The origin of Big Data, in its present form, is difficult to determine. Some attribute the first modern use of the term to John Mashey, a lead scientist at Silicon Graphics, during the 1990s. Since then, the volume, velocity, and variety of digital data has grown so rapidly that it has left executives and academicians alike scrambling to not only capture and measure Big Data but to use the information contained within data sets in a meaningful way.
Earlier this year, the Library of Congress announced it has archived over 170 billion tweets. The goal of the program is to provide researchers with data that depict the cultural significance of Twitter as a means of communication and creative expression. While important, I suggest that the real significance behind Big Data is how the incredible volume of structured and unstructured data can drive the economic engine that supports US and global growth.
Before you analyze Big Data, it’s important to figure out how the application of digital information underpins your marketing strategies and delivers value to your enterprise. At Adobe, we have six applications where Big Data analytics provides value to our brand.
1. 360⁰ Customer Profile
Data drives our ability to understand our customers better, providing a high-resolution view of their preferences, values, activities, and behaviors. Through the accumulation and analysis of the data, we can deliver relevancy to our markets. Without data, messaging is less targeted and conversion rates suffer. We might as well end up playing Led Zeppelin’s Immigrant Song to an audience of Millenials—“Why is that old dude screaming like that?!?”
The view of your customer becomes clearer as more data sets are infused into the customer profile. Marketing executives who rely on basic Web data such as site behavior and referral sources miss the nuanced behavior embedded in enriched data sets that include social behaviors, in-store purchases, call center activity, and loyalty program buying behavior. Big Data drives value by opening our eyes to our customers. I’ll deep dive into basic and enriched data in a future post.
2. Attribution Modeling
Whether it’s a linear, time decay, or last interaction model you follow, data is the bread to the attribution model butter. Each touch point along the buyer’s path provides value. You’ve got first touch, conversion points, abandonment points, last touch, and other interactions that reveal where messaging is strong and where you need to update your assets. Adobe Analytics was developed to provide this value within the modeling process. Connecting sales to engagement points leads to far greater marketing efficiency—and that can’t be done without Big Data.
Who wants to dive into a marketing campaign without insight into the most (and least) effective assets? That practice went out of style in the last century. Today’s marketers have too much information at their disposal to ignore the signals provided by data.
Now that we’ve got data sets that provide a high-resolution view of our customers, we must use them to create the most personalized, relevant engagements possible. I can remember being in high school where some of my teachers taught with a broad stroke, but the best ones paid attention to how each kid ingested the subject matter on a personal level. It’s no different with marketing, folks. Leveraging Big Data means following analysis with personalized messaging that resonates because it has focused appeal. We can’t get away with a stale, distant approach to our markets; the data that’s out there enables us to craft personalized campaigns.
4. Testing Enablement
Marketers know the value of testing. The good news is we’re no longer mired in guesswork when it comes to split testing, market evaluation, or product development. Our customers ultimately tell us which direction to go as we roll out value propositions. Sure, audience fragmentation makes the evaluation of testing outcomes more challenging, but that’s where Big Data provides maximum value. The transparency we achieve through Big Data has changed the testing environment and enhanced our ability to deliver solutions our markets seek.
A word of warning, though. There may be times where testing hypotheses can be tripped up, even in the face of Big Data. Correlation is not causation. Beware of making decisions based on poorly made assumptions. Data allows insight, but incomplete or disconnected metrics can scuttle the best-planned rollout. Ultimately, though, whether it’s concept, packaging, or channel you’re testing, the high price of failure drives us to depend on data to shape how we market.
5. Product Development
Along the product development continuum, the multiple points where success or failure is determined have to be supported by data. Solutions can be made more valuable to your customers when data is used as a development tool. I speak with leaders across the globe who have yet to leverage Big Data as a product development tool. They’re missing out! Data can push us to deliver tailored solutions that better meet exacting customer needs across multiple segments in any industry.
Customer sentiment is a significant metric that today is more easily captured through social, search, email, and Web engagements. Latent opportunities, consumer preferences, and geographic disparity are all embedded in the data we collect. As a marketer, think hard about the practice of leveraging Big Data for your product development efforts—big wins to be had!
6. Forecasting & Prediction
One of the more influential values Big Data contributes to enterprise success is its impact on forecasting and prediction. Predictive capabilities become sharper across business functions when data is used to support expectations. The forecasting lens is clearer because of the ability to segment data sets and analyze end-user behavior. Data accumulation provides real-time updates and enables sharper decision making. For enterprises using Agile management techniques, data allows you to adapt to market changes quickly with less second-guessing.
Whether you base go-to-market decisions on your first-party data sources or third-party research, the value derived from data analysis is unquestionable. Not only does Big Data allow for greater productivity within internal practices, it can provide a better assessment of the competitive environment as well.
Where would we be without Big Data? There is too much value to be unlocked for today’s leading enterprises to ignore. Where do you see the value when it comes to developing and marketing your offerings to your customers?