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Carolyn BreezeHead of Australia

Braintree

Contextual CommercePowered by Braintree’s commerce infrastructure tools

Braintree is a service of PayPal, Inc. © 2008 - 2017 PayPal, Inc.

Q2, 2017

Consumer expectations and behaviors are changing rapidly.The emergence of contextual commerce is creating a fundamental shift in discovery and purchase interactions. How are you seizing this opportunity?

Contextual commerce enables consumers to make seamless purchases at the moment of discovery, in the context of everyday activities.

It’s Buyable Pins on Pinterest and the ability to book an Uber or Lyft through Facebook Messenger. In-context, frictionless buying experiences are made possible by partnerships that create new distribution channels.

Braintree’s commerce infrastructure tools already power payments for contextual experiences with…

Time to go!Ava searches for flights from SIN to SFO on Skyscanner. She finds a good deal, but rather than being redirected to another site to complete the purchase, she books her flight in a few clicks directly on Skyscanner, with her payment method on file.

With Braintree, Skyscanner has seen up to 20% lift in flight booking conversion, up to 50% lift in mobile conversion rate and up to 100% lift in ancillary purchases. With a seamless contextual checkout, not only do users buy more often, but they buy more.

Forward API F > Travel

Paul TannockStudio 60

We always start with whySolutions are only effective if they start with a defined

problem. It’s why we ask the right questions to get a true understanding of what your needs are

It’s not just about great digital ideasOur strength is in executing them better than anyone else

It’s not just one solutionWe know every business is different which is why we focus

on the right technology to create the best experience possible

It’s about shared success We are successful when our customers are successful;

there are no sides, only shared goals

Supercharging the Future of Retail withCommerce Cloud EinsteinRetail Connect | Melbourne

Florent BenoitPrincipal Success Specialist

“Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.”Ray KurzweilAmerican Author, computer scientist, inventor and futurist

What is Einstein, and how does it work?Personalised recommendations based on the Shopper’s preferences and onsite behaviour

Product Recommendations for Digital

Leverage Commerce Data• Put the power of retailer’s data in their own hands

Personalise Across Channels

• Seamless shopper experience across mobile, desktop, and store touchpoints

Focus on Your Business

• Simplify merchandising for retailers- no data scientist required

Personalise recommendations across channels

Building Blocks of PersonalisationOne-to-All > One-to-Some > One-to-One

IndividualizationOne-to-One

SegmentationOne-to-Some

Dynamic Merchandising

Static Content

PersonalisationO

ne-to-All

Predictive Recommendations

Dynamic Customer GroupsSource Code Groups

DynamicSorting Rules

Commerce Cloud Einstein Data Sources

Product data• Learns about products, attributes, prices,

inventory

Order data• Learns about product relationships

(i.e. which products are bought together)• Learns about user affinity (i.e. who bought what)

Clickstream data• Learns about session behaviour

(i.e. who looked at what)

How Product Recommendations Work

Shopper comes to site and Commerce Cloud

Engine is called

Engine returns the product IDs

Storefront page displays best product

recommendations

Create & assign recommender

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Benefits of Commerce Cloud Einstein

Tracking & data learning already running (automatically activated after release 16.1)

• Recently Viewed Items

• User ID

Content slot integration• Scheduling

• Customer groups

• A/B Test

• Content vs. Products

• Campaigns

Flexible configuration of rules

Built into the platform

Type Home Page Footer

Any other page

(Account, Wishlist)

CategoryLanding

Page

Category Grid Page

Product Detail Page Cart Page

Recently Viewed Items

★ ★ ★ ★ ★ ★ ★

Based on all Categories ★ ★ ★ ★ ★ ★ ★

Based on current

Category★ ★

Based on current

Product(s)★ ★

Currently Supported Types and Locations

Types of Recommenders based on their Location

Type Description Anchor Expected

Typical Placement

Default Strategies

Product to Product Given a product or list of products, recommends similar/related affinity products

Product-id PDP • Customers who viewedalso viewed

• Product Affinity Algorithm

Products in A Category

Given a category, recommends products from within that category

Category-id Category Pages • Real-time personalised• Recent Top Sellers

Products in ALL Categories

Recommends products from across ALL categories

None Home PageAccount PageFooterCartMini-CartWish List

• Real-Time Personalised• Recent top sellers

Recently Viewed Shows products recently viewed by the shopper

None Any Page • Recently Viewed

Recommender Setup VideoVideo

Step by Step enablementWhat is required?

First Step – Data Enablement

Set up your data feeds• Product catalogue feed

• Order history (or legacy sites, store data)

• Clickstream data

The PI engines “digests” your data and uses machinelearning algorithms to process it:

• Collaborative filtering

• Unsupervised, semi-supervised, supervised learning

• Deep learning

The feeds have to be enabled by the Site Administratoron Production

More details in Commerce Cloud Einstein Help

Optimising Your RecommendationsElaborate a strategy and test, test, test!

Einstein AB Test Use CasesAlternate Product Recommendations on the PDP

Section Settings

Recommender Type Products to Product

Strategy Primary: Customers who viewed also viewedSecondary: Product Affinity Algorithm

Rule Any Product > DEMOTE > product_type = Match Anchor

Hypothesis Updated recommender will produce more revenue specific to recommendations and increase basket size of global experience.

Enabled Yes

Key Metric Average Units Per Order

Participation Trigger Pipeline Call: Pipeline: Product-Show

Control (50%) Existing slot configuration

Test Segment A (50%) New slot configuration containing new recommender with settings/configurations recommended above

Einstein AB Test Use CasesProduct Recommendations on the Basket Page

Section Settings

Recommender Type Products in ALL Categories

Strategy Primary: Real Time PersonalizedSecondary: Recent Top Selling

Hypothesis Including recommendations on the basket page increases AOV, but adversely affects Avg. Revenue per Visit.

Enabled Yes

Key Metric Avg. Revenue per Visit

Participation Trigger Pipeline Call: Pipeline: Cart-Show

Control (50%) No recommendation displayed

Test Segment A (50%) Einstein Slot – Products in ALL Categories

Hypothesis Including recommendations on the cart page increases AOV but adversely affects Avg. Revenue per Visit.

Commerce InsightsCorrelations You Had Not Thought Of

Discover the previously undiscoverable• Learn from your own Commerce data by

uncovering key product purchase correlations

Plan Store & Site Merchandising Smarter• Discern which products should be grouped

together for product bundles, deals and store merchandising

Truly understand your customers• Dig into purchase patterns to gain true awareness

Commerce Insights

The Commerce Insights Dashboard has various views:

• First view (previous slide), allows a retailers to choose a key item and see the items most commonly purchased with it.

• Second view (here), allows a retailer to click into that key items and discover additional insights (i.e. correlated products baskets and percentage rates)

Commerce Insights

Discover Product Sets You Had Not Thought Of

What are Shoppers buying together?

Use Einstein Ecommerce Insights to provide input on set combinations your merchandising team hasn’t thought of – that customers did!

Create Content to Support Seasonal Trends

Identify Seasonal Trends• Commerce Insights shows a high volume of

baskets with complementary winter camping products

Revisit and Refresh Existing Content• The ”Winter Camping Essentials” story has been

evergreened but obviously people are still purchasing items from it.

Feedback From Our Customers

“If you’re not using Commerce Cloud, you’re missing out on quite an opportunity.”Brian Hoven, Global Head of eCommerce, Icebreaker

Icebreaker Uses Einstein to Power Product Recommendations Outerwear and lifestyle clothing – 5,000 stores across 50 countries.

Web site powered by Commerce Cloud with product recommendations from Einstein.

40% more clicks, 11% higher average order value, 28% more revenue from recommended

products.

Predictive SortPromote the right product, first

Einstein Predictive Sort – Available now!

Create 1:1 Grid Pages• Personalise search and category pages for every

shopper, anonymous or logged in

Show the Best Products, First• Drive conversion by showing shoppers what they

want, especially in micro moments on mobile devices

Eliminate the Sorting Rule Guessing Game• Increase productivity with easy to use tools in

existing user interfaace

Infuse personalised product assortments into the shopper journey

How does Predictive Sort work?

With every click, Einstein collects the shopper’s browsing events and updates this shopper’s predictive model, in real-time, to calculate the most relevant products for each shopper.

Activities tracked:• viewCategory

• clickCategory

• viewProduct

The data is then used to re-order the results of site searches or grid pages.

Predictive Sort also available as dynamic attribute for your Sorting Rules.

Why You Should Use Predictive SortBenefits:

• Personalise search and category page for each shopper (know or unknown)

• Ensures your shoppers see the most relevant products to them, first

• Saves time by enabling sort personalisation within your existing business tools

• Increases revenue by leading your customers down a more direct path to purchase

• No data scientist needed!

• Eliminates time-consuming tasks of merchants determining the right sorting rules for various

customer groups and product categories

Einstein Predictive SortSteps to enable Predictive Sort on your PIG

Request Participation with your CSM

Data Enablement (if not already done)

Product Grid Template Change

Sorting Rule Configuration & Validation

Use Predictive Sort in your Storefront

“Predictive Sort eliminates the guessing. Being able to sort products, automatically per customer is huge.”Director ecommerce, CPO Commerce

Predictive Sort at CPO Commerce

America’s leading tool retailer known for offering customers high quality tools at great prices

Goal: Show each customers the best products for them

Predictive Sort ensures that anonymous and known shoppers see the best products in category and search resultsSimple implementation- “less than 5 minutes of work”

The Future of EinsteinProduct Roadmap

Forward-Looking Statements

Statement under the Private Securities Litigation Reform Act of 1995:

This presentation may contain forward-looking statements that involve risks, uncertainties, and assumptions. If any such uncertainties materialize or if any of the assumptions proves incorrect, the results of salesforce.com, inc. could differ materially from the results expressed or implied by the forward-looking statements we make. All statements other than statements of historical fact could be deemed forward-looking, including any projections of product or service availability, subscriber growth, earnings, revenues, or other financial items and any statements regarding strategies or plans of management for future operations, statements of belief, any statements concerning new, planned, or upgraded services or technology developments and customer contracts or use of our services.

The risks and uncertainties referred to above include – but are not limited to – risks associated with developing and delivering new functionality for our service, new products and services, our new business model, our past operating losses, possible fluctuations in our operating results and rate of growth, interruptions or delays in our Web hosting, breach of our security measures, the outcome of any litigation, risks associated with completed and any possible mergers and acquisitions, the immature market in which we operate, our relatively limited operating history, our ability to expand, retain, and motivate our employees and manage our growth, new releases of our service and successful customer deployment, our limited history reselling non-salesforce.com products, and utilization and selling to larger enterprise customers. Further information on potential factors that could affect the financial results of salesforce.com, inc. is included in our annual report on Form 10-K for the most recent fiscal year and in our quarterly report on Form 10-Q for the most recent fiscal quarter. These documents and others containing important disclosures are available on the SEC Filings section of the Investor Information section of our Web site.

Any unreleased services or features referenced in this or other presentations, press releases or public statements are not currently available and may not be delivered on time or at all. Customers who purchase our services should make the purchase decisions based upon features that are currently available. Salesforce.com, inc. assumes no obligation and does not intend to update these forward-looking statements.

Einstein Search Dictionaries (GA FEB 2018)

Discover Search Gaps Automatically• Uncover gaps between your search settings and

the way customers are searching for products

Seamless and Easy to Use• Fully integrated feature allows you to improve

search results with a few clicks

Never miss a search term again

Einstein Search Suggestions (BETA Q1 2018)

Show the right product, First• Autocomplete search, tailored to the individual

shopper

Promote search discovery• Power recommended, related, popular, and

recent searches

Anticipate shopper search intent before she/he types

Anda KiziCreative Director,

Amblique

James RotheraeBusiness & Digital Marketing Manager,

Sony

50© 2017. ALL RIGHTS RESERVED.

Implementing Data Driven UX


More About Us

James RotheraeBusiness & Digital Marketing Manager

• Heads up ANZ online trading & operations• Been at Sony for 10+ years• Oversaw the re-launch onto Commerce Cloud

Anda KiziCreative Director

• Heads up all design, UX & CX• Over 10 years in the industry• Balances brand with best practice


On-going improvement & innovation

Phase One:• Go live• Come to grips with the platform and it’s capabilities

Phase Two:• Enrich the customer experience• Leverage all the tools available to create a better experience• Fast changes + iterative/agile• Continual improvement and adjustment• Let the data and UX tell us what needs improving


Improving the Everyday


Data Driven Design


Design One


Design Two


Data Driven Design

Click mapping

Scroll mapping


Data Driven Design

Specifications

Breadcrumb navigation

Image thumbnails


Cross Category Behaviour

Different products, different behaviour

Different categories, different behaviour


Design Three


Toolkit - The 5 minute setup

• Heatmapping • Tracking Engagement

• Tag management• Ease of implementation

x


But… we’re not done

• Still having fun with this

• Many more pages to go

• Incremental changes are key

• Test and fail fast


Thank youJames Rothera

[email protected]

Anda [email protected]

Jamie CairnsCommercial Director

Fluent Retail

Confidential

Out-Convenience the CompetitionBy Fluent Retail

66 Confidential

OPTIMISELOCATIONSby- Utilisingallsourcesofinventory- Makesmartdecisions- StaffExperience=CustomerExperience

DistributedOrderManagement

67 Confidential

• ShipfromStore• eBay– pickupinstore• Rapidtimetomarket• DirecteParcelintegration• Simplestoretools• Comingsoon– Click&Collect

CaseStudy– ShaverShop

68 Confidential

• Click&Collect• Rapiddeployment• Convenientoption• Additionalserviceinstore• Minimaltrainingrequired

CaseStudy– MJBale

69 Confidential

• 100’slocations• Multi-brand• Global,splitFulfilment• PickUp/ShipFromStore• Returninstore• Endlessaislekiosks• AddToCartreservation

CaseStudy– JDSports

70 Confidential

• 400Stores,variousgrades,attributes

• Distributedfulfilmentmodel• PickUp/ShipFromStore• Inter-storetransfer• ComplexPick&Packreq• Bulky,highquantityorders• Replacecustom-builtsolution

CaseStudy- Target

71 Confidential

TypicalRetailSoftwareEnvironment

CustomCoding

eCommerce

CustomCoding

ERP

CustomCoding

PoS

CustomCoding

WarehouseManagement

CustomCoding

3rd PartyLogistics

CustomCoding

CRM

CustomCoding

OMS

72 Confidential

ConfigurableMicroservices

DistributedOrderManagementbyFluentRetail

FluentOrchestrationCloud

OrchestrationEngine

Commerce OrderManagement

In-Store

Flue

ntIn

sights FluentConnect

Microservice-basedApplications

ServicePointLockerClient

PickPack&ShipShippingMgmtEndlessAisle

FulfillmentOptionsSingleViewofInventoryLiveATS,ETA&FeesLocationNetworks

OpenAPIs

NativelyCloud,Global,Multi-tenant,InfinitelyScalable,Flexible,Agile

BusinessUserToolingSellAnywhere

FulfillFromAnywhereReturnAnywhere

73 Confidential

• Crossfunctional– wholeofretailproposition• Consideryouruniqueness• Useflexiblesystemsthataredesignedforthejob• Iterate,quickly• Staffexperienceiscritical• Lookatcoresystemsforrichdatatooptimisedecisionmaking

Keyconsiderations

From Analytics To ActionRetail Connect | Melbourne

Florent BenoitPrincipal Success Specialist

MethodologySampling• Yearly Aggregates from 2014-2016• Analysis focuses on vertical groups with populations of at least 50 sites.• Error thresholds are used to remove out of range data.• Outliers are trimmed by eliminating the top and bottom 10% of the distribution for all subsets.

Metrics• Basket Rate – rate of visits where at least one product was added to the shopping cart• Orders per Checkout – percentage of checkouts started where an order was completed (inverse

of checkout abandonment)• Search Usage Visits – percentage of visits where the shopper searched for something at least

once• Searches with Results – percentage of visits where search was performed and results came

back

Mobile Visit Share

Consumers are using mobile more than ever.

Mobile Visit Share continues to rise across all verticals while desktop and tablet traffic decline.

This trend will only grow stronger with the emergence of Apple Pay, Android Pay and biometric payment methods such as Touché.

Australia

Overview of the APJ RegionEvolution of the device usage by country

Mobile Basket Rate

As customers become more mobile,

The basket rate is also positively impacted across verticals with a “shorter” decision-making process

The verticals seeing the biggest increase are Accessories, Health & Beauty and Luxury

Australia

Orders per Checkout

Across all verticals, orders per checkout increased from 2015 to 2016.

The overall performance of the checkout funnel has been improved, but still leaves some room for optimisation:

• one-page checkout

• guest checkout

• email capture at the end of the process

Australia

Average Search Usage

Search usage patterns are mixed between industries indicating different shopping behaviours.

Search usage is approaching an average of 8% of total site visitors for the accessories vertical.

Australia

Average of Searches with Results

Search result quality and overall search merchandising strategies are getting better.

Approximately 70-80% of site searches across all verticals provide shoppers with search results.

Australia

New Business Manager DashboardsWhat is coming?

What is next?

Reports & DashboardsAvailable Globally Q4

Track revenue, products, and promotions

Use advanced filters for customer type, site, products and channel

Get results for one or multiple sites

Reports & Dashboards

Multi-site reporting Business users can now see one report across all of their sites

New filtering capabilitiesBusiness users can filter by customer type, site, product and much more - providing more granular insight

Report on custom date ranges Previously merchants could only pull reports for fixed date ranges. Merchants can also choose a comparison date range

"Movers and Shakers" Better visibility into hot and cold products

Improvements over current reporting tools

Analytics Demo Video

Turning Insights Into ActionsUsing analytics to drive and measure change within your retail business

What is next?

Louisa SimpsonEcommerce Consultant

Use Benchmarks to Identify Areas of Opportunity

Quarterly reports from SFCC give you insight into where you are overachieving & falling short


Quarterly reports from SFCC give you insight into where you are overachieving & falling shortBAU Initiatives

There are areas over which the businessmanager configuration gives you control (e.g. search conversion, average order value)

● Use these insights to set responsibilities and KPIs for your team

● Regularly review and measure the impact of BAU initiatives you introduce in your team

Quantum leaps

There are areas where development is required to improve the customer journey (e.g. checkout abandonment rate)

● Use these metrics to demonstrate a case for development investment to senior stakeholders

● Use the benchmarks to quantify the potential and build a business case


Quarterly reports from SFCC give you insight into where you are overachieving & falling short

BAU Initiatives

There are areas over which the business manager configuration gives you control (e.g. search conversion, average order value)



Quantum leaps




Example: Average Units per Transaction / AOV

Opportunity: Utilise predictive intelligence to test recommendations

Test in multiple locations• Homepage• PDP • Cart

Test multiple rules (and mixes of rules) within PI tool• Recently viewed• Others also bought / viewed• Product affinity algorithms

Example: Conversion rate

Opportunity: Test sorting rules

Use suite of rules available• Best sellers• Newness• Inventory• Predictive sort

Identify variables that may change results

• Categories• Seasonality

Example: Average Search Usage / Search Conversion

Utilise BM Analytics to identify and act on search improvements

No search results / top search results reports Synonyms / hypernyms etc.

Test search placement and behaviour to increase search usage

Increasing size or prominence Measure the impact on both usage & CVR

Methodology

Utilise AB testing in the Business Manager

VWO / Optimizely / AB Tasty are all great tools to use to simply test changes to content, layout or funnel (e.g. search bar placement, checkout flow)

• They do require some specialist knowledge & training

The SFCC BM tool is more suitable for testing recommendations / PI / sort order / merchandising

• And it can be administered by merchandisers / built into BAU

Methodology

Use insights to feed back to the business

Educate merchandise & buying teams • What are the most effective sorting rules for categories?• What are the most effective strategies for cross-selling?• What are the top failed search results (and could they be ranging opportunities)?

Educate marketing teams• Which content is most engaging?• What product sorting rules are the most effective?


Quarterly reports from SFCC give you insight into where you are overachieving & falling shortBAU Initiatives

There are areas over which the business manager configuration gives you control (e.g. search conversion, average order value)



Quantum leaps




Example: Orders per Checkout / Abandon Checkout

Build a case for change

Utilise analytics and benchmarking to identify areas of opportunity

• E.g. High add to bag ratio + high abandonment rate = checkout flow improvement opportunity

Use benchmarking to quantify size of the opportunity and gain senior-stakeholder buy-in and investment

Example: Investment to grow team capability

Raising awareness of customer and device trends can help support a case for investing in resources at the executive level

Mobile trends can help drive an understanding of the imperative to invest in UX research/capability

Using data from CRO tests and quantifying the revenue benefit (on an annualised basis) can help build a case for investment for dedicated CRO resources

Example: Building a case for CRO

Quantify incremental revenue benefit

Quantifying annualised incremental revenue benefit can help build a case for investment in team resources (e.g. a dedicated CRO resource)