When you look up the word “optics” in the dictionary, you get definitions surrounding the study of sight or behavior of light, how the public perceives events, and a lens or optical component. Those might provide some context to what “optics data” is, but they do not provide the details that are necessary to truly understand what optics data can provide.
Optics data, as we define it, is all the accumulated information of seemingly unimportant or insubstantial transactions surrounding a more noteworthy event. If a transfer between accounts is the event, optics data seeks to record the behaviors that took place before, during, and after that event. In other words, optics data provides insight into the events that take place around an event that matters (financially, systematically, or otherwise).
These details can provide insight into why someone performed (or didn’t perform) a specific action. That information can then in turn help to identify opportunities that might have been missed by not looking between the lines.
How companies gather the data
Let’s dissect a recent interaction and what optics are associated with the events. Keegan went to the grocery store and left without making a purchase. A simple event that ended with no financial transactions. But there is more to the story: the interactions and events that would be defined as optics. Below is a list of the optics associated with the original statement, “Keegan went to the grocery store and left without making a purchase.”
- Keegan walked into the grocery store.
- Keegan reviewed the new loaves of bread that were on the endcap of aisle 2.
- Keegan picked up a loaf of bread from the endcap of aisle 2.
- Keegan set down the loaf of bread that was picked up from the endcap of aisle 2.
- Keegan walked out of the grocery store.
The retail and grocery world has been studying optics data for years. There are consultants that help retail and grocery stores understand the flow of their customers to better position their products for sales. So, what types of questions would we ask ourselves based on Keegan’s trip to the grocery store? Or what could possibly be done with the optics data that was created as part of Keegan’s trip to the grocery store? Below you will find a listing of rhetorical questions and thoughts based on the optics data associated with Keegan’s trip to the grocery store.
Keegan’s trip to the store: the optics perspective
- Keegan walked into the grocery store.
- Why did Keegan go to the grocery store?
- Was Keegan just interested in shopping? Or did he have a defined list of grocery items that he needed to pick up?
- Keegan reviewed the new loaves of bread that were on the endcap of aisle 2.
- Why did Keegan stop at the endcap on aisle 2?
- Was he attracted to the packaging and was interested to see the new bread and how it is different than the bread his family normally buys from the grocery store?
- Keegan picked up a loaf of bread from the endcap of aisle 2.
- Why did Keegan pick up the bread?
- Was he verifying the expiration dates of the bread?
- Was he reading the nutritional facts?
- Keegan set down the loaf of bread that was picked up from the endcap of aisle 2.
- Was the price of the bread too steep compared to Keegan’s usual bread choice?
- Were the ingredients in the bread not consistent with the ingredients that are necessary for his lactose-intolerant daughter?
- Keegan walked out of the grocery store.
- Why did Keegan leave without making a purchase?
- Was there an emergency that he needed to tend to?
- Did Keegan leave the grocery store in a hurried state?
What to do with the data
The rhetorical questions are just a sampling of what could be asked about Keegan’s trip to the grocery store. But this is where the value of optics data begins to unfold. With the information above, the grocery store can then begin to market to Keegan and other customers who exhibited similar traits. Or perhaps the grocery store may wish to use the data to educate their customers so that they are more inclined to make a purchase or are more likely to make a purchase the next time that they visit the grocery store.
These two concepts, marketing and education, are the best ways for a business to benefit from the study and collection of optics data. This could otherwise be described as Punch – Counter Punch efforts. This is because someone or something performs an event or action and then the business responds. For example, the following attempts could be made based on the data collected:
- Send a marketing message to Keegan to let him know about anything new in the grocery store.
- This message may be of interest to Keegan because he entered the store and did not make a purchase. So, the business may be interested in just getting Keegan back into the store.
- Send an education message to Keegan reminding him of the hours of the grocery store.
- This message would be designed to remind Keegan that the grocery store is available when he is. This goal would be to subconsciously educate Keegan to visit again.
- Send a marketing message to Keegan that includes a coupon for the bakery.
- Keegan exhibited the traits of being interested in a bakery item but did not make a purchase. Would a coupon put him over the edge to make the purchase?
- Send an educational message about all the great new products in the grocery store.
- Because Keegan stopped to look at the new bread, he may be interested to learn about other new products.
The list above could go on and on. It is meant to inspire you with all the ways that optics data can be used.
Optics data in credit unions
When it comes to credit unions, there’s a wide area of interactions where optics data might begin to reveal new information on member behavior. One of the first my CUSO started looking into was in the conversations occurring between call center agents and credit union members. The goal was for credit unions to think more about how the interactions could lead to re-thinking call centers as an opportunity center where sales can be made.
To do that, we began studying the details of the call center interactions that were occurring with members to identify information not provided through the phone software. For example, who are the members that are calling into the call center? And why are they calling a call center agent? Marketing teams could then dive in and almost immediately identify opportunities.
- Reaching out to members who while talking with the call center agent, navigated to the Loan Rate Board.
- The member may have been interested in current loan rates but was still just shopping.
- Reaching out to the members who while talking with the call center agent, requested a password reset for online banking.
- The member could be educated on how they can reset their online banking password themselves using the self-service toolkits that are available.
- Reaching out to members who while talking with the call center agent, requested a transfer between their savings and checking account.
- The member could be educated on how they can make transfers themselves in online or mobile banking. It is possible that they just need to be educated so that next time, they can complete the work themselves.
The study/analysis of optics data leads to opportunities to create conversations. I encourage you to identify where in your business the opportunity for optics data exists. And then I challenge you to identify the routines that you will perform and how often you will perform them. Because this is where you will find the true benefits of optics data.
