IDC predicts that data volumes will double every two to three years, according to IBM.
The term “Big Data” is quickly becoming a popular buzzword in many industries. Data sets too large and complicated for conventional tools can be captured, stored, and analyzed. Gartner first introduced the term in 2001 and described it using the three “Vs:” volume (amount of information), velocity (speed at which data is entered and exited), and variety (range in data types and sources). They continue to use these characteristics even today.
For decades, marketers have collected and analyzed customer data to gain insights that can be used to enhance the experience of the customer as well as boost sales. Big Data presents a set of new challenges for marketers, as conventional tools and practices are unable to take advantage of these vast and diverse data sets.
Big Data offers brands a unique opportunity to understand their consumers better.
Why do we need big data?
Big Data is not just more data. It also includes new streams of data collected by digital sensors in connected devices other than phones, which make up the Internet of Things. The data is also gathered from a growing number of digital channels that customers use. This includes data from social media, transactions (e.g., credit card details), search and browsing history, and data collected using GPS technology.
Marketers can gain valuable insights from this data about the preferences, intentions, and journeys of their customers.
New tools have emerged to analyze and understand Big Data. Artificial intelligence in-memory computing, pattern recognition, and highly scalable NoSQL storage systems are some of the tools that can help marketers capture and analyze customer data in real-time and respond accordingly.
These advancements come at a perfect time. With the proliferation of social media and mobile devices, the customer’s behavior has become more fragmented, complex, and difficult to track. These sophisticated tools allow you to aggregate and analyze this data in one location.
Doing Big Data Right
Eight strategies to improve the customer experience using Big Data
1. What do you have?
It’s not necessary to throw the baby out with the bathwater. Most mature businesses already have years of valuable data and models representing customer behavior. List the existing data, create a plan to improve your collection and analysis methods, and develop new models as needed.
2. What you are looking for is important
As Big Data comes from many structured and unstructured sources, it is important to speak with your colleagues in other departments about the data and information you can get on customer engagement, including CRM, Web analytics, and contact/support centers.
Although Big Data technologies enable companies to analyze a larger amount of information, the 80/20 rule still holds: The majority of value comes from a relatively smaller subset of data. The analysis includes identifying the best data sets.
3. Test your intuition but also your assumptions
Computer analysis is only a supplement to human intelligence. All good processes are built on offline communication with staff and customers. Staff is one of the most reliable sources of information about your customers. Use your experience to create hypotheses and identify opportunities and problems. Use analytics to refine and test your assumptions and create a feedback cycle for your customers. Testing can be done based on data analysis but can also include offline activities and processes, such as active surveys.
4. Understanding your options
In an ideal world, companies would build sophisticated algorithms to identify and cultivate high-value customers, increase upsells, and prevent problems from causing customers to lose their business. Many large companies around the world with IT teams and deep resources already do this.
Time Warner, a cable giant, uses sophisticated correlation tools to understand how customers use competing services such as Hulu on its networks. These solutions combine publicly available data and local viewing habits to launch customized campaigns tailored to specific geographic or demographic segments of users.
IBM and SAP are two of the largest enterprise software providers. They provide platforms that allow users to aggregate and analyze large amounts of data from different sources, often in real-time, using in-memory computing technology.
5. Customer journey: Home in
Marketers often use customer journey maps to improve the customer experience. Big Data can help replace static descriptive models of customer journeys with dynamic prescriptive dashboards that respond in real-time to even the slightest change in customer behavior. While some companies have developed their dashboards, this is not a standard yet.
Real-time strategies can help marketers anticipate and meet high-value customers’ needs. They can also guide them quickly but in a graceful way. For more information, visit McKinsey’s tips on how companies can enhance customer journey mapping using Big Data. The firm recommends that you identify critical customer pain points and then assess the value of every improvement. This can help you prioritize initiatives to improve performance.
6. Empowering the Business User
When paired with a multitude of tools and interfaces, the proliferation of data can be a burden for business users. Use a single tool to create online experiences that integrate content from multiple sources and show real-time analytics from different platforms.
Remember that not every user is a power user and that business users do not always need access to all sources of data. Give business users only the information they require at the time they need it. They can create custom views of data to match their requirements and responsibilities. Automate whenever possible. Don’t force users to maintain and constantly adjust complex business rules.
7. Blending data and content
Content marketing is a key strategy that marketers use to increase customer engagement. The data collected from digital channels and social media can give valuable insights into what types of content are most engaging for audiences. Use actionable data to combine with content and then apply these insights directly to the Web publishing platform. Consider the context of the user to present the appropriate content at the correct time.
8. Understanding the limitations
Big Data cannot replace human intuition or intervention.
Remember that more data does not necessarily equal better data. The more information you have, the harder it may be to find the data that is most important.
Privacy is another major concern. We run the risk of exceeding the limits of what is considered appropriate data usage the more data we collect about customers. When analyzing customer behavior, keep all personally identifiable information hidden and secure.