Big Data: What are the Benefits for Businesses

October 2016

Big Data: What are the Benefits for Businesses

The increasing volume of information relating to the habits, preferences or expectations of online consumers gave birth to Big Data. Companies can take advantage of this large mass of data to better serve the customers. Big Data can have a real competitive advantage. The use of Big Data is no longer restricted to marketing professionals, it is now being integrated to the BtoB and BtoC strategies of many companies.

What is Big Data?

Big Data defines the technologies and methods used to analyze data sets. Companies can now identify and make certain market trends or consumer behaviour usable.
Big Data is particularly used by marketing professionals to refine their targeting and analyze the different aspects relating to consumer behavior. Data from their purchases made online or in stores, preferences on social networks, their internet browsing history (cookies) serves are being used as references for analyzing the global behavior.
The following terms are often highlighted: Smart Data, Data science or Analytics.

Why should a company make use of Big Data?

What are the challenges?

Several departments of the same company may be involved in the implementation and use of Big Data: IT, sales, marketing ... Marketing services are most likely to appeal to Big Data. They are considered as pioneers in the development of new strategies.
Through these services, Big Data addresses several issues for the company:
  • Improve the customer experience.
  • Better understanding the behavior of prospects and customers.
  • Anticipating the needs and adapt to marketing campaigns.

The implementation of new processes related to Big Data can also help improve channel coordination (supply chain mechanisms) and a provide significant competitive advantage.
Benefits for the company's marketing:
  • Improve the effectiveness of advertising campaigns (conventional and online).
  • Refine the targeting of prospects and customers.
  • Analyze the behavior of prospects and customers: purchases in store and online, internet browsing habits, preferences indicated on social networks ...

Data can be analyzed from various sources: transaction history, multi-channel interactions, social networks, data transmitted by the loyalty cards for example.

Moving towards new marketing strategies

Big data allows the company to improve or implement new marketing strategies:
- Behavioral analysis in real time to improve multi-channel promotion and influence consumer behavior: promotional offers, geolocation targeting ...
- Segmental analysis to better target and identify prospects.
Some examples of use of Big Data:
  • Predictive analytics - the adaptation of a marketing message that can influence the consumer to make a specific decision. Example: an advertisement for a hotel in Paris when the user is looking for a way to Paris!
  • Marketing automation - sending an advertisement by geolocation or the anniversary date.
  • Native advertising
  • Ad- retargeting

How to use Big Data?

Data Collection

The data can be collected from different channels (within the company or from external sources), which do not necessarily correspond to the same services of each company. The channels can be digital or not and each of them may have their own analytical application.
The aim is to centralize the data in one set.
  • Establish an overview of existing data channels.
  • Establish new media to collect additional customer feedback: online questionnaire, survey in store, application, website, social networking, loyalty card.
  • Opt for an outsourced solution or develop an in-house solution to manage the data flow.

Data Analysis

The large flow of data and information can be difficult to manage, this is why the client or prospect should be placed at the center of analysis: how can the data help improve the shopping experience? What information is required to adapt the product or service to meet the expectations of the consumers?
Big Data project can be developed around several axes:
  • Focus on the client's expectations and on the expected results.
  • Become more competitive.
  • Combine statistical analysis and predictive analytics to refine the results.

Big Data Solutions and Tools:

Related :

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