The need to improve reporting about customers is a recurring theme in analytics conversations among logistics providers. Everyone from large-scale 3PLs to small freight brokerages do their best to provide internal managers and executives with impactful and insightful findings about their customers. Yet, amidst this pursuit, challenges inevitably arise ranging from speed to value to quality.
Take, for example, shipment reports.
While every 3PL generates internal shipment volume and cost reports to varying degrees, the critical question looms: are these analyses truly delivering value? Several factors can lead to reports that (at best) clutter up executive inboxes, and (at worst) undermine internal confidence in the reporting team’s handling of the data.
Some of these detrimental factors include:
Limited customer metrics that lack insightful trend analysis
Outliers and other unusual patterns that skew otherwise solid findings
Data quality issues that lead to inaccurate insights
In this blog post, we'll address some of these limitations as they relate specifically to customer shipment analysis, and we'll outline three different analysis strategies that you can use to improve insights into customer trends and behaviors.
Limited Customer Metrics & How to Gain Better Insights
Conventional shipment reports encompass historical & descriptive metrics like shipment volume and delivery times – necessary and important for sure, but far from an insightful strategic picture. You can augment these findings with broader volume analysis metrics.
#1 – Look at ‘flipping’ and ‘missing’ customers through a new lens.
Basic reporting of customers’ volumes, via tables showing a time-period like a week or month, is always a good start. When paired with intelligent alerting and forecasting, this analysis can be a powerful tool to help protect against lost revenue. For example, if a traditionally high-volume customer begins trending downward, then that is absolutely something a 3PL customer sales team wants to be aware of as early as possible.
However, when you climb higher into rollup-level analysis then you gain new insights into the ‘big picture’ and see important patterns in the customer base. Try bucketing customers into volume groups (low-medium-high), and then identify and track trends across bucket groups over time. Seeing shifts among high-, medium-, and low-volume groups gives you a more complete and detailed understanding of how your customers (and revenue) fluctuate together. Not only does this alert you to imminent changes, but it also suggests strategic alterations to the 3PL's service focus may be in order, or at the very least, something to monitor.
‘New’ and ‘missing’ customer reports provide further depth, enabling proactive strategies for business retention and acquisition. When paired with high-, medium-, low-volume analysis, new actions may even be recommended. For example, in a freight broker scenario a ‘missing’ customer report showing a drop in the low-volume customer category can trigger specific action plans such as focused pricing changes or an automated increase response rate to inbound email quotes.
Skewed Insights & How to Identify Unusual Patterns
#2 – Use AI-driven detection to discover outliers & unusual activity.
Applied AI is brilliant for many reasons, with one of the most important being its ability to detect unusual patterns. For 3PLs, patterns are an early-warning system for the unexpected that may signal inefficiencies, potential losses, or customer dissatisfaction. We recommend that you focus your analytics to identify and differentiate between two types of metrics: scenario anomalies and outliers.
Scenario anomaly detectors monitor sets of shipments for specific business conditions and alert you when something starts to change. Anomalies are early warning flags to monitor situations going forward, and they can notice things like fluctuations in cost or demand and shifts in transit selections. Getting proactive alerts for these types of market and economic metrics enables operations to manage transportation costs more effectively. An AI-based solution can perform scenario anomaly detection without uploading manually maintained sheets such as route guides, and instead uses historical data to automatically identify typical data patterns. For instance, a scenario anomaly detector can flag shifting O-D patterns for individual customers. If a customer typically ships to a destination from origin A (using a known low-cost carrier), and they suddenly start shipping from origin B instead (using more expensive rates), then it may be worthwhile to let the customer know asap. If detected early enough, operations may even be able to change the shipment(s) and fulfill orders using better rates or revert to the expected origin location and carrier.
In contrast, an outlier is a data value (cost, weight, shipment count, etc.) in a set of results that is very much bigger or smaller than the other data points in the set. Outliers may skew your insights and affect your ability to make a good decision. In many situations it makes sense to remove one-time or extreme outliers from analysis. For example, 3PLs often track and report the “right” initial profitability-related metrics (total carrier cost over a time-period, average cost-per-shipment, etc.). But has the impact of outliers on these metrics been assessed? Outliers occur due to a variety of reasons like data entry mistakes or even a holiday landing mid-week, and you need to determine if further action is needed.
Data Quality Issues & How to Avoid Them
#3 – Run data quality reports regularly to find potential issues.
Good decision making relies heavily on accurate and reliable data. If you have data quality issues, it could lead to inefficiencies, increased costs, and even disruptions in your operations. Without high-quality shipment data, the ability to make informed decisions is seriously compromised. It is critical to have reports and processes that easily report on the overall quality of your data as it is added to your reporting data store.
At the organizational level, following best practices for data quality can go a long way toward eliminating data quality issues. Some of these best practices include:
Make people aware. Anyone who touches your data should be trained to understand the importance of its accuracy. It’s impossible to avoid manual data input errors completely, but emphasizing and rewarding consistency and accuracy can decrease errors.
Invest in your data infrastructure. More specifically, deploy an effective data standardization approach that ensures your data is consistent and accurate.
Conduct data quality inspections. Regular quality checks and audits help to proactively identify and resolve issues, minimizing the impact on operations and decision-making.
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With detailed tracking and understanding of customer behaviors and volumes, 3PLs can craft customized engagement strategies that potentially turn low-volume customers into high-yield relationships. 3PLs can improve their customer retention and growth strategies by adding the ability to predict customer trends and detect missing customers at varied levels of intensity.
Third Axiom produces reports with unprecedented depth that harness AI and machine learning. Leave your basic spreadsheet calculations behind and instead identify patterns, predict trends, and detect anomalies.