Question

Explain database marketing. Identify two companies that have been using database marketing. What are three challenges in using database marketing? What are three advantages of using database marketing?

Answer 1

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Database marketing is the process of identifying, collecting and then analysing relevant information about a company’s customers. The database is compiled using data obtained from a range of internal and external sources such as sales information, email correspondence, warranty cards, promotional efforts and, now more than ever, social media. The primary aim of database marketing is to then use the information within the database to implement marketing strategies that ultimately increases profits.

The key driving force behind database marketing is establishing, building and then maintains a relationship with the customer. Developing and understanding how customer relationships work with database marketing can be instrumental in boosting customer retention levels and therefore profits through repeat custom, as well as amassing new customers.

2 companies

1. Amazon

The online retail giant has access to a massive amount of data on its customers; names, addresses, payments and search histories are all filed away in its data bank.

While this information is obviously put to use in advertising algorithms, Amazon also uses the information to improve customer relations, an area that many big data users overlook.

The next time you contact the Amazon help desk with a query, don't be surprised when the employee on the other end already has most of the pertinent information about you on hand. This allows for a faster, more efficient customer service experience that doesn't include having to spell out your name three times.

2. American Express

The American Express Company is using big data to analyse and predict consumer behaviour.

By looking at historical transactions and incorporating more than 100 variables, the company employs sophisticated predictive models in place of traditional business intelligence-based hindsight reporting.

This allows a more accurate forecast of potential churn and customer loyalty. In fact, American Express has claimed that, in their Australian market, they are able to predict 24% of accounts that will close within four months.

The benefits of database marketing

• Improved profitability – database marketing allows a businesses to segment and target its marketing efforts in a cost-effective manner, whilst improving efficiency and profit margins.

• Increase sales – effective database marketing creates more sales by providing information that allows companies to identify and exploit new market opportunities, as well as attracting new customers.

• Improves marketing communications – accurate data increases the quality level of communications with customers’ existing and potential, allowing a company to massively improve its chances of building lasting customer relationships.

• Improve product development – with the successful implementation of a customer database, a business will be able to gather regular and up-to-date information that can be used as market research to identify where improvements can be made and where customer needs are not being met. This information can then be used in product development through by integrating conclusions derived from the research into the design of a new product.

The drawbacks of database marketing

• Need for talent: Data scientists and big data experts are among the most highly coveted —and highly paid — workers in the IT field. The AtScale survey found that the lack of a big data skill set has been the number one big data challenge for the past three years. And in the Syncsort survey, respondents ranked skills and staff as the second biggest challenge when creating a data lake. Hiring or training staff can increase costs considerably, and the process of acquiring big data skills can take considerable time.

• Data quality:In the Syncsort survey, the number one disadvantage to working with big data was the need to address data quality issues. Before they can use big data for analytics efforts, data scientists and analysts need to ensure that the information they are using is accurate, relevant and in the proper format for analysis. That slows the reporting process considerably, but if enterprises don't address data quality issues, they may find that the insights generated by their analytics are worthless — or even harmful if acted upon.

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