Drowning in data – By Plan Net Performan’s Mike Weiler

Mike Weiler is the general manager of Plan Net Performance

Brand managers and customers – but also marketing and advertising managers – have their sights on the same goal: to come into contact with one another at the right time and place and with the right message. The problem is that they all take different approaches and companies rarely carry out real-time evaluations of the data necessary for this.

Customers looking for specific products or services expect personalised treatment – and, of course, for this to be available on all devices and channels. The proverbial customer journey hasn’t been following a straight path for some time now.

After all, there are endless possibilities for customers to strike up contact with companies. They can approach the brands directly or do so indirectly – for example, via performance marketing channels such as price comparisons or social media platforms.

The upshot of this is that the labyrinthine customer journey throws up more purchase process-related data. Marketers, on the other hand, want to understand and reach digital customers but naturally always have their own ROI in the back of their minds.

Accordingly, they pursue a strategy that makes the most of their marketing budget. To do so, however, they must first find their way through the data jungle.

For the most part, the vast bulk of the constantly collected data ends up gathering dust in a virtual drawer somewhere.

At present, however, many marketing departments are still failing to apply any coherent strategy to this data, and rarely analyse and contextualise. This means that the data hardly points the way to clear optimisation measures and follow-ups. Before you start amassing data, you should ask yourself a few questions:

Do I need a constant supply of new data to run successful marketing campaigns?
At what point should I start and stop collecting? How can data be used in decision-making? What data is needed to manage campaigns successfully? The somewhat sobering answer to all these questions is that data collection never stops. It is more a question of making better use of the (available) data.

And the good news? The first step towards a successful data strategy can be taken with an initial inventory of available data. Existing customer data is particularly valuable because it provides important information about purchase behaviour.

For companies, data that already exists forms the basis for targeting customers with individualised advertising at a later stage. Here, it is important to compare existing data with other departments and to identify any overlaps.

All departments need to communicate with one another and to pool their data in a common SSOT (single source of truth) solution.

A very important structural change in the market is the shift of focus from a transaction perspective to customer lifetime perspective. This means that the customer now takes centre stage instead of the purchase transaction.

Accordingly, it is increasingly important to find out how much individual customers are worth when targeted directly. Customers looking for specific products or services expect personalised communication complete with relevant offers.

Well-informed customers research extensively and shop around, thereby leaving data trails relating to the purchase process. Apart from the transaction focus, which only gears the data evaluation and direct communication towards the purchase itself, the customer lifetime perspective takes into account the customer’s long-term value.

This gives rise to completely new approaches and questions that marketers must ask themselves: What does the customer think? What really interests them? Right now, however, the most important question is: How can I be sure of reaching the customer via all channels? With customers moving from channel to channel, marketing managers must bring together all this data from all contact points and assign them to a specific customer.

To provide customers with individualised offers, you must harmonise the available data. Its inherent value must be determined and analysed. Given the mountains of data that cannot possibly be dealt with manually, suitable business intelligence (BI) solutions are a necessity for companies.

After all, it is not just a question of automating marketing processes but of interpreting the data correctly.

Marketing managers need a tool that allows them to process large amounts of data and to visualise the information needed for business decisions – in the form of easily grasped graphics and diagrams, according to research by Tradedoubler.

The customer journey must always be kept in mind. Only then can the shift towards customer lifetime value (CLV) work.

Companies today have unmanageable quantities of data at their disposal. Only with the help of suitable software is it still possible for them to track online user behaviour.

This gives marketers insight into the extent to which individual platforms, channels, business models and publishers have influenced the purchase decision.

Only with the aid of this information can they optimise all digital marketing measures and personalised advertising at the right time, at the right place, for the right person and on a suitable device. The message in a nutshell: successful decisions in online marketing are not possible without smart data.

These five steps allow online marketing managers to keep track of their data:
1. Break open your internal departmental and channel silos.
2. Set up a dedicated BI team for analysing and classifying the data. This should consist of a healthy mixture of people from marketing, IT, data management and sales.
3. Data strategy needs to be based on and geared towards a clear objective. For the BI team, that means only requesting data that serves a clearly defined purpose.
4. The number of different software solutions in use should be kept to an absolute minimum to avoid data chaos.
5. Make sure that you choose a business intelligence tool that offers uniform reporting and systematic analyses. This is the key to successful data.