Big Data, Consumer Choice & Context
Consumer data is a valuable asset in the current age of data with “smart things” (sensors) delivering large amounts of information to consumers and businesses. According to IBM, in 2012 more than 2.5 exabytes (2.5 billion gigabytes) of data was generated daily. By 2015 this number has grown and, according to forecasts, will continue to grow to 40,000 exabytes by 2020. Under these circumstances, businesses develop innovative techniques to extract and analyse data “on the fly” in order to create quick value propositions for the consumers. The availability of large masses of data catalyses the rise of the domain of data-driven business models (DDBM) which looks at how the data can be used in order to develop new and improve existing business modelling mechanisms.
Yet, the creation of meaningful analytical tools for DDBM is complicated not only because of the volume of the data but also because of the complexity of human decision processes and the way these processes are reflected in the data. Particularly, household consumption data shows that people who shop in the same store may opt for different products and/or brands of products. For example, when making grocery purchases, consumers often tend to alternate brands of products they choose. This is one of the reasons why current online systems developed by some providers such as, e.g., Amazon, which suggest products and services to users and which are intended to nudge users to purchase suggested services and goods, have not gained much popularity.
One of the main disadvantages of the currently available purchasing data is that even though it allows analysts to observe consumer choices as well as providing them with useful demographic information about consumers; it is hard to tell whether observed choices are a result of consumer true preferences or merely a product of noise in these preferences. Analytics is particularly complicated for cases when consumers opt for products and services from different brands in different environments. Under these circumstances, it is important to not only pay attention to the models which help us analyse the data generated by consumer choices, but also to the types of data used for the analysis.
The next phase of the HAT has begun with HARRIET, an RCUK-funded project which will see the HAT platform being deployed into real households. Partnering with Birmingham City Council, HARRIET will develop the HAT Resource Integration and Enabling Tool that will assist individuals to better understand their household consumption behaviour and make ‘smarter’ decisions to plan and live better lives based on their own data stored on the HAT. Find out more from the HARRIET Project website, which currently features new blogposts on Smart Cities, Personal Data, and Smart Things and Data Analytics.