Big data is a term that’s often used in marketing related discussions, but exactly how big is “big data” and where and how can or should it intersect your operation? Is big data limited to only marketing related activities?
To get an idea of exactly how much data is out there, consider the following, according to Monetate’s Retailer’s Guide to Big Data:
90% of the world’s data has been created in the past two years
There are 6 billion mobile subscriptions, accounting for 87% of the world’s population
Retailers have been using analytics for years to measure a slew of metrics both on and offline. Recent rapid growth in smartphone and social media usage have been the driving factors in the explosion of data, leading to “big data” now becoming available. Retailers typically see big data as falling into two primary categories:
Structured data – this type of data includes names, addresses, transactions history, loyalty programs, and mostly any other data that involves an “amount” type of measurement
Unstructured data – this type of data includes product reviews, images, Facebook likes, tweets, and (typically) any other social media data
Structured data has been around for quite a while and has been used by retailers for decades in planning everything from marketing and merchandising to operations and supply chains. Unstructured data is proving to be a challenge for retailers to fully take advantage of. Over 75% of retailers do not know what percentage of data they collect is unstructured. Over 30% of retailers do not even know how much data they store at all!
Retailers are aware of the unique challenges and opportunities that an effective use of big data represents. 51% report that a lack of sharing data creates an obstacle to measuring marketing ROI. 45% report not using data effectively to personalize marketing communications. 42% report not being able to link data together at the individual customer level. It is very encouraging to see that the top three areas retailers report on planning to improve their big data initiatives and deploy big data projects are in marketing, merchandising and e-commerce.
These areas have tremendous value and strategic importance to retailers. Pricing may be included in one of these, but it is worth mentioning on its own – and we would add that pricing can and should be an area to focus on from a big data perspective as well. While pricing is always something that is tied to real-time market conditions, using big data in combination with that focus can provide a fantastic combination and lead to far richer dynamic pricing scenarios. Dynamic pricing as of today for online retailers is typically connected to a repricing solution, which allows retailers to respond in real-time to price changes in their market, price changes at specific competitors or to any number of other triggers that can be created. Consider the possibilities if in addition to all of that, dynamic pricing was also able to connect to big data with regard to a specific target market and have triggers “set” for how that market was reacting on Twitter or Facebook to a new marketing campaign. Trending on Twitter? Great, prices now adjust in real time either up or down. No social media response at all? Again, that sets off a trigger and pricing adjusts accordingly. While the structured segments of Big Data may not quite be at the point of being collected in real time, especially because some of them are retrospective, there are many elements on the unstructured side that actually are collectable and actionable in real time.
Connecting Big Data to an already dynamic pricing strategy seems to be the next step in creating an even more dynamic and responsive pricing experience, one that is able to adapt not just on changes in price, but to changes in perception and attitude to the product or service. We would love to hear your thoughts on connecting real time dynamic pricing to Big Data. Let us know what you think!