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Breakout Presentations Stream A

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Breakout Presentations Stream A

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Breakout Presentations Stream A

  1. 1. Carolyn Breeze Head of Australia Braintree
  2. 2. Contextual Commerce Powered by Braintree’s commerce infrastructure tools Braintree is a service of PayPal, Inc. © 2008 - 2017 PayPal, Inc. Q2, 2017
  3. 3. 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?
  4. 4. 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.
  5. 5. Braintree’s commerce infrastructure tools already power payments for contextual experiences with…
  6. 6. 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 APIF > Travel
  7. 7. Paul Tannock Studio 60
  8. 8. We always start with why Solutions 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 ideas Our strength is in executing them better than anyone else It’s not just one solution We 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
  9. 9. Supercharging the Future of Retail with Commerce Cloud Einstein Retail Connect | Melbourne
  10. 10. Florent Benoit Principal Success Specialist
  11. 11. “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 Kurzweil ​American Author, computer scientist, inventor and futurist
  12. 12. What is Einstein, and how does it work? Personalised recommendations based on the Shopper’s preferences and onsite behaviour
  13. 13. 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
  14. 14. Building Blocks of Personalisation ​One-to-All > One-to-Some > One-to-One Individualization One-to-One Segmentation One-to-Some Dynamic Merchandising Static Content PersonalisationOne-to-All Predictive Recommendations Dynamic Customer Groups Source Code Groups Dynamic Sorting Rules
  15. 15. 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)
  16. 16. 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 145637 876539 727457 554612 665390
  17. 17. 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
  18. 18. Type Home Page Footer Any other page (Account, Wishlist) Category Landing 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
  19. 19. 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 viewed also 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 Page Account Page Footer Cart Mini-Cart Wish List • Real-Time Personalised • Recent top sellers Recently Viewed Shows products recently viewed by the shopper None Any Page • Recently Viewed
  20. 20. Recommender Setup Video ​Video
  21. 21. Step by Step enablement What is required?
  22. 22. 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 machine learning algorithms to process it: • Collaborative filtering • Unsupervised, semi-supervised, supervised learning • Deep learning The feeds have to be enabled by the Site Administrator on Production More details in Commerce Cloud Einstein Help
  23. 23. Optimising Your Recommendations Elaborate a strategy and test, test, test!
  24. 24. Einstein AB Test Use Cases ​Alternate Product Recommendations on the PDP Section Settings Recommender Type Products to Product Strategy Primary: Customers who viewed also viewed Secondary: 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
  25. 25. Einstein AB Test Use Cases ​Product Recommendations on the Basket Page Section Settings Recommender Type Products in ALL Categories Strategy Primary: Real Time Personalized Secondary: 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.
  26. 26. Commerce Insights Correlations You Had Not Thought Of
  27. 27. ​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
  28. 28. ​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
  29. 29. 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!
  30. 30. 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.
  31. 31. Feedback From Our Customers
  32. 32. “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.
  33. 33. Predictive Sort Promote the right product, first
  34. 34. 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
  35. 35. 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.
  36. 36. Why You Should Use Predictive Sort ​Benefits: • 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
  37. 37. Einstein Predictive Sort ​Steps 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
  38. 38. “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 results Simple implementation- “less than 5 minutes of work”
  39. 39. The Future of Einstein Product Roadmap
  40. 40. 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.
  41. 41. 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
  42. 42. 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
  43. 43. Anda Kizi Creative Director, Amblique
  44. 44. James Rothera eBusiness & Digital Marketing Manager, Sony
  45. 45. 50© 2017. ALL RIGHTS RESERVED. Implementing Data Driven UX
  46. 46. 51© 2017. ALL RIGHTS RESERVED. More About Us James Rothera eBusiness & Digital Marketing Manager • Heads up ANZ online trading & operations • Been at Sony for 10+ years • Oversaw the re-launch onto Commerce Cloud Anda Kizi Creative Director • Heads up all design, UX & CX • Over 10 years in the industry • Balances brand with best practice
  47. 47. 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
  48. 48. 53© 2017. ALL RIGHTS RESERVED. Improving the Everyday
  49. 49. 54© 2017. ALL RIGHTS RESERVED. Data Driven Design
  50. 50. 55© 2017. ALL RIGHTS RESERVED. Design One
  51. 51. 56© 2017. ALL RIGHTS RESERVED. Design Two
  52. 52. 57© 2017. ALL RIGHTS RESERVED. Data Driven Design Click mapping Scroll mapping
  53. 53. 58© 2017. ALL RIGHTS RESERVED. Data Driven Design Specifications Breadcrumb navigation Image thumbnails
  54. 54. 59© 2017. ALL RIGHTS RESERVED. Cross Category Behaviour Different products, different behaviour Different categories, different behaviour
  55. 55. 60© 2017. ALL RIGHTS RESERVED. Design Three
  56. 56. 61© 2017. ALL RIGHTS RESERVED. Toolkit - The 5 minute setup • Heatmapping • Tracking Engagement • Tag management • Ease of implementation x
  57. 57. 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
  58. 58. 63© 2017. ALL RIGHTS RESERVED. Thank you James Rothera james.rothera@sony.com Anda Kizi ak@amblique.com
  59. 59. Jamie Cairns Commercial Director Fluent Retail
  60. 60. Confidential Out-Convenience the Competition By Fluent Retail
  61. 61. 66 Confidential OPTIMISE LOCATIONS by - Utilising all sources of inventory - Make smart decisions - Staff Experience = Customer Experience Distributed Order Management
  62. 62. 67 Confidential • Ship from Store • eBay – pickup in store • Rapid time to market • Direct eParcel integration • Simple store tools • Coming soon – Click & Collect Case Study – Shaver Shop
  63. 63. 68 Confidential • Click & Collect • Rapid deployment • Convenient option • Additional service in store • Minimal training required Case Study – MJ Bale
  64. 64. 69 Confidential • 100’s locations • Multi-brand • Global, split Fulfilment • Pick Up/Ship From Store • Return in store • Endless aisle kiosks • Add To Cart reservation Case Study – JD Sports
  65. 65. 70 Confidential • 400 Stores, various grades, attributes • Distributed fulfilment model • Pick Up/Ship From Store • Inter-store transfer • Complex Pick & Pack req • Bulky, high quantity orders • Replace custom-built solution Case Study - Target
  66. 66. 71 Confidential Typical Retail Software Environment Custom Coding eCommerce Custom Coding ERP Custom Coding PoS Custom Coding Warehouse Management Custom Coding 3rd Party Logistics Custom Coding CRM Custom Coding OMS
  67. 67. 72 Confidential Configurable Microservices Distributed Order Management by Fluent Retail Fluent Orchestration Cloud Orchestration Engine Commerce Order Management In-Store Fluent Insights Fluent Connect Microservice-based Applications ServicePoint Locker Client Pick Pack & Ship Shipping Mgmt Endless Aisle Fulfillment Options Single View of Inventory Live ATS, ETA & Fees Location Networks Open APIs Natively Cloud, Global, Multi-tenant, Infinitely Scalable, Flexible, Agile Business User Tooling Sell Anywhere Fulfill From Anywhere Return Anywhere
  68. 68. 73 Confidential • Cross functional – whole of retail proposition • Consider your uniqueness • Use flexible systems that are designed for the job • Iterate, quickly • Staff experience is critical • Look at core systems for rich data to optimise decision making Key considerations
  69. 69. From Analytics To Action Retail Connect | Melbourne
  70. 70. Florent Benoit Principal Success Specialist
  71. 71. Methodology Sampling • 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
  72. 72. 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
  73. 73. Overview of the APJ Region Evolution of the device usage by country
  74. 74. 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
  75. 75. 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
  76. 76. 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
  77. 77. 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
  78. 78. New Business Manager Dashboards What is coming? What is next?
  79. 79. Reports & Dashboards Available Globally Q4 Track revenue, products, and promotions Use advanced filters for customer type, site, products and channel Get results for one or multiple sites
  80. 80. Reports & Dashboards Multi-site reporting Business users can now see one report across all of their sites New filtering capabilities Business 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
  81. 81. Analytics Demo Video
  82. 82. Turning Insights Into Actions Using analytics to drive and measure change within your retail business What is next?
  83. 83. Louisa Simpson Ecommerce Consultant
  84. 84. Use Benchmarks to Identify Areas of Opportunity Quarterly reports from SFCC give you insight into where you are overachieving & falling short
  85. 85. 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
  86. 86. 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
  87. 87. 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
  88. 88. 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
  89. 89. 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
  90. 90. 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
  91. 91. 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?
  92. 92. 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
  93. 93. 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
  94. 94. 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
  95. 95. 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)

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