Omni-channel Retail: Fashioning a New Economics discusses how retailers must adapt their business models to an omni-channel retail environment. Traditionally, retailers focused on individual channels like stores or online, but now customers interact with brands across multiple channels. This requires integrated management of products, customers, and orders to provide seamless experiences. Product ranges now extend beyond physical stores through online sales. Customers are no longer anonymous but understood through a single view of their behavior and preferences across channels. Fulfilling orders can involve many parts of the business as transactions are no longer confined to physical stores. Retailers must optimize their economics across these dimensions through approaches like dynamic pricing, customer lifetime value analysis, and integrated inventory management

1. Omni-channel Retail:
Fashioning a New Economics
Michael Ross, Co-Founder & Chief Scientist, eCommera

2. Kiosk Mobile app PC/browser Tablet
Product
Customer
Assisted sale
Store Distribution centre Drop ship
Store Anywhere Home

3. ROPO to BIMBO
 Do you give credit to
online for an offline-
influenced sale?
 Stop online marketing that isn’t
justified by online value - but is
justified by overall business impact
Potential issues
Research online,
purchase offline
 Do stores get penalised for
online returns?
 Encourages antagonism between
channel teams
Buy Online, Return
to Store
 Is this an online or offline
sale?
 Store sales/profitability decreases but
store is clearly critical part of journey
Browse In-store on
Mobile, Buy Online
 Cost for store OR cheap
way to drive footfall?
 Underinvestment in click and collect
experience
Buy and collect
 Are high value offline
customers being targeted
with online
offers/discounts?
 Easy to send online offers to what
appears to be a “lapsed” online
customer rather than focussing on
them as an offline customer
High value offline,
lapsed online
Retailer question

4. Product

5. Product: before and after
Range
Inventory
Trading
 Propositions evolved , constrained
by store size
 Own brand to drive margin
 Sales rate per SKU and SKU
densities per sq. ft.
 Constrained by brand
 Unique product critical
 Long tail products
To
 Managed by store
 Stock in store owned
 A single view of inventory
 New stock ownership and fulfilment
models
 Broadcast prices
 Low variable cost of sale
 Simple trading
 Fixed discount schedule for sales
 More dynamic prices
 Revenue management
 Product “next best action”
From

6. Product economics

7. Customer

8. Customer: before and after
Management
Marketing
Selling
 Customers anonymous - insight
from gut experience or research
 Customers not measured - like-for-
likes are good enough proxies
 Single view of customers
 Customer centric planning
 Customer-centric input metrics
To
 Customers not part of the
conversation
 Fixed rent = footfall
 Fixed costs of store staff
 Broadcast
 Segment-driven for insight and
action
 Focus on most profitable customers
 Variable costs
 “Next best action”
 Personalised (segment for execution, not
insight)
 Channel targets: both store and
online
 Transaction focussed
 Customers are anonymous
 Service is uniform
 Customer targets
 Lifetime value/relationship focussed
 Service is differentiated
 Store staff are informed and incentivised
From

9. Customer economics
Expected 3 year purchases

10. Order

11. Order: before and after
Ownership
Timeline
Cost
 Simple
 Store staff
incentivised/focussed on
closing sales in store.
 Complex
 Transactions can touch many
parts of the business.
To
 Immediate: transactions
initiated/completed in store
 Delayed: separation of order
from delivery
 Longer gestation – interaction
with all channels (particularly
for high value purchases)
 Low marginal cost – shopping
bag, credit card charge
 High cost to serve – home
delivery, returns handling,
email/phone customer service
From

12. Order economics
Service request pattern
Staffing pattern (8am-9pm)
Waiting times

13. Michael Ross
Co-Founder and Chief Scientist, eCommera
michael@ecommera.co.uk