Marketing Analytics II Chapter 9: Distribution Analytics

Marketing Analytics II Chapter 9: Distribution Analytics
Stephan Sorger Disclaimer: All images such as logos, photos, etc. used in this presentation are the property of their respective copyright owners and are used here for educational purposes only Slide 1 Welcome to Module 7 of Marketing Analytics II, Distribution Analytics, lecture 1 of 3. Lecture 1 cover essential distribution concepts and terminology. Lecture 2 introduces a new distribution channel evaluation and selection model. Lecture 3 discusses useful metrics for distribution analytics. © Stephan Sorger 2015: Marketing Analytics: Distribution: 1

Outline/ Learning Objectives
Topic Description Distribution Concepts Cover essential distribution concepts & terminology Channel Model Introduce proprietary channel evaluation model Distribution Metrics Discuss useful metrics for distribution Slide 2 In this module, we will cover three important areas within distribution. We will cover distribution concepts and terminology that we have found essential for virtually any company dealing in physical products. We then introduce a new proprietary channel evaluation and selection model. We close the module by discussing useful metrics for distribution. © Stephan Sorger 2015: Marketing Analytics: Distribution: 2

Distribution Channel Members
Closeout Retailers Franchises Big Lots Taco Bell Convenience Retailers Mass Merchandisers Non-Internet Retailers 7-Eleven Sears Corporate Retailers Off-Price Retailers Niketown Marshalls Dealerships Specialty Retailers Ford Sunglass Hut Slide 3 In this section, we discuss the following types of distribution channel members: -Non-Internet retailers, such as convenience stores and mass merchandisers -Internet-based retailers, such as corporate websites and aggregator websites -Non-retailer intermediaries, such as distributors and wholesalers -Services-based channel members, such as dealerships and Internet-based services The figure on this slide shows different examples of non-Internet retailers. Each type plays a specific role and performs important services for the company and the consumer. On the next slide, we describe each one and provide examples. © Stephan Sorger 2015: Marketing Analytics: Distribution: 3

Sample Retailers, Non-Internet Slide 4 On this slide, we review several types of non-Internet retailers. Closeout Retailers: Closeout retailers liquidate unpopular merchandise that did not sell at full retail prices in traditional mass merchandise stores, such as department stores. For example, closeout retailer Big Lots (biglots.com) features an ever-changing mix of discounted merchandise. Convenience Retailers: Convenience retailers offer fast service (compared to supermarkets and mass merchandise retailers) for convenience goods, such as snack foods. For example, convenience store 7-Eleven (7-eleven.com) sells Big Gulp soft drinks, Slurpee drinks, and other fast food items. Corporate Retailers: Some corporations choose to manage locations owned by the corporation itself, in addition to franchisee-owned stores and other locations. For example, sports retailer Nike (nike.com) operates Niketown retail stores in several States. 9-4 Dealerships: Dealerships specialize in motor vehicles, such as automobiles, motorcycles, and powersports vehicles (snowmobiles, all-terrain vehicles, etc.). In addition to sales to end users, it performs several other roles, such as carrying inventory, negotiation for final price, service, and collecting market feedback. For example, Ford supplies vehicles to about 3,100 dealers in the United States. 9-5 Franchises for Products: Companies grant licenses to operate stores carrying the company’s brand and operating principles to franchisees. Generally, franchisees own and operate the stores, although many franchises include a mix of privately-owned and company-owned units. For example, fast food franchise Taco Bell operates a mix of restaurants, with 80% privately-owned, and 20% company-owned. 9-6 Mass Merchandisers: Mass merchandise retailers, such as department stores, sell a wide variety of goods to end users. For example, department store Sears (sears.com) carries appliances, automobile products, clothing, electronics, jewelry, lawn mowers, office supplies, sporting goods, tools, and many other categories. Off-Price Retailers: Off-price retailers sell name-brand merchandise for reduced prices. For example, off-price retailer Marshalls (marshallsonline.com) became successful by buying manufacturers’ discontinued, over-run, and closeout stock at a discount. The tactic allowed Marshalls to sell major brand merchandise at prices % less than that of department stores. 9-7 Specialty Retailers: Specialty retailers sell narrow assortments of goods to buyers looking for specific needs or usages. Often, specialty retailers provide consulting and advice on applications for the niche they serve. Specialty retailers will stock merchandise general stores do not carry, in order to accommodate unique customer needs. For example, specialty retailer Sunglass Hut (sunglasshut.com) focuses exclusively on sunglasses and their accessories. © Stephan Sorger 2015: Marketing Analytics: Distribution: 4

Aggregators Discount Internet-based Retailers Corporate Sites Specialty Slide 5 The top figure in this slide shows examples of several types of Internet-based retailers. Similar to non-Internet retailers, different types of Internet-based retailers play a variety of roles. Aggregators: Aggregator Internet retail distribution channels sell multiple brands of goods. Some sell a wide variety of goods. For example, Internet retailer Amazon.com offers 34 categories of goods on its website, such as Amazon Kindle e-books, cell phones, consumer electronics, music, office products, software, tools, and many others. As a testament to its selection, it offers 76 different models of riding lawn mowers. 9-8 Corporate Sites: Most consumer-oriented companies maintain a corporate website to sell directly to consumers. Companies sometimes offer unique models on their website not available at retail stores. For example, computer maker Dell (dell.com) offers its computers at Best Buy retail stores, at aggregator websites such as Amazon.com, as well as on its corporate website, Dell.com. Ordering through Dell.com allows consumers to configure the machine to their specifications and not be limited to the merchandise stocked at retail stores. Discount: Discount Internet retailers sell discontinued merchandise at discount prices. For example, discount Internet retailer Overstock.com sells housewares and other items at deeply reduced prices. The retailer also features its online clearance center, “The Clearance Bin,” where shoppers can find additional discounts. 9-9 Specialty: Specialty retailers focus on niche markets and categories. The retailers carry multiple brands of products and services which they believe will benefit the niche market. For example, Tiger Direct (tigerdirect.com) specializes in online sales of high technology consumer products, especially personal computers. Sample Retailers, Internet-based © Stephan Sorger 2015: Marketing Analytics: Distribution: 5

Distributor Value-Added Reseller Non-Retailer Intermediaries Liquidator Wholesaler Slide 6 The top figure shows examples of non-retailer intermediaries. Intermediaries generally either assist retailers in consumer sales or sell directly to businesses. Only in rare cases do non-retailer intermediaries sell directly to consumers. Intermediaries represent any organization involved in distribution between producers and consumers. They execute an assortment of necessary functions to get products to final users. In addition to retailers (covered earlier), intermediaries include organizations such as distributors and wholesalers. Here, we cover several common intermediary types. Distributors: Distributors purchase products from companies and then resell them to businesses. Some distributors sell directly to consumers, but most do not. The products remain in their stock form, without modifications (unlike value-added resellers, which we cover later). For example, distributor Arrow Electronics (arrow.com) sells to 120,000 businesses through a global network of more than 390 locations in 53 countries. Liquidators: Liquidators purchase all or most of the closeout stock of another company and sells it to other businesses. For example, product lifecycle logistics company GENCO (genco.com) purchases surplus inventories of companies and liquidates them through its subsidiary, GENCO Marketplace. Value-added Resellers: Value-added resellers (VARs) purchase products from others, add features or services to them, and then resell the products to other businesses. Some VARs also sell to consumers. VARs modify the products to make them more suitable for specific applications. For example, distributor and value-added reseller Tech Data (techdata.com) launched its StreamOne Solutions Store to customize options for clients. Wholesalers: Wholesalers purchase products from suppliers, and then resell them (generally in large quantities) to retailers. For example, wholesaler Jacobs Trading Company (jacobstrading.com) sells by the truckload to discount stores and other retailers. Examples of Non-Retailer Intermediaries © Stephan Sorger 2015: Marketing Analytics: Distribution: 6

Company-Owned Franchises for Services Services-Based Channel Members Dealership Internet Slide 7 The top figure shows examples of several types of distribution channel members specializing in delivering services. Company-Owned: Some businesses prefer to own their branches, instead of permitting independent ownership by franchisees. Company ownership ensures tight control over the brand, but can limit expansion rate. For example, coffee retailer Starbucks (starbucks.com) operates virtually all its stores as company-owned entities. Dealerships: Dealerships, such as automotive, motorcycle, and snowmobile dealerships, specialize in service at the point of sale. Franchises generally operate a mix of locally owned franchise locations (owned by franchisees) and company-owned franchise locations. For example, plumbing repair franchise Roto-Rooter (rotorooter.com) offers both company-owned and franchsee-owned locations. Internet: Organizations can deliver services over the Internet where operations do not require face-to-face relations. Many organizations maintain websites that fulfill elements of its product and service mix, such as software drivers, user manuals, software downloads, and customer support services. For example, Internet domain registrar Go Daddy (godaddy.com) provides domain registration services and web hosting services over the Internet. Examples of Services-based Channel Members © Stephan Sorger 2015: Marketing Analytics: Distribution: 7

Distribution Intensity
More Brand Control More Sales Outlets Distribution Intensity Exclusive Selective Intensive Snap-On Tools -Exclusive through franchise -Direct selling in tool trucks Coach -Coach stores -Bloomingdales -Macy’s -Nordstrom Verizon cell phones -Verizon stores -Apple stores -Best Buy stores -Costco -Radio Shack -Walmart -Independents Slide 8 Companies can operate different types and quantities of distribution channel members depending on their goals. We identify three levels of intensity: intensive, selective, and exclusive, as shown in the figure. Intensive Distribution: Companies select intensive level distribution intensity when they plan to saturate markets with their products or services. For example, cellular phone service provider Verizon (verizonwireless.com) offers its cellular phones and plans in a wide variety of locations. In exclusive distribution, companies limit themselves to only one type of retail outlet. The company often owns and/or controls the retail outlet. For example, tool maker Snap-on (snapon.com) manufactures and sells its line of professional-grade tools exclusively through its Snap-on franchise. Snap-on franchisees drive to mechanics’ shops within their territories in Snap-on branded vans and sell tools directly from them. Companies choose selective distribution as a middle ground between intensive and exclusive distribution intensities. For example, fashion manufacturer Coach (coach.com) offers its line of leather handbags, accessories, and other goods through only a handful of stores. © Stephan Sorger 2015: Marketing Analytics: Distribution: 8

Distribution Channel Costs
Channel Discounts Market Development Funds Typical Distribution Costs Co-Op Advertising Sales Performance Incentive Logistics Trade Margins Slide 9 The top figure shows an overview several typical types of costs incurred by the manufacturer when selling goods through distribution channels. Channel Discount costs: Many retailers set their own prices for goods and services, generally within guidelines set by manufacturers. Discounts on merchandise, such as those for consumer sales promotions, are “costs” in that they result in lower net sales amounts. Co-op (Cooperation) Advertising Costs: Companies can choose to co-market their products and services in advertising with distribution channel members, such as sharing the cost of a Safeway weekly ad that features Dannon yogurt. Logistics Costs: Distributors expect manufacturers to pay for any logistics-related costs to transport and manage product inventory. Market Development Funds (MDF) Costs: Some manufacturers choose to work with channel partners to promote certain products and services, such as newly introduced offerings. Manufacturers will prepare agreements stating the type of promotional activities channel partners will provide. Sales Performance Incentive Fund (SPIF) Costs: Similar to MDF payments, SPIF payments are incentives paid by a company to a distribution channel member to promote certain products and services. Unlike MDF payments (which get paid to the channel member’s marketing or business development function), SPIF payments get paid directly to salespeople. Trade Margins Costs: Distribution channel members expect payments from companies to carry the company’s products and services. © Stephan Sorger 2015: Marketing Analytics: Distribution: 9

Retail Location Selection
San Francisco San Jose Geographical Area Identification Potential Site Identification Individual Site Selection Slide 10 Many companies have found retail location selection especially crucial to the success of their sales efforts. For example, Apple locates some of its retail stores in high traffic zones within the San Francisco Bay Area, such as San Francisco and San Jose, as shown in the top figure. Successful companies follow a three step process for selecting a new retail store location, as shown in the middle figure. We start with Geographic Area Identification, where we identify the general geographic areas (such as cities or towns) that contain the types of people we want to target. Marketers refer to these areas as “attractive markets.” Marketing analysts often use geographic information systems, or GIS, in conjunction with market sizing models, to find profitable general geographic areas. Having identified a general geographic area, the second step is to identify the set of retail locations that will best meet the company’s retail goals. Here again we can use GIS systems, as well as the Gravity model, which we cover on the next slide. The third step is to narrow down the set of retail locations for our individual site selection. The criteria for individual site selection will vary depending on the company’s objectives. We discuss channel evaluation criteria to satisfy company objectives in lecture 2 of this module. © Stephan Sorger 2015: Marketing Analytics: Distribution: 10

Retail Location Selection: Gravity Model
Store A: 5000 square feet; 4 miles away Target Market Area B: 10,000 sq ft; 5 miles B C: 15,000 sq ft; 8 miles C Calculates probability of shoppers being pulled to store, as if by Gravity Probability = [ (Size) α / (Distance) β ] Σ [ (Size) α / (Distance) β ] Slide 11 Before the advent of GIS systems, the so-called gravity model was a popular model for selecting the location of retail stores. The gravity model asserts that shoppers are attracted to retail stores located with convenient access, and maintaining a good “image.” We use the word “attracted” because the model assumes that stores pull in shoppers with a force similar to that of gravity. In the model, the term “convenient access” is generally interpreted as the distance from the store to the center of a geographic area rich in potential customers. We can modify direct distance measurements to reflect impediments to travel, such as bridges and areas of slow traffic. The term “image” in the model is interpreted as the size of the retail store. The model assumes that larger stores translate to greater image for shoppers, due to the greater variety and lower prices larger stores offer. The gravity model states that the probability of a store pulling in a customer is calculated by dividing the size of a store by its distance from shoppers, and then dividing the resulting expression by the sum of similar expressions for alternative store locations. We can modify the equation using alpha and beta adjustment parameters. See our discussion below. Here is the formula: Probability = [ (Size)α / (Distance)β ] / Σ [ (Size)α / (Distance)β ] Size: Size refers to the size of the store in square feet or square meters. The units of size do not matter, as long as they are used consistently. The entire expression is dimensionless (no units) because the same terms are used in both the numerator and denominator, and are thus canceled out. Distance: Distance refers to the length in kilometers or miles from the store to the center of the target market. Again, the units do not matter as long as they are used consistently. α (Alpha): Alpha is a parameter to adjust the degree of importance the target market has for the size of stores. The default value is 1.0. If we found our customers held a strong preference for large stores, we could increase alpha to emphasize its importance. β (Beta): Similar to alpha, Beta is an adjustment parameter to reflect the degree of importance customers hold for the distance of stores. Σ (sigma): Sigma is an operator that sums the expressions of size divided by distance (modified by the parameters Alpha and Beta) for all stores in consideration. In the next slide, we demonstrate the use of the approach using a retail store location example. © Stephan Sorger 2015: Marketing Analytics: Distribution: 11

Store A: 5000 square feet; 4 miles away Target Market Area B: 10,000 sq ft; 5 miles B C: 15,000 sq ft; 8 miles C Step 1: 1. Step One: Calculate the expression [ (Size) α / (Distance) β ] for each store location Store A: [ (Size)α / (Distance) β ]: [ (5) 1 / (4) 1 ] = 1.25 Store B: [ (Size) α / (Distance) β ]: [ (10) 1 / (5) 1 ] = 2.00 Store C: [ (Size) α / (Distance) β ]: [ (15) 1 / (8) 1 ] = 1.88 2. Step Two: Sum the expression [ (Size) α / (Distance) β ] for each store location. Σ [ (Size) α / (Distance) β ] = = 5.13 Slide 12 In our example, discount mass merchandise retailer Acme Mart, a fictitious company, plans to open a new store. Acme Mart sells to its target market in nearby city Small City. The figure shows three possible locations for Acme Mart’s new retail store. Store location A has a size of 5,000 square feet and is four miles away from the target market area, the center of Small City. Store location B has a size of 10,000 square feet and is five miles away. Store location C has a size of 15,000 square feet and is eight miles away. We start by assuming that customers value size and distance equally. Therefore, we set our Alpha and Beta parameters to one. We use the gravity model equations to calculate the probability of attraction for each store, using a three step process. In step one, we calculate the expression [ (Size)α / (Distance)β ] for each store location. We arrive at the scores of 1.25, 2.00, and 1.88, respectively, for stores A, B, and C. In step two, we sum the expression [ (Size)α / (Distance)β ] for each store location and arrive at 5.13. We continue with step three on the next slide. © Stephan Sorger 2015: Marketing Analytics: Distribution: 12

Store A: 5000 square feet; 4 miles away Target Market Area B: 10,000 sq ft; 5 miles B C: 15,000 sq ft; 8 miles C 3. Step Three: Evaluate the expression [ (Size)α / (Distance)β ] / Σ [ (Size)α / (Distance)β ] Store A: [ (Size) α / (Distance) β ] / Σ [ (Size) α / (Distance) β ]: 1.25 / 5.13 = 0.24 Store B: [ (Size) α / (Distance) β ] / Σ [ (Size) α / (Distance) β ]: 2.00 / 5.13 = 0.39 Store C: [ (Size) α / (Distance) β ] / Σ [ (Size) α / (Distance) β ]: 1.88 / 5.13 = 0.37 Slide 13 Continuing our example, we move on to step three. In step three, we evaluate the expression [ (Size)α / (Distance)β ] / Σ [ (Size)α / (Distance)β ] for each store location. We calculate the scores as 0.24, 0.39, and 0.37, respectively, for the three locations. From the results, we see that Store B has the greatest probability of customer visits, based on the gravity model. If we had found that customers were unusually sensitive to store distance, as might be the case in an urban area, we could increase the beta score to a larger value, such as 2, and re-execute the model. The gravity model suffers from several disadvantages. First, customers do not always value short distances to stores. Customers will visit distant stores if they feel the stores offer the exact merchandise they seek, or provide some other attribute, such as lower prices. For example, many cities offer outlet stores of major brand names on the outskirts of town. Many customers visit the outlet stores despite the many closer stores available, in the hope of finding a bargain. In the second disadvantage, not all customers want the largest possible store. Some customers prefer shopping at boutiques for the specialized selection, informed salespeople, and more intimate environment. In the third disadvantage, the standard form of the gravity model does not reflect the implications of different modes of access, such as travel by personal vehicle or public transport (although the Beta parameter could be increased to reflect greater effective distance). The gravity model has enjoyed good success, but many companies now find GIS systems to be a more attractive alternative for today’s retail store location selection. © Stephan Sorger 2015: Marketing Analytics: Distribution: 13

Central Business District Agglomerated Retail Areas Near city centers; Urban brands Auto row; Hotel row Secondary Business District Lifestyle Centers One major store, with satellite stores Retail Site Selection Options Williams-Sonoma Neighborhood Business District Outlet Stores Strip mall Outlet to sell excess inventory Shopping Center/ Mall Specialty Locations Balanced tenancy Airport book stores Freestanding Retailer Separate building; Jiffy Lube Slide 14 Having found the general location using either the GIS method or the gravity model, we must now select the individual retail location. Each company will likely have different retailing objectives, making a universal site selection model infeasible. For example, a list of location objectives for an automotive maintenance retail store, such as Jiffy Lube (jiffylube.com) would be quite different from one for a designer clothing store, such as Kate Spade (katepade.com). As we can see from the figure, many options are available for retail location selection. In the next slide, we review the various choices and provide examples for each. © Stephan Sorger 2015: Marketing Analytics: Distribution: 14

Slide 15 In this slide, we review some of the retail location selections available to businesses and provide examples. Central business district (CBD) locations are unplanned shopping areas near city centers. The locations work well for so-called urban brands, such as Diesel brand jeans in the 2000s. Secondary business district (SBD) locations are similar to central business districts, but smaller. The locations work well for specialty stores, such as fashion apparel stores offering unique merchandise. Neighborhood business district (NBD) locations are shopping areas designed to satisfy the convenience needs of a local neighborhood. Sometimes referred to as strip malls, we often find convenience-oriented stores, such as dry cleaners, in NBDs. Shopping centers and malls are centrally owned or managed shopping districts. Shopping centers and malls work well for chain stores of major brands, such as clothing and footwear sales. Freestanding retailers are stand-alone buildings, with no adjacent retailers to share traffic. Freestanding retailers work well for retail environments that buyers perceive as destinations, such as specialty retailers. Agglomerated retail areas are shopping districts which combine competing brands of one category. For example, some buyers find so-called auto malls, with multiple auto dealerships clustered in one area, a convenient way to shop for a new car. So-called lifestyle centers feature trendy brands associated with certain types of lifestyles, especially relatively affluent ones. The centers include posh stores such as Williams-Sonoma, Restoration Hardware, and Pottery Barn. Outlet centers offer a method for manufacturers and retailers to dispose of excess inventory without the steep discounting of liquidation stores. For example, some outlet centers include stores such as Nordstrom Rack. In addition to the standard retail locations listed above, many other options exist. For example, airports often sell books and magazines for long flights, and hospitals sell flowers and other gifts to give to patients. © Stephan Sorger 2015: Marketing Analytics: Distribution: 15

Customer Revenue Growth
Channel Evaluation and Selection Model Channel Customer Acquisition Channel Expected Profit + = Most Effective Channel Member Channel Customer Retention Channel Customer Revenue Growth Model to evaluate and select channel members, based on unique needs of business Slide 16 In this section, we cover a simple technique to assess the performance of existing distribution channel members, as well as select new members. It incorporates the guidelines for retail location selection we just discussed, along with profitability and other objectives. We base the new channel evaluation and selection model on four categories of evaluation criteria. By “channel,” we primarily refer to retail channel members. The evaluation categories include profitability and customer evaluation criteria. To recognize the importance of customers in distribution channel evaluation, we include three different customer-oriented aspects. The aspects recognize the life cycle of customers—from acquiring them, to keeping them, to gaining additional revenue from them. We define Channel Expected Profit as the estimated revenue expected from the channel, less the total costs. Channel customer acquisition is the effectiveness the channels play in bringing in new customers to the store. Channel customer retention captures the role distribution channels play in keeping existing customers, ensuring they do not defect to competitors. Channel customer revenue growth acknowledges the role distribution channels play in developing new revenue streams from customers. Marketers also call this revenue growth technique as growing “share of wallet.” © Stephan Sorger 2015: Marketing Analytics: Distribution: 16

Channel Evaluation and Selection Model
Ability to attract new customers Location Physical Requirements Customer Acquisition Criteria Brand Alignment Market-Specific Criteria Slide 17 We identify four criteria that contribute to attracting new customers. For location, we covered earlier the essential role that location plays in the selection of new stores. For example, we can place an AM/PM convenience store near a major intersection to bring in more foot traffic. For brand alignment, we want to match the expectations of the brand with those of the store. For example, luxury product manufacturer Hermes would not want to sell its goods at the local 7-Eleven convenience store. For physical requirements, we want to check if physical necessities, such as parking, are available at the proposed new store. And for market-specific criteria, we want to see if any special requirements, such as certain types of skilled labor, are available in the area. © Stephan Sorger 2015: Marketing Analytics: Distribution: 17

Ability to retain customers Customer Retention Criteria Customer Support Customer Feedback Customer Programs Slide 18 Having acquired our customers, we must now retain them. Our model incorporates three criteria to assess the distribution channel member’s efforts to retain customers. The three criteria are customer support, feedback, and programs. First, we want to assess the effectiveness of resolving customer issues with the new store. For example, online retailer Amazon.com is known for providing good customer support. Second, companies can benefit by collecting feedback from their retail stores. For example, the Container Store partnered with the OpinionLab to collect comments from customers’ use of the Container Store’s online Design Center tool. And third, companies will want to determine the effectiveness of customer programs, such as loyalty programs, for customer retention, as Neiman Marcus does with its InCircle Rewards program. © Stephan Sorger 2015: Marketing Analytics: Distribution: 18

Ability to grow revenue from customers; also known as “share of wallet” Customer Revenue Growth Criteria Consulting and Guidance Customer-Oriented Metrics Channel Growth Slide 19 . In addition to growing revenue from acquiring new customers, companies can increase revenue by growing the amount of money earned from each customer. Companies can sell complementary goods and services to existing customers to increase customer revenue growth. Sometimes this concept is referred to as growing “share of wallet,” in that the company captures more total income from customers. The model considers three criteria to evaluate the customer revenue growth capability of the distribution channel member. The three criteria are consulting and guidance, customer-centered metrics, and channel growth. For consulting and guidance, we want to track the effectiveness of consulting by sales personnel for revenue growth. For example, sports retailer Fleet Feet Sports specializes in providing expert guidance to runners, and customers often purchase entire running ensembles, not just shoes, when visiting the store, thanks to the consulting. For customer-centered metrics, our goal is to track revenue at the customer level, and hotel operator Ritz Carlton does exactly that, tracking customer preferences to grow revenue. And for channel growth, we track the overall growth rate of the distribution channel itself. For example, companies formerly sold through mass merchants such as Montgomery Ward to maximize sales of its merchandise. But the mass merchant distribution channel has been shrinking due to competition from Internet retail sales and other factors. © Stephan Sorger 2015: Marketing Analytics: Distribution: 19

Customer-Related Scores
Channel Evaluation and Selection Model INPUTS OUTPUTS Revenue and Cost Data Channel Evaluation and Selection Model Expected Profit Criteria Assessments Aggregate Customer-Related Scores Slide 20 The goal of the channel evaluation and selection model is to act as a structured decision making tool for the evaluation and selection of new distribution channel members, such as retail stores. The inputs to the model include revenue and cost data, as well as the assessments of individual evaluation criteria described earlier. The outputs from the model includes expected profit from each channel member under consideration, as well as aggregate scores for the customer acquisition, retention, and revenue growth abilities of each member. © Stephan Sorger 2015: Marketing Analytics: Distribution: 20

Channel Evaluation and Selection Model INPUTS OUTPUTS Revenue and Cost Data Channel Evaluation and Selection Model Expected Profit Criteria Assessments Aggregate Customer-Related Scores Three-Step Execution: 1. Assess individual criteria: Calculate scores for each criterion (location, brand alignment, etc.) 2. Calculate total scores: Calculate the total scores for each criteria group 3. Calculate grand total score: Calculate grand total score for each channel alternative Slide 21 To execute the model, we follow a three-step procedure. First, we evaluate each of the candidate store locations against the 3 sets of customer-related evaluation criteria we just described. Second, we calculate the total scores for each group of criteria. And third, we calculate the grand total score. We will illustrate the process with an example. © Stephan Sorger 2015: Marketing Analytics: Distribution: 21

Channel Evaluation and Selection Model INPUTS OUTPUTS Revenue and Cost Data Channel Evaluation and Selection Model Expected Profit Criteria Assessments Aggregate Customer-Related Scores Model uses 3 Types of Data: Financial Data: Monetary terms (Dollars, Euros) for expected profitability Evaluation Criteria: User assessment based on rating scale (see next slide for ratings) Model Weights: Allows users to vary importance of different criteria Slide 22 The model uses 3 types of data in its calculations. The model uses financial data, such as dollars and Euros, for expected profitability. The model uses evaluation data, such as ratings from 1 to 5 stars, for user assessments. And the model uses weights, such as 10% or 50%, to vary the importance of different criteria. Again, we will demonstrate the different types of data with an example. © Stephan Sorger 2015: Marketing Analytics: Distribution: 22

Slide 23 We recommend stating clear guidelines to the rating criteria, such as the guidelines shown in the figure, to ensure consistency among different reviewers. Ratings © Stephan Sorger 2015: Marketing Analytics: Distribution: 23

Expected Profitability Gather revenue and cost information for each distribution channel member (e.g. retail store) Plug data into model to get totals in monetary and normalized formats Slide 24 The first step of the model is to calculate the scores for the decision criteria for each evaluation group. We start with the decision criteria for the profitability evaluation group, and then move on to customer acquisition, retention, and revenue growth. To calculate the expected profitability, we must estimate the expected revenues and costs for each candidate distribution channel member. We enter the values in a spreadsheet modeled after the figure shown on the slide. The figure shows expected revenue for different products or services as designated by Revenue (1) for product/service #1 and Revenue (2) for product/service #2. We can expand the figure to include more products or services. Similarly, we estimate total costs for each alternative. We complete the spreadsheet by subtracting costs from revenue to arrive at a total for each channel member. Because we do not want to mix up data types, we need to normalize the expected profitabilities. To calculate the normalized amounts, we find the sum of the expected profits for all channel members, and then divide each channel member expected profit by the sum. For example, if channel members A, B, and C generated expected profits of $10, $20, and $30, respectively, we add up the profits to arrive at $10 + $20 + $30 = $60. Next, divide each profit by the sum to find normalized values of $10/$60 = 16.6%, $20/$60 = 33.3%, and $30/$60 = 50.0% for channel members A, B, and C respectively. © Stephan Sorger 2015: Marketing Analytics: Distribution: 24

Assess scores and weights for customer acquisition criteria Total = Weight (L) * L + Weight (BA) * BA + Weight (PR) * PR + Weight (MSC) * MSC Slide 25 Continuing with our individual assessments, we move to the 3 categories of customer-based evaluation criteria, starting with customer acquisition. We must estimate the degree to which each alternative channel member satisfies customer acquisition-related criteria. For each channel member, assign a rating (1 – 5 stars or equivalent) assessing how well the member achieves each criterion. For example, we rate a store located in a highly trafficked area as a “5” in location, whereas a store in a near-abandoned mall with poor traffic we rate as a “1.” In addition, we assign a weight for each criterion in the group. The weights are designated with capital letters representing their function. For example, Weight (L) represents the weight for Location and Weight (BA) represents the weight for Brand Alignment. Weights must total 100%. For example, we could assign a weight of 50% for location, 20% for brand alignment, 20% for physical requirements, and 10% for market-specific criteria for a new brand with few market-specific needs, for a total of 100%. To execute the process, we recommend setting up a spreadsheet structure to capture the evaluations and the weights like that shown in the bottom figure of the slide. We can then total the customer acquisition score by multiplying the evaluations by the weights to arrive at the total for each channel member or store. To do that, we can embed the equation given in the middle of the slide into the Excel cell for the total score for each channel member. The equation is defined as: Total = Weight (L) * L + Weight (BA) * BA + Weight (PR) * PR + Weight (MSC) * MSC We move on to the other customer-related criteria, which are calculated in the same way as the customer acquisition criteria. © Stephan Sorger 2015: Marketing Analytics: Distribution: 25

Repeat process for Customer Retention and Customer Revenue Growth Slide 26 We repeat the same process for the customer retention criteria and the customer revenue growth criteria. Again, we multiply the various evaluation scores by the weights for each category to get the total score for each channel member. © Stephan Sorger 2015: Marketing Analytics: Distribution: 26

Calculate Grand Total, based on Total Scores from: EP: Expected Profit CA: Customer Acquisition CR: Customer Retention RG: Customer Revenue Growth Slide 27 We then calculate the grand total, based on the total scores from each evaluation category. The evaluation categories are expected profit, customer acquisition, customer retention, and customer revenue growth. We recommend building a spreadsheet model using the same structure shown in the bottom figure in the slide. We will now demonstrate the process using a numerical example. © Stephan Sorger 2015: Marketing Analytics: Distribution: 27

Acme Cosmetics Example: Weights and Assessment Scores Slide 28 We now demonstrate the process using an example. Acme Cosmetics (a fictitious company) manufactures and sells premium anti-wrinkle face cream derived from a rare form of green tea. Sales at the existing retail store are booming and the company plans to expand sales to additional retail stores. Acme Cosmetics completed an evaluation of available retail store locations in its target area. The company has narrowed down its choices to three alternatives—Store X, Store Y, and Store Z. The table in the slide shows the results of their research, presenting the attributes for the three candidates. The data in the figure represents the “inputs” into the assessment model. Acme has assigned weights for profitability, customer acquisition, customer retention, and customer revenue growth at 40%, 20%, 20%, and 20%, respectively. Users benefit from having such a large number of weights, because it allows them to tailor the model to the unique situation facing the company. For example, some companies will assign higher weights to customer acquisition to attract new customers when first launch the store. Older companies, with a stable of existing customers, will be more interested in keeping those customers and gaining more revenue from them. © Stephan Sorger 2015: Marketing Analytics: Distribution: 28

Acme Cosmetics Example, Acme Customer Acquisition Acme Cosmetics Example, Acme Customer Retention Slide 29 We calculate the sub-total scores for each store for each of the three evaluation criteria categories—customer acquisition, customer retention, and customer revenue growth. To calculate the sub-total scores, we multiply the criteria by the criteria weights, as we described how to do earlier. Acme Cosmetics Example, Acme Customer Revenue Growth © Stephan Sorger 2015: Marketing Analytics: Distribution: 29

Acme Cosmetics Example, Acme Profitability Calculations Acme Cosmetics Example, Acme Grand Total Calculations Slide 30 We also calculate expected profitability. For example, Acme expects Store X to generate generates $120,000 in revenue (abbreviated in the Figure as $120 for brevity) for brand #1 of our anti-wrinkle cream. Therefore, Revenue (1) is $120. Revenue (2), for brand #2 of our cream, is $60. The store charges a rate of 50% of total sales in costs. Therefore, we calculate the costs associated with Store X as 50% * ($120 + $60) = $90. We finish the process by calculating the grand total. We calculate the grand total by multiplying the sub-totals found in each evaluation criteria sub-total, and multiply those sub-totals by the weights for those evaluation criteria category. For example, Acme wants to emphasize expected profitability, so it applies an expected profitability weight of 40%, shown as 0.40 in the Weight (EP) column, by the expected profitabilities we calculated above. Recalling that the total weights must equal 100%, Acme decides to split the remaining 60% evenly among the three customer-related criteria, so we get 20% each. We multiply the weights by the criteria to find the grand total for Store X, Story Y, and Store Z. The table at the bottom of the slide shows the suggested format for the calculations. The total for Store X is the highest, so we pronounce it as the winner of the evaluation, given criteria and weights selected by Acme. Had Acme selected different weights, the outcome may have been different. This flexibility to accommodate different business conditions is an important advantage of the channel evaluation and selection model presented here. © Stephan Sorger 2015: Marketing Analytics: Distribution: 30

Multi-Channel Distribution
Channel A: Retail Stores Channel B: Internet Stores Sales B Channel C: Specialty Stores C Revenues by Channel, Product 1 (repeat for 2 & 3) Channel Sales Comparison Chart Slide 31 In this lecture, we discuss different types of metrics that many marketing analysts find useful when analyzing distribution channels. We start with metrics for multi-channel distribution, where companies employ multiple distribution channels to reach customers. We can track sales of company products and services through different channels to compare the performance of each. Marketers can use sales comparison charts to determine the effectiveness of different types of sales channels. The figure on the slide shows a sample sales comparison chart for different channels. The chart shows how sales vary by channel. Channel A represents the company’s retail store channel. The chart shows the retail store channel contributes the majority of its sales. Channel B characterizes the company’s Internet-based sales channel. Channel C is the company’s specialty store sales channel. © Stephan Sorger 2015: Marketing Analytics: Distribution: 31

Channel C: Specialty Stores B Sales Channel B: Internet Stores A Channel A: Retail Stores Incremental Revenue By Channel, Product 1 Incremental Channel Sales Chart Slide 32 The incremental channel sales chart on the slide shows an alternative approach to presenting the same data. The comparison chart discussed earlier highlighted how each distribution channel contributed to total sales. The chart highlights how each additional distribution channel adds to incremental revenue. Marketers can use incremental sales charts to show the role each channel plays in adding to company revenue. © Stephan Sorger 2015: Marketing Analytics: Distribution: 32

Multi-Channel Market Table: Cisco Consumer Markets Slide 33 Companies selling into multiple markets (such as business and consumer markets) apply different distribution channels to reach the different markets. Each type of distribution channel has a set of characteristics that make it well-suited for a certain type of market. Distribution channels selling to businesses, such as value-added resellers, must have the capability to modify products and services to meet unique business demands. Distribution channels selling to consumers, such as retail stores, must have the capability to provide easy ordering, returns, and customer support. We illustrate the concept with an example. Computer networking company Cisco manufactures connectivity hardware and software for multiple markets. It applies savvy multi-channel strategy to target its different markets with different distribution channels. The table at the top of the slide shows Cisco’s strategy for multi-channel sales to consumers. Cisco services basic consumers through its corporate consumer web store, through Amazon.com, and through BestBuy. It also services its more demanding mobile consumers through its corporate consumer web store. The table at the bottom of the slide shows Cisco’s strategy for multi-channel sales to businesses. Cisco generates the majority of its revenue through sales to businesses. Therefore, the company applies a robust set of distribution channels to the different business markets it serves. Small businesses can order directly off the cisco.com corporate website, or from distributors. Enterprises can purchase from distributors of from value-added resellers. More specialized businesses, such as service providers and industry specialists, such as health care organizations, order products through a value-added reseller due to the special requirements such organizations demand. Multi-Channel Market Table: Cisco Business Markets © Stephan Sorger 2015: Marketing Analytics: Distribution: 33

Distribution Channel Metrics
All Commodity Volume Measures total sales of company products and services in retail stores that stock the company’s brand, relative to total sales of all stores. ACV in Percentage Units: ACV = [Total Sales of Stores Carrying Brand ($)] / [Total Sales of All Stores ($)] ACV in Monetary Units: ACV = [Total Sales of Stores Carrying Brand ($)] Slide 34 In this section, we cover metrics useful for tracking distribution channel effectiveness. The metrics measure the availability of a company’s products and services in retail outlets. We discuss all commodity volume, product category volume, and category performance ratios. Each measures specific aspects of a company’s distribution effectiveness. The all commodity volume (ACV) metric measures the total sales of company products and services in retail stores that stock the company’s brand, relative to total sales of all stores. ACV is often used over a specific area, addressing stores in a specific neighborhood, city, or state. Two formulas express ACV. The first declares ACV as a percentage. The second formula abbreviates the expression to include only total sales of all stores carrying the company’s brands. The two formulas are distinguished by their units. The first uses percentages. The second uses monetary terms, such as dollars or Euros. ACV in Percentage Units: ACV = [Total Sales of Stores Carrying Brand ($)] / [Total Sales of All Stores ($)] ACV in Monetary Units: ACV = [Total Sales of Stores Carrying Brand ($)] We illustrate the ACV metric with a numerical example in the next slide. © Stephan Sorger 2015: Marketing Analytics: Distribution: 34

All Commodity Volume Measures total sales of company products and services in retail stores that stock the company’s brand, relative to total sales of all stores. ACV in Percentage Units: ACV = [Total Sales of Stores Carrying Brand ($)] / [Total Sales of All Stores ($)] ACV in Monetary Units: ACV = [Total Sales of Stores Carrying Brand ($)] Example: Acme Cosmetics sells its products through a distribution network consisting of two stores, Store D and Store E. The other store in the area, Store F, does not stock Acme. Total sales of Stores D, E, and F, are $30,000, $20,000, and $10,000, respectively. ACV = [Total Sales of Stores Carrying Brand] / [Total Sales of All Stores] = [$30,000 + $20,000] / [ $30,000 + $20,000 + $10,000 ] = 83.3% Slide 35 We illustrate the ACV metric using a hypothetical example. Acme Cosmetics sells its products through a distribution network consisting of two stores, Store D and Store E. The other store in the area, Store F, does not stock Acme. Total sales of Stores D, E, and F, are $30,000, $20,000, and $10,000, respectively. We calculate the all commodity volume as follows: ACV = [Total Sales of Stores Carrying Brand] / [Total Sales of All Stores] = [$30,000 + $20,000] / [ $30,000 + $20,000 + $10,000 ] = 83.3% The advantage of the all commodity volume metric is that it provides a measure of the customer traffic through the stores stocking the brand. It tells us if we are in the most popular stores (i.e., those with the highest sales). The disadvantage of the metric is that it tells us nothing about sales in our category. We discuss a metric that addresses this disadvantage in the next section, product category volume. © Stephan Sorger 2015: Marketing Analytics: Distribution: 35

Product Category Volume Similar to ACV, but emphasizes sales within the product or service category PCV= [Total Category Sales by Stores Carrying Company Brand] [Total Category Sales of All Stores] Slide 36 Product category volume (PCV) refines the all commodity volume (ACV) metric by emphasizing sales within the product or service category. Using the metric, marketers can understand the effectiveness of different distribution channels in category sales. 9-42 Product category volume is defined as the total category sales by stores that carry the company’s brand, divided by the total category sales of all stores. Here is the formula: PCV = [Total Category Sales by Stores Carrying Company Brand] [Total Category Sales of All Stores] We illustrate the PCV metric using a numerical example in the next slide. © Stephan Sorger 2015: Marketing Analytics: Distribution: 36

Product Category Volume Similar to ACV, but emphasizes sales within the product or service category PCV= [Total Category Sales by Stores Carrying Company Brand] [Total Category Sales of All Stores] Example: As we saw earlier, Acme Cosmetics sells its products through two stores, Store D and Store E. Stores D and E sell $1,000 and $800 of Acme products, respectively. The other store in the area, Store F, does not sell Acme products. Stores D, E, and F sell $1,000, $800, and $600 in the cosmetics category, respectively. PCV= [Total Category Sales by Stores Carrying Company Brand] [Total Category Sales of All Stores] PCV = [$1,000 + $800+ $0] / [$1,000 + $800 + $600] = 75.0% Slide 37 We illustrate the PCV metric using a hypothetical example. As we saw earlier, Acme Cosmetics sells its products through two stores, Store D and Store E. Stores D and E sell $1,000 and $800 of Acme products, respectively. The other store in the area, Store F, does not sell Acme products. Stores D, E, and F sell $1,000, $800, and $600 in the cosmetics category, respectively. We calculate the product category volume metric as follows: PCV = [Total Category Sales by Stores Carrying Company Brand] [Total Category Sales of All Stores] PCV = [$1,000 + $800+ $0] = 75.0% [$1,000 + $800 + $600] The advantage of the product category volume metric is that it can provide an indication of the effectiveness its channel partners have in sales within the category. The disadvantage is that sometimes the data on category sales for the different stores can be difficult to collect. In that case, we recommend physically visiting the stores and measuring the areas devoted to the relevant category. Multiply the areas by the expected sales per area (such as sales per square foot) for the store to arrive at the category sales for the store. For example, department store JC Penney (jcpenney.com) generates about $200 per square foot in its retail stores. If JC Penney allocated 100 square feet to cosmetics, then we would expect JC Penney to earn $200 * 100 = $20,000 in total sales for cosmetics. © Stephan Sorger 2015: Marketing Analytics: Distribution: 37

Category Performance Ratio Ratio of PCV/ ACV Gives us insight into the effectiveness of the company’s distribution efforts, relative to the average effectiveness of all categories Category Performance Ratio = [Product Category Volume] [All Commodity Volume] Slide 38 The category performance ratio is defined as the ratio of product category volume over all commodity volume. In formula form, we state the following: Category Performance Ratio = [Product Category Volume] / [All Commodity Volume] Again, we illustrate the metric with an example on the next slide. © Stephan Sorger 2015: Marketing Analytics: Distribution: 38

Category Performance Ratio Ratio of PCV/ ACV Gives us insight into the effectiveness of the company’s distribution efforts, relative to the average effectiveness of all categories Category Performance Ratio = [Product Category Volume] [All Commodity Volume] Example: Acme Cosmetics wishes to determine how the product category volume (sales in the category) for the relevant distribution channels compare to the market as a whole. We can use the category performance ratio to compute this. Category Performance Ratio = [Product Category Volume] [All Commodity Volume] = [75.0%] / [83.3%] = 90.0% Slide 39 Acme Cosmetics wishes to determine how the product category volume (sales in the category) for the relevant distribution channels compare to the market as a whole. We can use the category performance ratio to compute this. Category Performance Ratio = [Product Category Volume] / [All Commodity Volume] = [75.0%] / [83.3%] = 90.0% The metric gives us insight into the effectiveness of the company’s distribution efforts, relative to the average effectiveness of all categories. Category performance ratios greater than one indicate that the retail stores in the company’s distribution network perform more effectively in the category than the market as a whole. In our example, because 90% is under 100%, we can assert that the stores carrying Acme Cosmetics demonstrate a reduced emphasis on cosmetic sales than the overall collection of stores in the market as a whole. © Stephan Sorger 2015: Marketing Analytics: Distribution: 39

Outline/ Learning Objectives
Topic Description Distribution Concepts Cover essential distribution concepts & terminology Channel Model Introduce proprietary channel evaluation model Distribution Metrics Discuss useful metrics for distribution Slide 40 In this module, we will covered three important areas within distribution. We covered distribution concepts and terminology that we have found essential for virtually any company dealing in physical products. We then introduced a new proprietary channel evaluation and selection model. We closed the module by discussing useful metrics for distribution. © Stephan Sorger 2015: Marketing Analytics: Distribution: 40

Marketing Analytics II Chapter 9: Distribution Analytics

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