7 michael mokhberi apptus sebc


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Michael Mokhberi från Apptus, Torsdag 26:e maj 2011 SEBC

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  • Overview of Apptus search and Navigation capabilities
  • Live site, customer screen shotBringing back database matches....others don't......so brilliant performanceMost popular at the top
  • Another live site Huge catalogue , fast enough to autocomplete , for every letterFinding hits each match makes Most popular matches
  • Effectively bring back the user requirement – without having to set the synonym “implicit synonyms” – are a bonus!!! Don't have domain specific starter packs !Merchant specific synonyms – merchant IP
  • So this again underlines the importance of linking search and recommendations The recommendations engine, by knowing what was searched for, can make the most appropriate recommendations despite spelling
  • Dynamicnav options (iconised) for a brand landing page !Subset – size and brand sortCustomer is innovative – and they bought our technology!!
  • Animated illustration of how search results and navigation are dynamically optimised using eSales
  • This illustrates the basic “bought – bought” recommendations Look for bottom half of page next
  • Eg shows the films with same actors Pulls out other products with shared attributes / attribute values Can be same fabric e.g. cottonSame wood e.g. Looking at birch chairs, show other birch furniture It is a BIG DEAL !!
  • Illustrating recommendations and how their display can be optimised
  • Showing how content such as campaign banners can be targeted to a user – essentially providing automatic, personalised AB testing and optimisation
  • Snapshot of eSales ManagerLHS column shows available panels containing merchandising componentsMain area shows how they are placed in the page hierarchy
  • Showing how we report on the performance of our merchandising.
  • TODO – add stats for clients
  • 7 michael mokhberi apptus sebc

    1. 1. Intelligent Search<br />ScandinaivaneBusiness Camp, 26th of May<br />
    2. 2. Introducing Apptus<br />Pioneering relevance engine technology<br />Search, recommendations & content targeting for e-Commerce<br />Boosts site profits; streamlines marketing & merchandising back-office<br />Founded in 2000, profitable and VC backed<br />HQ & development in Lund - Sweden<br />Sales offices & partner network in Europe and North America <br />
    3. 3. Apptus clients <br />
    4. 4. 15% of the buyers know exactly what they are looking for<br />70%have a clue, <br />but they are open to guidance<br />
    5. 5. Is it possible to guide & influence without being relevant?<br />
    6. 6. Relevant interactions lead to more and better transactions<br />
    7. 7. When<br />Channel<br />What<br />Where<br />Who<br />
    8. 8. The many dimensions of relevancy<br />WHAT<br />Only 10% of the users know the exact name, article id or specifics of what they are looking for<br />80% of searches address 20% of the database<br />The margin of the long tail is between 50-400% higher than the top list<br />WHERE<br /><ul><li>Nearest store where the buyer can explore the product
    9. 9. Nearest location where the buyer can fetch or return the goods
    10. 10. Location specific purchase/delivery terms</li></ul>WHEN<br /><ul><li>Seasonal influences
    11. 11. Trends(site-specific and general)
    12. 12. Ongoing marketing activities</li></ul>WHO<br /><ul><li>Visit history(duration, time, length, outcome)
    13. 13. Search, Navigation and click track
    14. 14. Preferences (revisits to specific information entities)
    15. 15. Segment orientation and persona
    16. 16. Membership in any VIP or loyalty programs
    17. 17. Purchase history(duration, average order size, context)
    18. 18. Social network and influence
    19. 19. Contract specific terms for Business-2-Business</li></ul>Anonymous<br />Channel<br /><ul><li>Web, Mobile, eMail, MMS, Store tills</li></ul>Logged in<br />
    20. 20. Ever met a great salesman who suffered amnesia?<br />
    21. 21. We need to recall:<br /> * What we have shown* To Who, when and why* In what context* The outcome <br />
    22. 22. Learning from the crowd:<br />automating the personalisation process<br />Book<br />Michael Jackson<br />Behaviouraldatabase”Collectiveconsciousness”<br />New user<br />Fingerprints from pastusers –clicks, searches, purchases<br />
    23. 23. Intelligent Search<br />Personalized Search & Navigation<br />Multiple languages<br />Multiple channels<br />Combines multiple inputs<br />• Product catalogue search<br />• Browsing history<br />• Learning from the crowd<br />• Purchase history<br />to achieve the most relevant result<br />Search<br />Incremental <br />search<br />Spelling<br />corrections<br />Auto-<br />complete<br />Did you <br />mean?<br />Implicit<br />synonyms<br />
    24. 24. Auto-complete<br />Search chosen fields in catalogue<br />Top of list: match to most popular products<br />
    25. 25. Filtering auto-complete<br />Pick the most popular matches for ‘bruc...’<br />Show how many hits for each<br />Only show most important of the total matches<br />
    26. 26. Implicit synonyms<br />Implicit synonyms: look at what users did after searching<br />
    27. 27. Spell-tolerant recommendations<br />Recommendations allow for common spelling mistakes<br />
    28. 28. Dynamic Navigation<br />Dynamic Navigation<br />Personalised, dynamic navigation simplifies product selection<br />Refines search results<br /><ul><li> Help shoppers zero in on what they want
    29. 29. Highlight factors influencing buying decisions
    30. 30. Shoppers will never see ‘no results found’</li></ul>More ways to browse <br /><ul><li> Encourage shoppers to linger
    31. 31. Opportunity for up-sell and cross-sell</li></ul>Personalize by relevance for higher conversion <br /><ul><li> Rank relevant attributes higher
    32. 32. Include user ratings</li></li></ul><li>Navigation on brand landing page<br />Context-sensitive filtering<br />
    33. 33. …optimises use of page real estate<br />eSales search finds best match to what user is looking for in each category…<br />Product <br />Product <br />Product <br />Search, navigation and layout optimised for maximum conversions based on relevance & crowd learning<br />Faceted search personalised<br />
    34. 34. Category-based recommendations<br />Customers who bought things in this category bought...<br />
    35. 35. User-driven recommendations<br />People who bought this bought that...<br />
    36. 36. Attribute-based recommendations<br />Other products with similar attributes....<br />
    37. 37. Recommendations<br />& selects and positions most effective<br />Product <br />Product <br />Product <br />Product <br />Product <br />Product <br />eSales creates recommendations using pre-build & custom merchandising tactics<br />Product <br />Product <br />Product <br />
    38. 38. ContentTargeting<br />Image<br />Image<br />Image<br />Image <br />Image <br />Image <br />eSales automatically tests and chooses content to maximise sales outcome<br />
    39. 39. Controlling merchandising<br /><ul><li>Drag and drop deployment merchandising panels simplifies change
    40. 40. Easily guides personalisation – e.g. boost products based on stock level</li></li></ul><li>Displays 32156<br />Inspects 18356<br />Commissions 5467<br />Commissions / Displays 17%<br />Inspects / Displays 57%<br />Commissions / Inspects 30%<br />Displays 51150<br />Inspects 27997<br />Commissions 10233<br />Commissions / Displays 19%<br />Inspects / Displays 67%<br />Commissions / Inspects 37%<br /><ul><li>Continuous feedback on performance guides improvements </li></ul>Understanding performance<br />
    41. 41. Boosting relevancy from 20% to 80%<br />Combined<br />Behavioral<br />Sales<br />Text Match<br />
    42. 42. Avoiding cold starts by combining technologies<br />Combined<br />Behavioral<br />Sales<br />Text Match<br />
    43. 43. Proven approach<br /><ul><li>62% consumers find recommendations useful
    44. 44. 15% admit to purchasing when they see recommendations
    45. 45. “Retailers told us … that between 2% and 20% of their revenue could be attributed to recommendations”</li></ul>By year-end 2013 over 30% of the 100 most popular websites will use search technology or content analytics to target content at users.<br />Börge Olsen, Sales Manager<br />Personalised promotions double retention rates to 16%<br />
    46. 46. Referencecase: CDON<br /> CDON – the Amazon of the nordics<br />Challenges:<br />Slow search response times<br />Unstable IT environment and service outages @ peak load<br />Irrelevant products shown to buyers<br />Results with Apptus eSales:<br />Lightening fast response time regardless of load <br />99.97% uptime and excellent reliability during peak hours<br />Record sales in 2010 thanks to relevant products exposed to the buyers in different contexts<br />Mikael Olander, CEO<br /><ul><li> 10 million products
    47. 47. 4.2 million searches/day; peak 2,000 searches/sec
    48. 48. 5 million attribute updates/day</li></li></ul><li>Thank you<br />Michael.Mokhberi@apptus.com<br /> +46 701 66 41 02<br />