The relevance of database marketing in the digital marketing age

Still striving to deliver the right message to the right person at the right time

As someone who has over 30 years’ experience of data-driven marketing I don’t see any great dividing line between the age of traditional direct marketing and the digital marketing age in terms of strategy. What has occurred is the introduction of new methods of analysis and communication which facilitate concepts that previously were difficult to achieve.

Techniques like Artificial Intelligence, Machine Learning and Predictive Analytics support concepts that have been around for all the time I have been in database or data-driven marketing, addressing the need to direct audience selection as well as personalization of the creative treatment, the expected outcomes and channels of response, leading to the construction of customer journeys and profitable relationships.

The adage ‘right message, right person, right time’ formulated back in the 1980s is as true a strategic objective today as it was back then.

The New Strategic Cycle

Traditionally, a campaign would be implemented using traditional communication media and this campaign would generate a response. The marketer had full control to the greater extent, of who would receive the offer and hence the response could be viewed as controlled as the respondents were in the main a function of the audience.

That response would be analysed, and the outcome would be used to fine tune the strategy and drive the next round of campaign communication (see Fig 1).

 

Fig 1 Traditional Strategic Cycle

Whereas previously the marketer could anticipate with some degree of control when a response would be generated, the digital world has reduced this element of control. A marketer can no longer engineer when a contact may wish to encounter their message, proposition or brand, or, indeed, come across it. Hence, there is a need to establish valid methods for acquiring data from customers and website visitors.

The digital marketing age has delivered not only ease of data collection and analysis in order to drive the commercial relationships and measure the outcomes, but an absolute need for interpretation of the data that finds its way into the database not only from campaign responses but additionally uncontrolled responses, enquiries and tracking from website visits, to establish its value to the strategy (see Fig 2) and provide information that will drive automated communication strategies through a set of strategic rules and a broadened selection of communication media, in real time.

Fig 2: The Digital Strategic Cycle

These new concepts also have implication on clustering and segmentation. Combinations of demographic and behavioural criteria have traditionally defined clusters. Once defined, these have been used to drive product development and marketing activity.

Clustering

Cluster sets change their content and their direction; individuals join and leave as new information is learned about them and as the importance of the business rules inherent in the data relationships is recognised.

This means that the clusters must be recognised as volatile and allowed to be dynamic.

Their dynamism must be tracked, and the changes identified in order to keep the marketing strategy and communications schedule on course.

Marketers can find that markets for their products are dwindling, readership for their publications is flagging and response to their once attractive offers reduced. This may not be because the product is any lower quality, or the price has leapt; it can be that the customer profile once associated with that product or market has changed shape and has moved out of the target zone for that marketer. Has the customer turned 31 and so is no longer eligible for a Club 18-30 vacation? Has a child reached an age where its parents would no longer be interested in nappies?

These changes and new viable targets must be recognised to effect and maintain product and communications strategies. Early warning of changes in customer profiles can be provided from constant data feeds with triggers identified to drive personalization of proposition, communication channel and delivery.

Personalization

Personalization is not a new thing. Forty years ago, the large direct marketers like Readers’ Digest and Damart were thrilling their customers and prospects by incorporating their names, their street or their town in the text of the multi-page letters they sent.

This basic personalization continues today, with marketing communications incorporating the same text variables. However, these days no-one is particularly excited by the fact that direct marketers can reproduce their name on the page or the screen, irrespective of how large the font size is.

Others go one stage further to annoy the recipients of their marketing collateral, by making either guesses or misguided assumptions about the recipients’ interests.

To personalise relevantly, you need to understand the context of your products and how this will fit with the context of your customers, unlike this Amazon blunder exposed by a customer last year on Twitter:

So, data-driven personalization should be a business priority. Research over the last three or four years has determined that conversion rates can be increased as such relevant marketing messages make it more likely that people will engage; the efficiency of marketing spend can be improved; and customer lifetime value will be increased.

Considering the context of your products and services must be matched to the context of your customers – their actions, transactions and engagement and combinations of how long they have been a customer, complaints, referrals or advocacy and such classic measures as Recency Frequency and Value score.

You must also match to their personal profile or persona. Rather than targeting just based on demographics, consider how people behave and what this tells you about what might engage them.

Demographics are largely static and may not always influence how or why people buy, but psychographic and behavioural personas can give insight into who does what, and why based on aspirations, attitudes, self-view, price sensitivity, journey stage, satisfaction and sentiment; create strategies that target each behaviour-based profile.

Try to introduce personalization in real time – driving dynamics in the customer experience. It has been suggested that not personalising in real-time is not personalising. We didn’t have the channels for communication or the technology for driving real time dynamics back in the 1980s, but such tools are available today.

Personalization should enable immediate reaction – like face to face, and so you need personalization technology that can understand, react to, and optimise customer journeys in real-time by applying data analytics to deliver the right message or experience to the right person at the right time – that adage that we worked to 30 years ago!

Dynamic content presented to customers can be achieved using machine learning that decides what the best content for each customer is, based on such parameters as purchase history, preferences, persona and browsing and buying behaviour, along with the customer lifecycle.

But don’t just consider what to personalise, but how to personalise it; use the data-driven personalization to drive the creative presentation in copy, images, format, offer and the response channel.

Make personalization an integral part of the experience but don’t go out of your way to push it in the face of your customers. Don’t do something just because you can, like the meaningless incorporation of the customers’ names and towns discussed earlier in this article.

Similarly, don’t flaunt to customers how they are tracked or the data you hold – I have seen this just unnerve them and so it has a detrimental effect on the experience. It has been suggested that your tactics should go unnoticed and create an effortless experience.

The best personalization is that which enhances the customer experience without them querying how or why, but also demonstrates an understanding of the customer and reflects the truth about them.

Good, reliable data is the fundamental ingredient

Poor data is the single most quoted reason for failure of such communication strategies. Ask yourself:

Do I have a data strategy?

Can I rely on my data?

Have I undertaken a gap analysis?

Am I maximising the touchpoints?

If the answer to any of these is ‘no’ then this is your start point.

So, in summary…

  1. Basic personalization tactics are no longer enough to engage members
  2. Using personalization intelligently is the best way to predict and shape behaviour
  3. Following the elements of data-driven personalization helps you develop your strategy
    • Embrace predictive analytics
    • Use data to drive dynamics in segmentation, offer, creative and channel
    • Establish measures that will feed the intelligence behind your personalization
    • Ensure your goal is based on improving customer experience and you’ll see increased engagement, retention, commercial success
  4. Ensure everyone has a single view of the truth through sound data management and governance