Untangling omni-channel retail

Untangling omni-channel: How to stay prof itable in the new era of retail Michael Ross, Chief Scientist & Co-founder, eCommera

www.ecommera.com

http://www.ecommera.com

Retailers like to think and organise themselves in terms of channels. When the Internet launched we first saw retailers using multiple channels; then retailers saw the power of combining channels (offering click and collect or ipads in store).

Today we talk about omni-channel – a customer-centric approach and philosophy that recognises the profound change in customer behaviour. As John Lewis describes it, “Omni-channel is all about the customer – what they want, when they want it, where they want it.”

While retailers may have kept their channels separate, often managed by separate teams, customers have not. Shoppers now seamlessly switch between different forms of product information, purchasing, collection and returns. It is a complex interconnected web of behaviour that renders many decisions made by channel dangerous. Customers are increasingly Researching Online and Purchasing Offline (ROPOs); Browsing Offline and Buying Online (BOBOs); Browsing In-store on Mobiles and Buying Offline (BIMBOs) amongst their many options.

There are many drivers of this transformation – critically, as smart mobile devices proliferate, mobile speeds improve, and stores increasingly become mobile ready (things to scan, payment via virtual credit card, store check-in, product scanning etc.), the influence of the digital channel will increase further and cement the transformation of retail. Location, location, location will become customer, customer, customer.

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Cross channel dysfunctionAny retailer still optimising individual channel profitability risks being sub-optimal, and at worst, is likely to make completely incorrect decisions. Fixating on the channel often drives the wrong retailer behaviour for its customers across marketing, products and store activities.

The evolution of shopping

Cross channel behaviour

Research Online, Purchase Offline (ROPO)

Buy online, return to store

Buy on mobile, in store

Buy and collect

High value offline customers who occasionally shop online

Online order shipped from store

Out of stock in store, customers directed online or to other products

The retailer’s issue

Do you give credit to online for an offline-influenced sale?

Do stores get penalised for online returns?

Is this an online or offline sale?

Cost for store OR cheap way to drive footfall?

Are high value offline customers being targeted with online offers/discounts?

Do stores get credit for sale?

Are store staff incentivised to drive customers to the website or do an assisted sale in store versus attempt to sell the customer something unsuitable?

What can happen

Online marketing that isn’t justified by online value is cut, even though it is justified by overall business impact

Encourages antagonism between channel teams

Store sales/profitability decreases but store is clearly critical part of journey

Under investment in click and collect experience

Easy to send online offers to what appears to be a lapsed online customer rather than focusing on them as an offline customer

Encourages antagonism between channel teams

Encourages poor customer experience unless staff are properly incentivised and equipped

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Untangling Omni-channel. Michael Ross

This is already the new reality. Unfortunately, we are in a dark period of partial visibility, where retailers increasingly need to make omni-channel decisions but with messy and incomplete data. Navigating this transition requires a new way to think about decisions, measurement and profit: execute in the channel, but optimise the whole. Retailers need to differentiate between what can be locally optimised (either online and in store) versus those decisions that need to consider the wider omni-channel impact. They need a heightened awareness of how reversible a decision can be – while opening or closing a store is a decision that needs full certainty, bids on Google can be changed every few minutes. Underlying it all, be careful what you measure: the right metrics are critical to making sense of the omni-channel world.

The impact: products, customers and ordersTo succeed long-term, retailers’ core operating models will need to adapt. Focus on the endgame: all products will be available to all customers from all locations; customers will shop anywhere and everywhere, from a proliferation of devices and locations; and orders will be shipped, collected and returned in any location.

This is a terrifying vision for many retailers. While the endpoint may be appealing, getting from here to there is not obvious. As the old Irish joke goes – a traveller lost in Ireland asks for directions to Dublin: “If you’re trying to get to Dublin, I wouldn’t start from here”. Most retailers are asking the same questions:

• When? This is a game of chess – do we move now or wait?

• How? What do we do about our aging technology? Too often it’s back of house and doesn’t touch customers, is not very good, and is point-to-point integrated

• Who? Where do I find the skills, organisation and resources for the complex, process heavy, analytical omni-channel world?

• How much? How can I ensure my investments and the increased cost of omni-channel reap profits?

This article tries to offer some of the answers. We explore what omni-channel means for the three core elements of retail: the product, the customer and the order, looking at how each will be affected by omni-channel and the new economics of making a profit.

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1. Product

How will product change?Product is at the heart of every retailer. Omni-channel gives huge opportunities to rethink the stock model and should – in theory – really deliver benefits allowing retailers to widen their range, operate with less stock and deliver higher sell-through.

Proposition

Own versus 3rd party

Width

Where held

Ownership

Pricing

Promotion

From

• Propositions evolved over decades to suit physical locations and retail store sizes

• Own brand seen as one option to drive margin

• Well established minimum sales rate per SKU and SKU densities per sq. ft.

• Product is managed on a store by store basis. Many retailers don’t know exactly what inventory is in each store – the cost of counting often outweighs the benefit

• Stock in store owned by retailers (exceptions include supermarkets with vendor managed inventory)

• Broadcast prices• Low variable cost of sale

(cost to serve)

• Simple trading• Promotional calendar and fixed

discount schedule for sales

To

• Propositions constrained by brand, not store walls

• Unique product critical for all

categories to give a point of difference and avoid being ‘Amazoned’

• Opportunity to sell long tail products not available in store – new categories, new brands, new products, fringe sizes

• A single view of all inventory across the organisation

• All stock available to all customers; each store acts as a mini-warehouse

• Products shipped to consumer from country of manufacture

• New stock ownership models (e.g., stock on consignment, brand/manufacturer ships just-in-time to consumer)

• Revenue management which better aligns revenues and costs

• More dynamic (maybe personalised) prices

• Dynamic offers based on customer/product status

• Product next best action – a more dynamic and nuanced approach to trading

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The new economics of product The profit challengeTurning inventory into cash is the key to success in retail– as the saying goes “revenue is vanity, profit is sanity, cash is reality”. In physical retail, once a format is established, the rhythm of revenue/profit/cash generation turn out to be relatively simple.

However, establishing that rhythm with businesses transitioning to omni-channel can be extremely painful. Omni-channel breaks the stock in store model, requiring retailers to navigate a new cash cycle. All eCommerce pure-plays have had to work this out from first principles – Amazon consumed over $3bn before they became cashflow positive. Bezos was notably obsessed about not running out of cash, rather than making a profit.

The question to askThe critical question for retailers to answer is: What breadth, depth and store inventory allocation achieves the optimal balance of sales/profit/cash and return on capital?

To help answer this question, retailers should know: • How different is the omni-channel sell through

curve? • What stock should be held in store (for

immediate sale) versus a central warehouse (for ship to home)?

• What pricing and promotional strategy will maximise cash generation?

• How much stock should be held locally versus point of manufacture?

An example: Product cash-cycle model

MODEL CONCEPTThe key to generating cash in retail is the management of working capital. More specifically, understanding how to make the right trade-offs between cash and margin. The transition to omni-channel can cause a shock and it is critical to understand how different sell-through dynamics of online play through into cash generation. Our model below shows the degree of sensitivity between generating and consuming cash.

Driver of cash cycle

When product starts selling

Sales trajectory in first 2/3 weeks

Sell-through of broken ranges

Physical retail only

Start selling as soon as products arrive in store

New products immediately visible in store

Visible to store staff – moved to clearance rail

Omni-channel

Start selling once product is photographed, booked in to web warehouse, coded and uploaded to site

New products can languish if not actively managed or promoted on site

Easy to send traffic to broken products (customer journey is more difficult to control)

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ASSUMPTIONS• Purchase price: £40• Full retail price: £115 (equivalent to an in-take

margin of 65%)• Markdown schedule: 50 days (30%), 85 days

(50%), 100 days (70%)• Total sell through: 90% (remaining units

written off)• Payment terms: 30 days after inventory is

delivered

BASE CASE • Time from goods arriving to start selling:

6 days• Time from 1st sale to payment of invoice:

24 days• Time to sell half units: 30 days

BASE CASE CHARTS1. The retail price over time (following the

markdown schedule)2. The cumulative sales curve 3. The cumulative cash curve (the spike is when

the supplier invoice gets paid)4. The daily sales curve

0 50 100 150

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1. Retail price over time

3. Cumulative cash

2. Cumulative sales

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4. Daily sales curve

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IMPLICATIONSThere are many implications for retailers.

It is critical to invest in the people, process and technology to get product selling quickly, and establish the right trajectory for online sell through.

It is important to avoid masking flaws in the cash cycle and blend them into overall ‘operational losses’. Always distinguish between (i) cash requirements caused by losses due to lack of scale versus (ii) working capital driven by a fundamental failure in the cash cycle.

SCENARIOS: WORKING CAPITAL REQUIRED versus GENERATEDIn the base case of 30 days, this product generates negative working capital of £10,334

Time from first sale to payment of stock invoice

Tim

e to

sell

ha

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its

24 days

-£

£

£

39 days

Positive value – cash cycle has negative working capital

Negative value – cash cycle requires working capital

30 days

24 days

20 days

-£

£

£

15 days

-£ 19.11212,0936.694

-£ 1346,13210,334

£ 7,78113,37016.985

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2. Customers

How will customer strategy change?

Although customers have always been critical to retail success, they have traditionally remained anonymous. Only a handful of retailers – such as Tesco and Nordstrom – have made full use of customer data and many retailers including Walmart have done very well by focusing on other areas such as supply chain optimisation. Even where retailers do have customer data, it is often not obvious what to do with it beyond direct marketing, and certainly not how to use it to rethink the business.

For the last two hundred years of modern retail, location = footfall and footfall = sales. Omni-channel unshackles consumers from their physical location, requiring retailers to rethink their relationship with customers.

The new economics of customers The profit challengeRetailers have built very successful businesses without needing to understand or model customer behaviour. They have relied on a conceptually simple model of profit per store and category, and growth driven by like-for-likes and new stores.

Omni-channel has changed everything. Retailers will now need to embrace profit per customer. The new growth model is driven by customer acquisition and retention. It is already well understood in every industry with a customer contract – from mobile phones and insurance, to utilities and traditional mail order. Retailers will need to join them by thinking in terms of customer acquisition costs, life time value and retention economics.

The question to askThe critical question for retailers to answer is: What investment in customer acquisition and retention maximises long term profitability?

To help answer this question, retailers should know: • What is the profit per customer? • What is the role of the store in influencing

customer profitability?• How much should we spend acquiring a

customer?• What is the appropriate payback for a new

customer?• What is a customer’s lifetime value?

An example: Customer growth model

MODEL CONCEPTWe have developed a simple customer simulation model to highlight the sensitivity of sales to a customer’s transition from being ‘new’ to ‘loyal’. The model shows how overall sales are built up from customer cohorts, where each cohort represents the customers acquired in a particular year. The transition from a new to loyal customer is the key – how long does it take, how many customers do you lose along the way and how much do your loyal customers spend?

ASSUMPTIONS• New customers acquired in a week: 1000• Loyal customers’ annual purchases: 5 • Probability of making next purchase in a week:

probability increases with frequency (i.e., the more purchases you make, the more likely you are to make the next one), and decreases with recency (i.e., the longer it is since you made your last purchase, the less likely it is you will make your next one).

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Planning

Insight

Measurement

Focus

Cost

Personalisation

Targets

Timeline

Approach

From

• Location has been a proxy for customers, and growth has been planned based on like-for-likes and new stores

• Customers are typically anonymous

• Customer insight comes from gut experience or expensive ad hoc market research

• Customers not measured• Outcomes such as like-for-likes

are good enough customer proxies

• Customers not part of the conversation

• Fixed rent = footfall• Fixed costs of store staff

• Broadcast• Segment-driven for insight and

action

• Channel targets: both store and online

• Transaction focused

• Customers are anonymous• Service is uniform

To

• Customer centric planning – growth is based on customer acquisition and retention targets

• Single view of all customers/transactions across all channels

• Decisions are based on customer data

• Customer-centric input metrics critical to understanding what’s going on

• Focus on most profitable customers, and highest potential customers

• Understand business in terms of who’s making money

• Variable costs of online marketing• Optimal investment in acquisition

versus retention

• ‘Next best action’ – based on every customer interaction, what is the next best thing to do: nothing, promotion, email, event invitation, call from local store etc.

• Personalised (segment for execution, not insight)

• Customer targets • Store managers focused on acquiring/

retaining customers

• Lifetime value/relationship focused

• Service is differentiated based on a customer’s value and potential

• Store staff are informed and incentivised accordingly

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BASE CASE CHARTS1. The acquisition curve shows a flat acquisition

rate of 1000 new customers per week2. The transition curve show the evolution from

new to loyal customer – each line represents a different frequency cohort, and the chart gives the probability of a customer purchasing in a particular week. The base case indicates:

• 9 purchases to become loyal • 55% of customers move from 1st to 2nd

purchase3. The loyal curve shows flat purchasing of 5

times per annum4. The growth chart shows how the cohorts

translate into an overall sales curve played out over time.

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Years

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Yr 1 Yr 2 Yr 3 Yr 4 Yr 5 Yr 6 Yr 7 Yr 8 Yr 9 Yr 10

C1C2C3C4C5C6C7C8C9C10

Key Statistics

1 Percentage customers retained2 Annual loyal spend as a % of 1st year spend3 Expected 3 year orders for a new customer4 CAGR Year 7-105 CAGR Year 3-66 Year 10 revenue vs. Year 17 Week when revenue > costs (i.e. in profit)

5.6%23.7%

2%8.7%

13.2%3.2%

5%

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5.6%23.7%

2%8.7%

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5%

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Key Statistics


5.6%23.7%

2%8.7%

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5%

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1. Acquisition Curve (Growth Rate = 0)

2. Transition Curve

3. Loyal Curve

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5.6%23.7%

2%8.7%

13.2%3.2%

5%

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10 2 3 4 5 6 7 8 9 10

Loya

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4. Growth (C = cohort)

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Key Statistics


5.6%23.7%

2%8.7%

13.2%3.2%

5%

Years

10 2 3 4 5 6 7 8 9 10

Loya

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01

23

45

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SCENARIOS: EXPECTED NUMBER OF PURCHASES OVER 3 YEARSWe are then interested in understanding the expected 3-year purchases from customers. As you can see from the table below, the retailer’s evolution is extremely sensitive to these assumptions.

IMPLICATIONSThis highlights the sensitivity of growth and profit to these basic drivers: • Taking a high number of purchases to get

to loyalty is typically a service issue – erratic service drives attrition from potentially loyal customers.

• A low number of purchases to get to loyalty is good, but a low first-to-second purchase rate is bad, often driven by a poor CRM

• A retailer with expected 3-year customer purchases of 1.6 needs to focus on fixing the basics. A retailer with expected 3-year customer purchases of 6.6 can put its foot to the floor and focus on customer acquisition.

These dynamics are critical to understanding and prioritising relative investment in customer acquisition, service improvement, retention promotions and range expansion. It is easy for retailers to observe similar revenue growth but with very different underlying drivers of performance.

Expected 3 year purchases

Long term transition from 1st to 2nd purchase

Number of purchases to be deemed loyal

25% (bad) 35% 45% 55% (good)

3.2

2.2

1.8

1.6

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34.8

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3 (good)

5

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The new economics of orders The profit challengeThere are two elements to the new economics of orders:i. The cost to serve: The variable cost of

handling each order as more orders become omni-channel

ii. The economics of service: How much does it cost to deliver different service levels, and what is it worth (in terms of impact on customer retention)?

All service industries have evolved to offer different qualities of service, at different price points (think Direct Line versus Hiscox; BA versus RyanAir; Travelodge versus Four Seasons). In addition, businesses with a high variable cost of sale have evolved their business models to align revenue and costs.

3. Orders

How will the ordering process change?The order is the retail transaction – the exchange of goods for money. Omni-channel decouples the customer contract from the physical exchange with profound implications. Transactions in store are quick, simple and have a very low marginal cost. Decoupled transactions introduce delays, new costs, and process complexity. A transaction now becomes a series of touch points. While this is a very standard challenge in every service business (think hotels, airlines, insurance and pay TV), it is a new set of skills, processes and economics for retailers.

From

• Simple• Store staff incentivised/focused

on closing sales in store

• Immediate: Transactions initiated/completed in store

• Low marginal cost: Shopping bag, credit card charge

To

• Complex • Transactions can touch many

parts of the business

• Delayed: Separation of order from delivery

• Longer gestation: Interaction with all channels (particularly for high value purchases)

• High cost to serve: Home delivery, returns handling, email/phone customer service

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The question to askThe critical question for retailers to answer is: How do we make the trade-off between cost and service?

To help answer this question, retailers should know: • What’s the cost of delivering different

service levels?• What’s the impact on customer loyalty

of different service levels?• What’s the cost of managing volatility?

An example: Cost to serve model

MODEL CONCEPTAll retailers have processes that require human involvement or intervention such as: emails, fraud reviews, order issues, picking, packing, shipping and returns processing. Omni-channel retail means requests arrive 24/7, so retailers have to align shifts to get the right balance of cost versus service. Few retailers work 24/7 and our model below highlights the impact on the customer experience of the misalignment between requests arriving and then being handled.

ASSUMPTIONS• Requests per hour (24/7): 100• Shifts planned: 5 days per week (Mon – Fri)• Shifts start: 8am

BASE CASE CHARTS1. Staffing pattern shows the hours worked –

5 days per week, 8am-8pm2. Service request pattern shows requests per hour

over the week3. Distribution of waiting times: We examine here

the average and longest customer waiting time, and percentage of customers waiting more than 24 hours.

Mon Tue Wed Thu Fri Sat Sun

Service Request Pattern

Waiting times

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75-80

No. of items in the system

Key Statistics

1 Mean waiting time2 Maximum waiting time3 Proportion of time server is idle4 Number of items in system for more than 24 hours

No. of hours a request has been waiting

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3. Waiting Times

1. Staffing Pattern (8am-8pm)



Waiting times

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Shift worked

8am – 8pm

8am – 10pm

8am – midnight

8am – 2am

8am – 4am

30 (hours)

29 (hours)

28 (hours)

27 (hours)

26 (hours)

60 (hours)

58 (hours)

56 (hours)

54 (hours)

52 (hours)

61%

60%

57%

54%

52%

Average waiting time Maximum waiting time % waiting >24 hours

SCENARIOSWe can see that in all scenarios, the lack of weekend working has a dramatic impact on the customer experience.

IMPLICATIONSMany retailers experience (and have become inured to) the Monday backlog. Unfortunately, this can have a ripple effect for the rest of the week and create a systemically poor experience for customers.

Human processes are typically not visible and measurement of average outcomes obfuscate the real issues. It is critical for retailers to understand the value of service and instrument the business to get visibility of service failures.

***

The economics of omni-channel are different and complicated – what’s got us here won’t get us there. It is very easy not to make money, and it is very easy to not understand how to make money.

While many things improve with scale (negotiable and with fixed costs); other things get worse (customers get more expensive to acquire the further you get from home and

heartland). Tried and tested formulas for retail success will need to be rethought. However it is not all doom – there is significant money to be made for many retailers in this new era. Especially for those who can:

(i) Understand profitability: Where do we make/lose money

(ii) Optimise profitability: Where can we make things better

(iii) Prioritise investments: Where to spend next $/£

This requires new skills beyond the simpler counting of assessing profits by store or category. There is a new level of analytical and statistical complexity to untangling omni-channel profitability.

Finally, it’s important to have a mind-set that will evolve as new data becomes available, and as omni-channel customer behaviour becomes easier to track. In the words of John Maynard Keynes: “When the facts change, I change my mind. What do you do, sir?”

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Tel: +44 (0)203 530 5800

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[email protected]

Untangling omni-channel retail

Documents

online value

lapsed online customer

online returns

online bobos

online offersdiscounts

digital channel

channel dangerous

customer behaviour