Behavior Analytics in Retail - Service Intensity
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Transcript of Behavior Analytics in Retail - Service Intensity
Silicon Waves LLC
Behavior Analytics in Retail - Service Intensity
Optimize the
Schedule to Demand 1 3/21/2014
Real-Time Environment in the Store
Retailer’s Customer Service Model
Build the Optimized Schedule
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Sales Conversion Measures how many Visitors converted to Buyers
And Service Intensity Monitors and Predicts
when and where to position associates in order to maximize the sales opportunities
by Connecting
between the Regular Schedule to Actual Demand
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What is an Optimized Schedule?
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Service Intensity Answers
How Many Sales Associates per Period of Time
our local Store requires
in order to comply
with the Corporate Customer Service Policy
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So…..
Service Intensity is a
Customers to Staff Ratio?
Not quite
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Service Intensity
is a Holistic Metric
with Computation and Operational
and Strategic Models
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1. Calculate the
Service Intensity
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What is Service Intensity?
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For Example
Service Intensity (15 Minutes) = 5 Means
each Sales Associate On Average ‘Serviced’
5 Customers During 15 Minutes
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To calculate the
Service Intensity We must define
Customers and
Associates and
Period of Time
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Define the Customers
Average SI = 6.8
Average SI = 5.5
Average SI = 7.3
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Define the Customers
Arrivals: defines ‘service’ from the beginning
of the sales cycle, as a customer enters the store
Occupancy: defines ‘service’ during a period
of time; for Big Box or ‘Browse and Buy’ models
Exiting: defines ‘service’ to the last touch point
with the customer, at the checkout. Concurrent
with Sales Conversion as a percentage of Exiting
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Define the Associates
What are the Associate’s
Tasks?
What does it mean ‘Customer
Service’?
When a Manager operates as an
Associate?
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Define the Associates
On the Floor: all associates on the sales floor; for
small destination and luxury goods stores
Managers: defined as a sales associate per job
description and/or the store’s environment
Support Services: defined by the job function, retail
market, and compensation plans
Frontline Cashiers: Monitored and managed with
Queue Management & Predictive Scheduling techniques
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As workforce systems proliferate and the forecasting moves away from spreadsheets into
sophisticated systems that deploy bottom-up data, the schedule is now done in 15 minute increments.
This opens the door for innovations
Define the Period of Time
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Define the Period of Time
1st Hour
2nd Hour
If we calculate the metrics based on hourly data, the Service Intensity is 12 (48/4=12) for 1st hour
and 16 (72/4.5=16) for 2nd hour Silicon Waves LLC 17 3/21/2014
Define the Period of Time
We also calculate the Service Intensity for every 15 minutes. For example, between 11am and 11:15am,
with 12 arrivals and 4 associates the ratio is 3 (12/4=3) Silicon Waves LLC 18 3/21/2014
Define the Period of Time
1st Hour
2st Hour
Next, we calculated hourly ratios by averaging each 15 minute period. The Average Service Intensity between
11am to 12pm is 3; between 12pm to 1pm is 3.9. Silicon Waves LLC 19 3/21/2014
Which Method is more Accurate?
Average Service Intensity calculated in 15 minute segments, and averaged per the period of time - 3 for 1st Hour 3.9 for 2nd Hour
Service Intensity calculated per Hour - 12 for 1st hour
(48/4=12) 16 for 2nd hour
(72/4.5=16)
Using the Average Service Intensity method , each associate, on average, ‘serviced’ 3 customers during the first hour and
almost 4 customers in the second hour
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Define the Period of Time
1st Hour
2st Hour
At 12:30pm, the store adapted to the increase in traffic during lunchtime, and increased the number of
associates on the floor from 4 to 5
Lunch Crowd Traffic
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Calculating the
Service Intensity in
15 minute segments and using these values as
a basis for any period of time, such as daily analytics
paints a more authentic picture of how the store works
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Calculate Service Intensity
With Exiting as the Customers
Sales-Floor Employees as Associates And
15 Minute Segments for
Average Service Intensity Per Period of time
Best Practices for Specialty Retailers
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2. Identify the
Optimized Service Intensity
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What is Optimized Service Intensity?
Optimization answers what is the level of customer service that will maximize the store’s
performance?
Sales Conversion is the Key Performance Indicator for maximizing opportunities
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To link the schedule to service policies, we have to learn the relationships between demand,
service and store performance.
Since the ratio of Sales Conversion displays the link between demand and transactions, and
Service Intensity measures demand and service, finding the correlation between these
two metrics points to optimal values.
Optimize Service to Performance
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Sales Conversion is calculated as Transactions divided by Visitors, and Service Intensity is
Visitors divided by Associates. If we take out Visitors, we end up with Transactions divided by Employees.
We don’t need traffic! Silicon Waves LLC 27 3/21/2014
Before you jump… You do need Traffic
The traditional method of scheduling to transactions creates a self-fulfilling prophecy. Using only buying behaviors, avoids monitoring all potential sales opportunities. We see the influence of actual demand in the calculation methodology. Sales Conversion is calculated for the full period, i.e. daily conversion is calculated for all transactions divided by all visitors, for that day. Service Intensity, meanwhile, is calculated per 15 minutes. The daily ratio is an average of all the 15 minute segments of the day. Optimizing Service Intensity against traffic or sales also works, however the metric that best captures both demand and performance behaviors is the Sales Conversion
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50 Stores Sample
Lower Service Intensity means longer Average Service Time. In other words, when each associate can serve fewer customers, giving more service time to one customer, we should see an increase in sales
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50 Stores – What’s the Common Behavior?
Average Sales
Conversion is 40%
Average Service
Intensity is 5.9
Minimum Baseline is
3 months of data
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Optimize with the Average Service Time
One way is to turn the formula around for the Average Service Time
If
Average Service Intensity [1 Hour] = 4 We can also say
60 Minutes / 4 Service Intensity = 15 Minutes
The Average Service Time is 15 minutes
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Search for your Average Service Time
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A specific value, such as the Average Service Intensity, is hard to pinpoint. It is better to work
with a range of success
Retailers can define successful performance as long as the store stays within a range, such as one standard deviation from the average, or
within a predefine range
The Target – Point or Range?
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In a normal distribution, one standard deviation below and above the mean will include more than
68% of the cases
Target– Within 1 Standard Deviation
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Retailers can choose a Target Range based on the Actual Behavior of the Stores
Define the Range of Success
78% of Sample Stores have Service Intensity
between 4 and 7
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The Optimization Process
Once we defined the Target Service Intensity
And measured the Actual Behavior of the Stores
With Service Level Measurement We search for ways
to improve Customer Service without sacrificing Profitability
In order to increase Sales
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Optimization – Start with Why?
Same Service Intensity, why different Sales Conversion?
Same Service Intensity, why different Sales Conversion?
Same Service Intensity, why different Sales Conversion?
Same Sales Conversion, why different Service Intensity?
Is the Extra Staff worth the high
Conversion Rate?
Laggards Why?
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Optimization - Weighted Service Intensity
Retailers can assign different weights to per the employee
valued added to service -
Status: Full-Time Employees vs. Temporary Employees
Tasks: Fulfillment vs. Service Tasks
Position: Managers acting as salespeople
Goals: Customer Service vs. New Accounts
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50 Stores – Optimized Service Intensity
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With
Average Service Intensity Calculation Method
the Optimized
Service Intensity Range is typically between
3 to 7
Best Practices for Specialty Retailers
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3. Define the
Service Level Measurement
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Once Corporate identified the Optimized Service Intensity
the next step is Real-Time Monitoring
Of the Local Store With
A metric of success The
Service Level Measurement Silicon Waves LLC 42 3/21/2014
Measure the Success Rate of the Store
If the Customer Service Model is 3 < Service Intensity < 4
The success rate is 5 out of 8 periods , or Service Level Measurement
is 63%
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Service Level Measurement
is the metric of the local store’s
Success Rate to comply with the
Customer Service Model
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Monitor the Store, Monitor the Chain
How the Local Store Compares?
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Metrics are also Tools of Communication
Are we giving the store the resources
to compete?
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Store managers are overwhelmed with data, and often
resent any top-down new system The secret
to a proper introduction is education and training
and allowing the stores to identify their natural trends
before including these metrics in the
compensation plan. Silicon Waves LLC 47 3/21/2014
Too Many Associates?
Not enough Staff ?
Best Practices – Measure, Ask, Act
Optimized ?
Build the Baseline!
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93% days out of the year, the store performed with a Service Intensity
between 4 to 7
90% of Customers Wait less
than 3 Minutes
85% of Stores have
35% Plus Conversion
Measure the Actual Behaviors of the Store to the Corporate Policy
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4. Analyze the
Marginal Value of an Associate
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What is the Value of Adding 1 Salesperson?
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The Marginal Value of a Sales Associate answers
what is the value of adding, or taking off, a single
salesperson.
Choose a Productivity ‘Success’ Metric
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We choose Sales per Hour as the productivity metric,
keeping other variables—i.e. traffic levels, period of time, geography and demographic parameters—the same, and
only changing how many associates are scheduled
Adding one salesperson increases revenue but each revenue segment is less than the previous revenue slice.
This makes common sense. If demand stays at the
same level, there is still a natural buying behavior for that particular retailer, limiting how
much the associate can do to influence the sale.
For a profit-neutral retailer, the objective is to reach where the operating margin is zero.
Additional Revenue per Salesperson
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Playing ‘What If?’
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The Sample Store schedules 4 people between 11am and noon Arrivals: During the hour, 48 people visited the store. Sales: With 4 associates, the store sold $1,083 per Hour (Abby $248+ Bob $223+ Jane $321+ Rachel $291= $1,083 Sales Conversion is 25%.
What would be the impact on sales if we scheduled 5 associates?
Playing ‘What If?’
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Using Service Productivity, we know that Mike’s baseline is $198 Sales Per Hour. Therefore, if we add Mike as the 5th associate, he will increase the store’s sales to $ 1,281 ($1,083+ $198 = $1,281). Assuming Mike will serve 3 customers, the sales conversion would increase to 31%.
Schedule to Demand optimizes the schedule based on the behaviors
both the store and the employees
In other words, the performance of a sales associate depends on the store’s environment
and the individual ability
We forecast individual performance with Service Productivity. We measure the store’s ability to adapt
to actual traffic with Service Intensity
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Advanced Analytics – Service Productivity
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Service Productivity optimizes the schedule by
individual performance
Service Productivity is the probability that for a given level of traffic and service policy, the associate will repeat
past sales performance
Service Intensity
is a Holistic metric in Schedule to Demand -
Driving the schedule by answering
How many Associates should be scheduled
in order to Optimize Store Profitability
and Maximize Sales
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Service Intensity is a holistic ratio of visitors to associates, per period of time. Optimized Service Intensity identifies the correlation between Service Intensity (schedule) and Sales Conversion (store performance). Service Level Measurement is a percentage of how often the local store performed, according to the corporate customer service model. Service Intensity & Service Productivity, together, define the Marginal Value of a Sales Associate
Schedule to Demand
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Our mission is to nurture, train, and educate a community of behavior analytics professionals
Ronny Max is the author of “Behavior Analytics in Retail” (October 2013), and the founder of Silicon Waves, a consultancy specializing in Behavior Analytics, including People Counting, Queue Management and Schedule to Demand. Website: BehaviorAnalyticsRetail.com
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This presentation can be redistributed, in commercial and non-commercial form, as long as it is passed along unchanged and in whole, with credit to Ronny Max