Data is unstructured gold. For retailers, the key is to be agile enough — in a constant dialogue between marketing and IT departments — to analyze that data in real time. Synchrony Financial, in a new whitepaper, discusses the way data should be visualized and the payoffs it can yield.
Big Data can present big rewards or big headaches for retailers.
A recent whitepaper by Synchrony Financial titled “Taming Big Data” found that efficient, targeted data collection can result in customized experience, which, in turn, leads to a better return on investment.
The optimization of Big Data frequently mandates that marketing analytics and information technology departments must work together to collect and analyze that data in real time — in other words, using that data in an agile manner, shaping marketing programs on the fly.
The data deluge must be tamed in such a manner that the organization obtains a holistic, 360-degree view of its customers, which can be daunting against a backdrop where thousands of data points fly furiously. The sources of data can be as variant as the data itself, coming from transactions, of course, with information about every purchase, every swipe, every dissatisfied return to the retail brick-and-mortar location or online. There are nuggets of information across behavioral transactions, with insight as to how often individual customers shop, where and when — and thus giving insight into why people pull the trigger when they shop, regardless of channel. Social media lays out in broad daylight how customers feel about their shopping experience.
What to do with all of this data? The onus is on the marketing analytics department to translate the information into actionable insight. Thus, the need to partner with the IT department, noted Synchrony, to ensure efficient data collection.
The traditional data collection process is one where data is collected across several platforms and then transported to a database. Then, models are built to take that information from the database, and those models look to predict consumer behavior. The predictive behavior stems from past behavior. The catch, according to Synchrony, is that such constructs are expensive, both in terms of time and money. That’s because of the multiple steps involved, with transaction data being logged apart from the operational system and then the analytics being brought to bear. In essence, the data must be warehoused and then studied. Analytical tools themselves can be unwieldy in terms of the expertise needed regarding high-level stats, and those software platforms are expensive, too. Agile efforts in tandem with marketing and IT can move beyond the limitations of a traditional database structure, Synchrony stated.
An agile process can minimize the steps to take between data coming into the firm in raw, unstructured form and spinning into insight, which leads to better customer experiences and loyalty. Optimal minimizing of steps can occur with what could be thought of as a “data lake,” where analytics actually move to the platform where data is stored rather than the converse. Simply put, a data lake is a large-scale data repository and processing engine. The data lake also has the cost advantage of being useful and usable with open-source technology and data cycle times. With shorter cycle times comes the ability to respond to changing customer preferences with speed and thoughtfulness — and permissiveness, as in, just who within a firm can access that data.
In terms of responsiveness to the customer, an agile approach allows firms to tailor offers at the point of checkout, with, say, cash back, credit or other rewards, depending on the type of transaction (as in, purchase or return), delivering “in-the-moment pricing.”
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