Big data application process

Big data can be used to make every stage of the strategic communication management process more effective and efficient - from situation analysis, strategy development and implementation to evaluation.

How to use big data in strategic communication?

INTERPRETATION/EVALUATION: In the next step analyzed patterns, connections and forecasts have to be interpreted (prescriptive analysis) to derive recommendations for action.

Before showing the report to decision makers, the big data application process have to be evaluated. The evaluation shall show weather the right analysis is made with suitable data. The goals from the first step are the main reference point.

REPORTING: The last step is to prepare and convey the results for decision makers, f.e. in the form of network graphics, diagrams or with the support of real-time-dashboards. Fundamental data analysts and communicators should arrange, in which form and how often the reports should be produced.

It is important to implement the appropriate key performance indicators (KPI) for the communication goals. Automated processes are possible, f.e. alarm messages.

GOAL DEFINITION: Big data can support the separate phases of the communication management process, if there are goals and questions for the data analysis. There are separate goals and analysis for every phase of the process.

DATA COLLECTION/SELECTION: After defining the goals, a data pool has to be build. The experts examine what kind of data is available and what kind of data source can be used.

In accordance with variety, volume and velocity it hast to be specified: What kind of data has to exist to reach the defined goals? The generation of data is not banal because there are differente formats which need various possibilities to generate and save data.

DATA ANALYSIS (DATA MINING): In this step with statistical methods computer-based knowledge is gained (data mining). The analysis starts with defining goals for the data mining: What exactly should be analyzed? What method is suitable? Accordingly the suitable algorithm has to bo choosed to look for patterns, correlations or trends.

Subsequently there is a descriptive analysis. Here it is about frequency, the number of followers, likes or page views.

In the next step reasons, connections and patterns are examined with diagnostic analysis.

Also predicative analysis can be part of the data analysis.

DATA TRANSFORMATION: The step of data transformation describes the conversion of the raw data in other formats. The data has to be transformed so that they can be edited with the particular tool.

Another main goal of the phase is to reduce data by condensing single variables to characteristics.How to do the transformation in detail is related to the question in step one.

DATA CLEANING: After data collection the data have to be cleaned up and prepared for further processing. In other words, spam and irrelevant data have to be cleaned up, f.e. from bot-generated content. Mostly this can be made by algorithms.

In the following the texts are read randomly, proved manually, classed and eventually corrected. For experts this step is expensive but significant. Only if the data were collected correctly, they can be used for analysis.

Download image of the big data application model

Key facts

There are seven steps should be taken to apply big data in strategic communication: 1. Define goals, 2. Generate data, 3. Clean data, 4. Transform data, 5. Analyze data, 6. Evaluation, 7. Report results.

The process also involves experimenting with the data and installing feedback loops until a reliable data set has been created, which can then be used to gain real insights.

Despite pursuing set data targets, it is also important to leave enough room for new and explorative analysis.

Application

Applying big data in strategic communication:

Big data applications for situation analysis:

Market and competitor analysis provide insights into how a brand or organization is perceived compared to its competitors and also insights into who the main players are.
Reputation analysis and marketing-specific competitor analysis provide an overview of the strengths and weaknesses of competitors.
Explorative analysis can help to identify new topics, trends and insights. A combination of different types of analysis is also possible.

Big data applications for strategy development:

Analysis of stakeholders: who are the most important target and reference groups to talk to?
Topic analysis
Analysis of instruments and channels

Big data applications for implementation:

Real-time advertising
individual advertising

Big data applications for evaluation:

Testing advertising impact
Appealing to target groups
Monitoring the share of voice etc.

Data

There are three different types of data that are relevant for strategic communication:

First party data (internal data)

Data that is owned and controlled by companies:

Communications data, e.g. from campaigns or publications, from companies’ own websites, apps or social media channels.
CRM data, e.g. master customer data (address or contact data), customer characteristics (demography, psychography, sociography - such as household structure, social networking etc.).
Transaction data such as purchasing history, contact history, customer contact (e.g. from customer service).
Employee data, inventory data, market and competition data, media analysis, consumer analysis, data from studies carried out on behalf of the company, etc.

Second party data (shared data)

Data that comes from other companies or is shared with other companies, e.g.:

Information from loyalty or reward programs.
Data that comes from sponsoring, partnerships or other activities of the company in cooperation with other organizations.
Data from external portals or channels used to distribute their own goods and services.

Third party data (external data)

Data purchased from different sources outside the organization. Data cannot be controlled by the company. A company can also generate external data or commission data from service providers such as market research institutes. Examples include:

Social media data from media and news
Data originating from public authorities
Scientific studies

Methodology

From 2015 to 2017 a team at the University of Münster headed by Professor Ulrike Röttger and Dr. Christian Wiencierz explored the potentials, challenges and requirements for the use of big data in Corporate Communications.

Find out more about the methodology of this research project

Downloads & further readings

Downloads:

Communication Insights - Issue 4: Full Version (German)

Further reading:

BVDW – Bundesverband Digitale Wirtschaft e.V. (2017): Social Media Monitoring in der Praxis. Grundlagen, Praxis-Cases, Anbieterauswahl und Trends.