The Role of Big Data in Implementing and Developing Marketing Strategies in Companies

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Without big data analytics, a company is blind and deaf because it cannot hear consumer wishes and see future industry trends. Big data platforms can be applied in various industrial sectors and departments within a company. Of the many sectors and departments, marketing is one of the important factors that can use big data analytics for the needs of preparing a company’s marketing strategy. If you are engaged in marketing, big data can provide 3 important information in the form of customer data, financial data and operational data. Each of these data is obtained from different sources and stored in different formats.

  1. Customer data: Customer data helps marketers to understand their target audience. Certain data, of course, in the form of names, email addresses, purchases and so on. Data that is no less important is consumer behavior which you can get from social media activities, surveys and online communities.
  2. Financial data: This data helps marketers to know the performance of their campaigns so they can work efficiently. Sales and marketing statistics and margins are included in this financial report. If possible, big data can also estimate and analyze the finances of your competitors.
  3. Operational data: This data relates to business processes. It can also relate to shipping, logistics, CRM and feedback from monitoring your assets. Big data solutions can be integrated with operations to analyze performance and reduce costs.

The Role of Big Data in Improving the Company’s Marketing Strategy

Big data platforms take marketing to the next level. With big data analysis, marketers can make important decisions correctly. Here are some areas that you can use big data analytics in marketing your product as well as how to apply it.

  • Design marketing campaigns that are right on target

Big data solutions lighten your work in identifying who your customers are and what they want. It’s not the era of marketing offering door to door to increase sales. When marketers only need to use big data to target potential consumers who are definitely interested in your product, at the right time.

 

One example of a big data solution is placing cookies in files. Every time a user surfs the internet, a piece of information related to their activities on the website can be collected for further processing. With this information, marketers can create personalized marketing targeted to individuals based on their shopping patterns and spending abilities. With a qualified big data analytics tool, data is reported along with the solution.

  • Help determine the price

The old way to determine the selling price of a product is to calculate the production price and compare it to the prices set by competitors.

 

For small-scale businesses, maybe this traditional method can still be applied. But for large companies, of course, it is not that easy. This is where big data can be a solution. Big data analytics collects more detailed data, not only the products consumers buy but also what their product preferences are. In addition, Big data analytics can consider current economic and sales factors. Everything is calculated automatically with an almost 0% error rate and saves time and resources.

 

  • Show personalized content

You may have noticed that Google always knows what products you have just seen on the internet and then will display them continuously in front of you in the form of advertisements.

So is Netflix. This app knows exactly what your film tastes are and always recommends them to you. That’s how big data works in an effective way. By utilizing information that is not notified by consumers, but by observing consumer behavior.

The two examples above illustrate how effective big data analytics can work. You can also take advantage of big data platforms by providing personalized content for your customers. Big data remembers each individual’s browsing history so that when they log back in, they will be presented with a service according to their wishes. This is related to the next point.

  • Improve customer service

Big data analytics can improve consumer experience when using your services. When a customer is satisfied with your service, sales increase over time. One of the ways big data is changing customer service is by using chatbots or automated answering machines. Chatbot software can be integrated with messaging applications and interact with consumers. Chatbots are becoming popular as a business tool because they are able to provide fast feedback and save time and money.

Big data can make your company a data-based company that has appropriate solutions. big data analytics services that can help your company achieve success efficiently and determine the right target market. Data platform offers analytics services in various formats and reports to help your company determine the right strategy. Consult your business needs and build appropriate solutions together! 

 

Big data in implementation in the company

Big data challenges – In implementing big data, every company will encounter it, but to reduce or even anticipate these problems, we should study big data as a whole, like before going into battle, generals should study the enemy first, the deeper they know the enemy, the more likely they are to win, as well as strategic IT / decision makers in the company, they need to know in depth about big data, if not, they should choose partners. to assist and guide in implementing big data, one of which is end to end big data solution.

The challenges and problems when implementing big data are:

1. The problem is the lack of understanding and insight into big data

2. Lack of experience in dealing with large and complex data

3. Confused in choosing big data technology and platforms

In these 3 problems, companies usually implement big data to catch up with trends, follow competitors or other things, but due to the lack of big data knowledge, such as what infrastructure is needed, knowledge of designing appropriate and optimal architectures for company conditions, unplanned budgets, manpower, experience and other knowledge. Without sufficient knowledge and understanding of big data, implementation of big data has a high risk of failure and of course wastes a lot of money, time and effort on this.

Solution to the lack of understanding & practice of big data:

  • Conducting Big Data Workshops and seminars with big data solution partners for all stakeholders and all employees involved, all levels need to know this so that the coordination and implementation of big data can run smoothly without any miscommunication from every level. With big data company partners who have skills and experience. Architectural design, hardware selection and budgeting are optimized without wasting resources.

4. Budget problems because investing in big data requires quite a large amount of money

Budget-limited solutions:

  • Do Investment big data by designing a scalable architecture and implementing big data in stages

 

5. Problems: upscaling & data growth / Upscaling & data growth issues

The more the company’s business develops, the more data the company generates, with limited storage capacity, increasing data variations and amounts, over time it becomes increasingly difficult to deal with this problem, slow handling or wrong handling of this problem can be fatal.

Data upscaling solution:

  • Companies in handling large enough data usually do data compression, tiering and deduplication, compression can reduce file size, tiering can make it easier for companies to manage data by categorizing data based on tiering and storing it based on data storage tiering, storage places based on often can be public cloud, private cloud, flash storage depends on the size and importance of the data. However, the most important thing is the design of the big data architecture at the start of the design process, has this problem been thought through and resolved during design?

 

6. Experiencing problems in taking and integrating from data sources

On this problem, sooner or later, as the business develops, it will certainly face data quality and data source integration problems, for example, initially, retail companies and retail product brands, which initially only analyzed logs on the e-commerce platform they use, must also carry out integration and analyzing data with 3rd party platforms for call centers, social media and even scanning various e-commerce used by competitors, of course these platforms have different structures and types and of course have large and large amounts of data.

Solutions to problems in taking and integrating from data sources:

  1. Using software integration tools that can accommodate call center integration, social media and other platforms to make data integration easier.
  2. Minimizing social media raw data by using available analytics, for example Social media Analytic Big Social analytic software that focuses on social media with a target audience specialization in the form of Indonesian people, of course this analytic tool is different from other analytic tools whose audience focus is more on American or European countries, the Indonesian audience certainly has personality, culture and various other factors that are very different from European countries and also America.
  3. Minimizing e-commerce raw data by using marketplaces or e-commerce analytic tools, one of which is Big Market. Imagine, for example, a consumer goods brand that sells various types of products for daily needs, from toothpaste, soap, shampoo, food, drinks, cosmetics and so on, has to do scanning of competitors who sell the same goods, with quite a lot of product types and number of competing brands plus a large number of e-commerce in Indonesia, of course scanning, storing the data and doing the analysis yourself will certainly be a heavy burden for brands that have infrastructure, limited budget and the most terrible thing is that it doesn’t necessarily work well to analyze these data. Based on the difficulties, of course it is better to use e-commerce analytic tools that are connected to various marketplaces in Indonesia i and focus on customers in Indonesia who are ready to use, namely tools Big Market.

 

 7. It is difficult to maintain data quality / Difficulty in Managing & maintaining data quality

We know that Big data does not necessarily have 100% accuracy, because the data taken is not necessarily 100% correct, there are many duplications and also wrong contradictions especially those with large amounts of data, such as customer data, this data is not real-time, in in a matter of days or weeks there will be data changes, whether someone has changed their cellphone number, moved their email or home address and so on, not including the data spread across various divisions or branches, each branch has its own data reference or database

Solutions to maintain data quality:

  1. Create a proper big data model
  2. Perform data cleaning
  3. Collect data on a single point of truth
  4. Match & merge data to avoid duplicate data
  5. Update data regularly

8. Data security and data privacy issues

This problem is quite complex and is discussed in full in Big Data Security, usually this big data security is implemented when big data has entered the mature stage or is already large in size because companies are often busy understanding data, storing data, analyzing and dealing with various big data challenges when implementing or developing big data, of course this is not a good move because data security and data privacy are crucial for a brand’s reputation, namely:

  1. loss in the form of fines by the government
  2. potential profit lost if the data is leaked to the wider community or competitors.
  3. reputation the brand is destroyed because the brand cannot properly maintain customer data.

therefore it is better

Big data security solutions / big data security problems can be overcome by

  1. Data encryption
  2. Data segregation identity
  3. Access control
  4. Real time security monitoring”’

9. lack of professional staff big data in the form of data scientists, data engineers and data analysts

With massive data growth, the demand for data scientists and also data analysts for big data is getting higher but the number of professional workers is limited resulting in a shortage of professionals

The solution to the shortage of big data professionals

  1. upgrading existing company engineers by providing boot camps or granting scholarships or can
  2. rent consultants and big data solution providers such as to solve big data problems in companies.

10. There are no KPIs or other indicators in managing big data

Big data usually has quite significant growth in line with the development of the company’s business, but KPIs or indicators in big data management do not change or even do not exist, thus making management or development of big data run slowly

no KPI solution regarding big data

  1. Determine big data KPIs by making appeals to similar companies or market leaders
  2. rent big data consultant so that the company’s big data management is not left behind by competitors in the market.

 

Various big data challenges can be overcome, but the most effective way to deal with big data challenges is at the beginning when creating a big data architecture to take these problems into account, you can also recruit cybersecurity and data professional teams or partner with big data companies to solve these problems.