(KMBN MK02) Unit 5: Retailing & Advertising Analysis

Market Basket Analysis

Market Basket Analysis (MBA) is a technique used to understand what products are often bought together. It's like looking at the shopping habits of customers to figure out which items they tend to purchase at the same time.

Example: Imagine you run a grocery store. You notice that many customers who buy bread also buy butter. You might also see that customers who buy chips often pick up soda. Using Market Basket Analysis, you can identify these patterns and use this information to make better business decisions.

For instance, you could:

• Place bread and butter closer together on the shelves, making it easier for customers to buy both.
• Offer a discount if a customer buys chips and soda together.

In short, Market Basket Analysis helps you understand which products go hand in hand, so you can make smart choices about how to arrange your store or create promotions.

Computing two-way and three-way lift

Market Basket Analysis is a technique used to understand the relationships between products that customers often buy together. It’s widely used in retail to analyze customer purchase patterns.

Two-Way Lift

Lift measures how much more likely two products are to be bought together compared to if they were bought independently. A higher lift means the two products are more related to each other.

To calculate two-way lift, we use the formula:

Where:

• Support(A and B) is the percentage of transactions where both A and B are bought together.
• Support(A) is the percentage of transactions where A is bought.
• Support(B) is the percentage of transactions where B is bought.

Example: Let's say in a store:

• 20% of the transactions have both Milk and Bread.
• 50% of the transactions have Milk.
• 60% of the transactions have Bread.

Now, calculate the Lift:

A Lift of 0.67 means that Milk and Bread are less likely to be bought together than if they were bought independently. In this case, they don't have a strong relationship.

Three-Way Lift

Three-way lift is similar, but it looks at the relationship between three products instead of just two. It calculates how likely it is that all three products will be bought together compared to if they were bought independently.

To calculate a three-way lift, we use the formula:

Where:

• Support(A, B, and C) is the percentage of transactions where A, B, and C are bought together.
• Support(A), Support(B), and Support(C) are the percentages of transactions where each product is bought independently.

Example: Let’s now say that:

• 10% of the transactions have Milk, Bread, and Butter bought together.
• 50% of the transactions have Milk.
• 60% of the transactions have Bread.
• 30% of the transactions have Butter.

Now, calculate the Lift:

A Lift of 1.11 means that Milk, Bread, and Butter are slightly more likely to be bought together than if they were bought independently. The relationship is positive, indicating that these items tend to be bought together more often than expected.

Summary:

• A two-way lift tells you if two products are likely to be bought together.
• Three-way lift extends this to three products.
• A Lift > 1 means the products are likely to be bought together, while a Lift < 1 means they are less likely to be bought together than expected.

RFM Analysis

RFM Analysis is a marketing technique used to understand and segment customers based on their behavior. RFM stands for Recency, Frequency, and Monetary value, which are the three key factors considered in the analysis. It’s a way to understand customer behavior and group them based on how recently they made a purchase, how often they buy, and how much money they spend. This helps businesses target the right customers for marketing campaigns. Here's how each factor works:

• Recency (R): This measures how recently a customer made a purchase. The idea is that customers who bought recently are more likely to buy again soon. Example: A customer who bought something last week is considered more valuable than someone who bought it six months ago.
• Frequency (F): This tracks how often a customer makes a purchase. The more often a customer buys, the more valuable they are to the business. Example: A customer who buys every month is more valuable than someone who makes a purchase once a year.
• Monetary (M): This looks at how much money a customer spends. Customers who spend more are considered more valuable. Example: A customer who spends $100 on each purchase is more valuable than someone who spends $10.

Example in Marketing: Let’s say you're a marketer for an online clothing store.

• Customer A: Last bought 2 weeks ago (recency), buys every month (frequency), and spends $100 each time (monetary).
• Customer B: Last bought 6 months ago (recency), buys once a year (frequency), and spends $10 (monetary).

In RFM Analysis, Customer A would be more valuable because they buy more often and spend more. Based on this, you might send Customer A exclusive offers or promotions to encourage more purchases, while Customer B might receive a re-engagement campaign or discount to bring them back.

By using RFM, businesses can improve customer retention, increase sales, and better target their marketing efforts.

How businesses use RFM

Businesses group their customers into different segments using these three factors. 

For example:

• High Recency, High Frequency, High Monetary: These are your best customers. You might send them exclusive offers to keep them coming back.
• Low Recency, Low Frequency, Low Monetary: These are the customers who haven't bought much. You might try to win them back with special discounts.

By doing RFM Analysis, businesses can create targeted marketing strategies that are more likely to engage and retain valuable customers.

Allocating Retail Space and Sales Resources

Allocating retail space and sales resources involves the strategic process of determining how much physical space and how many salespeople or other resources should be assigned to different areas within a retail environment to maximize sales and efficiency.

1. Allocating Retail Space: This refers to the distribution of available space within a retail store to various products or product categories. The goal is to optimize the layout for customer flow and maximize sales.

Factors to Consider:

• Product Category Demand: High-demand products should be given more space.
• Profitability: Allocate more space to high-margin products.
• Seasonal Trends: For example, giving more space to winter clothing during colder months.
• Store Layout: The space allocation also depends on the store’s design, such as the front or back of the store.

Example: A clothing retailer allocates 50% of floor space to men's clothing, 30% to women's clothing, and 20% to accessories based on customer demand and seasonal trends. If women's clothing is in higher demand during the spring, the retailer might reduce space for accessories and allocate more space to women’s apparel.

2. Allocating Sales Resources: This refers to assigning salespeople, managers, or promotional efforts to different areas within the store or to different product lines based on their potential to generate sales.

Factors to Consider:

• Traffic Flow: Assign more salespeople to areas with higher foot traffic.
• Product Expertise: Sales resources are often allocated to product categories where staff have specialized knowledge.
• Customer Service: More salespeople can be assigned to help customers in busy areas or for high-value products.

Example: A department store might allocate more sales staff to the electronics section during a promotional event or sale, as it is a high-traffic area with higher customer engagement. Conversely, for a less popular section like home goods, fewer staff members may be required.

In both cases, the goal is to balance resources (space and staff) to create an environment that maximizes sales while ensuring customer satisfaction and efficient operations.

Identifying the Sales-to-Marketing Effort Relationship

Sales and marketing work together to generate leads, convert prospects, and build customer loyalty. However, the way they interact varies depending on the business strategy. The relationship between sales and marketing is critical for business success. Marketing efforts help generate awareness, interest, and demand for a product or service, while sales efforts focus on converting that interest into actual purchases.

The relationship can be understood in the following steps:

• Marketing's Role: Marketing activities such as advertising, promotions, content creation, and brand positioning aim to create a demand for the product or service.
• Sales Role: Sales efforts focus on closing deals with potential customers by providing them with detailed information, addressing their needs, and persuading them to purchase.
• Marketing Drives Awareness: Marketing strategies like advertising, content marketing, social media, and SEO are designed to create awareness about products or services.
• Sales Convert Leads: Once marketing creates awareness, sales teams engage with leads, nurture relationships, and close deals. Sales efforts focus on converting leads into actual revenue.
• Feedback Loop: Sales teams provide feedback to marketing about customer preferences, objections, and market trends, which helps marketers refine their strategies.

A successful strategy involves aligning both departments to ensure that marketing generates the right leads, and sales can convert those leads effectively.

Key Metrics to Measure the Relationship

To identify the relationship between sales and marketing efforts, key metrics are tracked:

• Lead Generation: The number of leads generated by marketing campaigns.
• Conversion Rate: The percentage of leads that sales teams successfully convert into paying customers.
• Customer Acquisition Cost (CAC): The cost incurred by the company to acquire a new customer through marketing and sales efforts.
• Return on Investment (ROI): The revenue generated from marketing and sales efforts compared to the cost of those efforts.

Modeling the Sales-to-Marketing Effort Relationship

Once the relationship between sales and marketing is identified, it can be modeled using various methods. One common approach is a linear regression model.

Example: Sales and Marketing Efforts Relationship

Let’s say a company wants to model how their marketing efforts (e.g., advertising spend) affect sales. The company collects data over several months:

To model the relationship between Marketing Spend and Sales Revenue, a linear regression model can be used:

Y = a + bX

Where:

Y = Sales Revenue

X = Marketing Spend

a = intercept (constant)

b = slope (the change in sales revenue for each unit change in marketing spend)

Calculation: Let’s assume after analysis, the model is found to be:

Sales Revenue = 50,000 + 2 * Marketing Spend

This equation implies that for every ₹1 spent on marketing, sales revenue increases by ₹2.

Evaluating the Model

To evaluate the accuracy of the model, you would use metrics such as:

• R-squared (R²): Measures how well the independent variable (Marketing Spend) explains the variance in sales revenue.
• P-value: Tests whether the relationship between sales and marketing spend is statistically significant.

Modeling the relationship between sales and marketing efforts involves understanding how marketing activities influence sales performance. The modeling typically follows these steps:

a. Define Key Variables

• Marketing Effort (M): The total effort invested in marketing activities such as advertising spend, content creation, social media engagement, etc.
• Sales Volume (S): The total number of units or revenue generated from sales efforts.

b. Data Collection

To create an accurate model, historical data is required, including:

• Marketing budget and spending details.
• Corresponding sales figures.
• External factors like market conditions, competition, seasonality, etc.

c. Correlation Analysis

The next step is to examine the correlation between marketing efforts and sales results. For instance, a business might find that an increase in digital ad spending correlates with higher sales.

d. Regression Modeling

A common approach to quantifying the relationship is through regression analysis, where the dependent variable is sales (S), and the independent variable is marketing effort (M). The model can be represented as:

Where:

𝛽0 is the intercept (base sales without marketing efforts).

𝛽1 is the coefficient showing the impact of marketing efforts on sales.

𝜖 is the error term (factors that affect sales but are not captured by marketing efforts alone).

e. Interpretation

The coefficient  𝛽1 indicates how much an increase in marketing spending leads to an increase in sales. A positive  𝛽1 means that more marketing effort results in higher sales, while a negative  𝛽1 could suggest that marketing efforts might not be effective or even counterproductive.

3. Example: Let’s take a hypothetical example of a company that sells consumer electronics.

• Marketing Effort (M): The company invests $100,000 in online advertising, TV commercials, and social media campaigns over a quarter.
• Sales (S): As a result, the sales for that quarter amount to $1 million.

After collecting data over several quarters, the company performs a regression analysis and finds that:

S=100,000+9⋅M

This means for every additional dollar spent on marketing, sales increase by $9. The marketing effort has a positive and significant relationship with sales.

If the company spends $200,000 on marketing, sales are expected to be:

S=100,000+9⋅200,000=1,900,000

Thus, the model helps predict how changes in marketing efforts affect sales.

4. Limitations

• External Factors: Other factors like economic conditions or competition might influence sales, but they aren’t captured in the model.
• Diminishing Returns: After a certain point, increasing marketing efforts might not yield proportional increases in sales.
• Sales Process Complexity: Sales performance might depend on factors beyond marketing, such as sales team effectiveness or customer service.

The relationship between sales and marketing is fundamental for business success. By modeling this relationship through data analysis and regression techniques, companies can better understand how their marketing efforts drive sales and optimize their strategies accordingly.

Optimizing Sales Effort 

Optimizing sales effort means finding the best ways to improve how a business sells its products or services to customers. It involves using time, resources, and strategies in the most effective way to increase sales and reach more customers. or Optimizing sales effort means improving how a company or salesperson uses their time and resources to sell more effectively. The goal is to work smarter, not harder, so that the sales process becomes more efficient and productive.

Example: Imagine a store selling shoes. Instead of trying to sell every shoe to every customer, the salesperson focuses on customers who are most likely to buy shoes. They might do this by:

• Identifying Best Customers: They notice that customers who are looking for running shoes are more likely to make a purchase. So, they focus on showing running shoes to people who need them.
• Targeting High-Value Customers: Instead of wasting time on customers who aren't interested, the salesperson spends more time with people who are already showing interest in shoes.
• Improving Communication: The salesperson makes sure to explain the features and benefits of the shoes clearly, answer questions quickly, and suggest options based on what the customer needs, saving time and increasing the chances of a sale.
• Using Technology: The store might use a system to track customer preferences, so when a regular customer comes in, the salesperson can recommend shoes based on past purchases, making the interaction faster and more relevant.

By focusing on the right people and using time and resources effectively, the salesperson can sell more in less time, which is the essence of optimizing sales efforts.

Advertising Analysis

Advertising analysis is the process of looking at an advertisement to understand how it works and how effective it is in reaching its goal. The goal could be to inform, persuade, or remind people about a product or service. Analyzing an ad helps businesses figure out what is good, what can be improved, and how to get better results from their advertising efforts.

Key aspects of advertising analysis

• Target Audience: Who is the ad trying to reach? For example, if an ad is about baby food, the target audience might be new parents.
• Message: What is the ad trying to communicate? Is it about how the product works, or is it focusing on emotions like happiness or safety?
• Visuals: How does the ad look? Are there colors, pictures, or people that grab attention? For example, an ad for a vacation spot might show beautiful beaches to attract people looking to relax.
• Tone and Style: What feeling does the ad create? Is it serious, fun, or inspiring? For instance, an ad for a car might be serious and focus on safety, while an ad for a soft drink might be fun and playful.
• Call to Action: Does the ad encourage the viewer to do something, like buy the product, sign up for a service, or visit a website?

Example of Advertising Analysis

Imagine an ad for a fitness gym. The target audience is likely people who want to get fit. The message might be: "Join now and get your first month free!" The visuals could show people working out, looking happy and healthy. The tone is motivational and energetic. The ad’s call to action might be: "Sign up today and start your fitness journey!"

In this example, the analysis would focus on how well the ad speaks to the target audience (people looking to get fit), how the visuals and tone help communicate the message, and whether the call to action encourages people to act.

In short, Advertising Analysis helps understand if an advertisement is successful and why or why not.

Measuring the Effectiveness of Advertising

Measuring the effectiveness of advertising means figuring out whether an ad has successfully achieved its goals, like increasing sales, brand awareness, or customer engagement. In simple terms, it's like checking if your effort in spreading a message is paying off.

How do we measure it?

• Sales Increase: One way to measure effectiveness is by looking at if there was an increase in sales after the ad campaign. For example, if a store runs an ad about a sale and sees a rise in customers buying products during the sale period, it indicates the ad worked.
• Brand Awareness: Another way is by seeing how many people recognize the brand after the campaign. For example, a new soft drink brand might run a TV ad. After a few weeks, they may survey people to see how many have heard of the brand or can recall the ad.
• Customer Engagement: This measures how much people interacted with the ad. For instance, if a clothing brand runs an ad on social media and gets lots of likes, shares, or comments, it shows people are engaged and interested in the ad.
• Return on Investment (ROI): This checks if the money spent on the ad brings in more money. For example, if a company spends $1,000 on an online ad and earns $5,000 in sales from it, the ROI is high, meaning the ad was effective.
• Customer Feedback: Sometimes, businesses ask customers how they found out about the product. This helps measure how many people were influenced by the advertisement. Example: A restaurant runs a TV ad promoting a new dish. When customers visit, the waiter might ask, "How did you hear about our new dish?" If many people say, "We saw the ad," this shows the ad was effective in getting people to visit.
• Website Traffic: If the ad directs people to a website, the business can measure how many people visited the website after seeing the ad. Example: An online shoe store runs a social media ad, offering a discount for first-time customers. If there is a spike in website visits and people making purchases, the store can see that the ad led to more traffic and sales.

Example: Imagine a bakery that runs a Facebook ad offering a discount on cakes. After running the ad, they check how many customers visited their shop using the discount code mentioned in the ad. If many customers used the code and made purchases, the bakery can say the ad was effective.

In short, by looking at things like increased sales, better awareness, customer reactions, and the return on money spent, companies can measure how well their advertising is doing.

Pay per Click (PPC) Online Advertising

Pay per Click (PPC) online advertising is a way for businesses to get their ads shown to people on search engines (like Google) or websites, and they only pay when someone clicks on the ad.

Pay-per-click (PPC) Online Advertising is a type of online marketing where businesses pay a fee each time someone clicks on their ad. Think of it like paying for a ticket when someone enters your shop. Instead of paying for just showing your ad, you only pay when someone actually clicks on it and visits your website.

Here's how it works:

• Advertisers create ads: Businesses design ads for their products or services and choose keywords that people might search for. For example, if a shoe store sells running shoes, they might choose keywords like "buy running shoes" or "best running shoes."
• Ads appear in search results or on websites: When someone types in those keywords on a search engine, like Google, the ads appear on the search results page, usually at the top or bottom, or on other websites that allow ads.
• Advertisers pay for clicks: If someone sees the ad and clicks on it, the business pays the advertising platform (like Google) a certain amount of money for that click. The price depends on the competition for those keywords and the quality of the ad.

Example: Let's say you have a business that sells handmade candles. You create a PPC ad with the keyword "buy handmade candles." If someone types "buy handmade candles" into Google and clicks on your ad, you'll pay Google for that click. The price for the click could be $1, $2, or more, depending on how competitive the keyword is.

In simple terms, PPC is like paying for a chance to show your ad, but you only pay if someone actually shows interest by clicking on it.

Unit 4: Customer Analytics