Diversey’s custom dashboard features unlock major insights

Diversey uses Real-time Data, custom filters, and raw data exports to make smarter decisions about their team.

Diversey’s customized Enterprise dashboard allows them to see what’s happening inside Support mailboxes in real-time, so they never miss an important email.

Diversey is a multinational company providing cleaning and hygiene products to businesses worldwide. David McEvoy, the Global CS, Automation & Insight Analyst at Diversey noticed it was becoming increasingly difficult to quantify and track key email KPIs from offices and teams across the globe. A pressing need to see Response Time performance in the Cairo office was the catalyst that led them to seek out email statistics in all of their customer service inboxes around the world.


When starting out with email statistics, David’s main frustration was that he couldn’t pinpoint where issues were coming from. The Diversey team knew that they could improve their email practices, but in David’s own words: “the first step in fixing a problem is being able to identify it”. Initially, they had some issues manually monitoring their inboxes using their own tools despite being incredibly data-savvy. They started to look into different analytics solutions, but landed on Email Meter as the perfect fit.


Email Meter Enterprise allowed Diversey’s team to mix-and-match the features they needed most, meaning their dashboard reflected exactly what they were interested in.

After onboarding a few managers on the “standard” Enterprise dashboard, they made the switch to a customized dashboard with more robust functionality. The most important features for Diversey are access to Real-Time Data for their inboxes, and the BigQuery direct connection. These features allowed them to encourage their team and set achievable goals which they are now consistently hitting.

Response Rate Improvement
48 hr
Real-Time Implementation
Response Time Improvement

Meeting and exceeding expectations

Response Time was the main KPI that Diversey wanted to closely monitor. After looking at current performance, they decided on a 1.5 hour Response Time goal. With the custom filters in their dashboard they were able to filter by specific offices and regions. This makes it easy to see if there are any outliers or specific issues to address. As a result, in 2021 Diversey’s entire global team of more than 20 offices hit this goal!

The team was able to identify one office in particular whose response time was 4 times longer than others, and worked with them to resolve the problem.

Email volume is the other metric Diversey is most interested in. Using the dashboard, they’re able to detemine what times and days of the week their offices were busiest. Now, they consistently benchmark performance between teams to see if any office or region needs extra support.

Connecting to Big Query

As mentioned above, Diversey has an extensive background in data analytics, so they were interested in having access to raw data they can analyze themselves. The team is using this raw data for monthly performance evaluations, by creating charts to compare performance between regions or custom histograms. The power of Email Meter data paired with the freedom of BigQuery is the perfect solution for data teams in a company like Diversey.

"Direct access will allow us to automatically extract data on a daily basis to integrate the information into other dashboards that provide higher level overviews of the Customer Service workload and performance."
— David McEvoy
Global CS, Automation, & Insight Analyst at Diversey

Real-time data

Diversey added the Real-Time Data page to their dashboard. Generally, Email Meter shows users their data from the previous day. However, with this Enterprise upgrade users are able to see inbox activity by hour for the current day. The tables show managers the received and sent messages, unread emails, Response Times, and more. Diversey’s SLAs are present as well—whenever the unread email count goes over 100, or Response Time exceeds 1.5 hours, the table turns red. This makes it easy for managers to log in and see who is struggling right now, not after-the-fact.

Using this feature, David noticed an interesting trend: orders that came in after the “cut-off hour” at the end of the workday were left unread and weren’t receiving a response until the subsequent day. The team began implementing pre-written responses that would be sent out automatically when an order came in after the cut-off time. This served the dual purpose of lowering Response Time, and ensuring clients were being attended to immediately.

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