In the second part of Fields of Application of Big Data, we have discussed about some topics which can be put under the header “Business Life Application”. They were (i) Research and product development (ii) Financial and Risk Controlling (iii) Production. In this 3rd part of of Application of Big Data, we have discussed use cases in two fields – marketing and sales, distribution and logistics.
Marketing and Sales
Good marketing is a very important part of the sales success of a company. In this sense, data-driven marketing is becoming more important with big data. By integrating social media and CRM, it is possible to get a better insight into the customer perspective and derive corresponding conclusions for the marketing strategy.
With regard to the pricing policy tool, the scenario of faster and more efficient pricing is conceivable. The real-time evaluation of different data sources enables precise and constantly updated pricing for a product. The measurement of the marketing success and the sales of a marketing campaign could thus be predicted faster. However, the extent to which big data evaluations can be effective in such price decisions remains questionable, as the consideration of the target is often a significant factor in pricing.
---
For the sources considered, marketing and sales have to consider a variety of platforms and influencing factors. The most important thing is the data from CRM systems. Also feedback from personal telephone conversations and social media channels are important sources for the data collection and the subsequent evaluation for the marketing. Especially in this area, the unstructured data are less important, as they require an interpretation, which can build no real understanding of the customer. If, for example, a customer buys a new piece of electronics and then continues to receive marketing measures from the same electronics item on various sales channels for weeks, this can be annoying and disturbing for the customer. Thus, the customer could turn away and lose his brand loyalty. Not only the personalization, but also the regionalization of marketing plays a role.
The evaluation of weather data can have a visual impact on the layout of the online shop. This presentation can be made individually in the countries depending on the prevailing geographic weather conditions. Furthermore, gender-specific product offers can be placed on such digital platforms.
In summary, the customer does not have to be made aware of the product by the price alone, and there are additional factors involved in the marketing and sales of a product. The company can advertise the product with an “individually tailored” manner and address the customer in a personalized way. In the marketing departments of companies, big data is no longer a foreign word these days. By collecting and analyzing data, marketing hopes for stronger customer loyalty and optimization of behavioral and customer profiles. Also, longer brand loyalty and sustainable added value are hoped for added value.
Distribution and logistics
Distribution logistics is facing a turnaround. The trend towards fully automatic logistics is due to the increased use of Big Data mechanisms. Compared with the economy as a whole, demand for logistics services is doubling as trade is constantly globalized and influenced by customers’ on-demand expectations. Modern information technology, above all big data, should support the expectation of more efficient logistics. The means of transport not only include the known fleet of trucks, but include other means of transport, such as ships or aircraft. Coordinated processes within the supply chain save companies high costs, Such costs arise from increased fuel consumption, empty runs of the trucks and increased downtime. Currently, there are projects that work towards a driverless vehicle. One of the aims is to ensure safe movement in traffic, such as automatic braking of the machine-driven vehicle in heavy rain. This is made possible by fitting sensor data on the vehicles.
In 2013, DHL highlighted five logistical factors that helped make distribution logistics more efficient:
- Optimization of service quality, such as delivery time, resource usage and geographic reach
- Direct customer interaction
- Merging of production and distribution processes in various industrial sectors
- Distribution and transport as a potential data source for new insights
- Worldwide and decentralized logistics offerings
For all these factors, Big Data data analytics can add value. In logistics cost accounting, the “last mile” is the most expensive route of the entire transport route. Big data and real-time optimization of transport routes can reduce such costs. Due to the real-time evaluations, the driver can be informed of short-term change routes. The service “DHL MyWays” offers on the basis of a B2C platform the possibility to have the delivery to the customer handled by a passerby or user of the service. The recipient of the package can individually determine the delivery address. Further logistical added value is the reduction of idle consumption in trucks, accident prevention and compliance with driving and minimum rest periods. Fleet operators are also interested in driving behavior of vehicles, such as heavy braking or strong acceleration.
Not only to find big data potentials in the theoretical planning of logistics processes, but also during the practical transport route, where traffic data is used.
A suitable application example is the toll collection companies, which are responsible for the collection of truck tolls on the motorways. In order to guarantee revenue, a correct billing is required, which is realized with the help of information on toll roads. Information required is, among other things, photos of the vehicle, the name of the driver and the corresponding license plate. On the motorways, the toll bridges help to capture such information in real time and transmit it to a common database. It contains cameras for each lane, which record vehicle registration numbers and can also determine the type of vehicle through special image analysis procedures. Through the continuous determination of the data and the transfer into real-time systems an error detection can be guaranteed. Errors such as detecting a vehicle twice within a very short time can thus be detected and avoided.
In summary, big data has a positive effect on time and money in distribution logistics. Both factors can be used more efficiently by using data analytics. This positive trend may continue to evolve in the future by taking into account site information or weather data.
Conclusion on this part of fields of application of Big Data
Up to this part, we have discussed about all topics which can be put under the header “Business Life Application”. Namely – (i) Research and product development (ii) Financial and Risk Controlling (iii) Production (iv) Marketing and Sales and (v) Distribution and logistics.
In the next part, we will discuss the use cases related to personal life including employment.
