Data-driven marketing is a hot topic, as is the automation of marketing activities based on it, because the range of applications is constantly expanding and improving.
Providers of bonus programs unite shopping-oriented consumers with diverse channels, relevant services and bonuses. As a result, these programs have a very high everyday relevance for consumers whose expectations have risen massively in recent years: They expect individualized offers that are tailored to their life situation, their needs, and their channel preferences. Inappropriate and impersonal advertising messages are an absolute no-go for them.
Today’s best offers require knowledge of current and future customer needs and behaviors; therefore, data is key. Automated processes and a meaningful database are prerequisites for the implementation of individualized, cross-channel contact strategies. If you gave your opt-in for data use when registering, the program has a large amount of information that can be used sensibly and in compliance with data protection for personalized addressing. Multi-partner bonus programs benefit from their high relevance and constantly deliver noticeable added value, so that consumers are willing to give their consent to the use of data and contact. Only a multichannel marketing platform can do more. When using the program, relevant information is collected across channels and industries, and online and offline data are being combined.
Automated processes are the solution
To be able to deal with this wealth of information and generate insights, it must be structured and evaluated. For this purpose, high-performance analysis and prediction models have been developed. Among other things, the platform relies on artificial intelligence (AI), for example, in the form of so-called supervised and unsupervised machine learning methods. Unsupervised methods are used, among other things, for cluster analyzes to identify specific target groups. The insights into the characteristics and behavior of these target groups are ultimately used in content marketing. Supervised methods determine the probability that a previously defined target variable will occur among different participants. For example, a score can be calculated that shows the likelihood that consumers will redeem a voucher or become relevant new customers. Another advantage is that the models are executed automatically, so the analysts always have the most up-to-date values, which saves a lot of time and manual effort. This makes it clear that real customer insights that deliver relevant added value can only be derived from the structuring and evaluation of the available information.
Transformation is bound to happen
So-called trigger campaigns are an example of automated, data-driven campaigns. As soon as participants show a predefined behavior (trigger), they are addressed directly via push apps or newsletters to encourage their behavior or, for example, to prevent them from migrating. For this purpose, scores are automatically calculated using supervised machine learning methods which campaign management can also access. As soon as certain thresholds are exceeded, a corresponding campaign is triggered. The possible uses are extremely varied, but the procedure serves, among other things, mainly to prevent churn. If the period between card usages by a company’s users is unusually long, the campaign takes effect and addresses them promptly via their preferred channels, for example, with needs-based coupons. If this offer is accepted, another communication will follow directly to keep them in the activity loop. If the kick-off is not considered, another kick-off follows with a higher incentive or other relevant bonuses. These logics can be broken down to the assortment level, so that campaigns can also be implemented at the product group level. The marketing budget can also be used optimally, since a campaign specifically addresses those participants who are very likely to react and generate additional sales.
With the help of diverse data sources and automated processes, it is becoming increasingly possible to understand consumers and their needs and to enable the short-term control of individualized offers. With the right technological and analytical infrastructure, consumers can be optimally accompanied on their customer journey and contacted with needs-based, tailor-made offers. In this way, campaign costs can be reduced, and additional sales can be significantly increased. At the same time, this also promotes customer satisfaction and optimizes the shopping experience for consumers, which is the core of a positive and sustainable relationship.
By Daniela La Marca