Post on 21-Jan-2018
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
51© 2017. ALL RIGHTS RESERVED.
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
52© 2017. ALL RIGHTS RESERVED.
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
53© 2017. ALL RIGHTS RESERVED.
Improving the Everyday
54© 2017. ALL RIGHTS RESERVED.
Data Driven Design
55© 2017. ALL RIGHTS RESERVED.
Design One
56© 2017. ALL RIGHTS RESERVED.
Design Two
57© 2017. ALL RIGHTS RESERVED.
Data Driven Design
Click mapping
Scroll mapping
58© 2017. ALL RIGHTS RESERVED.
Data Driven Design
Specifications
Breadcrumb navigation
Image thumbnails
59© 2017. ALL RIGHTS RESERVED.
Cross Category Behaviour
Different products, different behaviour
Different categories, different behaviour
60© 2017. ALL RIGHTS RESERVED.
Design Three
61© 2017. ALL RIGHTS RESERVED.
Toolkit - The 5 minute setup
• Heatmapping • Tracking Engagement
• Tag management• Ease of implementation
x
62© 2017. ALL RIGHTS RESERVED.
But… we’re not done
• Still having fun with this
• Many more pages to go
• Incremental changes are key
• Test and fail fast
63© 2017. ALL RIGHTS RESERVED.
Thank youJames Rothera
james.rothera@sony.com
Anda Kiziak@amblique.com
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
Use Benchmarks to Identify Areas of Opportunity
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
Use Benchmarks to Identify Areas of Opportunity
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)
● 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
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?
Use Benchmarks to Identify Areas of Opportunity
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)
● 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
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)