Finally – products. I use a little hair product to tame my crazy hair. If used properly, the tub of product should last about 6 months. The barber should know that if my wife is getting ahold of the “repurchase” responsibility for my product, she will likely be purchasing it somewhere other than their barber shop. How does one combat that?
When approaching the 6 month “repurchase” date – perhaps 5 months after the last purchase – the barber should send me a text message, email or some other personal communication offering me a discount on the product with my next cut. My wife will be impressed that I’m using a “coupon”!
If I ask the barber for a new tub say 5 months after my last purchase, the astute barber would realize that I might be using too much product. At the end of my cut, he might take the extra few seconds to show me again how much to use – thus making it last longer. Yes, they might loose an extra sale once every 10 years, but the fact that he’s taking care of my hair care needs will likely get me talking about him.
In the same conversation – me asking for a new tub earlier than expect – the barber might learn that my teenage son has discovered girls, and is taking a bigger interest in his appearance. But, the son hasn’t been in for a cut to this barber shop. Again, our astute barber might suggest a greatly reduced rate for the first cut if I bring my son with me on my next visit…
Wrapping It Up
We realize that the healthy barber series is a simple, digestible way to understand a couple of the models we might use to analyze and improve business, but what about a business that’s more complex than our barber shop? How about a heavy truck parts supply house that may have a 1000 or more products (SKU’s) that they sell, and a 1000 or more regular customers? This same type of analytics can be applied to any of their products.
Let’s pick brake shoes for a moment. The same condition applies when we look at the frequency an individual customer is buying this item. We can be predictive about when they’ll come in to purchase again.
With the parts supplier example, we might be able to go even further. Imagine a customer comes in to buy brake shoes. If we see a growing trend that similar customers are coming in to purchase a $1.50 brake spring, then the parts staff could suggest that those customers buy the spring at the same time as the brake shoes – “It’ll be cheaper than another trip in to visit us, and it will keep on your bench if it’s not needed right now.” Perhaps this type of data might point to some type of quality problem, or perhaps a training issue for the folks changing the brakes.
Back to a subtle part of the very first part of this series: “bucketizing” a customer. Using mathematical and statistical techniques – analytics, and with domain expert input, groupings of customers can be created. Their purchase habits can be compared, and “outliers” (customers who should be purchasing a particular product, but are not) can be identified. Why are they outliers? Do they simply not know that you sell the product, or are they purposefully buying from someone else? Time to investigate – but now we can put recourses into a very specific problem that has a measurable return on the investment.
We might also be able to create a “growth pattern” for groups of our customers, showing how their order habits will change as they grow (based on early customers habits). You might recognize that they’re a month or so away from a large credit increase with you. This can give you some lead time to ensure that you’re servicing them properly as they need it.
Two different customers will likely grow at two different rates. The software, and the analytics and mathematical model built into it, will recognize this, and adjust for it based on the individual customer.
But regardless of the size of the operation in question, the fundaments at work operate in a similar fashion. By organizing a means to capture data and analyze trends within the company, we can build prediction models, assess business process and build options that allow decisions to made quickly and effectively.
Analytics provides an enormous amount of visibility into your business. If you want to find out more, get in touch with us.
Thanks for following along for this series!