Data Analytics for Postal Organizations

The new realities of the mail business mean postal organizations around the world need to find new sources of competitive advantage. In the highly competitive postal market, success comes down to the ability to analyze, understand and act on information. Existing quantitative data can help postal agencies shape management thinking, improve operational performance and even predict customer needs and behaviors.

Recent Accenture research finds that high performance businesses that substantially outperform competitors over the long term and across economic, industry, and leadership cycles, are five times more likely to use data analytics strategically compared with low performers. However, for most postal organizations, data remains an underused and underappreciated asset.

If data is king, then data analytics will be the key to the kingdom for postal organizations. The data is already in their hands. The technology to harness it and get insight exists. Now all that remains is to for postal organizations to put all the pieces together to start seeing a much bigger picture.

The strategic agenda for postal organizations is challenging: services must be improved, customer loyalty sustained and costs reduced. Postal organizations already have volumes of data about their customers’ buying behaviors, peak volume times, staffing practices and equipment utilization. But they have taken that data only so far, primarily still using it in relatively unsophisticated ways.

Going far beyond data mining, advanced data analytics uses an integrated framework of quantitative methods that can help postal organizations derive insights from their data, then take action on those insights to shape business decisions and, ultimately, to improve outcomes. With experts predicting the digital universe will double every 18 months, the time for Posts to get serious about analytics is now.

Many postal organizations recognize that they should be doing something with their data. Others have made some initial investments in developing their reporting and business intelligence capabilities. Nevertheless, manual aggregation of reports based on historical data remains the norm for most postal organizations. Some continue to rely on what has been done in the past and what they intuit to be right, with individual management behaviors varying across situations and decision makers. The consistent theme across the majority of the industry is that any analytical work that is done is largely reactive rather than predictive.

This does not have to be the case. Postal organizations can use analytics to get smarter about every aspect of their business, not only improving the performance of their existing activities, but also helping to build the business of the future.

With experts predicting the digital universe will double every eighteen months, the time for postal organizations to get serious about analytics is now. Accenture recommends:

Use analytics to build volume profiles.
At Accenture, we have seen postal organizations struggle to handle peak mailing times, often because they have persistent blind spots in operations: they measure on hand volume and labor availability through a laborious manual counting process, rough estimation, or intuition. These blind spots lead to an imbalance between incoming volume and machine utilization and staffing. Successful postal organizations establish analytics capabilities to build volume arrival profiles that let them allocate labor and machines to match real-time information. The result is reduced overtime and work hour variances better utilization and processing throughout; and improved service performance. All of these then result in increased customer satisfaction.

Use analytics to maximize future sales.
Accenture has worked with clients to improve their sales opportunity management through analytics. Few postal organizations ever take the time to analyze historical data on revenue-generating leads. By analyzing this data, postal organizations can build highly accurate predictive models to differentiate among the sales leads they already have—and then focus their efforts on the ones most likely to convert to sales. By prioritizing existing opportunities, postal organizations can make better decisions on where their sales groups should spend their time and effort. Moreover, the predictive model can be used in other aspects of lead management, such as developing a better alignment strategy to match leads with specific sales people.