OK, I know it’s a weird title. For those that don’t know, Underpants Gnomes is the title of a South Park episode that first aired in 1998 about a group of less than stealthy Gnomes that had a grand three phase plan:
- Phase 1: Collect Underpants
- Phase 2: ?
- Phase 3: Profit
Change Phase 1 to “Collect Data” and I think it’s an eerily similar plan to how many approach big data.
To avoid the big question mark in phase 2, use an expert. Installing Hadoop and tinkering around with how to get from collecting data to profit can be a long road if you don’t have the experience to hit the ground running. Even to those that are somewhat seasoned, putting together Phase 2 is the magic.
The Three “P”s of Retail Big Data
This “magic” is a matter of experience with large data sets and knowing how to correlate large amounts of data to uncover information that has value. Many online retailers make a smart choice to go with proven technology to begin to reap the benefits of big data analysis and turn phase two from a question mark into a reality. The good news is that there are several amazing products in this space that can help retailers make smarter decisions regarding 3 ‘P’s of retail big data:
- Personalization: Find out what buyers want based on data about their past purchases. This may include others who purchased similar items, social media data, customer service transcripts. There are a lot of possible vendors that can help here. Monetate is one well-known choice. There are many others.
- Pricing: Use data based on a shoppers on-site behavior along with inventory and cost data to provide dynamic pricing to customers. Several companies provide great products here, such as QuickLizard, that integrate with existing platforms making dynamic pricing a reality with a minimum of work.
- Prediction: The holy grail of big data for retailers is predictive analytics. I’ve seen Coherent Path’s demo of this technology and it’s astounding what can be discovered about customers and how they may be guided to products that would increase the value of a customer.
Some large retailers employ data scientists to work with teams of developers to uncover intelligence about their customers. The vast majority employ solutions developed by such experts that can be made specific to a retailers needs. Making a good start with big data is all about getting started. Quickly.
Know of other products or practices for producing retail big data results? Post a comment below!