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There are two key things to consider when pondering big data’s role in the modern marketing landscape: 1) the types of new data sets and 2) the variety of new data sources. Each is driving the development of new analytics tools and technologies, which in turn, are driving the business value of big data across organizations.
Today, big data involves multi-structured data sources. In the past — and regardless of their professional roles — people would leverage just one analytical tool to glean insights. Statisticians would use a numerical tool, analysts would use a business intelligence tool, and so on. But in modern marketing, our resources have to match the diverse and myriad sets and sources of big data generated by our ever-evolving, multichannel environment. And likewise, an organization’s analytical architecture must feature many different technologies, including traditional data warehousing, a data discovery platform, and a layer that performs multi-structured data refining enabled by Hadoop.
As our world becomes increasingly more connected — and consumer touchpoints continue to multiply and evolve — big data’s reach and influence will become even bigger. This means that every line of business in your organization can benefit from it. In fact, statistics from McKinsey indicate that use of big data has the potential of improving global productivity by 1% — a massive amount when you’re talking about a global business.
In the consumer goods (CG) space, big data enables retailer collaboration. CG manufacturers can leverage it to merge consumer shopping behavior and social network insights with loyalty and transaction data. This all-encompassing blend of data sets enables them to truly understand what’s driving path to purchase across their different customer segments. These insights can lead to increased engagement, thus driving more sales and loyalty opportunities for both the retailer and the CG firm.
The evolution of big data has also affected our information’s shelf life. In the past, the retail industry extolled the benefits of keeping data for years on end — so many historical insights and patterns to be leveraged, after all. Today, that’s simply not the case. Data can go stale in just a few months, a phenomenon that speaks directly to the high-velocity aspects of big data. Because in a number of cases, the true value of big data-generated insights comes from their use in real time.
Take, for example, the practice of sending a promotion that is triggered by the in-store scanning of a product’s QR code to a customer’s mobile phone. This can be a relatively simple automated process that doesn’t require extensive analysis. However, let’s say the promotion is not only addressed to that shopper, but is also designed specifically for her based on factors including her previous purchases and her online and mobile searches — or even her potential to be a high-value shopper due to her demographic similarities to other shoppers. Now we’re getting a bit more involved. And so is big data.
Such highly personalized promotions require bringing together data from customer loyalty and digital analytics solutions, shaped by sophisticated predictive analytics applications, and all tied into in-store and mobile promotional tools for delivery when the shopper is at the point of decision. And while this type of effort requires a significant investment in both analytics and good data management, it could also produce conversion rates (read: an ROI) that would dwarf those of broader promotional efforts.
This type of retailer promotion ties directly to the CG partnership, as many product promotions are bankrolled by trade fund dollars provided by the CG firm — all with the aim of driving transactions/product movement, trial/acquisition, in-store traffic and sales.
Another example where speed is of the essence: store-based fulfillment, which is becoming increasingly popular as retailers realize the value of their existing store network in getting products into customers’ hands. Operating such a fulfillment network, however, requires retailers to establish business rules that analyze all the costs involved. They also have to have a direct relationship with CG suppliers to fulfill specific orders and — when stores are furnishing the goods — ensure on-time, in-full inventory replenishment.
In some cases, the shortest geographic distance between two points might not be the most cost-effective route. Drop-shipping an item from a CG manufacturer or distribution center — even one located a few states away — could conceivably be better for the retailer’s bottom line than shipping it from a store located in the same city as the customer, particularly if the store risks an out-of-stock situation if the item is taken off the shelf or out of the backroom.
Determining how these multiple factors interact depends on real-time inventory information, variability in shipping costs and even weather conditions that might slow down a delivery. The fulfillment decision, however, needs to be delivered immediately and automatically, either directly to the customer or to a call center or store associate.
“Transforming raw data into timely insight is at the core of a good [business intelligence] strategy, and doing it quickly even with high volumes of data is the mark of a good big data initiative,” noted Aberdeen Group’s Rowe. CGers that can master not just the velocity, but also the volume and variety that define big data will be well on their way to unlocking its tremendous — and to a great extent still untapped — value.
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