“What’s special about Big Data is that it has a vast range of possible usage scenarios,” explains Holger Kisker, an analyst at Forrester Research. This means that you don’t have a single definitive business case for Big Data: the business case must fit the scenario in question. However, warns Kisker, it’s particularly important for businesses to ensure that they select measurable, business-relevant success factors. For example, “increase in the success rate of marketing campaigns” is a business-relevant success factor, whereas “improvement in customer data” is not. Market researchers including Gartner, IDC, and McKinsey have established that 90% of Big Data is unstructured. Thus, before diving into the Big Data deluge, users must consider exactly what it is they can and want to do with it.
Management consultant Wolfgang Martin has defined five main use types for Big Data:
- Transparency: insights into ongoing business operations
- Decision-testing: What happened (will happen) when (if) we made (make) this decision?
- Individualization in real time: tailoring offerings and services to customer wishes in real time in order to increase customer satisfaction and reduce customer churn
- Intelligent process control and automation
- Innovative data-driven business models.
Applying use cases to specific industries: Energy sector
Generally speaking, one or more of these use cases will lend itself particularly to a specific sector of industry. “Individualization in real time”, for instance, is highly suited to the needs of the energy sector. In the future, energy suppliers will be able to use the measurement and operational data they continuously collect from smart meters to establish how their customers use energy and how they pay for it. This information could help them offer cheaper energy rates to suit individual customers’ needs.
Analyzing customer data could also help suppliers reduce customer churn and collect outstanding utility payments more effectively. Conversely, customers could urge their suppliers to use Big Data to find ways of minimizing power outages, particularly during the winter months and in rural areas.
If they are to benefit from real-time sensor data collection, energy suppliers need to have an extremely agile Big Data concept. Specifically, they need to conduct analyses quickly and incorporate the insight gained from them into their direct customer-contact processes. Wolfgang Martin refers here to “operational Big Data”, because it addresses the issue of how businesses can create an infrastructure in which everyone benefits from what is learned from Big Data.
Analyzing market and customer data
A second aspect, called “high-resolution management”, is relevant for every single company – no matter which sector they operate in, says Martin. It answers the question, “How can we change the way we manage our businesses based on the high-resolution view that big data provides?”
“The benefits that Big Data pioneers like Amazon, eBay, Facebook, and Google enjoy today are chiefly in the areas of customer focus, customer relationship management (CRM), and customer experience management,” explains Martin. “Currently, the area of marketing can benefit particularly strongly from taking unstructured data from sources other than ERP and CRM systems – from Facebook, Twitter, blogs, or forums – and turning the insight it delivers into competitive advantage,” adds Holger Stelz, who is the director of business development and marketing at Uniserv GmbH. Accordingly, explains Stelz, Big Data allows marketing departments to extend their 360-degree view of the customer into a 360-degree view of the entire market. It makes hidden trends visible and supplies information about customer behavior and about what companies can do to fulfill customer wishes better and more quickly.
For marketing specialists, Facebook and Twitter are the places to look if they want to know the opinions of consumers who no longer watch TV and who no longer read e-mails. To harvest these opinions, they need a suitable interface and analyses tools that can handle functions such as sentiment analysis.
Instantly react to negative analyses
Sentiment analysis acts as an early-warning device for producers of consumer goods who have just brought a new product to the market by picking up on any negative consumer responses. It can therefore indicate potential loss of revenue and customer churn.
“Once your social media monitoring is in place,you can move on to setting up your social media interaction,” adds Wolfgang Martin. If a company reacts quickly to negative analyses with a social network campaign, it can mitigate – or even prevent – any negative consequences. This, says Martin, is a major advantage in customer service and during product launches, because companies can immediately establish and foster communication with Web communities.
Furthermore, GPS and other smartphone data allow marketing specialists to create “movement profiles” that are not restricted to a city, region, or country, but that can cover the entire world. These geo-coded profiles make it possible to draw conclusions about customer behavior and attributes. However, experts like Wolfgang Martin warn of the importance of complying with data protection laws in this regard.