Data Driven Marketing: the Next Generation Marketing Techniques

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The power of data and digitalization is creating new connections between the marketing and sales functions. Next-generation sales technique that consist of data-driven sales processes and lead generation aligned to the customer journey have proven three times more effective than traditional sales methods.Content has long been king in selling, however that king has currently been dethroned. Data has become the new buzz word. With advancement in technologies marketers are able to track individual actions, from first contact to final transaction, in an Omni-channel marketing environment.

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They are able to shape content in order to get desired business outcomes. New marketing analytics and automation tools are changing how companies use their huge stores of data.With the growth of data every day, marketers are finding it a necessity to transform data quickly and easily. They have realized that companies that spend more on marketing technology analytics are the top performers. Adequate investment in the right data transformation and analytics tools can send companies to the front of the line and ahead of their competition.The purpose of the current research is to investigate the significance and effectiveness of data driven marketing as the next generation marketing techniques and the major challenges associated with it.Key words: digitalization, next generation, Omni channel marketing, marketing analytics Introduction:Data has made a name for itself. From professional sports to healthcare, retail and beyond, almost every industry is talking about big data and analytics. Marketers are increasingly relying on data for their campaigns, measuring the results of every effort so that they can streamline their messages.

Five years ago, the McKinsey Global Institute (MGI) released Big data, the next frontier for innovation, competition, and productivity. In the years since, data science has continued to make rapid advances, particularly on the frontiers of machine learning and deep learning. Organizations now have troves of raw data combined with powerful and sophisticated analytics tools to gain insights that can improve operational performance and create new market opportunities. Most profoundly, their decisions no longer have to be made in the dark or based on gut instinct; they can be based on evidence, experiments, and more accurate forecasts.The growing expansion of available data is a recognized trend worldwide, while valuable knowledge arising from the information come from data analysis processes. In this context, the bulk of organizations are collecting, storing and analyzing data for strategic business decisions leading to valuable knowledge. The ability to manage, analyze and act on data (“data-driven decision systems”) is very important to organizations and is characterized as a significant asset. The prospects of big data analytics are important and the benefits for data-driven organizations are significant determinants for competitiveness and innovation performance. However, there are considerable obstacles to adopt data-driven approach and get valuable knowledge through big data.Challenges’ of data driven marketing:

  1. Storage of data: Data comes from multiple sources.
  2. Collecting, transforming and sending data to a designated location has become increasingly complex.
  3. Data quality: Once the data is collected from its initial sources, the challenge is to make sure that it is properly cleansed and harmonized. We need to ensure the highest possible quality of data.
  4. Integrating data at the deepest level possible: The best way to integrate data is at the product level, because then the BI tool can automatically merge it with the higher levels as needed. Integrating data directly at the company level could not bring the desired result.
  5. Finding the right data talent: To find a right data talent, one option is to outsource the data activities to a third party, and the other one is to hire more talent with strong data skills.To become a data-driven marketing organization, companies must identify the problem they’re trying to solve, and then decide what they need to do from a budgeting, staffing, and business process perspective before jumping into the digital arms race. If they’re working a multi-channel marketing strategy (as most companies are), attribution capabilities that determine how online and offline strategies affect one another are great, but data-driven processes need to be in place so that action can be taken on insights before a company can have the conversation about attribution.

Certain key ideas that could be adopted by marketers for such strategy to grow are:

  1. To balance resources among channels and managing the continuous internal struggle for marketing budget.
  2. A company should have no more personas than it can service with distinct messaging strategies.
  3. Continually analyze your data, make adjustments, experiment and test, and never be satisfied because you can always do better.
  4. Even if you have large amounts of data, you need to be in a position to respond immediately to noticeable patterns and trends.
  5. A key element in switching from a last-click attribution mentality to a completely holistic measurement approach is to understand that some challenging change management issues lie ahead.
  6. Identify and align a specify set of potential opportunities to investigate and realize with data and analysis.
  7. For marketing group to become data-driven, one must look across and beyond the business to identify quality data sources one can use for marketing purposes.
  8. Marketers need to reassess both their current media mix and their media agencies.
  9. Without the ability to analyze all the touch points together, one cannot get a full sense of the customer journey.
  10. The most important step in developing a data driven marketing organization is being able to capture both online and offline data

Conclusion: Data and analytics capabilities have made a leap forward in recent years.

The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. The convergence of these trends is fueling rapid technology advances and business disruptions. These are changing the basis of competition. Leading companies are using their capabilities not only to improve their core operations but to launch entirely new business models. The network effects of digital platforms are creating a winner-take-most dynamic in some markets. Data is now a critical corporate asset. It comes from the web, billions of phones, sensors, payment systems, cameras, and a huge array of other sources—and its value is tied to its ultimate use. While data itself will become increasingly commoditized, value is likely to accrue to the owners of scarce data, to players that aggregate data in unique ways, and especially to providers of valuable analytics.

Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass. An even bigger wave of change is looming on the horizon as deep learning reaches maturity, giving machines unprecedented capabilities to think, problem-solve, and understand language. Organizations that are able to harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at disadvantage. Organizations that achieve to manage the challenges and adopt a data-driven culture, they can expect good prospects.

There is strong evidence that business performance can be improved via data-driven decision making, big data technologies analytical tools and techniques on big data. As more companies learn the essential skills of using big data and how to engage with current technologies, which are continuously developing, may soon stand out from their competitors and have a decisive competitive advantage.

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