In data-driven marketing, relevant data of appropriate quality leads to commercial success

Companies that rely on data in marketing improve the customer experience. This is because, based on data, customers can be addressed in a very targeted manner, with the right offer at the right time via the desired channel. For this to work, data quality plays an important role. Poor data quality causes communication errors. These can result in rejection or even resistance. Read here what is important in data-driven marketing.

Incorporating data from customers and business partners into your marketing activities means tapping into a huge source of knowledge. This is because data is an important key to success, especially in marketing. For example, it reveals the wishes and preferences of customers and prospective customers, but also provides information on what someone rejects or which are the preferred channels on which someone would like to be addressed. In this respect, it is about such important aspects as customer loyalty.

Sufficient data is usually available for data-driven marketing

Due to increasing digitalization, companies today have more access to (customer) data than ever before. Interactions on the website, social media posts, digital payments and so on - customers and business partners leave data or traces of data everywhere. And thus also information, ergo knowledge. This can be used, for example, to tailor offers to individual customer requirements. Customers know this and now insist on receiving personalized information about products and services.

However, a large amount of data alone does not automatically guarantee a company's success. If companies indiscriminately request and store data without knowing what benefit they are deriving from it, this has several consequences: Firstly, frequent data requests will make customers skeptical, if not ex-customers, if they perceive that their data details only lead to the collection of information without any concrete personal added value. The data should be of use to the customer.

It is also fatal for the customer relationship if the marketing department has incorrect or incomplete data. An incorrect form of address, misspelled names or even a mix-up of first and last names simply looks unprofessional. This quickly leads customers to one of the many competitors. Because in the digital world in particular, competitors are just a click away. It is much more important to collect the right, relevant data and use it in the best possible way. So what can companies do to focus on the really important data in order to derive real added value from it?

Data-driven marketing demands: First things first: quality first, then enrichment

The first port of call is the company's own customer data from various channels and systems - such as address data, purchase or contract history, data from customer service, accounting and many other departments. Various tools help to consolidate this data, improve its quality and keep it continuously high. Step 1 to successful data-driven marketing is therefore: data quality management. This lays the foundation for a correct and reliable customer master data basis.

With data enrichment, however, this database is enriched with additional information, refined, so to speak, and made more valuable. This can be primary statistical data, such as the usual demographic data and possibly information such as disposable household income, which is collected directly when a contract is concluded or an application is made, or it can be data from the CRM or ERP system. In addition, there is secondary statistical data from internal or external sources that describe, for example, social behavior, sociographic living conditions, values and preferences, individual lifestyles and the personal environment of people in more detail.

Experience has shown that the following dates are suitable for this:

• Behavioral data: This is unique, historical data, such as orders, transactions, payment history, dwell time, etc. It can be used to enrich data for special occasions, such as customer birthdays. For example, companies can congratulate their customers on their birthday with a voucher or discount code, or reward long-term loyalty with special services.
• Descriptive data: They describe attributes, self-disclosures, demographic information, etc., such as gender or age.
• Characteristics: This includes opinions, preferences, needs, wishes, etc. This data can be collected from customer service, reviews or social media channels, for example.
• Interaction data: This includes offers, results, context, click streams, notes, etc.

Secondary statistical household data from third-party providers offers another possibility. This is data from external sources that provides knowledge about social behavior and socio-economic living conditions. Values and preferences, individual lifestyles and the personal environment of the respective customers can also be derived from such data. The Sinus Institute, for example, offers social milieu data grouped according to characteristics such as social situation, basic orientation, value orientation, everyday attitudes and socio-demographic information. Market data from providers such as Sigma, Microm or Arvato can also be relevant additions in order to better assess the needs and (purchasing) behavior of customers.

If customer data is prepared in a quality-oriented manner in the sense of being up-to-date, complete, correct and unambiguous, i.e. reliable, additional knowledge can be gained by enriching this supplementary information. It can be put to excellent use, for example for personalization in the context of customer experience management.

When the postal address doesn't work in data-driven marketing

Address data is still important customer information. However, this information can also be expanded, especially for goods delivery. Although traditional street addresses have a house number, they do not provide any information about the location of the house entrance or other delivery points. Traditional addressing methods also reach their limits for delivery destinations that are not yet linked to a specific address as a delivery location, such as "greenfield sites" for plant and factory construction or in new development areas. The use of geocoordinates as an alternative addressing method can therefore be useful. Compared to postal addresses, they are clearer and easier to match. There are now several methods for determining geocoordinates. Google Plus codes, for example, convert coordinates into a sequence of Latin letters and Arabic numerals. Another option is the proprietary what3words approach, in which geographical coordinates are represented in three words.

What are the benefits of data-driven marketing?

By enriching customer profiles with household and milieu data, marketers can create more targeted approaches and more tailored offers. But behavioral data is also valuable: it improves the customer experience and enables more precise use of forecast-based tools such as behavioral targeting and predictive analytics.

Household and milieu data can usefully enrich online chronology data in order to understand customer behavior and make them tailored offers. This creates a good basis for behavioral targeting and reduces wastage and costs per action (CPA). Tools such as predictive analytics can also be optimized using household data, enabling marketers to predict customer behaviour more accurately. This makes it easier for them to proactively suggest future offers or promotions and also supports long-term product development by assessing product preferences.

In the long term, successful implementation of data-driven marketing with the help of data enrichment turns customers into fans. This increases brand loyalty and customer retention - with an efficient use of resources and low wastage. In turn, this not only makes customers happy, but also companies. However, an essential prerequisite is a reliable database with up-to-date, complete, correct and unambiguous customer data.