One of the biggest challenges marketers face today is customer acquisition and retention. The key to both acquiring new customers and retaining current customers is possessing the critical data that can help you, one, communicate effectively with the highest qualified contact possible and, two, further identify the needs of your current customers to foster long-term loyalty.
Unfortunately, today’s data industry is both far too complicated and highly fragmented, offering a confusing glut of choices that are overwhelming marketers, who are in desperate need of this mission-critical information. The existing data marketing ecosystem of data and direct marketing list owners, managers, and brokers is wildly inefficient and often ineffective, costing businesses untold millions in unnecessary time and money, and untold more in opportunity loss.
Even so, given the fundamental truth that data is the backbone of both digital advertising and marketing and traditional direct marketing, marketers have struggled along with what the market has been able to provide, for better or for worse. Global advertising revenue for 2017 was $591 billion with $209 billion of it dedicated to digital advertising. A conundrum as effective data sources are becoming even rarer as the need for—and actual dependency on—data becomes more essential. The escalating demand for big data sources that provide quality and complete data has skyrocketed in today’s digital age.
It’s the fundamental big data sources that have been the very crux of the problem for marketers. Today, an individual, entity, or brand looking to acquire a specific data set will have to spend extensive time and resources locating sources that meet its target audience, negotiate costs, and establish privacy standards for the transferring of the data. This leads to a decrease in quality and data record duplication. These three challenges not only make it extremely cost prohibitive to identify and acquire the various parameters required to compile the exact dataset that is needed but, for small and medium sized businesses, it creates a barrier to enter the data marketplace.
As problematic, attempting to generate revenue today from existing datasets brings its own unique set of challenges. The first is the time and money it takes to create data cards and collateral for the data owner to monetize. At the same time, they need to identify the right organization or marketplace with the widest reach—one that represents the highest demand for their data. The second major challenge is integrity and accountability. Data owners do not trust outside organizations to properly store, manage, and monetize their data. The last major concern surrounds the security of the storage environment. Data abuse and lack of transparency in the revenue share business model are underlying fears that will ultimately prevent a list owner from making his/her unique data set available for purchase.
So with all of the problems running rampant in the big data industry, what is needed to put this key facet on course? Below are five reasons why merging big data, artificial intelligence, and blockchain technology will revolutionize data-driven marketing worldwide, across all industries:
- Empowerment. A blockchain-based system empowers data source providers to monetize their data and better capitalize demand, allowing data source providers to access the large global marketplace. In the same way that eBay provides a marketplace for vendors of physical products, a blockchain-based digital marketplace can create growth potential for data source providers of all sizes, while also reducing barriers to entry into the industry.
- Transparency. A blockchain approach provides data providers with full transparency, traceability, and auditability, overcoming many of the hurdles data providers currently face in the existing marketplace. Anyone who has operated in the big data space knows that duplicate data, false data, and questionable sourcing are unfortunate industry truths. However, a blockchain-based approach provides complete transparency, allowing buyers to see where the data has been and where it came from prior to purchasing.
- Confidence. A more transparent vetting and grading system for data will improve confidence building between the end user and data sources. Currently, most data purchases are practically blind transactions, whereby buyers won’t really know what kind of data they’re receiving until they actually buy it, because no vendor would ever reveal the data prior to money changing hands. Once you have the data, it’s then up to you to determine its quality but by then the money has been spent. Rather than this archaic process leaving much to be desired, having a three-party scoring system improves quality and increases trust in the marketplace, facilitating more transactions and leading to overall higher levels of confidence in the industry as a whole. Giving businesses and consumers quality and verified data that’s vetted and scored externally allows for the reduction, if not elimination, of false or outdated data—a significant problem currently plaguing the industry.
- Simplification. By simplifying and aggregating world data transactions into a single point of sale, the result will be an “Amazon” like marketplace, where economies of scale and data aggregation will facilitate a smoother, cleaner, and simply better checkout process, creating more data trade worldwide. Giving end users a simplified, easy-to-use and robust interface with a quick and secure payment system between the business or individual and data sources is a requisite means toward this end.
- Artificial Intelligence. “Smart Indexing” engines are now utilizing predictive analytics (a type of artificial intelligence using data analysis and machine learning) for “Confidence Scoring” to provide continual real-time accurate data. Based on immediate business conditions, this will allow for record sets that can be a single individual that matches all parameters or millions of records that match desired parameters.
Ultimately, democratizing big data levels the data playing field by providing the most comprehensive marketing data solution to all businesses and individuals. It will provide a robust interface between the business or individual and the data sources. The backend systems will ensure full confidence in data quality for the end user as well as transactional finality for the data providers.
