The overarching disruptive power of big data demands that organizations engage with it at a strategic level. However, how organizations will utilize this technology trend with their existing business model will depend on their orientation. While, consultants at A.T. Kearney argue that big data would have positive effects across strategy and operations (Hagen et al.2013). In summary, the advantages of utilizing big data to obtain competitive advantage has been discussed into relation to big data driven target marketing, design-driven innovation, and crowd innovation, all of which will be discussed in detail in the following sections.
1.3.1
Big Data Driven Target Marketing
Big data can change the way companies identify and relate to their customer base. Undoubtedly, companies can boost the old marketing strategies using new big data tools and expertise. Market penetration strategies can leverage big data to feed marketers information on how to keep existing customers and improve repetitive sales. Likewise, new customer engagement techniques, like gamification, promise improved loyalty levels (Paharia2013).
Cross-selling, for example, leverages a company’s affinity and knowledge of its market to sell different products to the same people. Banks, for example, have started not only to analyze vast amounts of their clients’transactions in relation to social media to understand their customer preferences, but also to create new offerings to their clients. Citi, for example aggregates data from its global customer base, to help corporate clients identify market trends (Lesk2013).
Identifying, however, new market niches must be the real power of big data and its real challenge. Companies no longer need to approach the market in large demographic chunks. They can instead use emerging analytics to identify new niches or even subdivide existing target markets into smaller more coherent groups to unlock their potential (Adamson et al. 2012). Targeting becomes a matter of aggregating multiple small niches. Combined with advancements in automated marketing communications, we are heading towards the era of mass customization. Thus, the ultimate big data promise for marketers is that of mass customization. big data collection, synthesis and analysis promises to provide businesses with real insight about its customers’behaviors based on actual past and real-time, step-by- step behaviors. Much like in virtual ethnography (Hine 2000), that seeks to understand online communities through participation, social media data is collected covertly. Because most of us post spontaneous opinions and views in social media, information is free from response biases often encountered in surveys or under focus group conditions. This gets rid of the cost of ineffective market research, which can then mislead sales efforts, marketing plans and company strategies. Sentiment analysis on what we post about areas of our lives on social media unveils our attitudes and can lead to discovery about new product and service requirements (Morabito2014; LaValle et al.2011). Fine grained behavioral analysis aspires to feed predictive analytics, enabling marketers to spot deviations in our purchasing patterns. An initial issue was corresponding social media views and opinions to demographics, so one can understand who says what to profile information and improve targeting. The ability to correlate social media data, for example tweets with Facebook and LinkedIn profiles, has mitigated our doubts about the source of information.
Thus, big data gives Marketing Relationship Managers an excellent tool to feed us the right information, to influence us at the right moment towards making that final decision to buy. There is now a whole sector of social media analytics dedi- cated at eliciting these insights. Mashable.com alone offers 20 Application Pro- gramming Interface (API) that can help people scan different channels, like Facebook and Twitter or even texts in newspaper articles and blogs, for what people want. In the same way, they can also get real-time feedback about how promotions and other promotional activities are received (Provost and Fawcett2013).
But data has also changed the way we can target people. We don’t need to group people into large or even small target groups, we can target people directly based on the electronic trails of our computer’s IP address about our lifestyle choices, from what we buy to what we vote for to what we are interested in, where we are located, what our demographic characteristics are.
While social media, however, dominate current discussions about the potential of big data to provide companies with a competitive advantage, it is likely that really differentiated business models will take advantage of design-driven innova- tion relating, for example, to the Internet of Things (IoT), see also (Morabito2014).
1.3.2
Design-Driven Innovation
The combination of big data sources with other emerging technologies can inspire design-driven innovations. These innovations are disruptive game changers that manage innovations that customers do not expect but they eventually love. Design, in its etymological essence, means “marking sense of things” and design-driven innovations are the R&D process for meanings (Verganti2009).
For example Apple did not change how we make calls from our mobile, but what we do with our mobile and how we think of it. For everything you want to do, there is now an app for that, from designing color schemes for your baby’s room to passing time playing angry birds to checking the news to watching a film to measuring the dimensions of your rooms. An iPhone is not a phone anymore, it is a multiple purpose tool (Verganti2009) and Apple is not a phone-making company, it’s the company that has changed our lives and most people love it for it. Products are not seen as outputs of some faceless industrial process anymore, they are symbols of the ethos and caliber of the people who designed it. Buying a product is also a representation of who you are and who you favor. Hence, product innovation is not just about products, it is the strategy of sharing common meanings with your customers and being part of the community.
The same way product innovation is not just about products, but also about sharing common meanings, business model design is also about sharing meanings about what an organization stands for. For example,Asos.comis a fashion retailer though is not just about cloths (Asos2012). The company has invested in a mar- ketplace site which is notjustabout fashion it is about fashion Democracy, enabling anyone, anywhere in the world to sell fashion, to anyone, anywhere in the world and for a 10 % commission per sale, it is a self-sustained business model as well (Asos2012).
What will constitute big data Design driven Innovations then? To date, “Big data = Social Data”in most people’s minds, yet really transformative innovations are likely to be inspired by the Internet of Things (IoT). Intelligent systems equipped with sensors and decision support systems promise autonomous, rather than auto- mated, innovations. Such intelligent systems change our paradigm, the very core assumptions about what is possible, what is right and what is wrong. They promise an “always-on, always-aware, always-connected, always-controllable” (Paharia
2013) machine-to-machine coordinated world. This will affect almost every aspect of infrastructure as we know it. Such technologies will turn mundane everyday things into novel offerings. Commuting, for example, may change dramatically over the next few decades. Your future car may be more of a driverless taxi you pay for on demand. You will be able to call it to pick you up and drive you to work as
technology allows cars to communicate with other cars and the road infrastructure in a safe way, and, of course, self-regulate their own green energy consumption and storage as they will most like run by solar or hydrogen power (Neiger2014; Griggs
2014). And perhaps, you won’t even have to own it! You will be able to hire it from city stations, from companies such as, e.g.,www.Car2go.com.
Since 2008 more things are connected to the internet than people, making it a huge business opportunity. According to the UK government’s Department for Business, Innovation and Skills (BIS) the global market for smart city solutions will be at more than $400 billion annually by 2020. This may sound huge to some but it is still a fraction of global infrastructure spending (Townsend2013). Though, an increasing movement of civic hackers, open-source technologies and open government data are still working together in order to demonstrate the value of smart technology to make cities more efficient, democratic, safer and sociable (Townsend2013). And while this may be a challenge for organizations who strive for profit, it is extremely valuable to societies and possibly to social entrepreneurs who strive for social impact and social change, rather than money.
Perhaps ideas for innovations won’t even come from within organizations and institutions; perhaps we have entered an era of peer-to-peer innovation where ideas and even solutions are crowdsourced and crowdfunded. The following section describes how the locus of innovation has shifted over the years from an internal process to becoming the creative engagement of communities of users, and how thus its ownership and directions shifts from the organization to the community (Morabito2014).
1.3.3
Crowd Innovation
Big data can not only change how we approach the market with a product or service, but also how we design the product to start with. Open innovation was based on the premise that innovation ideas that can be useful for organizations may lie outside the organization and companies should not restrict themselves from harnessing these ideas for the sake of control (Chesbrough2003). This perspective suggests some very different principles about how a successful organization should behave. For example, it abolishes the“non-invented here”notion to recognize talent with useful ideas wherever these may come from (universities, suppliers, customers, other companies, the public). Intellectual Property (IP) is a trading asset to be bought and sold for profit. And IP can be a matter of co-creating with outsiders for mutual benefit (Chesbrough2003). Big data can take this conception into a whole new level. Seen as product requirements, social media can be scanned for customer complaints and product related wish lists. But it is not only that we can get better insight into the market, we can also respond rapidly.
Open innovation is now facilitated by innovation intermediaries, like Innocen- tiveplatform which match makes‘solvers’and companies with a problem seeking a solution. Big companies can take advantage of these developments to outsource such expertise, and they do. For example AstraZeneca, has set up an innovation
pavilion that hosts their challenges on Innocentive. As part of this, it has set up a $100,000 innovation fund to source a solution for a Targeted Delivery of Oligo- nucleotides that will improve their therapeutic effectiveness on tumors cells (Innocentive2014). But such outsourced expertise is available to smaller companies too. While this is a good thing, outsourcing expertise is a great leveler between large and small companies, and big data has given small organizations a leg up.
In the big data era, not only data and opinions are open, but so are ideas, even business ideas! Innovation hubs pop up across the globe to provide support to people with ideas to incubate new businesses offering, mentoring, and avenues to funding. Crowdfunding sites have broadened the funding avenues and the funding base even more, by enabling consumers to support these creative business ideas directly. For exampleKickstarter.comis a community of people working together that is a crowdfunding platform to enables people to donate, pre-order or get a stake in a company of their liking (Kickstarter2014).
Anything from Art and Comic design to Food and Technology business ideas are included.Lix, for example, a pen-like 3D printer idea has pledged for£30,000 only to collect £485,249 from 5,388 backers in 26 days, most of them early adopters who pre-ordered the pen (LIX2014).
Social media and big data feed off each other. Identified ideas, prototypes, products and scenarios are discussed, developed and constantly updated in collaboration within communities, and tested using against historical and real-time data to predict market reactions (Choi and Varian2012; Hafkesbrink and Schroll
2011). Using predictive analytics, for example innovators can get insights about best case scenarios and comparisons of different alternatives (Kearney2014).