What if when you went into your favorite apparel store at the mall you could:
- earn rewards for showing up
- see where your size of everything is on each rack as you approach it
- earn rewards for trying on clothes
- hear your favorite genre of music while trying on clothes
- step in front of the mirror and immediately get pictures of all sides of you
- take the opportunity to post the pictures to Facebook for immediate feedback
- provide the opportunity for your friends to earn rewards for providing feedback immediately
- customize the apparel like the stitching on your pockets to make it unique
- take whatever you wanted or ship to wherever without paying for anything
- automatically pay your bill at the end of the month which has progressive discounting based on your shopping habits and rewards status
Before such an experience can ever take place, retailers need to inspire customers to partake in their shopping experience. This is where predictive analytics can help retailers get customers in their stores.
Predictive Analytics is commonly known as a variety of statistical techniques for analyzing historical data to predict future events or behaviors such as future shopping needs based on a customer’s historical behaviors crossed with millions of others’.
First, you need to gather big data to massage it into customer relevant small data. There is a plethora of data to consume if you can get access to their social networks. An easy way is to allow customers to login to your systems with social network credentials or sync their existing accounts with social network accounts and while doing so ask for visibility into their behaviors. This paves the path for your collection of all sorts of data.
Next, you need to crunch the data specific to a customer and historical behaviors relevant to them and develop predictive models for potential future behaviors and their probability. For example:
- Notice that similarly segmented customers that bought item X often did behavior A and the potential it has to lead to the purchase of item Y or Z. Then, open a dialogue with the customer saying we noticed behavior A and thought you might be interested in items Y & Z. Then give them the chance to respond or not. Either way if you capture every interaction, you’ll have more data to work with next time.
- Identify your most valuable and trusted customers that have potentially untapped buying potential based on their spending habits, their diligence in paying their credit card bill and their credit score. Then offer those few customers an elite, invite only, status in your rewards program that comes with a no hassle shopping experience that eliminates the line at the register or the need to pay in the store at all. As an added bonus, you can throw in a personal shopping assistant, available only to them, that is able to see their past purchases and make recommendations.
Predictive Analytics has great potential, but unfortunately today it is mostly focused on pushing products and often products that a customer already bought. The right product at the right time is very important. However, customer-focused predictive analytic models that distill actionable services to provide to specific customers will empower retailers to strengthen their brand and their relationships with customers. With a customer service focused approach retailers must reorient employees’ total focus on products to also focus on the associated services they provide. Predictive analytics will empower those employees with each customers’ potential behaviors and associated services, which will increase their probability of selling.
Today; I expect you to know me, what I’m looking for and how to help me get it.
Tomorrow; I expect you to know before I do, how to get me there and how to instill my confidence in you.
However, if you are bold, leapfrog it all and instill my reliance on you.