Data-Driven Marketing Strategies

Introduction

Today's businesses operate in a data-driven market, where they are always looking for new ways to gain an edge. Effective prioritization and strategy alignment with core business objectives are required to accomplish this. An organization's ability to make educated decisions, allocate resources, and monitor progress in real time is greatly enhanced with a priority dashboard. Strategic alignment is the top priority that this dashboard helps to track. The term "strategic alignment," also known as "strategic fit" (Luftman, 2000), refers to how well an organization's goals and actions line up with its stated purpose and guiding principles. When an organization's strategies are well-aligned, its various parts work together to achieve a shared goal, which boosts efficiency and productivity.

Strategic alignment is helped by several priority dashboard items. First, it shows executives all the metrics and KPIs that important for an organization so they can determine if their strategies are working. It also allows real-time monitoring of strategic goals, indicating potential issues (Ariyachandra & Watson, 2010). A priority dashboard gives organizations this kind of visibility to make data-driven decisions and reevaluate and alter their strategies to meet priorities. The dashboard's role in strategic alignment goes beyond performance monitoring. It also identifies patterns and risks that could affect the organization's goals. If a strategic effort fails to fulfill expectations, the priority dashboard may alert key stakeholders to reevaluate its efficacy. Early misalignment detection is essential for firms to stay flexible in today's fast-paced industry (Groh, 2016).

A holistic view of all assets via the priority dashboard aids strategic planning. This data can help decision-makers allocate funding to the company's top priorities. Thus, strategic initiatives improve (Mehregan et al., 2011).

In conclusion, the priority dashboard is a powerful resource for businesses that want to stay aligned strategically. Leaders are able to make data-informed decisions, adjust to new conditions, and keep their plans in line with their highest objectives thanks to the system's real-time insights, trend analysis, and visibility of available resources. Priority dashboards are becoming more and more important in helping businesses achieve and maintain strategic alignment as they attempt to navigate today's increasingly complex business world.

Questions

• Q1 | How can Coca-Cola better understand their price point in the market compared to their competitors to better understand how this will affect future strategies?
• Q2 | How can we measure the purchase frequency consumers from any given country to better understand the loyalty of the brand in our category market?
• Q3 | How can we use available data to better measure motivations behind purchasing Coca-Cola?

Insights

Question 1: How can Coca-Cola better understand their price point in the market compared to their competitors to better understand how this will affect future strategies?

The data supplied shows that there is significant variation in the average ratings across the listed products. Average scores range from below 3 to over 5, inclusive. For a quick recap, here it is:

• Most products have average ratings between 4.5 and 5.0, indicating that they are well-liked by buyers.
• There are certain products with relatively lower ratings, as evidenced by the fact that their average rating is below 4.
• A perfect 5 rating is held by "PAX 3 wardrobe frames - black-brown 78 5/8x13 3/4x79 1/4," making it the product with the highest average rating.
• With an average score of 3, "SEKTION / MAXIMERA High cabinet with door and 5 drawers - white/Axstad dark gray 18x24x80" is the product with the fewest positive reviews.

By looking at the average ratings for each product, we can see which ones are very well-received and which ones could use some work.

Question 2: How can we measure the purchase frequency consumers from any given country to better understand the loyalty of the brand in our category market?

In order to gauge reader commitment to a given author's canon, we've made repeat purchases our primary metric for Q2. The presented visual data, however, reveals that the authors' names are not significantly correlated with the number of book purchases. This is because the majority of Q2's visual data is made up of totals for individual author IDs, which does not provide an accurate reflection of sales volume.

Q2 needs to be addressed and purchase frequency measured with precision, and this can only be done with access to consumers' actual purchase history or transaction details. Unfortunately, there is insufficient detail in the data provided to properly assess customer spending habits. It is crucial to monitor repeat purchases over time in order to learn about customer loyalty to a certain brand. It's difficult to give a definitive response to Q2 without this purchasing history information.

Question 3: How can we use available data to better measure motivations behind purchasing Coca-Cola?

Visual data is provided showing the "name" and "Avg. average_rating" for various products, which can be used for Q3's focus on understanding how average ratings vary by product name. This graphically shown data is useful for seeing how users generally rate certain goods.

Average ratings appear to be between 3 and 5 on the visual data, with the vast majority of products having average ratings of 4 or higher. This indicates that these items have a high approval rating among buyers. The lowest average rating is for the "SEKTION / MAXIMERA High cabinet with door & 5 drawers" in white/Axstad dark gray (18.5" x 24" x 80"), while the highest is for the "PAX 3 wardrobe frames" in black-brown (78.5" x 13.75" x 79.25") and the "VIDGA Single track set" in white (25.5" x 7.5" x 3.5").

From this information, we can infer that some products are routinely preferred over others. Product management and marketing groups may find this data useful because it shows which goods are preferred by customers. It may also indicate places to enhance lower-rated products.

Instructions

Step 1: Define Your Purpose and Audience: We wanted to graphically convey the most relevant product evaluation data facts and insights.

Since stakeholders and decision-makers will use the data and insights, they are the target population.

Step 2: Collect and Prepare Data After collecting and cleaning the data, our product review dataset is complete. Product, reviewer, rating, and other facts are presented.

Step 3: Select Tools: This dashboard will be made with Tableau. We used it for our investigation because it's great for data analysis and visualization.

Step 4: Design the Layout: The dashboard should have charts and insights that answer our inquiries. Diagrams answering Q1, Q2, and Q3 are examples.

To help readers navigate your ideas, keep the layout simple.

Step 5: Visualize: Create a bar chart of typical product reviews for the first quarter.

A bar chart of the top nations by purchasing frequency is recommended for Q2. Alternatively, a world map chart might show nation buy frequency (if available).

Use a stacked bar chart to show third-quarter Coca-Cola sales drivers.

Step 6: Add Interactivity: Tableau allows dashboard interactivity. Users can filter data, select time ranges and categories, and hover over data points for additional information.

Step 7: Test and Review Get user input by testing the dashboard with a pilot group.

References

Mehregan, M. R., Kahreh, M. S., & Yousefi, H. (2011). Strategic Planning by use of Total Systems Intervention Towards the Strategic Alignment. International Journal of Trade, Economics and Finance, 166–170. https://doi.org/10.7763/ijtef.2011.v2.97

Groh, M. (2016). Critical Analysis of “Profitable Customer Management: Reducing Costs by Influencing Customer Behaviour” (Persson, 2013). SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2872669

Luftman, J. N. (2000). Assessing Business-IT Alignment Maturity. Communications of AIS, 4(14), 1-52.

Ariyachandra, T., & Watson, H. (2010, May). Key organizational factors in data warehouse architecture selection. Decision Support Systems, 49(2), 200–212. https://doi.org/10.1016/j.dss.2010.02.006

Related Topic:- Data-Driven Marketing Strategies