Analytics in Retail for Strategic Growth - SAS · Analytics in Retail for Strategic Growth ......

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Analytics in Retail for Strategic GrowthLori Schafer Executive Advisor, SAS Retail


SAS FORUMRUSSIA 2017

Understanding the New Consumer

Personal consumption is up, but retail sales remain sluggish (lowest since recession of 2008 N. America)

Conspicuous consumption to conscious consumption

Shoppers spending more on experiences and less on products

Consumers, especially Milennials, see experiences as building longer-term memories than products

A major shift is happening in the way people shop

and what they want to buy

PresenterPresentation NotesA major shift is happening in the way people shop and what they want to buy!



Understanding Milennial / Gen-Z Shopping Habits

16 36 years old

Most tech saavygeneration

WantDiscounts

Personalization& In-Store Experience

unimpressedby marketing or

advertising

Gather opinions on social media

before decision

Prefer spending on

experiences over tangible goods

In 2020, will represent 30%

of all sales; $1.4 Trillion in U.S.

Rent vs.OwnIndividualism



Competitive Landscape

Strong Web presence now the price of entry into retail

Omni-Channel capabilities are an expectation

One-stop shops like Amazon, Target and Walmart will continue to dominate

Traditional malls will become a thing of the past

Pop-up shops and showrooms will emerge as an appealing alternative

Fast fashion retailers will continue to build their competitive presence



Priorities of the Retail CEO

We need to be more agile in responding to our customers needs online and in store

David Jaffe, CEO of Ascena Retail Group

We have seen a drastic reduction in the amount of people physically visiting the store. Theres so much competition online, I am constantly thinking of new marketing techniques to get people to shop in my store

CEO, Apparel & Accessories Retailer

We need to enhance our in-store experience and Internet/Home delivery, offering state-of-the -art digital tools

CEO,Top 5 Grocery Retailer

Improving technology to grow the linkage between the Internet & the Store to enhance the customer experience is our top goal

Jerry Storch, CEO, Hudsons Bay



PresenterPresentation NotesIt all start with good dataIn a true Omnichannel experience, we must understand how the customer engagesthrough any and all channelsSAS Omnichannel Analytics brings together all the data for how consumers engage with the retailerData is cleansed and harmonized to provide a single view of the customer journey



Out of the Backroom.into the Boardroom Enabling the CEO Agenda for Change

Its all about shareholder value!

Adding Top Line Growth Through Predictive Analytics



Focus on the right customersWhich customers should you target for the ideal customer lifetime value?

Offer the right assortmentsBased on online and in-store sales, are the right products available to the trade area and are they profitable?

Cover DemandEnough inventory to cover demand?

Efficient StoresIs your Energy usage, Staff and Equipment performing at their best ?Personalize Customer Experience

What is the best tuned path to purchase for each and every visitor ?

Protect your businessWith the vast number of returns, transactions, purchase orders and network connections, how do you determine what is legitimate?

Analyze for opportunitiesHow do we detect, discover and determine the root cause of issues effecting your business?

PresenterPresentation NotesRetail is facing its most revolutionary period. The potential of the Data retailers have and can get is Phenomenal .Analytics is the enabler for Retail to be successful with all of its new technology opportunity as well as the new engagement model that the customers and consumers demand.

SAS has recognised that as Retailers reorganise themselves to stay competitive and profitable - Omnichannel Analytics are required to best serve the Customers with the most relevant and timely message- and equally to enable the commercial decision making across the organisation to ensure Sales and Profits can grow.

As the diagram depicts the Customer moves central to the whole organisation , and what were once channels become Communication routes to the customer.Often one single customer might use 3 or 4 of these communication routes in one shopping mission with the retailer- Challenging for the retailer in terms of Content and efficiency,

How often of you found yourself using a retailers web shop to try to see if the product you want is in a store near you ? A recent Survey told us that only 15% of Retailers In the UK have visibility of the assortment at a store level and not all of these have a read of live stock massive opportunity to satisfy more customers.

Our Customers find themselves exposed ,in situations such as major Marketing campaigns For example : where that Dress featured on Bus Shelters in every city and on landing pages of the web shop never quite got planned as that kind of featured product by Merchandising - and Supply chain scrabbling to Fulfill orders.

These situations arise because the Data is often in silos , marketing separate from merchandising and supply chain.

Omnichannel Analytics facilitates bringing this data together and even allowing users to model the possible outcomes of situations in order to future proof their decisions- - That Dress thats now in short supply and availability- Should I consolidate stock to limited outlets , online only ? When it comes back to stock where should we put it for maximum Sales and Profitability?

Omnichannel Demand is a key strength of SAS Our ability to Model and Forecast the signals of demand is perfect for the complexity that retailers now face. Where in the past the strongest demand signal was in the store at fillpoint or at the empty fixture or shelf its now shifted to be a complex layering of signals across these communication channels What if you could use Browsing behaviour form your webshop as a signal of demand ?

Lets delve into the Kind of Business challenges that SAS is resolving in each business function:



PresenterPresentation NotesSAS Viya Open Platform supporting the Analytics lifecycle from data discovery to deployment

Cloud Analytics is are Software and Results as a service strategy

We are embedding AI into the entire portfolio to further enhance analytic capabilites.

The wide range of IOT data becoming available is only valuable when retailers can act upon it in the right manner and timely fashion.



Assortment Planning tied to Allocation 17% Inventory Savings (2016 Financials) 24% increase in EBIT Over 1,2M to reinvest in the business

without any additional inventory

Size / Case Pack Optimization 1% reduction in stock outs 30bps (.3pp) increase GM 70bps (10% gain) in pre-tax Profit 10% flow through to pre-tax profit

Price Optimization 310 bps (3.1pp) gain in GM 91% increase in annual pre-tax profit 5.0pp increase in EBIT

Promotion Optimization / Demand Forecasting 10-30% inventory reduction 2-5% revenue increase 5-20% increase in product availability10-50% improvement in forecast accuracy

Case Studies



Assortment Planning, Optimization& Inventory Allocation



Vertically Integrated Retailer; 295B in revenue, 5,000 stores globally

International Specialty Retailer

Increased Revenue and Profitability- Better targeted assortments with clusters

- Higher average sales and profitability

Reduced Inventory Cost- Unique store inventory plans based on actual facings

- Tighter managed product life-cycle (discontinued)

- Eliminated duplicate stock with substitute/replacement

Increased Productivity- Translating Assortment Plans into Vendor Forecasts/Purchase Orders

- Consistent tools and business processes across divisions

- Automation allowing for lower level decisions/execution



Sample Income Statement Using Actual Retailers ResultsRubles (RUB) % of Sales

Net Sales 290,000,000,000Gross Margin 130,500,000,000 45.0%Inventory @ Retail 72,500,000,000 25.0%Inventory @ Cost 36,250,000,000 12.5%Inventory Reduction (17% reduction) 6,162,500,000 2.5%

EBIT 29,000,000,000 10%

Sales Improvement 8,700,000,000 3%GM Resulting from Sales Increase 3,886,000,000 45%GM Improvement 2,900,000,000 1.3%Increase in EBIT 6,960,000,000 24%

Planning / Forecasting / Allocation Value PropositionAssumptions: IMU of 50%, GM of 45%, Average Turnover: 4

Yields 6,162,500M Redeployment of $ Assets to New Store Openings



Demand Forecasting

PresenterPresentation NotesDemand Forecasting

10-30% inventory reduction2-5% revenue increase5-20% increase in product availability 10-50% improvement in forecast accuracy Improved customer satisfactionSignificant time savings.



Before Store Forecasting reacts to

events, does not predict them 50% of short life

line/store/day forecasts more than case different from actual

Warehouse forecasts inconsistent with store forecasts

Use increased stock during promotions to overcome uncertainty in demand

Forecast Accuracy too low

Out-of-stocks / over-stocks during promotions and weeks following

Over-spending on promotions / price-cuts

Increased waste on fresh foods

Lost sales and customer dissatisfaction

Lower productivity

PresenterPresentation NotesGrocery Chain (184 stores, $5.8B sales, 25,000 sku/store)Goal: improve performance during promotional periods, especially in prepared foodsBusiness ProblemOut-of-stocks / over-stocks during promotions and weeks followingOver-spending on promotions / price-cutsSignificant loss of MarginTechnical ProblemForecast accuracy needed to improveForecast models need to be created for 4,500,000+ SKU-Stores. Forecasts need to be updated weeklyThere are a number of problems with this set up The forecasting does not spot events and only reacts to them afterwardsThe forecasting is not very accurate (for example in short life goods for store forecasting, half of our forecasts are wrong by more than half a case causing wastage or shrinkage and affecting availabilityThere is no link between the forecasting taking place for our stores and that taking place for our warehousesTo cope with uncertainty around one of our key events we put excess stock through the supply chain to provide a buffer.



After.promotions several sample itemsJohn West Tuna

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demand Old System New System

New SAS Forecasting system Predicts the start of the promotion

Lemon Juice

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demand New System Old System

New SAS Forecasting system Predicts the start of the event

Old promotional systemdoes not spot the event

PresenterPresentation Notes we have added a red line to indicate the forecasts for the new system. AS you can see, although the forecasts are not perfect (and never will be) the system does much better at predicting the start of the promotion.

Chart2

62.988387072.6884394403

34.229387083.8215235985

83.89387094.2660659233

23.918387105.8822936245

05.666387110.0701294304

04.567387120.0326259487

52.932387134.53361974

32.988387143.3769114008

44.229387154.3060114347

43.89387164.9560719748

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54.567387193.7490422453

72.932387204.2321381095

92.988387213.1943399493

34.229387224.1483741865

43.89387234.8985778227

53.918387246.1511417192

56.464387254.9975462739

45.639387264.2357476418

33.789387274.7357801765

33.978387283.6614506524

45.468387294.6453224703

84.772387305.2890160968

64.686387316.1851534414

15.614387324.4637917612

14.412387333.8788159689

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03.721387353.6481424562

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35.005387374.7154605615

44.439387385.4387778479

63.119387393.9463842486

22.471387403.0882553013

31.74387413.4720311902

11.095387422.5951416435

72.412387433.5245906537

43.929387444.0133428777

64.273387454.8980929565

33.74387463.2269325152

32.388387473.0508303465

12.988387483.5294736981

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65.395387524.808688874

54.203387533.1782816552

182.1683875425.568203969

241.763875529.229634412

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3015.663876122.952836878

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139.7873876323.632733018

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2820.5393876724.608261125

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2020.7473876921.867792945

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demand

Old System

Offsale Ind

New System

John West Tuna

213 20587

branchnumlinenumLONGNAMECASE_SIZECASE_SIZE_UOMdatedemandNew SystemOld SystemOffsale Ind

21320587John West tuna chunks in brine 4x185g12U12/21/0562.68843944032.988











21320587John West tuna chunks in brine 4x185g12U1/1/06005.6661































































21320587John West tuna chunks in brine 4x185g12U3/5/061918.6704343123






213 20587

demand

New System

Old System

Offsale Ind

Off Sale Ind

John West Tuna

Chart1

35.01173385133.168

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demand

New System

Old System

Lemon Juice

233 6116

branchnumlinenumLONGNAMECASE_SIZECASE_SIZE_UOMdatedemandNew SystemOld SystemOffsale Ind

2336116Jif juice lemon 250ml12U12/21/0535.01173385133.168




2336116Jif juice lemon 250ml12U12/25/05001.424













































2336116Jif juice lemon 250ml12U2/8/064.192.92626232273.2591































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demand

New System

Old System

Offsale Ind

Off Sale Ind

Jif Lemons



Reduced $ inventory by 41% and increased revenue by $2,378 Billion

Forecast Error:Safety Stock:Lost Sales due to OOS: Revenue:

25% 1,160M5% 78,880B

15% 684,4M2% 81,200B

Before After

Results Prepared Food



Revenue Optimization

PresenterPresentation NotesDemand Forecasting

10-30% inventory reduction2-5% revenue increase5-20% increase in product availability 10-50% improvement in forecast accuracy Improved customer satisfactionSignificant time savings.



Identifies SKU/store price points that maximize profits

Before After




Price is one of the most important retail profit levers.

A 10% change in . . . . . . Leads to a change in profit of

50%

30%

20%

Note: Illustrative estimates based on a 50% gross margin and non-product variable costs at 20% of sales

Price 100MM 110MM 10MM 20MM

Unit Cost 50 45 10MM 15MM

Sales Revenue 100MM 110MM 10MM 13MM

Fixed Expenses 20MM 18MM 10MM 12MM

Profit Lever Original

After 10%

ChangeOriginal Profit

New Profit

100%




Sample Income Statement Actual Retailers ResultsRubles (RUB) % of Sales

Net Sales 116,000,000,000Initial Markup 63,800,000,000 55.0%Cost of Goods 52,200,000,000 45.0%Promotional Markdowns 23,200,000,000 20.0%Permanent Markdowns 34,800,000,000 30.0%Total Markdowns (Sell) 58,000,000,000 50.0%Total Markdowns (Cost) 26,100,000,000 22.5%

Gross Margin 37,700,000,000 32.5%

The difference betweenIMU and Gross MarginIs 22.5 Percentage Points

Better managing markdowns directly improves margin which directly flows through to pre-tax profitability

Markdowns are a fashion retailers single largest expense.

Revenue - Markdown Optimization



Before Optimization (RUB)

% of Sales

After Optimization

% of Sales

Net Sales 116,000,000,000 123,888,000,000Initial Markup 63,800,000,000 55.0% 68,134,400,000 55.0%Promotional Markdowns 23,200,000,000 20.0% 24,777,600,000 20.0%Permanent Markdowns 34,800,000,000 30.0% 28,742,016,000 23.2%Total Markdowns (Sell) 58,000,000,000 50.0% 53,519,616,000 43.2%Total Markdowns (Cost) 26,100,000,000 22.5% 24,083,827,200 19.4%

Gross Margin 37,700,000,000 32.5% 44,054,572,800 35.6%

PRETAX -Operating Earnings (EBITDA)

6,960,000,000 6.0% 12,760,000,000 11.0%

Percentage Point Gain 3.1

Net Result: 91% increase in Earnings

Revenue - Markdown Optimization



Size & PackOptimization

Get the right size to the right store at the right time.

Recognize customer differences by store location, by department and by category.

Improve in stocks by size for basic, seasonal basic and fashion merchandise.



Current Size Distribution Actual Demand Size Profiles Womens Department



2016 Stock-out Rates For All Stores By Sizes

Stockouts are very high

For those styles with 4 sizes, at least 1 size is stocked out by store/style/week over 55% of the time

Number Sizes Stocked Out Frequency Percent

Cumulative Frequency

Cumulative Percent

0 5,081 44.53 5,081 44.531 2,738 24.00 7,819 68.532 1,702 14.92 9,521 83.443 1,228 10.76 10,749 94.214 661 5.79 11,410 100.00

Number of Sizes Store/Weeks Are Stocked Out



Cluster-based Size Allocations

versus

Average Extra Small Medium Med/Large Large

One scheme fits all



Sample Income Statement Using Actual Retailers ResultsRubles (RUB) % of Sales

Net Sales 58,000,000,000Gross Margin 23,200,000,000 40.0%Pre-Tax Income 4,060,000,000 7.0%

Net Sales Improvement 580,000,000 1.0%Gross Margin Improvement 232,000,000 0.4%Pre-Tax Income Improvement 232,000,000 0.4%Revised Pre-Tax Income 4,292,000,000 7.4%

Margin Improvement 174,000,000 0.3%Revised Pre-Tax Income 4,466,000,000 7.7%

10% flow through to pre-tax income

Size Optimization Value Proposition

Results: a 1%Reduction in Stock outs and 30bpsImprovement in Gross Margin


SAS in RetailRecognized as Industry Leader by Forrester and Gartner

Industry Thought-Leaders

Global Presence

Analytic Expertise

Open Analytics Platform

Analytics in Retail for Strategic GrowthUnderstanding the New ConsumerUnderstanding Milennial / Gen-Z Shopping HabitsCompetitive LandscapePriorities of the Retail CEOSlide Number 6Out of the Backroom.into the Boardroom Enabling the CEO Agenda for ChangeSlide Number 8Slide Number 9Case StudiesSlide Number 11International Specialty RetailerSample Income Statement Using Actual Retailers Results Slide Number 14Before After.promotions several sample itemsSlide Number 17Slide Number 18Identifies SKU/store price points that maximize profitsPrice is one of the most important retail profit levers.Sample Income Statement Actual Retailers ResultsSlide Number 22Slide Number 23Slide Number 242016 Stock-out Rates For All Stores By SizesCluster-based Size Allocations Sample Income Statement Using Actual Retailers Results SAS in Retail

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