SMAL 2.4.7: Data Analysis in Strategic Planning Study Guide

Introduction to Strategic Management and Leadership 2.4.7

• This session covers section $2.4.7$ of the Strategic Management and Leadership ($SMAL$) module, specifically focusing on data analysis within the strategic planning process.

• This topic was explicitly highlighted in the Competency Statement for $FAE2022$. It is largely considered a carry-over or evolution of existing skills, as data analysis has been used by predecessors in previous examinations.

• The area is described as a "fertile area" for a blended Indicator in exams. For instance, data analysis was combined with the marketing mix in the Summer $2021$ exam.

• The learning approach focuses on a "light bulb moment," which is a focused combination of two existing sets of knowledge:

  • Data analysis knowledge from module $3.2$ ($DAAIET$).

  • Strategic planning knowledge from module $2.1$ ($SMAL$).

The Competency Statement (2.4.7)

• The specific requirement from the Competency Statement states: "Assess the importance of data analysis as a driver and consequence of the strategic planning process."

• The requirement is broken down into five key highlighted elements:

  1. Assess: Requires the application of judgment, a skill developed in sections $2.1$ and $2.3.1$.

  2. Data Analysis: Centered on the techniques taught in section $3.2$ of $DAAIET$.

  3. Driver: Investigates how data analysis pushes the creation of strategy.

  4. Consequence: Investigates the data generated as an outcome of the process (the feedback loop).

  5. Strategic Planning Process: Built upon the foundations established in section $2.1.1$.

Section B: 10-Question Knowledge Check (True or False)

• Assertion 1: Section $2$ of the Competency Statement was radically revised for $FAE 2022$. (True)

• Assertion 2: Section $2.4$ of the Competency Statement on the wider aspects of strategy is entirely new. (True)

• Assertion 3: You are expected to learn new data analysis skills for $FAE$. (False - Skills are drawn from existing $DAAIET$ content).

• Assertion 4: Most strategic planning exercises involve a degree of data analysis, whether formalized or not. (True)

• Assertion 5: The use of complex models is crucial in this part of the Competency Statement. (False - The focus is on judgment and application rather than model complexity).

• Assertion 6: Gallagher comprehensively covers this topic. (False - It is only briefly referenced on page $5$ of Gallagher).

• Assertion 7: There are five key elements in $2.4.7$ of the Competency Statement. (True: Assess, Data Analysis, Driver, Consequence, and Strategic Planning Process).

• Assertion 8: This topic in $2.4.7$ is completely ring-fenced from other parts of the Competency Statement. (False - It links directly to Marketing $2.3.5$, $DAAIET 3.2$, and $SMAL 2.1$).

• Assertion 9: There is a lot of volume in section $2.4$ of the Competency Statement. (True)

• Assertion 10: This is a theoretical examination and I do not need practical examples of data analysis. (False - Practical application and commercial common sense are essential).

The Strategic Planning Process Framework

• The strategic planning process is viewed through Ansoff’s Rational Model, which consists of several sequential and circular stages:

  1. Mission and Objectives: Defining the destination and purpose.

  2. Corporate Appraisal: Divided into Environment Analysis (external) and Position Audit (internal).

  3. Strategic Options: Generating potential paths forward.

  4. Strategic Choice: Selecting the most viable option.

  5. Strategic Implementation: Executing the chosen plan.

  6. Strategic Control: Monitoring performance and closing the feedback loop.

• This process effectively sets the boundaries for how section $2.4.7$ can be examined.

Data Analysis as a Driver of the Strategic Planning Process

• Commercial Common Sense: Successful business people do not make major strategic decisions without data.

• Historical Case Study: Land Rover Defender: The original Land Rover Defender (featuring a separate chassis) reversed the trend towards monocoque bodies (started by the Lancia Aprilia in $1921$). This was not a hunch; it was based on an analysis of data regarding the limited availability of steel and the benefits of constructing bodywork from aluminium.

Data analysis drives the individual aspects of the Rational Model:

1. Foundations of Strategy (Vision, Mission, Goals, and Objectives)

• Vision: The final long-term destination. While motivated by founders/stakeholders, it can be driven by data.

  • Example: A non-profit protecting the diversity of life might use data on species at risk of extinction to set its vision.

• Mission: The journey to the vision.

  • Example: A telecom company seeking a vision of "$5G$ for everyone" might use data on current $4G$ coverage and the price of $4G$ compatible handsets to determine the transition path.

• Goals: Broad statements of intent. Data is used here to establish priorities.

• Objectives: Specific, measurable marker posts. Internal data determines if targets (dates and quantities) are realistic within a given timeframe.

2. Strategic Analysis

• Environment Analysis: Uses external data analysis tools like PESTEL and Porter’s Five Forces. Key data sources include $GDP$ per head, relative market share, industry margins, product pricing, and discounting levels.

• Position Audit: Relies more on internal data to assess capital requirements for expansion, financial/marketing strength relative to competitors, and performance metrics (e.g., target costing vs. standard costing).

• Corporate Appraisal: Combines these analyses to measure likely return against risk and assess competitive position. Without data, this process lacks rigour.

3. Strategic Options and Choice

• Options: Options must meet a basic viability threshold drawing on financial resources and competitor margins in market segments.

• Choice: Requires informed decision-making using internal and external data.

  • Example: Every proper choice should include a detailed cash flow analysis, incorporating external data on selling prices, credit terms, intermediary margins, and average promotion costs alongside internal financing data.

4. Implementation of Strategy

• Market analysis determines the weighting of different elements in the marketing mix.

• Customer expectations and relative costs drive inventory holding plans.

• Relative costs drive the trade-off between in-house production and subcontracting.

Data Analysis as a Consequence of the Strategic Planning Process

• The Feedback Loop: A major benefit of a structured planning process is the generation of new, valuable data. When captured in a Strategic Enterprise Management (SEM) system and channeled to decision-makers, it refreshes the strategic process.

• Timeliness: Strategy can no longer be static. COVID-$19$ proved that companies must reinvent themselves constantly. Accurate, timely data is the benefit and outcome of a strategic planning process that recognizes the value of data.

Examples of Data as a Consequence:

• Shift to Online Retail (Grocery): Tesco and Supervalu gained granular data on regular vs. impulse purchases due to the pandemic, leading to shifts in marketing mix (pricing/promotion).

• Cost Structures: Strategic re-evaluations helped companies distinguish clearly between truly variable and semi-fixed costs.

• Supply Chain Resilience: Combining outsourced production shifts with global supply chain problems provided data on failed order satisfaction, revealing the true financial and reputational cost of marginal savings in production.

• CSR and Sustainability: Customer feedback data is increasingly driving the Corporate Social Responsibility ($CSR$) aspect of strategy, moving it beyond "lip service" to actual sustainability concerns.

• The Automotive Industry: The growth of Tesla and changing customer preferences led manufacturers to cancel research into combustion engine technology based on the impact identified in the data.

• Defensive Strategy against Amazon: Competitors identified the financial/marketing weight of Amazon through their own performance data, leading them to adopt defensive strategies focusing on service over price (e.g., toy stores and pharmacies).

Case Study: Games Platter (GPL)

• The Games Platter ($GPL$) mini case provides a full-length Indicator assessing data analytics as both a driver and consequence.

• Flaws Identified in GPL: The case shows that very little attention was paid to data analytics, which increased the risk of strategic choices. Decisions were made on guesses rather than insights into customer behavior.

• Data Quality: Data that possesses value must have four specific attributes:

  1. Accuracy

  2. Granularity

  3. Interoperability

  4. Accessibility

Questions & Discussion

• Question: How was data analytics used in Summer $2021$?

• Response: It was blended with the marketing mix indicator to test a student's ability to apply data points to strategic marketing decisions.

• Question: Is the Ansoff Rational Model the only framework used?

• Response: While Ansoff’s model sets the structure for this session, the competency statement allows for any strategic planning process coverage, though the Ansoff model is the standard taught in section $2.1$.

• Question: What is the primary takeaway for the exam?

• Response: You must assess data as both a driver (inputs for starting strategy) and a consequence (outputs that inform the next cycle of strategy). Use the "light bulb moment" to combine $DAAIET$ and $SMAL$ knowledge.