Surviving the MarTech Jungle – Data Science: How do I go about it?

Data Science: Overview of internal resources

In the field of marketing technologies (MarTech), we like to speak metaphorically of a jungle, because the subject proves to be a multifaceted landscape with different terrains and disciplines. Due to the sheer mass, one easily runs the risk of losing the overview. In the context of this metaphor, Data Science can be understood as a useful tool set that should not be missing for exploring the marketing technology jungle. In our blog post “Data Science – The toolkit for the first expedition into the MarTech jungle“, we have already explained what Data Science is and what advantages it holds for customers and companies alike. Likewise, we have looked at what framework conditions are necessary for Data Science  Now we need to get an overview of the existing internal resources.

The following questions can be helpful:

• Does your company have data analysis systems that can process millions of data records efficiently and within which you can use existing mathematical models for analysis or create your own models?
• Are there employees in your company who operate such systems and can understand and further develop the models described?
• Or do you work with external service providers or partners to gain access to the necessary systems and corresponding know-how?

If the answer is yes, you can basically develop your own scoring or prediction models on the existing platforms. If the infrastructure and staff are not available, we recommend the following procedure:

1. systematically capture the business logic you want to use!
2. clarify where and which data is already collected and which you will need in the future!
3. compare possible software solutions with each other!

Depending on individual requirements, different types of systems can be considered:

Ready to Use:

Platforms for use cases with low flexibility:

• Bloomreach Engagement
• Salesforce Einstein
• IBM Watson

For complex use cases:

Dedicated predictive modeling solutions such as:

• GPredictive

For highly specialized use cases:

Own data science machine learning environment, for maximum flexibility

• Python

The selection of the appropriate systems should, of course, also be made in dependence on the existing or future planned IT architecture & Marketecture (Marketing Architecture).

In general, it should be noted:

The science of data is a complex field anyway. That’s why it pays to bring data science experts on board: They know the right methods to get the most out of your data.  The same applies to the selection of suitable technologies.

It is therefore important to define concrete use cases and goals along the entire customer journey, derive measures, and thus optimize the experiences of your (potential) customers. This can only succeed if you look at the organization as a whole. The opportunities and potentials that data science and machine learning methods bring with them are – as you have learned in past blog posts – enormous. The advantages on both the company and the customer side are indeed many and varied.