Why you actually can start ‘small’ with ‘Big Data‘ to boost the success of mail

We have recently conducted a survey among British marketing agencies and marketing departments to find out more about the future of direct mail (or ‘advertising mail’, as it is often called in Great Britain). Together with the British DMA (Direct Marketing Association), we invited both marketing agencies and companies to share their view on this specific communication medium.

The results of this survey have been released as a report (called ‘Mail Matters’, published on March 25th by the British DMA) and those of you who are interested in the details are more than welcome to download the study using the following link:


The report was full of useful findings, e.g. that the majority of respondents still consider direct mail a trusted and effective medium (which it is) but a minority of respondents is concerned that direct mail may be considered as ‘junk mail’ (which it is only  if you don’t make it relevant for the recipient!). A conclusion was that the so-called ‘Digital Natives’ seem to have acquired relatively little knowhow about the production and use of direct mail, so that they concentrate on online marketing activities.

DMA research Five Segments of advertising mail

However, most of the respondents  can still be considered ‘loyal fans’ of direct mail although the research revealed that they are expecting more from the medium (and, in the case of agencies,  from their clients). One of the barriers for more successful direct mail obviously is the ‘lack of data and data analytics’. The data, however, is already there … but it can seem difficult to fully harness, so let’s have a closer look at the issue of analytics:

In recent months more and more focus has been put on how much value ‘analysing Big Data’ could bring. Despite the fact that ‘Big Data’ has become a marketing buzzword and that many decision makers are unsure of exactly what Big Data is, a whole industry has formed to help marketers to identify the best target groups and create customer profiles.

Starting with ‘Big Data analytics’ might at first sound like David’s fight against Goliath (also known as the data monster).  A marketer will learn that consumer data is stored among multiple databases and that the format and content of the databases heavily depends on the application (CRM, sales database etc).

In order to get a full, 360 degree view of your customers an immediate task is to consolidate the data from those different data sources (basically ‘copy’ all datasets into one big Excel  or .csv file). Once you have done that you will be confronted with the fact  that one and the same customer’s data occupies more than one database; in other words, you have found ‘duplicates’. A deduping exercise is therefore needed. In addition to that, important data fields like the gender (which determines the salutation) are not always filled, foreign names are not recognised  correctly  or the address is written with many different variations.

So a first small, but very important, step in data analytics is ‘data cleansing and data enrichment’. Don’t think that this must be a manual process. As long as you can transform the data from the different sources into one and the same format (e.g .csv-files) you can use appropriate tools. Such software comes with built-in intelligence (and algorithms) to understand which data belongs to one and the same customer. Most of them come as a SaaS (software as a service), run by European companies following the strictest EU data privacy laws (Ricoh is currently developing such a tool with an authorised RiDP = Ricoh Development Partner). Large corporations might prefer local installations and regular local updates and upgrades.

My recommendation is to take ‘data cleansing’ and ‘data enrichment’ seriously. Today’s tools can not only improve the quality of your general data but can also check whether email addresses are valid or if GSM mobile phone numbers are still in use. The cost of this first small step (data cleansing and data enrichment) is relatively modest.  More good news is that such state-of-the-art algorithms to clean your data can be built into all your existing (and future) web forms so that no bad data will find its way into your databases.

Once your general data quality has improved, you will be ready for true data analytics. Ricoh and alliance partner SAS can ‘calculate’ the likelihood of your customers buying further products or services (called predictive data analytics), so that you can concentrate your focus and improve the effectiveness of your actions.

So: mastering the (big) data monster should start with small steps, with ‘data cleansing and enrichment’ at the beginning of the list. After that, getting full customer insights using data analytical software should be second. Once you have created customer profiles and target groups, talk to us so that we can combine your data insights with our dynamic document composition tools to create the truly personalised pieces of customer communication which can make direct mail so successful.

This process actually is what we call ‘Precision Marketing’. Learn more about Ricoh’s Precision Marketing practice on YouTube and see e.g. how retailers can benefit from data-analytics and mail.

Your opportunities are endless! Let’s take the first steps together…