The Magic of Data Science in Marketing
Marketing operations has been a prominent topic for pretty much every company that sells products or services. It does not matter if these are sold B2B or B2C; awareness about the brand, products, etc. plays a crucial role to stay competitive in a world full of offers and to retain existing clients. There are as many marketing campaigns and initiatives as there are brands. Years of research have proven marketing to be inevitable to generate brand relevance and leads. However, in recent years the field of data science in marketing, or “marketing analytics” has blossomed. With the digital age, a lot more data is accessible, which opens opportunities that marketers didn’t even dare dreaming about before. We all know targeted marketing, albeit it is not always obvious to us.
Everyone speaks about data science and big data in marketing, but only few have yet managed to offer “the whole package”, combining in-depth marketing knowledge with expertise in data science and the respective field of application (such as the financial sector or retail). With various technologies and methodological approaches, such as real time and social media analytics, to name only two, a lot of different targets in the realm of marketing operations can be reached.
The following article summarizes the prominent role big data plays in modern marketing and provides an overview of how data can be turned into value by gateB’s data scientists.
In the digital era, when most products are available everywhere and always, the products itself are losing in significance for the marketing. Distinction nowadays happens more and more through enhancing customer experiences. The customer is the new focal point of marketing, and big data analysis plays a vital role in gaining insights that allow marketers to orchestrate the customer experience in a way that results e.g. in a raise in cross- and upselling revenue as well as an increase of customer engagement and loyalty. Marketing is becoming personalized.
Considering sociodemographic features and buying patterns – and in some cases micro-geographical data – and taking into account bought product combinations and segments with similar customers, analytics may predict future activities of a customer more accurately. With offering him the right product at the right time, his experience is enhanced, which increases his engagement and loyalty as well as the chances of a purchase. By interaction-based programs on the level of individuals there can be created a sustainable differentiation – the key to success in the digital era, where the products itself are getting less and less important for success, while the customer experience and engagement are becoming essential.
Clustering algorithms allow to build segments in little time out of huge amounts of data. Customer segmentation can be done by any relevant features, which of course differ from branch to branch and from company to company. This permits to aim marketing actions and campaigns directly at the receptive customers only and does increase response rates and revenues as well as strengthen customers’ brand loyalty. And purchases are likely to increase when customers can be offered what they are likely to want even if they were not looking for it. Data scientists can tell marketers what the next best activity for any customer is, what products he might be interested in, and what group of clients bought similar products.
By gaining actionable insights, data science can evaluate the customer lifetime value (CLV) and estimate their long-term profitability, i.e. the products a customer is likely to buy in his lifecycle and the expected total revenue. With the analysis of data from various sources, including social media, the lifetime value of any customer can be predicted, and by taking into consideration the expected costs of marketing actions, individual profiles can be made that help marketers to make the right decisions.
