Data Analytics in Marketing
It wasn’t long ago that data analytics was completely unknown in marketing. Progressive companies have only been doing real data analytics in marketing since around 2010. In economics, marketing professors at universities taught until a few years ago that success in marketing could not be predicted. They said “Of 100% of the marketing budget invested in different marketing measures, 50% would be successful – but which 50% could not be predicted”. The professors were still of the opinion that last year’s successful channels would not automatically be successful in the coming year. Thus it would be unavoidable that a considerable part of the marketing budget would be badly invested. Well, in order to protect the professors, one has to admit that Data Anaytics was not possible in marketing to the same extent at that time. In particular, data on marketing campaigns could not be viewed in real time. Real-time control of marketing campaigns through intelligent data analytics in marketing was impossible.
In today’s age of real-time success controls for digital marketing campaigns as well as print campaigns or broadcast advertising campaigns such as television or radio, the teachings of professors are no longer correct.
Nowadays, the challenges in digital marketing lie in six areas in particular. All 6 areas are based on Data Analytics in Marketing
- The complete collection of essential customer data across all marketing channels and their different touchpoints
- The automated reading and recognition of sales-relevant information
- The machine abstraction of customer and sales information including all data sources – the whole is more than the sum of its parts. See also our data analytics topic “find out and define target groups“.
- Dynamic marketing as part of dynamic pricing. Dynamic pricing is rather than real-time bidding systems today can. Dynamic pricing is a combination of data analytics and software development. Multichannel digital marketing including programmatic marketing and real-time bidding are only part of dynamic pricing. Dynamic pricing, even if it only takes place in marketing, contains numerous parameters. For example, internal fixed and variable costs, contribution margins, competitive prices and behaviour, market development analyses and forecasts, current sales figures (supply and demand) and automated target group orientation. However, data analytics in marketing is usually a good introduction to dynamic pricing.
- Real-time detection of campaign progress – early detection of tops and flops in campaign management. Automated campaign management through data analytics in marketing. Is the marketing channel currently successful? Is another marketing channel somewhat less successful but cheaper in the end? Which marketing channel and which touchpoints are too expensive and must be stopped automatically?
- compliance with data protection regulations, – country-specific data protection regulations as well as internal company data protection policies.
The mathematical stochastic methods behind Data Analytics in marketing ensure that your marketing budget is used effectively. Through data analytics in marketing, all parameters are analyzed and decisions are proposed almost in real time. Thus, your marketing budget is used efficiently.
To date, there are no applications that map these complexities. Regardless of which manufacturer applications you work with. No application in the areas of online marketing, sales or customer relationship management can map the complexity of the multitude of interdependent parameters as standard. It does not matter whether you work with applications from Microsoft (Dynamics CRM), SAP (Marketing / Customer Data Cloud), IBM (Watson) or Sales Force (e.g. Journey Builder).
Data analytics in marketing must therefore always be performed manually. This does not necessarily require the use of proprietary Data Anyltics editors from the manufacturers listed above. It can be advantageous to build your own independent Data Analytics plafform within the company that can be docked to existing Data Warehouses, for example. With a standardized interface it is possible to dock to the applications of the manufacturers listed above. Interfaces to third-party data analytics platforms are also possible. E.g. to Tableau, OpenRefine, NodeXL, SAS, etc.
The key for the establishment of marketing automation and marketing digitization consists of three essential components. Data Analytics in Marketing, Requirements Engineering and Software Development.
We at HighPots know the requirements and how important the interaction of these three components is. We offer you many years of experience and knowledge in the field of data analytics in marketing. Take advantage of our Data Analytics Marketing Consulting.
Our experience in the areas of
- Statistical procedures
- programming languages like R, SQL, Java or Python
- Online marketing and sales
- Databases and Big Data Data Rooms
- Mathematical modeling and simulation
help you to implement your data analytics procedures for the digitalization of marketing. Just give us a call or write us.