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64 Cards in this Set
- Front
- Back
Salesforce Models: Personal Selling -- Selectivity
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Company can selectively target its marketing effort
e.g., High-value accounts only |
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Salesforce Models: Personal Selling -- Interactive
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Salespeople can tweak messages based on feedback
Relay field knowledge to improve product and/or service |
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Salesforce Models: Personal Selling -- People
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Recruiting, Training, Motivating, Rewarding, Retaining
More complex than placing ads |
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Salesforce Models: Personal Selling -- Costly
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Cost per sales contact (2000): $169-500 per sales call
Compare with magazine ad insertion: $500K for 20M viewers |
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Salespeople Functions
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Prospecting Finding & cultivating new customers
Presenting Providing information to existing and prospective customers Selling Closing the sale Problems Problem Solving: Determining how to address problems Servicing Rendering technical assistance Relationships Establish long-term partnerships with customers Information Gathering info about customers and competitors Analyzing Assessing sales potential Allocating Directing sales effort to different resources |
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Model: GEOLINE
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Organization Decisions
-Salesforce structure -Salesforce sizing -Territory design |
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Model: SYNTEX
Model: CALLPLAN |
Allocation Decisions
-Deploy resources -Products, Prospects -Call planning |
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Model: MSZ
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Control Decisions
-Compensation -Evaluation -Motivation |
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Salesforce Sizing & Allocation: Intuitive Methods -- Afford
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“What we can afford”
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Salesforce Sizing & Allocation: Intuitive Methods -- % Sales
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Salesforce expenditure = percentage of sales, e.g., 5%-8%
Similar: Historical norms or competitor spending Size = (5% of Sales) / (Average cost of salesperson) |
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Salesforce Sizing & Allocation: Intuitive Methods -- Breakdown
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Breakdown of sales forecast to number of salespeople
Number = (Forecasted Sales) / (Ave. Rev./ Salesperson) |
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Salesforce Sizing & Allocation: Intuitive Methods -- Problems
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Individual accounts can differ from average
Can not know optimal size before calculating allocation |
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Salesforce Sizing & Allocation: Market-Response -- Sales Entity
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Anything associated with potential sales
Customer, Prospect, Segment, Product, Geography |
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Salesforce Sizing & Allocation: Market-Response -- Function
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Determine response function: Sales = f(sales in new entity)
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Salesforce Sizing & Allocation: Market-Response -- Allocate
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Deploy certain level of resources to each entity to max. profit
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Salesforce Sizing & Allocation: Market-Response -- SYNTEX
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Pharmaceutical sales firm, with field salesforce
7 drugs (eg, Naprosyn), 9 physician specialties (dermatology) Considering increasing size of salesforce: 433 473 (background for developing SYNTEX model) |
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Salesforce Sizing & Allocation: Market-Response -- Sum
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Add up all entities to arrive at total Sales
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Salesforce Sizing & Allocation: Market-Response -- Goal
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Allocate sales efforts to maximize total profits
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SYNTEX: Steps for Sales Allocation Model
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see slide 7
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SYNTEX Model Examples -- C-Tek
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Industrial materials supplier
US sales = $100M, 78 salespeople, 14 branches |
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SYNTEX Model Examples -- Managers
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90 minute training sessions with managers
**Estimate sales in 3 years with varying salesforce allocations** |
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SYNTEX Model Examples -- Results
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2 of 14 branches over-staffed; 3 under-staffed
**Profits increased 4% just by re-allocation** Profits increased additional 7% by adding 25-30 new people |
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SYNTEX Model Examples -- S&Z
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Sinha and Zoltners (S&Z) (2001) studied 50 projects
Average improvement through re-allocation: 4.5% 28% from size change; 72% from re-allocation |
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Extending the SYNTEX Model: Re-Allocation -- Limitations
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SYNTEX Limitations: Static, Sales effort only
**Big limit: Assumes no interactions between segments/ products** Sales entities = Territories: Little impact Sales entities = Products: Potential big impact (one product may be considered sub for another) |
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Extending the SYNTEX Model: Re-Allocation -- ReAllocator
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ReAllocator = SYNTEX + Interaction Effects
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Extending the SYNTEX Model: Re-Allocation -- ReAllocator Advantages
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Incorporates interaction effects
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Extending the SYNTEX Model: Re-Allocation -- Disadvantages
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Best suited for repetitive buying (hospital supplies)
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Sales Territory Design -- Territory
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Physical area assigned to salesperson
Territory carved out of total market |
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Sales Territory Design -- Balance
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Opportunities & workload equal across territories
Important for “fairness” of commission |
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Sales Territory Design -- Imbalance
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Most companies (up to 80%) imbalanced
Too many salespeople in some areas; not enough in others Could reduce travel time by 10-15% with balance |
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Sales Territory Design -- Dynamic
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Territories tend to become unbalanced over time
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Sales Territory Design -- Goals
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Easy to administer: By ZIP code or county
Easy to estimate: Known number of targets (ie 5 hospitals in area) Easy to travel: No/ few geographic boundaries |
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Sales Territory Design: GEOLINE -- Objectives
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Equalize workload/ potential over territories
Create physically contiguous (touching) territories Minimize travel time across territory 9on exam ... adjust bourndary between territory to accomodate sales people travel) |
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Sales Territory Design: GEOLINE -- breakdown
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Breakdown Breaks down entire market into SGUs
SGU Standard Geographic Unit: ZIP code, County |
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Sales Territory Design: GEOLINE -- COSTA
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Adds profit influence; Allocate over Sales Coverage Units SCU
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Salesforce Compensation -- Objectives
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Compensation: Reward for performance
Motivation: Encourage to work harder (quota + bonus) Direction: Emphasize certain products/activities Example: Higher commission % for featured product |
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Salesforce Compensation -- Monetary
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Salary: Money for time worked
Commission: % of Sales generated Bonus: Money for attaining quota (company-set goal) |
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Salesforce Compensation -- Non-monetary
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Recognition: Salesperson of the month
Contests: Highest sales per quarter Awards: All-expenses paid trip |
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Salesforce Compensation -- Combination
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Most popular compensation plan (70%)
Salary + Commission (+maybe bonus) |
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Salesforce Compensation -- Evaluation
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Plot out pay vs. performance for each salesperson
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Salesforce Compensation: MSZ Model -- Conjoint
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Uses conjoint analysis to design a bonus plan (on exam)
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Salesforce Compensation: MSZ Model -- Purpose
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Set individual sales quotas reflecting territory differences
Design common bonus plan awarding same pay for perform. Example: Territory A: 10 big accounts, Terr. B: 20 small accts. Can not do bonus based on number of accounts |
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Salesforce Compensation: MSZ Model -- Inputs
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Sales potential per territory (secondary info available)
Salespeople’s assessments on their projected sales impact **(i.e. individual sales response functions)** + personal worth of leisure time: “Utility” of bonus vs. leisure |
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Salesforce Compensation: MSZ Model -- Trade-Off
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c influenced by salesperson desires: Utility of bonus vs. leisure
c = rate at which sales approaches max with increasing effort |
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Salesforce Compensation: MSZ Model -- Profit
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Maximizes profits, incorporating idiosyncracies of salesforce
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Improving Efficiency & Effectiveness of Sales Calls -- Sales Call
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Physical visit by salesperson to account
Average 3.4 calls/day, 750 calls/year |
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Improving Efficiency & Effectiveness of Sales Calls -- Varies
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Some calls routine; Existing long-term relationship
Some calls difficult; Prospects need a lot of convincing |
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Improving Efficiency & Effectiveness of Sales Calls -- Ideal
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Spend more effort where likely response is high
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Improving Efficiency & Effectiveness of Sales Calls -- Human
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Salesperson tendency to “win” a sale, even at low likelihood
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Improving Effectiveness of Sales Calls: CALLPLAN -- CALLPLAN
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Interactive system to maximize return on sales efforts
Number of calls to make to each client & prospect per period (exam q ... what is call plan) |
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Improving Effectiveness of Sales Calls: CALLPLAN -- Period
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Effort period = Planning period of salesperson (Quarter)
Response period = Planning period of firm (Year) |
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Improving Effectiveness of Sales Calls: CALLPLAN -- Allocation
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Allocate effort across different accounts to maximize results
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Marketing Channel Decisions -- Strategy
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High-level decision: How to sell to target market
Directly to customers: Direct mail, Internet Intermediary: Agents, Brokers, Wholesaler, Retailer Contractual: Ownership of channel (Apple stores)Brokers |
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Marketing Channel Decisions -- Location
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Number of outlets
Location of outlets Owned by company (Wal-Mart) Owned by intermediary, influenced by company (McDonald’s) |
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Marketing Channel Decisions -- Logistics
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Physical distribution
Inventory Efficiency of channel operations; high service, low costs |
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Marketing Channel Decisions: GRAVITY Model -- GRAVITY
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Model to support location decisions
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Marketing Channel Decisions: GRAVITY Model -- Factors
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Customer profiles, Store image, Drive times, Competitors
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Marketing Channel Decisions: GRAVITY Model -- Attractive
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Size of store: Bigger = Better; selection, prices
Distance to store: Closer = Better (thing to know) |
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Marketing Channel Decisions: GRAVITY Model -- Gravity
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Shoppers “pulled” to high-probability stores
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Marketing Channel Decisions: GRAVITY Model -- Limits
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Customers do not always want biggest stores, or closest
Hermes: Few small stores in selected locations Ikea: Monster stores in few areas Ignores access methods: Availability of CalTrain (or Parking!) |
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GRAVITY Model: Example -- alpha
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Effect of store image (or size) (Default: a=1)
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GRAVITY Model: Example -- beta
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Effect of distance (Default: b=1)
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GRAVITY Model: alpha and beta
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alpha - effect of store image
beta - effect of store distance |
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GRAVITY Model: Example -- Database
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Geodemographic databases: Shows where shoppers are
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