In this final unit, we will explore the impact of technology on the practice of brand management. Technology advances continue to improve Brand Managers' capabilities in developing marketing plans and brand management strategies. Analyzing the marketplace of consumers often relies on technological big data analysis. Both correlational forecasting technology, such as Salesforce, and research insight studies, such as Qualtrics, can identify brand strengths, opportunities, and threats. With the advent of e-commerce and mobile devices, brand managers realize the importance of digital marketing and social media management in influencing the brand narrative. Big data analysis is considered the most valuable path to fact-based decision-making in the digital era.
Completing this unit should take you approximately 6 hours.
Market intelligence is the most consumer-centric component of developing branded strategic marketing efforts. Market intelligence requires gathering data, analyzing it, and applying the findings. Market Intelligence is data-driven knowledge Brand Managers use to determine the best way to target market groups, combat competitive inroads and make educated decisions on strategic growth. As noted in previous units, the strategic planning models such as SWOT and PESTLE all rely on market intelligence to inform their business strategies. Market intelligence gives firms a broad picture of the marketplace using the following steps: gather data via market research from secondary sources or primary research, deploy coordinated competitive intelligence, analyze internal database consumer behavior, and establish metrics for applying market intelligence.
Brand managers rely on qualitative research to gain insight into the dynamics of their most significant consumer segment. You read about the real-world example of the study of Sports Footwear Brands in South Africa. Now review the findings of this study and list five ways these results could guide a brand like Nike in its goal of athletic wear market domination.
Overlay the assumptions expressed in this article regarding the importance of including data from social media campaigns in a brand's strategic game plan. Use your notebook to create an MIS system to capture the data most important from the social media tracking of a brand.
Once guided by strategic modeling and market research, brand management has evolved in the last ten years to be much more data-driven. Predictive analytics is a relatively new brand strategy model that enhances insight into consumer engagement and opportunities for competitive advantage. In fact, the value of big data predictive analytics is now considered a critical tool in every brand manager's strategic toolbox. As you will learn in this unit, predictive analytic modeling processes massive amounts of data into consumer behavior patterns. These patterns are used in the brand management planning phase to achieve more "statistically" based decision-making,
Brand managers must understand the role governance has on digital marketing initiatives. Review the article on the effects governance has on the relationship between brand social media response strategy and digital customer engagement. Using Figure 1, which introduces a model of key components of data governance, identify the factors that have the most impact on social media governance of data collection.
The importance of consumer touchpoints was addressed in the Consumer Journey section earlier in this course. Brand managers rely on marketing technology applications to interpret consumer data into more targeted strategic goals. Customer relationship management (CRM) software is the prominent marketing technology platform, particularly revealing in its ability to pinpoint consumer satisfaction touchpoints.
Big data analytics (BDA) is similar to machine-based learning or AI (artificial intelligence). BDA is only as accurate as the coded practice of collecting consumer behavior. This research proposes a novel method to apply the collection of BDA.. Using the research model found in figure 1 of this reading, identify how the research findings using the BDA predicts sales performance. Then compare this model to the two DBA collection theories: the resource-based view (RBV) versus dynamic capability theory for best practices.
Using big data to target brand success and build equity has become valuable. Review the results of this predictive analysis research and assess how the loyalty rules were derived from this model. Was the classification of the consumers predictive or reflective?
This review video is an excellent way to review what you've learned so far and is presented by one of the professors who created the course.
Watch this as you work through the unit and prepare to take the final exam.
You can also download the presentation slides so you can make notes.
We also recommend that you review this Study Guide before taking the Unit 10 Assessment.
Take this assessment to see how well you understood this unit.