Business Intelligence Basics
The LES takes analytics data or business intelligence results and it turns it into an actionable event for the users. It does this by presenting that information in places where they need it.
So we know, for example, when we look at a report that the report is valuable to the organization. You are able to take that report in the LES and present that information back to the user when they’re using it. That is the critical difference. It’s easy to have a report that tells us the information we need. But, it’s difficult to have that report accessible to us when we need it. And then when we need it is the part that we want to focus on. We make sure that we’re finding if that user does need that information at that time. Then we present it back to them.
Presenting Customer Information
For example, we may want to show a user the information about the customer that they’re currently working on. We don’t want to show them simple things about how many shipments they’ve run. But this is where we’d want to alert the user to things that we’re concerned about. For instance:
- Payment trends
- Credit trends
- If a customer’s volume has dropped recently
- Things that our analytics tool is telling us
That is something that our analytics tool or business intelligence tools would provide for us. But we’ll cycle that information right back to the user while they’re on the phone with the client. That way, they can use that information and drive more business to the organization. Also, this will bring more value to the organization.
Making Business Intelligence Data Available to Users
The most challenging part is taking business intelligence, reporting, or analytics data. After that, making it so it’s valuable to the users. The key is finding out how to reduce the noise. Finding out how to give them the information that’s going to help them at that time is the key. And there’s no easy answer to that. That’s one of the most difficult problems because we all know how to generate their data. We all know how to generate lots of reports.
But making sure that we’re giving the users the right data at the right time is something that is a lot more difficult. The way we do it is by learning and using their machine learning tools. This allows us to look back at the data and decide: when we showed this information to this user, did it provide value? And we know it will provide value because we can see the trend has changed in a positive direction.
So let’s say we see that trend moving in a positive direction. Then we know we should show this information more often to this user in these circumstances. We’ll try to continue to leverage that or leverage it in different ways to experiment a little bit. Then we can see if there are other places that we can expand on the benefits that we saw the first time.
Providing Value in Context
Let’s take an example of the kind of information that we would bring to a user that provides value to them in context. That would be something like shipment volume. So we know, for example, when a customer calls us, that we’re going to want to take a look at creating a new shipment. Because that’s why they’re calling. That or we’re going to be tracking a shipment for them. Those are the most common reasons that the customer’s gonna call us.
But while we’re doing that, we’re going to want to take the opportunity to show shipment volume. We may want to warn the broker or the person taking the call with the company. We tell them that this customer moves a significant volume with us during February. But this year, they’re not. So, that way, we can ask the customer at that time, “hey, what’s going on? Is there something I can do to earn more business from you?”
Information like that or questions like that are all things that we can do in real time. We do this by seeing the trend that Tai or our analytics reporting present right back to the user in context. But then also, we want to take a little bit more into account than only their average monthly volume. Because we have seasonality that’s super important in this business.
Timely and Accurate Information Matters
So say we have somebody who ships in September or October. Well, we don’t want to be asking them in February why we’ve lost all their business. That makes us sound like we don’t know what we’re talking about. So what we’re able to do with the more advanced business intelligence tools is use these machine learning algorithms. Then we tell the user, the broker, that in February, we don’t see a lot of shipment volume from this user. This is a time when we can talk to them about other opportunities. Or talk to them about what they need to prepare for their busy season when it comes up. And it’s that contextual information that gives you a deep understanding of what the users need. It gives you a deep understanding of ways that you can improve the business throughout the entire year.