Big Data Analytics and The Benefits for Marketing

 Chapter 1

Introduction

In this paper We will discuss about big data analytics and elaborate why marketing need big data and analytics. In the beginning, we will get understanding about big data analytics, some characters of big data, and the benefit of big data analytics for marketing.

Key Words: Big Data, Analytics, The Characteristic of Big Data, The Benefit of Big Data analytics for Marketing

Chapter 2

Big Data 

Big Data is everywhere these days, whether in the form of structured data, such as organizations traditional databases (e.g., customer relationship management) or unstructured data, driven by new communication technologies and user editing platforms (e.g., text, images and videos) (Lansley & Longley, 2016).  “Big data can be defined as data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Big Data has been described by some Data Management pundits (with a bit of a snicker) as “huge, overwhelming, and uncontrollable amounts of information.” (Foote, 2017)

Picture 2.1

Illustration 

Source:https://growthmarketinggenie.com

Big data is characterized by some elements, according to (Amadoa, Cortez, Rita, & Moro, 2017), (Laney, 2001) mentioned  about the 3 Vs in Big Data management: Volume, Variety, and Velocity. Recently, two more Vs were included onto the Big Data equation: Variability, and Value. Gartner summarizes these five dimensions in its definition of Big Data in 2012 as “high volume, velocity and variety information assets cost-effective demand, innovative forms of information processing for enhanced insight and decision making” (Fan & Bifet, 2013). So, it started in the year 2001 with 3 V’s, namely Volume, Velocity and Variety. Then Veracity got added, making it 4 V’s. Then Value got added, making it 5V’s. Later came 8Vs, 10Vs etc (Guttta, 2020). In this paper We will elaborate to 5 characteristics below:

Picture 2.2

The 5’V Characteristic of Big Data

Source : https://medium.com/analytics-vidhya

• Volume: The term big data signifies a large quantity of data. It is about the large amounts of data that is received and processed. If in the past before the digital /internet era data was only produced by humans, now data is also generated by machines along with the IOT era (internet of Things). Human and network interaction in systems such as social media makes the data that must be analyzed very large (Wibowo, 2018). So, the rapidly increasing volume data is also due to cloud-computing traffic, IoT, mobile traffic etc. (Guttta, 2020). Below is the estimated/predicted global data growth until 2025:

Picture 2.3

Predicted Global Data Growth

Source: https://medium.com/analytics-vidhya

• Variety: Very diverse data sources be it structured or unstructured data. (if it used to be only in the form of spreadsheets and data bases, now it is also in the form of images, audio, video etc.)

• Velocity: Velocity refers to data speed (how fast data can be generated and can be processed and analyzed to meet a need). Currently, the data flow has exploded in size and frequency. Researchers and enterprises can use this real-time data to deliver up-to-date insights for decision-making. In the year 2000, Google was receiving 32.8 million searches per day. As for 2018, Google was receiving 5.6 billion searches per day! (Guttta, 2020).  

• Validity: Data discrepancies might emerge at any time, obstructing the process of properly processing and managing data.

• Veracity: Data veracity refers to the quality of data that is to be analyzed. Data quality is determined by a number of factors, including where the data was obtained, how it was acquired, and how it will be analyzed. The authenticity of a user's data determines how trustworthy and significant the information is.

Chapter 3

Big Data Analytics

Big data analytics assists businesses in leveraging their data and identifying new opportunities. Data analysis is the process of examining raw unstructured data in order to draw conclusions, make prediction find the trends and answer the questions (Valcheva, 2022). As a result of the analytics, smarter business decisions are can be made and the impact then are: operations are more efficient, consumers are happier because the business offer the high value and ultimately profitable is increasing. 

Picture 3.1

The main Benefit of Big Data Analytics

Source: https://www.sas.com/id_id/insights

According to (Davenport & Dyche, 2013) In their report Big Data in Big Companies, IIA Director of Research Tom Davenport interviewed more than 50 companies to understand how they use big data. He found that these companies gained value in the following ways:

1. Cost reduction.  Big data technologies like Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.

2. Faster, better decision-making.  With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new data sources, companies can analyze information immediately – and make decisions based on what they have learned.

3. New products and services.  With the ability to measure customer needs and satisfaction through analytics, comes the power to deliver what customers want. Davenport points out that with big data analytics, more companies are creating new products to meet customer needs.

Chapter 4

Big Data Analytics for Marketing

We'll look at the benefits of Big Data Analysis for marketers and marketers in this chapter. In general, a marketer's ability to use or optimize big data analysis in generating marketing insight will aid marketing in gaining a better understanding of the market and making sound strategy or decisions that will lead to the company providing high value to its customers and ultimately winning the market.

Some of the benefits of utilizing big data analysis in detail are conveyed by (Valcheva, 2022) as follows:

1. Data Is The Key To Behavior Analysis,

A customer behavior analysis is an examination of how customers interact with your business (including your brands, websites, products, applications, etc.) Purchased products, page visits, email sign-ups, ad clicks, and other user activities are examples of behavioral data. Websites, CRM systems, call centers, marketing automation systems, and billing systems all provide behavioral data.

2. Data Make Conversational Marketing Possible

Real-time interactions are used in conversational marketing to increase engagement, client loyalty, and revenue. In conversational marketing, big data analytics allows you to have a better understanding of your customers. Furthermore, big data adds a "listening" component to conversational interfaces (such as chatbots and live conversations). The example of data-driven conversational marketing software and chatbot platforms are: Drift, Intercom, MobileMonkey, Botsify, ChipBot, Aivo.

Picture 4.1

Botsify and ChatBot Platform

Source: Google.com

3. Data Make Predictive Analytics Truly Effective

Predictive analytics is the use of data algorithms and techniques to define the likelihood of future events or results based on historical data as past customer behavior and habits. It enables marketers to identify future risks and opportunities and thus to make the most effective data-driven decision-making process.

Some examples of  predictive analytics software: IBM Predictive Analytics, Optimove, NGData, SAP Predictive Analytics, Dataiku DSS, AgilOne

Picture 4.2

IBM and SAP Predictive Analytics

Source: Google.com

4. Data Revolutionize Digital Marketing

Big data offer big insights into any of the digital marketing type or platform (SEO,Pay-Per-Click Advertising, Native Advertising, Social Media Marketing, Email Marketing, Mobile Phone marketing, Affiliate Marketing, Inbound Marketing, Influencer Marketing). It is impossible to set the right strategy, to make content, to set digital advertising without the insight about the target market/audience from Big Data Analysis.

Picture 4.3

Digital Marketing Type/Platform

Source : https://www.dhadigital.com

Some digital marketing software: Engagebay, BrightEdge, MarketMuse, Vennli : 

Picture 4.4

Digital Marketing Software examples 

Source: Google

5. Data Build Up Personalization

Customer behavior and patterns can be better understood with data analytics software, which allows you to customize recommendations for each purchase. This enables purchasers to make quick and easy purchasing decisions. To eliminate buyer friction and boost the amount and quality of qualified leads in their funnel, marketers utilize data analytics solutions for personalization.

Marketing personalization can be different types such as: targeted ads, personalized emails, product recommendations, dynamically changing websites, targeted notifications, etc (Valcheva, 2022). Some marketing personalization software: PathFactory, Dynamic Yield, Recombee, LiftIgniter, Segment, Yusp, Evergage, BrightInfo.

Picture 4.5

Data Build up Personalization software

Source: Google.com

6. Data Transform Market Research

In today's high-tech, hyper-connected world, traditional market research is no longer relevant. Big data Analytics with some software will help marketer gain the insight about the market/customer such as their lives, values, preferences, attitude, behavior . Some data-driven market research software: Question Pro Locus, SurveySparrow, Response-ai, Qualtrics, SmartReader, FocusVision.

Picture 4.6

SmartReader & Qualtrics

Source: Google.com

Chapter 5

Conclusions

The use of big data analysts to generate insights that support business and marketing decisions is a strategic step at a time when the advancement of the digital era can no longer be contained (which is characterized by an abundance of data and the increasing need for data to support every business decision, including in supporting digital marketing activities. The digital era and big data have also made the power of data increasingly affect the ability of businesses to be more competitive in the industry.

One condition that benefits the company or marketing is that we do not have to manage the data alone and look for insights but can take advantage of consulting services and application providers according to our business needs or marketing goals.

References

Amadoa, A., Cortez, P., Rita, P., & Moro, S. (2017). Research Trends on Big Data in Marketing: A Text Mining and Topic Modeling Based Literature Analysis. European Research on Management and Business Economics. Vol.24, 1-7.

Davenport, T., & Dyche, J. (2013). SAS Website. Retrieved from https://www.sas.com: https://www.sas.com/id_id/insights/analytics/big-data-analytics.html

Fan, W., & Bifet, A. (2013). Mining Big Data: Current Status, and Forecast to The Future. ACM sIGKDD Explorations Newsletter Vol.14 (2), 1-5.

Foote, K. D. (2017, December 14). Dataversity. Retrieved from https://www.dataversity.net: https://www.dataversity.net/brief-history-big-data/#

Guttta, S. (2020, May 04). Medium Website. Retrieved from https://medium.com: https://medium.com/analytics-vidhya/the-5-vs-of-big-data-2758bfcc51d

IBM. (2021). IBM website. Retrieved from www.ibm.com: https://www.ibm.com/analytics/big-data-analytics

Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group Research Note. Vol.6, 70-73.

Lansley, G., & Longley, P. (2016). Deriving Age and Gender from Forenames for Consumer Analytics. Journal of Retailing and Consumer Services . Vol.30, 271-278.

Valcheva, S. (2022). Intellspot. Retrieved from https://www.intellspot.com: https://www.intellspot.com/data-analytics-marketing

Wibowo, A. (2018, June 28). BINUS University : Master of Information Technology. Retrieved from https://mti.binus.ac.id: https://mti.binus.ac.id/2018/06/28/2222/