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Jargon Buster: A Complete Guide To 13 Database Marketing Terms

by Simon Spyer on Jul 15, 2014 10:24:00 AM

Our previous blog debunked some of the myths around marketing metrics and KPIs. Database marketing and marketing technology are other areas beset by buzzwords, acronyms and conflicting definitions.

So we thought that we would set the record straight and provide some quick, non-techie definitions, useful references and considerations for some of the jargon that you may well come across. 

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Data strategy

This connects your data to your marketing strategy for the purposes of testing, measuring, targeting and personalising customer experience. A data strategy is broader than just data - it should also encompass the people, processes and technology required - and it should also consider the full data lifecycle from planning and harnessing data to applying and disposing of it.

 

Business Intelligence (BI)

The terminology has been colonised by the technology industry in much a similar way to CRM (see below) and there is a danger that a dashboard is the default for the ‘BI’. BI looks at how information is used across an enterprise to turn data into insight into action. It encompasses the systems, standards and governance to manage data plus the analytics and reporting used to support decision-making. And it applies to all business areas.

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Marketing Intelligence (MI) 

The marketing specific application of BI. This can also incorporate third party marketing information (market research, desk research) that may not be included in an enterprise BI approach.

A quick watch-out: MI can also mean Management Information which is subtly different and tends to be a deliverable of BI (from spreadsheet reports to executive dashboards).


Data Warehouse

A data warehouse is a key enabler of BI. It is ‘fed’ data by operational systems (such as a transaction database, customer database, call centre system) to create one repository of information. And this repository is then used for different activities like reporting and analytics. This is sometimes described as a single customer view (although you don't necessarily need a data warehouse to have a single customer view).


Data Mart

These can be output from a data warehouse for specific activities. For example, it could be quicker, easier and more manageable for a piece of analytic software to query a data mart rather than the whole data warehouse.


Marketing Database

This typically holds details of all customers, their contact information and ‘contact history’. The contact history is a log of all marketing campaigns sent to each customer; each customer’s response to the campaign may also be held in the marketing database.


ETL

Extract, Transform, Load. A key process in data warehousing, ETL takes data from lots of different source systems and manages it into a common format that can entered into the data warehouse.


Tag management

These are snippets of code that sit on websites and allow you to track, report and re-market to individuals based on their digital body language. This article will give you the essentials.


ESP

Email Service Provider. This is the software that manages the creation and broadcast of bulk emails. Mailchimp and Dotmailer are typical examples.


Marketing Analytics

This is very broad. It can include any type of analytics that you undertake to understand and optimise your marketing performance. It's not the same as reporting and involves the statistical analysis of customer behaviour, communication channel performance etc. You should create an analytics plan as an output of your data strategy.


Web & Mobile Analytics

These are specific components of marketing analytics. They tend to focus on digital advertising and customer acquisition. Tags and cookies are the key sources of information and Google Analytics will be the primary source for many companies.


Marketing automation

This is the process of organising marketing communications into workflows that are triggered by specific customer actions. It should deliver highly personal and relevant content as a result. The automation can and should work across all communication channels and can be applied to customer acquisition and retention and to both on- and off-line customer touch-points.


CRM

Customer relationship management. The terminology has been bastardised by the software industry. For marketers, CRM boils down to the process of personalising relationships with customers across the customer journey. In B2B, there is a tendency to think of CRM as a sales tool for recording and managing the sales funnel; this is important but we think that CRM is broader and about the concept of ‘show me you know me’ through the sales funnel and throughout a customer’s lifetime.



Is there any other jargon that you would like busted? Let us know and we will include more non-techie definitions in an updated blog post.

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This post was written by Simon Spyer

Co-founder & Insight Partner at Conduit, professional insight-monger, dad, lover of all sport and Spurs.

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