By: Laura Patterson, President
The amount of data being generated is expanding at rapid logarithmic rates. Every day, customers and consumers are creating quintillions of bytes of data due to the growing number of customer contact channels. Some sources suggest that 90% of the world’s customer data has been created and stored since 2010. The vast majority of this data is unstructured data.
It is not surprising, then, that study after study shows that the majority of marketers struggle with mining and analyzing this data in order to derive valuable insights and actionable intelligence. A recent report by EMC found that only 38% of business intelligence analysts and data scientists strongly agree that their company uses data to learn more about customers. As marketers we need to learn how to leverage and optimize this flood of data and incorporate it into customer models we can use to predict what customers want.
Many marketing questions require being able to perform robust analytics on this data. For example, understanding what mix of channels are driving sales for a particular product or in a particular customer set or what sequence of channels is most effective. These types of questions often require large sets of data, or what is being referred to as Big Data.
Big Data isn’t new; it’s just gone mainstream. A recent study found that almost half (49%) of US data aggregation leaders defined Big Data as an aggregate of all external and internal web-based data, others defined it as the mass amounts of internal information stored and managed by an enterprise (16%) or web-based data and content businesses used for their own operations (7%).
But 21% of respondents were unsure how to best define Big Data. IDC defines big data as: ‘a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis.’
Big Data incorporates multiple data sets—customer data, competitive data, online data, offline data, and so forth—enabling a more holistic approach to business intelligence. Big data can include transactional data, warehoused data, metadata, and other data residing in extremely massive files. Mobile devices and social media solutions such as Facebook, Foursquare, and Twitter are the newest data sources. Most companies use Big Data to monitor their own brand and that of their competitors. The use of “Big Data” has become increasingly important, especially for data-conscious marketers. Big Data is a valuable tool for marketing when it comes to strategy, product, and pricing decisions.
Big Data offers big insights and it also poses big challenges. A recent study by Connotate found the top challenge with Big Data was the time and manpower required to collect and analyze it. In addition, 44% found the sheer amount of data too overwhelming for businesses to properly leverage. As a result, many companies aren’t maximizing their use of Big Data.
The effort however associated with managing Big Data is more than worth it. The promise of Big Data is more precise information and insights, improved fidelity of information and the ability to respond more accurately and quickly to dynamic situations.
How to Handle Big Data
So while Big Data might seem a bit daunting, these steps will help you navigate using Big Data:
- Clarify the question. Before you start undertaking any data collection, have a clear understanding of the question(s) you are trying to answer. Using Big Data starts with knowing what you want to analyze. By knowing what you want to focus on, you will be better able to better determine what data you need. Some common questions asked are ’which customers are the most loyal’ and/or ‘which customers are most likely to buy X‘? Big Data is about looking beyond transactional information, such as a click-through data or website activity.
- Clarify how you want to use the data. Will you be using the data for your dashboard, to define a customer target set for a specific offer or to make program element decisions (creative, channel, frequency, etc.)?
- Think beyond the initial question. Invariably the answer to one question leads to more questions. If you’re not sure, hold a brainstorming session to explore all the ways the data could be used and potential questions the answers might prompt. Structure your data in a dynamic way to allow for quick manipulation or sharing. Aggregate data structures and data cubes aid with this step. Construct your data cubes so that
they contain elements and dimensions relevant to your questions.
- Identify data sources that need to be linked. Once you identify the question and how you want to use that data you will have insight into what data you need. To run analysis 3 against data you will need to consolidate and link it. More than likely you will need to collect the data from disparate data sources in order to create a clear, concise, and actionable format. It may be necessary to invest in some new tools so you can pull and analyze data from disparate locations, centers, and channels. These tools include massively parallel processing databases, data mining grids, distributed file systems, distributed databases, and scalable storage systems.
- Organize your data. Create a data inventory so you have a good understanding of all your data points.
- Create a mock version of your data output. This is a key step to helping you determine the data sets. It will also help you with thinking about how you will convert the results into a business story.
Smart marketers use the data to tell a story that will illuminate trends and issues, forecast potential outcomes, and identify opportunities for improvement or course adjustments. They use the data to gain big insights into customer wants and needs, market and competitive trends. Tackle Big Data and tap into big insights that enable you to take advantage of market opportunities, deliver an exceptional customer experience, and give your customers the right products when, where, and at the price they want.
This entry was posted in Analytics, Marketing Measurement, Marketing Performance, Marketing Strategy, Pipeline Metrics, Uncategorized and tagged Analytics, Big Data, customer centricity, customer relationships, data, Marketing, marketing metrics, marketing operations, Marketing Performance, marketing plan, marketing ROI, measuring customer loyalty, measuring ROI, media and marketing, Metrics, performance management, performance marketing, social marketing, social media.