predictive analysis.