Since its start in 1994, Amazon has spent large sums of money on perfecting business and customer data collection. From books, to groceries, to cloud servers, Amazon uses real time data to drive business decisions across their enterprise. As Amazon moves into the shipping space, they will no doubt leverage those same capabilities to push any advantage they can against industry incumbents.
For trucking and intermodal shipping companies, that means the first step in fighting the coming battle against Amazon will be to make sure that you have access to your data to make informed business decisions as readily as Amazon does.
For too long, trucking companies have relied on inefficient manual processes, disparate and confusing data sources, and error prone manual entry to drive business decisions.
As a prime example, when a quote request comes in, what steps do you take to execute and provide a quote that’s right for both your customer and your company?
If you are like most trucking companies, in order to provide a good quote, you must do the following:
- Look through one set of data to determine Origin-Destination (OD) or lane information;
- Read through another set of files to find historical rates for the customer;
- Collate another set of spreadsheets to find the right fuel, cost, and competitor data.
In the worst case scenario, different copies of this information are stored on individual pricing analysts’ computers, leading to customers getting a hodge-podge of different quotes for similar loads. The time needed to respond to quotes means that there may be some you never even have a chance to answer due to the information overhead.
Now contrast that to the way Amazon has conditioned their Amazon Web Services (AWS) customers to expect near instantaneous answers on pricing and capacity. At any time, I can go to an AWS console, request my desired server setup and know exactly what I’ll be paying for either a long term, or a spot market rate in a matter of minutes.
Even worse than the time required to respond, the lack of good, readily available data means that all too often, quotes are driven by analysts’ personal knowledge or gut feel about the right price. In this kind of data environment, it’s impossible to have a directed, analytical approach to pricing. Amazon has made an art of leveraging all their available data and analytics to dynamically adjust pricing based on their available capacity and demand, especially in fast changing areas like the AWS spot market.
This combination of elements means that the first weapon in the battle against Amazon isn’t pricing or advanced analytics, but rather an automated workflow and dataset that gives your pricing team the power to respond to quotes with all the data they need at their fingertips, in near real time.
So what does that really mean?
First, you need to start with your available customer data, assembling the history of all the business you’ve done with your existing or potential customers. Amazon knows every item that you’ve ever looked at, whether you purchased it or not. Similarly, you need to know all the data on not just those customers you won, but also the ones you didn’t.
Next, comes an automated method to present cost data, including OD information, fuel and driver pricing, and any special load factors. Finally, external factors like competitor pricing, competitive shipping alternatives, and seasonal shipping factors need to be taken into account.
While the complexity of the different factors can vary based on the information available, the critical piece is that all your analysts have access to the same information in a standardized, centralized location, and that the information is automatically updated, available on demand, and presented in an easily usable fashion.
By automating your data intake and availability, you can minimize the time it takes to respond to each quote, giving your team the time and resources to go from passively responding to quotes, to actively hunting for fresh business, and taking the fight to your competitors, both new and old.
In the next entry, we’ll talk about improving your demand and capacity forecasting in an era of shrinking trucking capacity, to best position your drivers and trucks for changing market conditions.
Also, I’ll be attending the 2017 RailTrends conference and would love to connect with you while I’m there. The trucking and rail industry face similar unknowns, and Revenue Analytics can help you eliminate them.