According to IBM, we create about 2.5 quintillion bites of data every day, and IDC estimates that the volumes of data will more than double every two years.
"Big Data" has quickly become a buzzword across industries. It is defined as data sets that are too large and complex for conventional tools to capture, store, and analyze. Just as the term was first emerging, Gartner described Big Data in 2001 via the three "Vs"—volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources), and continues to lean on these characteristics today.
Marketers have collected and analyzed customer data and used the resulting insights to improve the customer experience and boost sales for decades. Big Data poses a new set of challenges to marketers as many conventional tools and practices fail to capitalize on these vast and varied data sets.
At the same time, Big Data affords a strong opportunity for brands to understand their customers more than ever before.
Why Do We Need Big Data?
Big Data doesn't include simply more data, but also new streams of it, collected by digital sensors on connected devices beyond phones that make up the "Internet of Things." It also incorporates data from an ever-expanding number of digital channels frequented by customers. That includes conversations on social channels, transactional data (e.g., credit card information), browsing and search history, and data collected by GPS technology.
Insights gleaned from all that data can offer marketers valuable clues around the tastes, intent, and journey of their customers and prospects.
Alongside the rise of Big Data, new tools are emerging to help analyze and understand that data. Tools such as artificial intelligence, in-memory computing, pattern recognition, and highly scalable NoSQL data storage systems can empower marketers to capture and analyze data on customer activity in real time, and respond as appropriate.
Those advancements are happening at the right time; with the ubiquity of social channels and mobile devices, customer behavior is becoming increasingly fragmented, complicated, and hard to track. Sophisticated tools can help aggregate and analyze all of this information in one place.
Doing Big Data Right
Here are eight strategies for capitalizing on Big Data to enhance the customer experience.
1. Start with what you have
There's no need to throw out the baby with the bathwater. Most mature businesses have already accumulated years of valuable data and models to represent customer behavior. Take stock of existing data sets, develop a strategy to improve collection and analysis models, and build new ones as appropriate.
2. Know what you're looking for
Because Big Data is derived from a multitude of structured and unstructured data sources, speak with other departments to understand the sources that contain customer engagement data and information: CRM data, Web analytics, contact/support center data, and business intelligence systems data.
Also, although Big Data technologies allow companies to analyze larger amount of data, the old 80/20 rule still applies: Most of the value will come from a relatively small subset of your data. Part of the analysis process is identifying the most useful data sets.
3. Use your intuition, but also test your assumptions
Computer analysis can only augment human intelligence, and all good processes are rooted in offline communications with customers and staff. Your staff is still one of the best sources of information on your customers. Rely on experience to define business goals, create hypotheses, and identify problems and opportunities. Then, use analytics to test and refine your assumptions and create a feedback loop for your customers. Testing can be based on analysis of data, but it can also incorporate offline processes and activities, such as active surveys.
4. Understand your options
Ideally, companies will build sophisticated, automated algorithms that identify and cultivate high value customers, increase upsell, and head off problems before they lead to lost customers. Many large global companies with deep resources and IT teams already do so.
To understand how its customers consume competing services, such as Hulu, on its networks, cable giant Time Warner uses sophisticated correlation solutions that meld publicly available data with local viewing habits to help clients launch custom campaigns tailored to geographic or demographic micro-segments of users.
Big enterprise software providers, such as IBM and SAP, provide platforms for aggregating and analyzing large amounts of data from multiple sources—often in real time, using in-memory computing technologies.
5. Home in on the customer journey
Marketers often lean on customer journey maps to identify areas for improving the customer experience. Big Data has the opportunity to help replace static, descriptive models of the customer journey with dynamic, prescriptive dashboards that respond in real time to the slightest shift in customer behavior. (While some companies may have built home grown dashboards, they aren't a standard technology yet.)
A real-time strategy can help marketers anticipate high-value customer needs and quickly but gracefully guide them in the right direction. For more insight, see McKinsey's tips for how companies can use Big Data to enhance customer journey mapping. The firm suggests that alongside identifying critical customer pain points and causes, you assess the value of each improvement, which can help you develop prioritized initiatives to improve performance.
6. Empower the business user
The proliferation of data sources can increase the burden on business users when paired with too many tools and user interfaces. Manage data centrally and use a single tool for creating online experiences that can integrate content from a variety of sources and display real-time analysis from more than one platform.
Also, remember that not all users are power-users, nor do all business users need access to all data sources all the time. Only give business users the information they need, when they need it. Allow them to create custom views of the data that matches their needs and responsibilities. Automate when possible; don't require users to constantly tweak and maintain complex business rules.
7. Blend data with content
Content marketing has emerged as a top strategy for marketers to enhance customer engagement. Data collected on digital channels and social networks can provide valuable insight into the types of information most engaging to audiences. Combine actionable data with content, and apply those insights directly into a Web publishing platform. Consider the user's context so you can present the right content to the user at the right time.
8. Understand the limitations
Big Data is not a panacea for all problems, and it can't replace human intuition and intervention.
Keep in mind, too, that more data doesn't necessarily mean better data—and that the more there is, the more difficult it might be to identify the information that really matters.
Finally, privacy is also a key concern. The more data we collect on customers, the more we run a risk of overstepping the boundaries of what people consider appropriate data use. Keep personally identifiable details hidden and secure when analyzing customer behavior.
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