Change You Want to Solve
You may already know what your top challenge is, but if you don’t, here are some ways to get started.
- Is there a particular area of your business you need to improve such as customer service or delivery times?
- Are your team members spending too much time or resources on something that should be faster or easier to achieve?
Recommended Read: Find out how one company leveraged their big data in this white paper Fleet Benchmarking with Telematics.
Example: Where Are My Customers?
Take for example a company that spent many hours each month on geocoding. For those unfamiliar with the term, geocoding is the process of taking an address and turning it into its corresponding latitude and longitude coordinates.
So, let’s say your customer is a golf club. Google might correctly say that the address is at 123 Main Street. In actual fact, the delivery location for the golf club is 1 km down the road at the clubhouse. Geocoding discrepancies can occur when there is a difference in the corporate street address and delivery point or when there has been a data entry error.
Example map showing actual vehicle destinations discovered from big data clustering of fleet data.
Two Reasons Why Data Accuracy Is Important
If you’re creating routes for your drivers, your routing algorithm will rely on the fact that you know where your customers are and have provided accurate coordinates for them. If this is not the case and your customer locations are not on point, not only will your drivers be confused during delivery, but you will undoubtedly have un-optimized routes leading to late delivery times, missed delivery windows, and unnecessary fuel waste.
If you don’t know where your delivery trucks should be stopping, it becomes very difficult to know when they were there. You can’t run the necessary analytics to determine if systemic issues exist and in turn optimize that performance. Are deliveries taking too long? Is there more back-door congestion from other delivery vehicles delivering to a store at certain times of the day? You will never know.
There was one simple question that needed to be answered: Where are my customers?
Yet, answering this one simple question could lead to significant returns for them both in terms of productivity and customer satisfaction. If you’re a small enough company, this may not be a big data problem, but if you’ve got thousands of vehicles making deliveries and new customers added daily, it can quickly turn into an issue.
Big Data Solution
Uncovering Hidden Data with Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
To solve this issue, we examined a year’s worth of driving data to identify where each vehicle was actually stopping on each of their routes. Over the course of a year, we generated a series of points in clusters. We then grouped those clusters using scikit-learn’s implementation of Density-Based Spatial Clustering of Applications with Noise (DBSCAN).
This allowed us to remove any outliers from our analysis and to generate a bounding box that was essentially the zone in which the driver actually stopped for the customer. Comparing this data to the manifest allowed us to correlate the specific stop to a customer.
We found that for several delivery hubs, over 15% of the customers differed by 500 metres or more from their planned stop location (some by up to 20 km due to poor geocoding).
As you can well see, answering a simple question like “Where are my customers?” can have a dramatic effect on your business. Correcting the geocoding is a stepping stone to improving productivity, fuel savings, and customer satisfaction.
This is just one example of how you can find value in your big data. A big data graveyard can actually be a goldmine for your business, but only if you ask the right questions.
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