Introduction
Adelman et al. (2002) introduced integration of big data analytics with digital marketing that provides business intelligence, which in turn helps the rise in revenue and reduces risk. According to the International Data Corporation (IDC) report “Worldwide IT Industry” (2019), “total revenues from big data and business analytics will rise from $122 billion in 2015 to $187 billion in 2019. And enterprises who invest in big data and obtain the power to quickly analyze the large-scale data and extract actionable information can get an additional $430 billion in terms of productivity benefits over their competitors.”
Cui et al. (2007) demonstrate that big data enables enterprises to better understand customers and provide real time customer insights to retain customers. Big data facilitates enterprises to utilize the data in a more innovative and stable way when designing a product for a potential customer (Anjariny & Zeki, 2013). Due to digitization enterprises have enormous data to deal with. Big data extracts all the valuable data that can drive a company’s benefits.
Influence of Big Data on Digital Marketing
The following have a great impact on digital marketing collaboration with big data:
Personalized Learning Management
The outstanding features of big data in the digital marketing is specified by Malhotra (2000); marketers visualized a decline in their expenses, in addition to being able to promote their product development process by improving quality and reducing costs. The application of voluminous datasets, enormous computing power, advanced analytics, and classy data modeling can assist the management significantly to engage the customer on a large scale. Goodhue et al. (2002) points out that big data can also be helpful in creating personalized campaigns targeting individuals. Marketers can easily grasp patterns of customer behavior, which in turn engages audiences at the distinct level.
Improved Customer Behavior Analysis
Hartemo (2016) focused on big data analytics, which deals with customizing the user experience with related offers that are personalized to the right audience. The customer behavior analytics of Jenster et al. (2009) provides essential customer insight by highlighting both lagging and leading customer trends and offers predictive models for the marketing department to follow and act upon.
Enriched Employee Retention Processes
The predictive analytics that Harding (2003) focused on supports the smarter employee retention process, which includes directly interviewing people, surveys, feedback from the target audience to assess skill, analyzing expertise, and a deep understanding of audience behavior. The new drivers of data analytics affect every area of the recruitment process including vacancy marketing, talent development, and filtering of prospective candidates (Kannan & Li, 2016).
Rise in Revenue
The integrated technologies of big data like “Apache Hadoop”, “Ambari and Cloud Computing Analytics” result in noteworthy cost advantages especially with regard to sustenance for storing large amounts of data. Market analytics creates greater prospects of companies getting better in decision-making, goal setting, boosting revenue, and providing better services to their customers (Durmaz & Efendioglu, 2016). With the power of big data, businesses are now able to foster great innovations, taking customer experiences to greater levels.
Once the marketer successfully gains an accurate and deep understanding of their audience’s behavior, they can strategize how to make use of the existing data and provide insight into how to impart it into their digital marketing operations (Brea, 2012). Artificial intelligence and machine learning technology strategies (Yuniarthe, 2017) used to access volumes of data about consumer preferences and to allow enterprises to discover how to optimize branded items, such as customized logos with colors and font styles that boost consumer engagement, lead to lower design costs and improved revenues.
Present Data Content with Statistics
Lou (2017) insisted that content can serve as an initial touch point that funnels a customer toward a desired action. When creating content, based on customer interest, the content is designed with graphs, pictures, and histograms. Using data enterprises can help discover key insights into customer behavior that helps to create quality content and increase audience engagement (Ruhi 2014).
Methodology
Integrating big data techniques with digital marketing strategies involves five steps as shown in Figure 4.1.
Figure 4.1: Digital marketing with big data steps.
Learn
Sivadasa et al. (1998) proposed that enterprises focus on customer preferences, shopping habits, and market surveys to learn customer behavior. This step involves web analytics to process volumes of data, which is unstructured, semi structured, and structured from databases, websites, warehouses, and social sites. Various distributed storage and processing tools used in this step includes Hadoop, “Apache Spark is a unified analytics engine for large-scale data processing” n.d. Spark, “snowflake data cloud,” n.d. snowflake, and “Apache strom” n.d. strom.
Decide
With predictive analytics the enterprises analyze the past, pick up current trends, and foresee the impact in the future in a simulated platform and rely on the desired results to apply strategy leading to dynamic decision-making, Search Engine Optimization, paid search. Market basket analysis and content marketing are all other forms of digital marketing aided by the big data of M. Goyal et al. (2012). Grandon and Pearson (2004) state that marketing teams should concentrate on understanding the online marketing channel and use these non-traditional data sources, like search engine information, customer transaction, and other big data sources available. In addition, focus on online data, which is the energy that drives any successful digital marketing campaign.
Buy
Enterprises are initiating data analytics as a service by integrating data base management system solutions, voluminous storage, and ETL tools for small business that can utilize these services through dashboards (Hatta et al., 2017).
Use
Harris and Attour (2003) emphasize on most of the enterprises. The main task is to catch real time customer insights and enhance customer experience due to heavy competition and to retain the customers. The sentimental analysis, customer shopping analytics, and forecasting market trends with statistics of big data tools can help businesses to offer such insights and catch customer thinking (Lanquillon & Mallow, 2015).
Reengage
Information integration, governance, stream computing, social transactions, and enhanced algorithms are helpful for reengaging with big data for digital marketing. “Zoho cloud software suite,” Zoho, “SAP Hana in memory database,” Hana and “Apache Casandra software foundation,” and Casandra are some tools used for reengaging.
Case Study
Monitoring and managing strategic customers in a corporate sector is very important for planning customer satisfaction, customer retention, and repeat new project works. In view of Hennig-Thurau et al. (2012), most of the corporate sector across the globe require such systems with enhanced data, image analytics, and artificial intelligence by developing customer vision project. A recent McKinsey Survey shows investors have the desire for good. The McKinsey survey found that companies that extensively use customer analytics are reporting 115 percent higher return on investment (ROI) and 93 percent higher profits as shown in Figure 4.2.
Figure 4.2: McKinsey survey.
The main activities of the customer vision project are as follows:
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Identify top customers.
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Create image profiles of these customers.
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Create a training set of customers with suitable marketing parameters, such as customer acquisition costs and lifetime value.
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In the corporate sector, enterprises should have various marketing meetings and symposiums.
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Develop imaging analytics and track enterprise key customer movements and participation.
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Develop analytics using python illustrated by Hawking and Sellitto (2010) and Anaconda described by Tvrdikova (2007), data science, and image processing tools and libraries.
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Identify key customer interests and associations.
Data Flow Diagram
According to Crespo et al. (2010), the data flow model involves three phases as shown in Figure 4.3.
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Enterprises track every touch point of the customer from websites, mobiles, and social sites with big data tools like Google Analytics, HubSpot, Mailchimp, Optimizely, etc., which are used for data collection (Borka et al., 2012).
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In view of Kearns and Sabherwal (2006), corporate customer data platforms, like segment, Zeotap, SAP to preprocess data, and data analysis, prioritized with MongoDB, BigQuery, and Postgres.
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Customer analytics are carried out with customer relationship management (CRM), Labview, and R and Python programming (Artun and Levin, 2015).
Figure 4.3: Data flow model.
Implementation
Corporate sales and marketing teams are generally interested in customer vision and various risk factors involved at each stage of implementation as described in Table 4.1.
Table 4.1:Risk factors in implementation.
| Area | Risk Factor | Risk management/m |
|---|---|---|
| S/H environment | Low | The software’s used in the development are Python and Anaconda, highly proven for prediction in data analytic problems. |
| Development process | Low | Implementation is carried out with data supervisory and decision table models for a comprehensive solution. |
| Training and test data sets | Medium | Explore global training data sets to minimize risk. |
The key metrics of data collection at every touch point of customer at stage 1 are illustrated in Figure 4.4.
Figure 4.4: Customer dashboard.
Results and Discussions
To apply customer analytics two key parameters are considered and they are customer acquisition cost (CAC) and the lifetime value LTV, which are applied to a music app. Most of the corporate operational customer analytics are carried out with these two factors. Enterprises use proactive measures to reach potential customers when they primarily start a trail. The customer retention based on eight favorite songs is illustrated in Figure 4.5.
Figure 4.5: Customer retention.
It is worth noting that the ratio of LTV and CAC plays an important role in customer analytics, and in customer vision project the recommended ratio is 3:0 for investing in marketing for faster growth. After noticing the ratio for consecutive years the time needed to increase marketing by focusing advertising budgets rigorously at that time is described in Figure 4.6.
Figure 4.6: Ratio of LTV and CAC.
It is evident that digital marketing with big data provides proactive risk assessment, gains visual insights into real time customers, and identifies customers with precision.
Conclusion
Digital marketing with big data provides managers smart decision-making, implements customer retention strategies, and gains real-time customer insights. Big data integrated with digital marketing improves the revenue of enterprises and minimizes the business risk. In a customer vision project, the right time to focus on digital marketing is when LTV and CAV ratio is greater than 3:0. This project can be extended with the image analytics libraries of Python to determine and monitor customers.
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