Everyone seems to want, need or have Big Data these days. Marketers and technologists have been seduced by vendors with promises of customer delight delivered through Big Data. However, the morning-after haze is now clearing up, and business people and technologists alike are realizing that building a Big Data infrastructure does not magically deliver the desired business results. To achieve that, data must first be accepted as a strategic asset of the organization and infused into the company's DNA in an operational way.
Demographic and psychographic data is typically well-formed, and historically, integrating it was a matter of dealing with its quality, completeness, and scale. Cheaper processors, memory, storage, and database technology have addressed that hurdle. The introduction of digital tracking, social media, blogs, and other unstructured data sources introduced Big Data's Vs: Volume, Velocity, Veracity, and Versatility. Technologies, such as Hadoop, have enabled technologists to organize that data in a business-friendly cycle-time. Done! So what's the problem?
Experts agree: "It's not the amount of data, it's what you do with it that matters."1 The challenge is to get an organization to agree on insight objectives, organize the data in a way that delivers on the promised benefits, and execute on the strategy. Unfortunately, there is a major disconnect between technology and marketing teams. A Forrester study found that "many marketers don't really understand what Big Data is and still believe that Big Data is a lot of hype and that the CIO's Big Data initiatives are just a distraction from optimizing customer insights or delivering better customer experiences."1 The approach to a solution is what Forrester calls the introduction of the three Cs: Culture, Capabilities, and Competency.2
If an organization does not understand what question it is trying to answer, too much time can be spent analyzing volumes of data.
This may be counterintuitive, but isn't that what Big Data analysis is supposed to solve: You don't know what insight you are looking for when you decide to use it? The answer is simple: NO! The underlying misconception regarding Big Data is that more is better. Instead of trying to make sense of every single piece of data at their fingertips, organizations need to focus on the business problem they are trying to address (new markets, churn, brand, price, etc.) and use Big Data to test their theses.
A McKinsey Institute study of leading global companies concluded that "Big Data can unlock significant value by making information transparent and usable at a much higher frequency."3 That, in turn, leads to better decision making, delighting customers, and according to the study, could result in a potential 60 percent increase in revenue.
Leading companies have figured out that relying on legacy metrics will not deliver the results they did even five years ago. At Hacker Agency, we believe that Big Data should help marketers find nuggets of trends and outliers that can be tied back to the "traditional" marketing data, and tested in near real-time microsegments. It's integrated data, big or small, that drives marketing value by helping companies execute.
A connected view of the customer journey delivers unprecedented insight into the buyer's process that marketers can act on.
Of course, this is not a simple challenge. This approach requires an environment we call a Customer Data Ecosystem (CDE), where our clients integrate, store, manipulate a,nd analyze the customer data they have, or can access, to inform marketing execution.
The CDE is a foundation for continual learning and leverages the connected data to place targets in segments and microsegments based on dynamic rule sets. Input from all levels of quality, depth, channels, and freshness are subject to a rigorous set of business rules that reflects the marketing strategy. The data can be available for predictive and prescriptive analytics that satisfy an organizational culture of who to target and why.
Online and offline behavior, including complex direct marketing campaign history, messaging, offer, creative, in-market dates, and other test variables can be stored with data captured from landing pages, social media, metadata, multi-variate testing data ,and other third-party sources. In his latest book, Tom Davenport introduces the concept of "analytics 3.0" to describe how companies can integrate small data, traditional analytics and Big Data discovery methodologies to activate the skill set in an organization.4
As clients execute and interact with the target, the more they learn and the more relevant interactions become. Data leads to insights, insights to better creative ideas, ideas, in turn, engage the target, and a persona is transformed into a one to one interaction. Quantitative data collected becomes quality information that can be fed back into segments to be validated in order to drive future execution.
Over time, messaging performance can be comprehensively analyzed by program test variables down to a very granular level-from initial touch to the point of sale and beyond. This allows marketers to examine all programs for detailed sales attribution across all marketing channels.
Data science has never played a more integral role in marketing insights and execution to dispel myths and intuition.
This multi-channel response engine allows marketers to gain consolidated insights into response analysis across all channels on a regular basis. We're using statistical routines and data mining techniques to uncover hidden trends and correlations and activating marketing campaigns more efficiently and effectively. This includes budget-aware media allocation recommendations and predictive modeling for micro-segmented targeting.
When done right, starting and ending with data is a winning strategy. Businesses should bring the three Cs to explore the use of Big Data by asking the ultimate question, "What can I do together with you?" Instead of the more traditional, "What can I do to you?"5 Building a culture focused on trying to answer the difficult business questions, ensuring that the right data and tools are available and continual testing and refinement can drive the growth of your bottom line, even in the face of fierce competition.