Retailer Big Data
Let’s say you’re at a midsize retailer. Already, do you think big data is not for you – since you are not at a global, multinational company?
How about these use cases? We can agree most are for you I’m sure.
- Market basket analysis
- Website customized experience
- Supply-chain management
- Event- and behavior-based targeting
- Market and consumer segmentations
- Data syndication
But where’s the big data in all that? It’s behind the use case. It’s the new data supporting the use case. It’s taking the use case to new levels of business contribution. While I realize these use cases may still be forming at the company, many cannot simply focus on shoring up the use cases with traditional data.
With sensor devices, market basket analysis can be extended from what is in the basket at checkout to what was in and out of the basket throughout the retail experience.
Similarly, clickstream tracking can show the timing and activity around the browsing behavior that preceded the online purchase. Either way, there are real ramifications there for understanding customer movements and optimizing the interactions. Amazon, for example, knows not just what you buy, but every detail leading up to the purchase.
The supply chain is rife with opportunity once detailed patterns of products and parts are learned through sensor networks. The supply chain becomes much less costly and more just-in-time with big data. Frequently sourced GPS data can also improve all product in motion.
Triangulating customers to their social media can create unique marketing, retention and potentially fraud prevention opportunities. Social activity can feed into customer lifetime value (CLV). While usually a financial metric, CLV can now have a developing component of influence.
Finally, GPS-enabled apps for the retail concept can tease out an entire new product line – company data. With demographics combined with location analysis, customers of the data can determine where target consumer segments are.
A recent study showed that 10% of midmarket organizations had greater than 100 petabytes of data, compared to 8% of large organizations. 19% of large organizations were between 1 and 100 petabytes while only 13% of midsize organizations did. Large organizations have a similar lead at the 100 terabyte level. At the 10 terabyte level, it’s very close (27% to 26%). Over 1 terabyte: large organizations 14%, the midmarket 29%.
Those numbers are close, and may be surprising. Company size does not seem to correlated with data size. I’ve been mentoring some startups and, despite their small stature, they have terabyte to petabyte levels of data. Newer companies are formed with the information asset in mind. Many exist to exploit information to their advantage above all other assets. They also enter the world unencumbered with legacy challenges and with an abundance of software to manage it all. Many nurture the ‘data scientist’ mentality from the start.
Big data is a disruptive change. I realize that can make it uncomfortable, but it’s exploitation helps to ensure similar or greater levels of competitiveness for the organization. For the most part, for retailers, it’s not new use cases – it’s additive data to existing use cases.
This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies or opinions.