Recently I was standing in my kitchen as I opened what I expected to be an innocuous quarterly water bill. Instead, imagine my surprise when I discovered that my usage had unexpectedly increased by nearly 50 percent.
Naturally, I was worried. Not only was my bill higher than I had anticipated, but I immediately assumed we had a leak in our house. I needed to get in touch with the water company immediately.
First, I checked my account online. My water company actually offers some data in its online portal, so this seemed like a natural place to start. Unfortunately the chart looked pretty but was not useful because it displayed static data of water usage in prior quarters. It did not offer information on my current month or current quarter usage, and I had no ability to drill into the data or to change it to analyze by time of day, day of week, etc. Instead, any time I required real-time data (or even historical data) compiled in a flexible fashion I needed to call the water company.
The Hidden Costs of Leaky Analytics
So, how does my excessive water usage have anything to do with analytics? When I think of embedded analytics, my first thought is of revenue. However, an equally important calculation for companies may be less obvious: reducing overhead.
How did I cost the water company money? And how does that relate to how analytics may cost your company money?
Call 1: After the portal failed to illuminate me, I had to call the water company. During this call the company assessed my water usage and highlighted that usage was consistent even when my family was on vacation. They made some suggestions to conduct a dye test on my toilets, then offered to send a tech for a fee and a flat file of the data so I could evaluate. Nothing was resolved.
- Time Spent: 15 minutes
Call 2. A few days after fixing my toilets (the dye test showed 2 faulty gaskets), I called back to get an update on usage. I followed the familiar pattern of telling my tale to the rep, having my information reviewed, and ultimately getting more data delivered to me. The results were inconclusive.
- Time Spent: 10 minutes
Call 3: After spending a few days away from home, I called back, certain that now the data would show I had plugged my leak. I again explained the situation to the representative. Again, he reviewed my water usage, assessed the situation, and offered to send me a report of usage. I’m still not sure the problem has been fixed.
- Time Spent: 10 minutes
Now, I’m just a single resident. The company services tens of thousands of households. My one problem cost them 35 minutes of representatives’ time.
Let’s estimate the cost if 1,000 customers got excessively high bills in a quarter. Based on my experience, the issue takes 35 minutes to resolve. That’s 583 hours of time spent each quarter on this one issues. If an average employee works 40 hours per week, that’s 14 weeks per quarter (which is at least 2 employees) dedicated to only resolving this issue—spending time on a problem that, if their portal data were improved slightly, I could have investigated on my own.
In terms of real dollars (assuming the rep makes $12/hour), that’s $7,000 per quarter or $28,000 per year resolving a problem that I should have been able to take care of on my own. And the time spent is so high that they would need to hire a second rep—so add in additional healthcare, overhead, etc.
How Better Analytics Saves Companies Money and Resources
If I had access to the data I needed and the ability to explore the data the way I wanted—also known as self-service analytics—I probably wouldn’t have needed to call the water company at all.
This is a proven benefit of embedding sophisticated analytics capabilities: As found in the recent 2017 State of Embedded Analytics Report, 64 percent of companies that embed self-service analytics see a decline in the number of ad hoc requests they get from end users.
What’s interesting is that the water company already collects the data they would need to deliver a more comprehensive dashboard. The .CSV file they sent me had several columns that would be useful for data analysis for an increasingly data-centric world. Sure, it wasn’t perfect—why they would put data and time in the same field vexes me—but it could easily be updated to a format that would provide good insight to customers.
So, what can companies that offer analytics in their applications learn from my experience?
First, customers expect access to data. But more than that, they need self-service capabilities that let them manipulate that data in a custom way.
Second, many customers—if given access to the right information—can resolve issues on their own.
Third, by providing real-time information, companies can help prevent future issues (like my unexpectedly high water bill) because customers will be forewarned by the data. And by providing more information, companies empower customers to analyze data in a meaningful way, control their outcomes in real time by changing their behaviors, and reduce overhead by eliminating nuisance requests.
Stop leaking profits. Learn how embedded analytics can plug an unidentified gap in your application offerings in the full State of Embedded Analytics Report.