Big Data for Marketing
Big data holds the promise of better service and understanding of our customers. But across verticals and industries the term “customer” covers a wide surface area. We tend to place it in the context of a retail engagement, but arguably a patient in a hospital is a customer, a patron of a bank or credit union, or even another business receiving services from a solution vendor. Indeed, customer needs across all these scenarios differ greatly which is why there is no one focal solution for understanding and addressing the needs of customers across marketing engagements. At the same time, organizations are taking internal directive to transform their marketing and customer retention with data. The desire to have data fuel marketing and service strategy means that systems designers need to search for efforts to modernize their platforms to meet the goals of the business. Rarely is this done overnight, and in Cloudera’s user base we see several patterns of adoption that a company will take in their goal of becoming data-driven.
As a marketing leader you may not have ever been exposed to an open source big data solution like Apache Hadoop. You may understand that more data is likely to help you better target marketing activities and understand the markets in which you participate. In a 2013, Mckinsey found that companies that put data at the center of their marketing and sales decisions improved their marketing returns by 15-20% adding up to 150 to 20 billion in additional revenue.
Machine learning and advanced analytics are often the end aspiration of these data investments. But the tech is complex and dynamic. Looking past technology components like Impala or Kudu, marketers and service heads can benefit greatly by implementing their use cases on a platform like Cloudera Enterprise. They can combine data and assemble views of the business like never before possible. They can uncover insights that predict future behavior, and they can serve customers better in real-time leveraging these insights.
But where to begin? I present for your consideration an example of a journey to implementing true customer 360 intelligence.
Understanding Your Customer
The first and necessary exercise in gaining an enhanced profile of your customer is to assemble your data. The problem is that this data often lives across the organization in many siloed systems. At Cloudera, it is not uncommon for our customers to report they have over 8 central systems of truth for customer data before implementing a centralized solution. In Cloudera’s platform a wide variety of data formats can coexist and companies can achieve immense scale economically as combined data sources grow. No longer do companies need to manage to a scarcity of resources or participate in competing for departmental usage. You can use components to get data out of relational databases and for capturing new streaming data sources. Soon, you have all your customer data in a central data repository be it on-promise or in the public cloud.
Fantastic, but now how do we start making sense of all this data? This is where a robust partner ecosystem provides immense value. Rather than dictate the way you view and organize your data insights (although we we do have preliminary, out of the box tools for data discovery), we open up the data to whatever visualization or BI tool works best for your organization. We allow you to bring those tools to the data, get self-service access and discovery, and build the best views for your business to understand what your customers are doing and how your brand and products affect their lives. This is generally where most companies start. It can create quick wins and is really powerful in showcasing how data can affect strategic decision making.
For example, Cloudera customer SFR is empowering employees across the country to operate based on a centralized, real-time customer view that spans many devices and data sources. For the first time, SFR has the capacity to ingest, store, and analyze log data, from which the company may glean previously hidden customer insights by combining that data with other data sets.
Learning Key Behaviors
We now have the views to understand our data. We can see which kinds of customers buy our products and engage with our marketing campaigns. At this point, we can digest and react but we have limited ability to predict future behavior. A common next step after creating the right foundational data structure is to bring in the data science team to help explore, model, and eventually deploy predictive solutions that later enrich the customer profile.
Here is an example: A customer record at a telco provider consists of a number of fields. These fields may be important features in a data science model. A machine learning solution is deployed to predict customer churn and the predictive analytics that it creates should be merged with the core customer record. Now the bank has a view of its customer and their likelihood to churn, upgrade services, all based on growing customer intelligence.
Predicting customer behavior can yield impressive revenue opportunities and competitive advantage. Telco provider Telkomsel was experiencing 100% data growth annually and needed to derive new, predictive insights on customers and network usage. They deployed Cloudera for churn analytics and lifetime value analytics and saw a 1% uplift in net revenue.
Improve Customer Experience
Moving back to the old trope, insights are ultimately useless if they aren’t actionable. Competitive pressures are setting the stakes and today’s customer requires the interaction to be in real-time. Readers want communication to be contextualized, personalized and relevant to the moment they feel the need. So how do we get the type of intelligence to know all this about our users? The answer is buried in the data.
Managing real-time data is a new challenge for many companies. Modern platforms like Cloudera Enterprise bring unlimited options for engineers to build dynamic applications that leverage real-time customer data to provide personalized engagements. New research shows that 57% of the buying cycle is completed before a prospect even speaks to a company. Which is complicated because no one raises their hands to speak with marketing. Which is why it is extremely important to equip. your organization with the ability to understand and engage in real-time.
Improved audience measurement capabilities helped DISH provide advertisers more specific, anonymized insight about who views their ads. The company is also able to deliver personalized content recommendations to help improve the customer experience. Dish’s new platform enabled DISH to save US$1 million compared to traditional approaches.
Your Own Journey
The journey for every company is unique to the use cases and data sources they have in their aspirations. Becoming data driven wont happen overnight but customer data is out there for you to harness. Customer are participating in hundreds of activities daily that create data, the opportunity you have is to modernize your systems and your processes to better leverage customer data.
To learn more about how Cloudera is enabling the customer insights journey visit our Customer Insights Solution Page.