The aim of this paper is to provide a structured overview of several marketing analytics tools and guidance in creation of company’s own digital marketing dashboard and benchmarks. Digital analytics ...tools display in a clear and creative manner the trends and dynamics of the numbers (budgets, costs, investment), the percentages (shares, ratios), as well as various statistical data, rankings and KPIs. By exploiting the data from these tools, the entire business of a company can be leveraged. Ideally, the data should be regularly monitored and benchmarked to relevant criteria and business results of the industry leaders. If some benchmarks are not available within an analytics tool, a company can develop own benchmarking system and ranking scales based on own database. Such dashboards would be highly personalized to the needs of the company – they would reflect performance of own product assortment per lines of business, digital capabilities of B2B partners, and allow scores comparison through markets and through time (MoM / YoY trends and dynamics). By combining external and internal analytics tools, a company can achieve insights of greater value in relation to its core-business, products/services and its digital marketing strategy. The paper also covers detailed review of digital marketing metrics within the tried-out tools, as well as tools’ advantages and disadvantages. The author discusses development of internal digital marketing standards, ranking scales and benchmarking criteria which can support decisionmaking in digital marketing practice, and it also addresses the probable direction of future development of digital marketing analytics tools in general.
The aim of this paper is to provide a structured overview of several useful marketing analytics tools and guidance in creation of company’s own digital marketing dashboard and benchmarks. These tools ...display in a clear and creative manner the trends and dynamics of the numbers (budgets, costs, investment), the percentages (shares, ratios), as well as various statistical data, rankings and KPIs. By exploiting the data from marketing analytics tools, the entire business of a company can be leveraged. Ideally, the data should be regularly monitored and benchmarked to relevant criteria and business results of the industry leaders. If some benchmarks are not available within a marketing analytics tool, a company can develop own benchmarking system and scales based on own database. Then it would be able to personalize such dashboards to great extent – to reflect own product assortment, lines of business, digital capabilities of own B2B partners, and allow scores comparison through markets and through time (MoM / YoY trends and dynamics). By combining external and internal analytics tools, a company can achieve insights of greater value in relation to its core-business, products / services and its digital marketing strategy. This paper covers detailed review of digital marketing metrics within the selected tools, by also mentioning the major advantages and disadvantages of each tool. The paper also discusses development of internal digital marketing standards, ranking scales and benchmarking criteria which can support decision-making in digital marketing practice, and it also addresses the probable direction of future development of marketing analytics tools in general.
HADOOPING THE MARKETING GAME Sorić, Iva; Oreščanin, Dražen; Štajcer, Marko
CroDiM,
03/2018, Letnik:
1, Številka:
1
Web Resource
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Big data allows marketers to define and describe their customers at a more detailed and precise level than it was even considered possible just a couple of years ago. With the speed this field is ...evolving, big data is becoming increasingly important in the marketing domain.
While it is not a problem to collect the data, the problem remains to leverage these vast amounts of data, coming from different sources, in an efficient manner. Most marketing departments use many marketing applications, causing data fragmentation problems. This paper describes a different, data-driven approach that refers to this problem by using the Data Lake concept allowing the marketers to leverage data as their biggest and most valuable resource in a more agile and flexible manner. The goal is to collect the data, across all applications and channels, consolidate it and analyze all at once, regardless of source and type, which will enable a more complete picture of customer behavior than ever before, answer questions that were previously unanswerable, give rise to many other possibilities, and by extension – lead to more profit.
We describe in detail the strategy and its benefits, give an overview of technologies in terms of functionalities inside the Data Lake architecture, and finally, we elaborate the advantages of this concept, in comparison to traditional marketing techniques, through some of the many use cases that show how this approach can respond to the challenges of marketing today and in the future.
This paper explores implications for marketing education curricula of developments in marketing practice. Research from a variety of sources is drawn together and suggests that many marketing ...graduates are not being well equipped for 'the new marketing'. Marketing curricula should be reconsidered with respect to the nature of the learning outcomes, skills and knowledge that are required by graduates. Specifically, a range of marketing research and information issues are raised, together with implications of the new marketing metrics and the nature of marketing's wider role (via alliances and partnerships) in Knowledge Management strategies. This is furthered by the need to revisit the marketing concept itself in terms of its wider social responsibilities.
This chapter looks at the future of marketing services and resources. It starts by exploring the challenges, issues, and strategic considerations. It then discusses the trends that will have an ...impact on the future of marketing. By the end of this chapter, the key points are summarized and the references are provided.
To research this chapter, the author spoke to Charles Morgan, who was responsible for transforming a small company of analysts providing services to local businesses into a multinational Big ...Data‐driven powerhouse with a billion‐dollar turnover. Acxiom created their proprietary list order fulfilment system, which took data from the credit agencies and combined it into the first online mailing list generator. Since pioneering Big Data‐driven marketing, Acxiom have moved with the times. Most of the early pioneering data work was done with tape‐based systems, but when Citibank dramatically scaled up the requirements for analytics power and storage, Acxiom invested heavily in DEC Alpha supercomputers running Oracle to implement their first truly Big Data‐oriented systems. Big Data analytical techniques such as those developed by Acxiom have delivered tremendous growth to the businesses that adopted them in recent decades.
While predictive marketing uses predictive analytics under the hood, you do not need to know predictive analytics at all to be a predictive marketing expert. There is a huge career opportunity that ...comes from being an early adopter of new technologies, like predictive analytics, and new business practices, like predictive marketing. The most important thing is to demonstrate curiosity and ask the right questions about your business and customers. Management is starting to look to marketing to inform major strategic decisions for the company. This type of visibility in the company can be great for your career. Successful marketers learn to combine the science of numbers with the art of creativity. Using data, you can discover new customer personas and marketing strategies and test that your creative ideas are working.
To the customer‐centric enterprise, data about individual customers are like gold nuggets that, if collected and used effectively, can increase the value of the customer base significantly. Data ...mining is a frequently used term for the process of extracting useful nuggets of information from a vast database of customer information; but as the relationship revolution has taken hold, the data‐mining process itself has also undergone an important transformation. In the pre‐interactive age, data‐mining techniques were used to uncover information about the types of customers to whom particular offers should be made, answering the question: Who is the next most likely customer to buy this product? Today, the question asked by companies engaged in managing ongoing, interactive relationships with individual customers is: What is the next most likely product that this particular customer will want to buy? As we have seen throughout this book, rather than optimizing around each product, the customer‐strategy enterprise needs to optimize around the customer.
In truth, both product optimization and customer optimization have roles to play in any competitive enterprise's efforts to get, keep, and grow customers. But in the interactive age, much more so than in the past, individual customer information drives the central engine of competition. Without reliable insights into the value and needs of individual customers, the customer‐based enterprise will be completely rudderless.
In this chapter, we look at customer analytics—the way we think of data mining now—and learn about the fundamental issues facing customer‐strategy companies when they are working with and using large amounts of customer data.
This chapter provides some insights from researches on essential marketing processes, the difference between the leaders and the laggards, the role of technology, and it provides a phased approach to ...upgrading one's marketing campaign management (MCM) processes while focusing on marketing budgets. These processes are an essential component of effective data‐driven marketing since, as just collecting metrics or buying technology tools is not enough to produce results. A maturity model exists for MCM. MCM capability is a key process capability of the leaders to gain a competitive advantage from marketing. MCM capability consists of four essential processes: technology tools and infrastructure supports campaign selection, portfolio view, monitoring, and adaptive learning. Marketing technology tools and infrastructure are not directly linked to firm performance, but they support MCM capability. A winning strategy is to take a phased approach to developing MCM capability, starting with defined, then intermediate, before adding advanced capabilities.
One does not need millions of dollars of data‐driven marketing infrastructure to get started, one can use Microsoft Excel as the tool of choice as a first step. This chapter clarifies from an ...infrastructure perspective, what it is going to take to build the data‐driven marketing infrastructure. The infrastructure one needs to support data‐driven marketing depends on the size of the customer base and the complexity of requirements. Getting started with balanced score‐carding, ROMI analysis, and Internet data analysis can be accomplished with simple tools, such as Microsoft Excel. However, churn management, value‐based marketing (CLTV), and event‐driven marketing require infrastructure. Marketers need to understand the scale of their data‐driven marketing challenge so that they clearly understand what they are getting into and what they are asking the technology team to build. The risks for data‐driven marketing infrastructure projects are well known and documented. The major pitfalls are lack of vision, lack of senior executive sponsorship, politics, lack of resources, scalability, and quality of databases.
