You can’t fight the future, opt-in, non-interruptive, permissive communication is where marketing is headed today.

Faced with empowered audiences that have a growing toolset to filter the content they see, advertisers are rethinking the classic approaches and adding newfangled inbound strategies focused on drawing in eyeballs, rather than seeking them out.

But, two things can be missed in the shuffle to adapt in a changing environment. First, new tools don’t make the old ones obsolete overnight. Second, some old tools not only survive the transition, they are made stronger by it.

Want proof? Sales of vinyl records have been on the rise for ten years straight and manufacturers continue to innovate.

 

Old Dogs Can Learn New Tricks

Direct marketing, initiating communication and speaking directly to your audience, is an old way of advertising, maybe even the oldest, it dates back to the first marketplaces where vendors shouted the virtues of their wares to passerbys.

Database marketing, direct marketing that is tied to a store of information about customers or potential customers is also a time-tested strategy. Businesses have long kept (and carefully guarded) their lists of customers and leads.

But, like modern day turntables, database marketing has gotten upgraded over the years, and still has a role to play in the marketplace. Overstuffed paper rolodexes have been replaced by voluminous data warehouses and advanced modeling algorithms.

Database marketing has also been a robust tool that can help you better reach your audience, stimulate demand for your products, and keep tabs on your partners and competitors.

But, thanks to new technologies and a shift in marketing goals, it is also being used towards new ends, such as relationship marketing. Audiences today are demanding to be wooed rather than cajoled. Relationship marketing uses database knowledge to increase customer retention, satisfaction, and loyalty, rather than just close sales.

 

Making a List, Checking it Twice

There are two broad categories of databases used in marketing, consumer and business. Consumer databases are somewhat regulated for privacy reasons and can’t only store basic information like names, addresses, and transaction histories.

Databases of business, useful for B2B marketing, are typically more extensive, and include employee contacts, information about industries the company participates in, and a record of past dealings.

However, because such lists are usually smaller and more fragmented there is less data to analyze, and potentially a large number of individuals to linked to each organization, making it less clear who to contact.

In either case, the bulk of information is generated either by internal means (such as sales and delivery systems), or by purchasing it from a third party, such as a list broker.

Lists can also be compiled through application forms for free products, contests, and other lead generating activities, or from warranty cards or subscription forms.

One of the pitfalls of database marketing, is improper targeting. A properly targeted message is delivered to someone who ends up happy to receive it, which is beneficial to both the recipient and the sender. Unwanted communications will be perceived as junk mail or spam and ignored.

 

The Big Data Gold Rush

Databases hold the collective memories of a company. It has information about everyone the organization has encountered, partnered, competed, or transacted with (or hopes to).

That information can then be analyzed to work out behavioral patterns, which is then in turn used to generate personalized communications.

This is one of the reasons everyone seems to want access to new streams of data today. Microsoft’s purchase of LinkedIn, for example, or Salesforce’s flirtation with acquiring Twitter, were both seen as companies looking for user information to feed into their analytical models.

More data means more accurate modeling.

 

Conclusion: The Five Vs

There are five factors that indicate your database marketing effort is moving in the right direction:

  • Volume – more data, means better predictions
  • Veracity – bad or outdated data won’t give you good predictions no matter how much of it you have
  • Variety – data should be coming from multiple sources to avoid blindspots
  • Velocity – your database has be able input, retrieve, and process mountains of data in nearly realtime
  • Value – there has to be a positive ROI for the time and money spent gathering, analyzing, and using data