In the sense of first-party data or data generated in-house, the email channel is often the starting point for a controllable dialog sequence. Leads are often generated via programmatic advertising, Google Ads, or social media channels, but as soon as an e-mail address is available, this “owned channel” is the ideal starting point for further personalization. A controllable range is available, and a controllable mail sequence in several steps is offered. Nevertheless, the user – contrary to expectation – can shine through inactivity on this channel. Therefore, he is targeted via other channels, such as social media, thanks to the matching of the email address. However, in the practice of many companies, complexity is already reached here, which is why it is usually refrained from: If no activity on the email channel, then the dialog ends.
Until then, it was assumed that the presence of the e-mail address was sufficient to contact the person. In fact, this would be the famous SPAM, which has been successfully fought in the last decades both technologically by the ISPs (Internet Service Providers or just the incoming central inbox for the recipients such as Gmail, GMX, Bluewin and others) and regulatory by the justified demands for more “data privacy”.
Customer data is worthless if the authorization to use this data for marketing purposes is not consistently filed to the respective contact and this legal status is not continuously maintained.
Technically, consent to the company’s handling of customer data means that it must be allocated as “flags” to each user and written to a database. So this consent management defines new data fields, in the scope of the First Party Data Strategy. All too often, companies are poorly organized here or have incomplete or outdated data sets. One must consider various aspects of consent: The purpose, the channel of origin, the timing, the content of the legal declaration. Because this information is not only important for the own use of the permission, but also for the obligation to provide information in data protection: The user can request this information from the advertising company at any time. Changing the permission is an ongoing process, because the user can unsubscribe from a newsletter or revoke the use of the data for the purpose of personalized offers at any time. For this reason, the Consent Management System must also be maintained in different versions.
Customer Data Platform
The Customer Data Platform CDP has become more relevant since the diminishing importance of third-party data. The company’s own data has become an important differentiation factor compared to competitors. Companies are prompted to integrate their own data on a technical platform and use it to segment target groups for specific sales targets. The focus is on contact data, transaction data, tracking data e.g. from e-mail campaigns or those from the surfing behavior of anonymous users on the website or known users, which can already be matched with a profile.
After an initial big data setup, the aforementioned data is continuously imported as a feed on a CDP and maintained and further processed under a uniform data model. This is often easier said than done, because this is where different software solutions, non-uniform data and non-homogeneous data models meet. The good news is that this step forces companies to address the quality of their data in advance. Smaller companies with smaller data sets have it easier here. It is advisable to focus on the really relevant data before a long data hygiene project sets all efforts back by years.
Data analysis software
A prerequisite for the intelligent use of a company’s own customer data is its central storage on a Customer Data Platform CDP. For what purpose is customer data analyzed? Many companies have long wanted to clarify certain questions from the combination of contact data with transaction and behavioral data. The marketing specialist makes hypotheses about target groups and data relevance, which he can test by combining data sources. Which are my top customers? Which ones have up-selling potential? What will the customer buy in the next step? Which customers are in danger of dropping out? But also how big is the catchment area of a store? How do my customers migrate, geographically, but also physically/digitally?
Mentioned are simple RFM methods (Recency, Frequency, Monetary), the basics of the Limbic Map, predictive modeling approaches, churn prevention models. Venn diagrams and decision trees are used to present the results.
In a clear software, certain graphs can be compiled from the database using manageable SQL queries and monitored continuously. However, the limits are reached here with the visualization of the data.
One step deeper goes the identification of target groups directly in the analysis software with the aim of preparing special campaigns or automation steps for these segments. This is where data-based personalization begins. This analysis can also be supported by artificial intelligence, i.e. essentially thanks to the identification of patterns resulting from the behavior of customers.
It is not enough for companies to aggregate your users’ data. They must also obtain permission to use this data at the same time. They must obtain an overview of whether and for what purpose they may actively use this personal data or use it for advertising purposes. There is a content-related legal level and a technical level here.
The legal requirements are set out in the EU in the Data Protection Directive (GDPR) and the respective nationally applicable competition law provisions. In Switzerland, the new Data Protection Act (DPA) will come into force in September 2023, and the Unfair Competition Act is also applicable (UCA). The Telecommunications Act FMG regulates the cookie issue. Compare the blog for a distinction.
In terms of content, a double opt-in procedure is used to obtain the user’s authorization to receive a newsletter or general advertising information in the future. However, this “you may” does not authorize the tracking and automatic analysis or “profiling” of customers’ behavior on the website or in the e-mail in order to address them in a personalized manner.
These two aforementioned consents are not to be confused with the cookie banners or granular settings by cookie category on advertisers’ websites. These are third-party cookies, which are currently up for discussion in the course of the planned restrictions by browser providers.
For technical details, please refer to the chapter on Consent Management.
First Party Data
The consolidation of the so-called “owned data” is the prerequisite for the further use of the data. This essentially includes, but is not limited to:
Contact data from a CRM or even just from an eShop or ERP. Historical sales or transaction data is obtained from the ERP. These must be matched with the contact data (unique customer ID or e-mail address). Tracking data from the e-mail dialog, i.e. the KPI, but especially behavioral data from the clicks indicate interests. Web tracking data from the surfing behavior of anonymous users form the basis for anonymized targeting. They can be “matched” with an actual contact as soon as he or she identifies himself or herself via a form registration (for the legal aspects, see Consent Management).
All other data sources are left out at this point so as not to increase complexity unnecessarily.
These data can be supplemented with the declared interests of the known contacts (the so-called zero party data), whether via a preference center on the website or via the interest query via e-mails. Ideally, this data is allocated to the contact data in the CRM so that it can be used in a targeted manner.
Zero Party Data
As a distinction from first party data, this also includes personal data about customers and contacts, but of a less structured nature and usually declared or caused by the person himself:
- Interest data, which is intentionally entered in a form by the contact in an online preference center or in a corresponding mail; or
- Surfing or behavioral data, which are passively left by the movement of a user in a store or on a website.
Both categories of data can be used for personalization once the user has given consent. Ideally, this data is matched with an existing profile and serves to qualitatively enrich the customer image.
Information and privacy settings to the company. It requires a certain relationship of interest and trust that the customer is willing to enter into a detailed dialog with the company. Such selection options can be:
- Selection of newsletters
- Naming of explicit interests in the context of the company’s product portfolio
- Permission handling for newsletters
- Language selection
- Cadence selection for newsletters
These settings can be adjusted by the customer at any time. For the company, this protected space has the advantage that it has the most up-to-date data possible. It can regularly ask customers to update the data in self-service mode, which reduces the maintenance effort enormously.
A marketing cloud technology comprises various modules for the active management and dialog management of customers via a digital marketing platform, which is itself obtained as a SaaS service – i.e. via the Internet and not installed proprietarily in the own company. It serves the goal of omnichannel marketing, i.e., the recording and targeting of every customer via every touchpoint in the customer journey, as individually and personally as possible. As a rule, the platform is connected to a customer relationship management CRM, so that in the lead nurturing process the correspondence with the lead is reflected and managed directly in the CRM and the marketing and sales specialist receives an up-to-date overview of the status of each lead in the sales funnel. These modules of the Marketing Cloud can include:
- An email studio where e-mail campaigns are created.
- A Journey Builder that maps automated dialog processes in the sense of marketing automation.
- An Automation Studio, where campaigns are planned, controlled via various channels, and the cadence and multiple exposure of the same recipients is also controlled.
- A Social Studio, with which content is captured and posted via various social media networks in order to reach the appropriate target group outside the e-mail channel.
- A Customer Data Platform, where a company’s relevant customer data is aggregated as a basis for segmentation and data-driven personalization.
- Data analytics software that enables in-depth audience building or targeting based on RFM methods, clustering, Venn diagrams and decision trees.
- An Advertisting Studio, through which customer audiences or lists of recipients are matched with profiles on social media to serve them display ads on these channels.
- A Content Builder, where content for manual and automated campaigns is created.
- A Web Studio, where websites or specific landing pages are built. Live chats are set up, web pushes are generated.
What sounds convincing has rarely grown organically and even more rarely is a truly unified platform with seamless data exchange. It is often a jigsaw puzzle of specialized and bought-together software that is more or less poorly integrated under the marketing cloud buzzword and can cause major irritations in day-to-day business.
Marketing automation is a framework consisting of strategy (first party data and marketing), use cases and processes, data or customer data platform, technologies, content, organization and skills, and the legal framework or consent management. Marketing automation in the context of a use case defines a process that begins, for example, after the download of a white paper and should run until the resulting lead can be converted to a customer. These dialog paths are mapped in software, a marketing cloud suitable for this purpose. They control a defined dialog with conditions, rules and branches and run largely individually according to the defined rules. In contrast to a campaign, marketing automation is not a single communication shot, but a sequence of dialog units, or precisely this process, which permanently picks up new users at the starting point – usually certain triggers – (for example, leads) and only ends when the target is reached (conversion or no reaction).
Organisation and skills
Strategy and execution of a first party data concept and the processes of personalized and data-driven marketing need the right people. This is an interdisciplinary event with specialists from marketing (and sales), product management, IT, data analysts and legal. The field is extremely fast-moving and is subject to permanent change. A very heterogeneous technology landscape, both internally at the company and in the market offering of marketing clouds, necessitates the involvement of a specialized external service provider. They can advise and implement conception, technology audit and implementation; we recommend Mayoris!