Data Analytics2019-03-15T18:22:50+00:00

Data Science Consulting and Data Analysis

The use of statistics in marketing increases the company’s success. Success in the form of increasing conversion usually comes very quickly and sustainably. Our Data Analytics Consulting as well as our Big Data Analytics Consulting brings you, depending on the marketing maturity of your company, fast sales success. Our Data Science Consulting also enables sales forecasts in certain areas with a high probability of occurrence.

Our Data Scientists ensure transparency through meaningful data analyses and help you to make the right decisions. Before making a decision, it is important to ask the questions relevant to success and to evaluate statements. We support you so that your marketing budget ends up on the right channel and with the right campaign.

For some years now, marketing departments have been constantly overrun by new requirements. The increasing digitalization in marketing brings with it vast amounts of new data sources and data volumes.

  • Which data sources should be trusted?
  • What data is important?
  • How do the data have to be related to each other in order to increase their significance or to gain new insights?
  • What data is relevant for which campaigns on which channel and when?
  • What information is significant but not relevant?
Big Data Analytics Consulting
Data Analytics Tools

Examples for the use of Data Analytics

Data Analytics in Digital Marketing

Data Analytics in marketing not only requires experience in the application and development of complex statistical methods and their combination with each other. Our data scientists are more than just experienced statisticians; they are also database specialists with expertise in the R and SQL programming languages.

Our Big Data Analysts and Data Scientists can thus create both simple descriptive and complicated stochastic analyses and evaluations for you.

The analysis results are for example:

  • The marketing channels, campaign types and time points that are particularly valuable for your products or services
  • Alignment of your marketing budget to optimize advertising channels as well as internal and external marketing resources
  • Identification of those information and marketing channels that are successful for real-time campaigns (newsletter or programmatic advertising)
  • Time Series Analyses for your Omnichannel Marketing Strategy
  • Creation of automated reports and reporting systems
  • Target group analyses and creation of target group product marketing channel bundles
  • Digital Transformation of Marketing Processes and Workflows
  • Construction of trend analysis systems
  • Continual improvement and automation of marketing processes
  • Predictions of campaign successes and conversions
  • Cost-benefit analysis of data providers and third-party data sources

Trial & Error versus Stochastic

Many companies use Trial & Error methods to try to find answers to the questions listed above. The trial and error method is popular and belongs to the simplest heuristic way to gain insight. But it costs a lot of time and money. It is often the reason why marketing companies are overtaken by their competitors.
Moreover, in areas such as artificial intelligence, machine learning and new intelligences, the trial-and-error process can quickly have catastrophic effects. Of course, the effects in marketing or dynamic pricing are not as bad as in the field of autonomous weapon systems. However, mistrained artificial intelligence for pricing purposes can drive a company into ruin. Or destroy the reputation of a brand in the field of marketing automation. Automation, artificial intelligence and machine learning in marketing are based on statistics.

Today, successful marketing requires intelligent statistical methods.

It is important to answer crucial questions.

For example, how much customer data is to be analysed with which statistical methods in order to reliably apply the marketing knowledge gained to a complete target group. Statistically speaking, the question is which methods can determine a minimum sample size.

The skills in the application of stochastic methods such as Kolmogorow, Laplace or various Bavarian methods, e.g. for the analysis of customer benevolence expectations and the associated combinatorics, have been lost. Also the ability to apply geometric probabilities (e.g. infinite customer data) or to handle ARIMA processes.

Consumer Data Analytics
Data Analysis

Data source and information abstractions

The reliability of data sources is just as important to Data Analytics as the way in which data is collected. It does not matter whether it is internal company marketing data or marketing data from advertising partners. For proprietary data, the data collection and processing method is generally more transparent than for advertising partners.

We use special stochastic procedures, standard applications and self-developed programs for the seriousness investigation of marketing information.

The statistical procedures apply both to affiliate marketing – regardless of whether the marketing information results from programmatic advertising, newsletter campaigns, web traffic or CRM systems – and to offline marketing or the merging of online and offline channels.

Our data scientists support you in the data analysis of classic data records stored in SQL databases as well as in the investigation of big data data rooms (NoSQL or hybrid data rooms).

We would be happy to support you in all areas related to Data Analytics. Give us a call or send us a short message. We look forward to hearing from you.