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Big Data, personalization, recommendations: Companies are finally coming to grips with the fact that data is essential for digital marketing. However, it is not uncommon for collected data to end in data silos – and the process of collecting in the “data-driven-everything” paradigm. This raises the question of how companies actually generate concrete benefits from user data. For, owning data and using it profitably is not the same thing. The value of data lies in the insights that can be gained about a company’s customers and the personalization that is made possible by these insights. With the following example, we will show you exactly what this means.
The assumption: Luxury goods sell particularly well at airports – accordingly, shops and boutiques in airports should gear their content and marketing activities to this segment. Does the data confirm this?
So, should shops and boutiques shift their marketing focus away from luxury watches to baby milk? Of course not. The target audiences for both product groups are important, but not necessarily identical. Accordingly, both must be addressed differently, with appropriate content and appropriate marketing measures.
It is important to remember that customers are not stereotypes. They are versatile and their respective touchpoints with companies and brands are fragmented across multiple channels as well as time and place.
The challenge in the approach is to find overlaps at every point of user interaction and assign them to appropriate segments based on their data. Machine learning algorithms can then automatically make predictions and recommendations. The result: personalized, i.e. individually suitable offers for customers. Web and app analytics make it possible to verify the measures taken and continuously learn even more about customer behavior – all fully-automatic in the background.
Remember: Collected data only allows a view into the past. Forecasts and derived measures only result from the analysis.
If you are not sure whether a measure will have the desired effect, simply test it with a small target group. Because: behind all these collected data points are people who want to get in touch with your company. Ultimately, your digital marketing must act as a substitute for the customer advisor. At first glance, the customer advisor would assess the respective family and life situation, put it into the current context and advise accordingly.
Back to our example: If the customer of an airport shop is interested in ready-to-drink baby food, much more can be deduced from this than “interest in baby food”: Presumably this is a family on a journey and the product allows conclusions to be drawn about the age of the child. Using this single data point, you can now communicated personalized content and as well as targeted advertising – and not just “Customers who bought this milk also bought this milk”. Possibilities include tips for travelling with children, recommendations for families and children at the airport, and specific products and services that make travelling with family easier. If a customer profile is also created, you can get to know the behaviour even better over a longer period of time and can continuously adjust the measures.
The foundation for the automated collection and evaluation of data is therefore:
Frequently, existing structures, stakeholders and regulations stand in the way of a rapid implementation of new technologies. It does not have to be complicated at all: The right tools can make it easier to get started with data-driven business. The Adobe Experience Cloud is a perfect example. It offers suitable tools for every field of activity, which are networked among each other.
Let’s take a look at our example: If a customer buys baby milk online in the airport shop, this transaction is recorded by a web analysis tool and the customer is assigned to the “Family” segment, for example. In order to use this website data for personalized marketing measures, it must be exported from and imported into a target group tool. To measure the success of the measure, the data must in turn be compared with the web analysis tool. This means: effort, time delay, susceptibility to errors due to import and export processes. If the data is also to serve as the basis for personalized E-Mails, SMS or push messages, however, networking of the tools and a customer database is essential.
With the Adobe Experience Cloud, the scenario is as follows: Reports and segments from Adobe Analytics are shared with Adobe Target at the click of a mouse, and the results of the performance measurement are played back directly to Adobe Analytics. Connected to a CRM database, this information enriches existing customer data. With Adobe Campaign, customers can then be offered family-friendly additional services by E-Mail before their next trip, for example.
So, data-driven marketing does not mean offering a second watch to the customer who has just bought a luxury watch. However, it can certainly mean regularly reminding customers of consumables such as baby food. And who knows: maybe the personalized services will eventually inspire such relaxed parents to buy a luxury watch if the offer comes at the right time.
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