Website personalization trends and techniques 2018
•Transferir como PPTX, PDF•
1 gostou•636 visualizações
Learn the latest best practices with examples from the Smart Insights summit talk on personalization across web, email and media platforms using AI and machine learning.
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (20)
Semelhante a Website personalization trends and techniques 2018
Semelhante a Website personalization trends and techniques 2018 (20)
Mais de Smart Insights
Mais de Smart Insights (20)
Último
Último (20)
Website personalization trends and techniques 2018
- 1. 1 #DigitalPriorities Digital Marketing Priorities 2018 brought to you by The 3 key ways to improve the customer journey Joey Moore, Director of Product Marketing Greg Moore, Manager for Personalisation and Campaign Strategy of Episerver
- 2. 2 Before we start… house keeping • A recording for the webinar will be sent via Email • Slides will be available via Smart Insights Slideshare • Please get involved with the interactions: • Give your views in the votes • Do ask questions at any point via the Questions panel
- 3. 3 Recommended Smart Insights toolkit update
- 5. 5 THE IMPORTANCE OF ARTIFICIAL INTELLIGENCE
- 6. 6 6 THE IMPORTANCE OF MACHINE LEARNING
- 7. 7 7 WHY DO YOU EVEN WANT THESE 3 TO CONVERGE?
- 8. 8 8 THE IMPORTANCE OF CUSTOMER EXPERIENCE
- 10. 10 10 MOST COMPANIES TREAT THEIR CUSTOMERS LIKE THIS
- 11. 11 BUT YOU WANT TO TREAT THEM LIKE THIS
- 12. 12 12 Start Finish HOW DO YOU START?
- 13. 13 Segmentation
- 14. 14
- 15. 15 SEGMENTATION
- 17. 17 Strategy and Authoring Campaign Orchestration 2. Visitor Intelligence Behavioral Search Optimization Personalized Cross-channel Outreach Rankings & Recommendations 3. Autonomous Personalization Delighting the Customer Marketer Machine Cross-channel Execution Content Personalization 1. Omnichannel Content Management Real-Time Interaction Management Profile and Persona Big Data Store Creativity Helping Differentiate with the Ideal Marriage of Man and Machine
- 18. 18 THE 3 TIERS OF PERSONALIZATION Segmentation BehavioralBasic/Standard Hello Joey, Welcome Back Based on what what you’ve browsed and what people with similar behaviors like you Here’s your product or content that best fits you Brits, who have too many kids and like to travel This is what they should see
- 19. 19 Segmentation BehavioralBasic/Standard Hello Joey, Welcome Back Brits, who have too many kids and like to travel This is what they should see Based on what what you’ve browsed and what people with similar behaviors like you Here’s your product or content that best fits you Developers Marketers / Merchandisers AI
- 25. 25 25 50% increase in AOV 65% increase in units per order 5% increase in sales 35% increase in AOV 7% increase in sales 66% increase in units per order
- 26. 26 Episerver Advance* | Content Sequencing SEE THINK DO CARE * Planned release, not yet available
- 28. EpiserverEpiserver Brands are spending more year-over-year increase in brand created content 3X For less return of all brand created content gets noticed 5% of purchase decisions are peer influenced 81% Drive sales ofconsumerstrustearnedmediamorethan ownedmedia 92% Strengthen consumer trust
- 29. 29 WWW WWW WWW WWW WWW Target Segment Female Segme nt Male Segme nt Other A/B Test A/B Test 30 days 30 days 30 days INTELLIGENT CAMPAIGN (REACH & CAMPAIGN)
- 30. EpiserverEpiserver Drives click through rates up by up to 60% Increase conversion by up to 25% Boost the performance of all your email campaigns Welcom Offers News Browse Abando n Confirmatio nn Shipping Rate/Review Cross-sell Fully Personalize All Types of Emails
- 32. 32 Episerver Collect Individual User Level Data Aggregate to Segmented or Persona Level Identify User Journey’s Visualize Relevant Data Take Action and Permeate Through Relevant Systems
- 34. 34 Episerver 34
- 35. 35 Episerver 35 target amulti- dimensionallysegmented audience basedon their historical interactions
- 37. 37
- 40. 40 Episerver
- 43. 43 Episerver understandand act on your businessobjectives
- 44. 44
- 45. 45
- 46. 46
- 47. 47
- 48. 48
- 49. 49
- 50. 50 From Data Management to Data Intelligence Episerver Profile Store | Data Management Platform (DMP) Episerver Personalization Engine | Machine Learning Algorithms Data Analytics • Records what happened • Stores visitor behavior • Low customer value Customer profile Interaction preferences Transaction history Case history Recent browsing activity Recent chat activity Social activity Location and device GDPR compliance Data Science • Calculates and predicts intent • Relies on Data Analytics • Mid value to customer Episerver Advance Episerver Find Episerver Perform Data Intelligence • Visualize results • Relies on Data Science or Data Analytics or both • High value to customer Search Rankings Product Recommendations Content Sequencing Event Triggering Individual or Group Profiles and Journeys Personas and Persona Journeys Multi-Dimensional Segments Episerver Reach Episerver Campaign Episerver CMS Episerver Commerce Episerver Insight
- 51. 51 Capture Data capture and data processing Customer segmentation RFM Store Transactions Margin Product sales Inventory & Stock Prices and promotion Social interests Social Likes Store location Competitive intelligence Ratings & Reviews Loyalty Data Sales & Inventory Social data Secondary data
- 52. 52 Capture Analyze Run analytics-algorithms learn from data capture Single customer behavior Product attributes Other customers’ behavior Clustering Product Identification Customer Profiles Product Association Collaborative Analytics & Algorithms Customer segmentation RFM Store Transactions Margin Product sales Inventory & Stock Prices and promotion Social interests Social Likes Store location Competitive intelligence Ratings & Reviews Loyalty Data Sales & Inventory Social data Secondary data Data capture and data processing
- 53. 53 Capture Analyze Visualize Data capture and data processing Run analytics-algorithms learn from data capture Push relevant content and products to consumer Product attributes Clustering Product Identification Customer Profiles Product Association Collaborative Customer Insights Recommendation Push relevant content and suitable products back to customers Analytics & Algorithms Visualization Action Customer segmentation RFM Store Transactions Margin Product sales Inventory & Stock Prices and promotion Social interests Social Likes Store location Competitive intelligence Ratings & Reviews Loyalty Data Sales & Inventory Social data Secondary data Single customer behavior Other customers’ behavior
- 55. 55 Ad Targeting SEO Listings Perform: Highly relevant alternatives based on product attributes and behavior
- 56. 56
- 57. 57 Perform: Related Recommendations - Product and Category predictive recommendations Advance: Related Recommendations Perform: Product and Category predictive recommendations Personalised Find: Site search and category landing based on previous orders and interactions Inspired
- 58. 58 Perform: Related Product Recommendations Advance: Conversion intent Content/Promotions Abandoned browse trigger Related Product Recommendations Conversion intent Content Store or Online?
- 59. 59
- 60. 60
- 61. 61 Perform: Related Product Recommendations Advance: Conversion intent Content/Promotions Order confirmation Reach: Abandoned Purchase Thank you for your order: Order #12345
- 64. 64 Auto identify customer Reach: Related Recommendations Ad Targeting Cross-device Merging Rules based landing content & PLA Optimisation Campaign or Reactivation Automation SEO Listings Insight: Dynamic audience selection based on all behaviour
- 65. 65 Capture Analyse Visualise Single customer behavior Product attributes Other customers’ behavior Product Data Click Stream Behavioural Transactional Contextual Manually Crowd Behaviour Establish Intent Individual Behaviour Optimised PPC Landing Personalised Emails Relevant Triggers Individualised Recommendations
- 66. 66 First Name: Surname: Location: Last visit: Last online Purchase: Last instore Purchase: Store name: Served by: Days since last purchase: AOV: Last search Keyword: Last category viewed: Last product viewed: Most viewed Category: Most viewed Brand: Number of devices: Holly Golightly London, UK 10/09/2016 10/09/2016 16/11/2017 Carnaby Street Jane 43 £78.00 Black Dresses Dresses M100Blk Womens Reiss 2
- 67. 67 Episerver Q&A
Notas do Editor
- Every business understands the importance Tells you what happened. Can
- Lots of noise Companies claiming to deliver on promise We have been doing for 10 years
- Specifically Machine Learning Using to make sense of huge data sets, every click, view, basket SCALE automatically learn and adjust your business decisions based on the data you are collecting.
- Where is the value in this convergence? What can it deliver?
- Customer experience has been talked about for many years – most organizations say they focus on it, only some do. Part of that customer experience is to allow your customers to feel a part of the company – to really get an understanding of what it’s like to be a part of the culture – in this case we were at a concert for TOP and they roll this Hamster Ball out into the crowd…their fans were highly integral to an overwhelmingly positive customer experience. What does it mean for you?
- Why are we treating everyone as if they were the same? That’s pretty much the current state of affairs so; When people are Browsing Using search Navigating categories At the moment, everyone pretty much gets the same experience when in fact [next slide]
- But everyone is different so why do we still give them the same experience when browsing a website or when they are using search? Not only are people different, but they also behave differently depending on their wants and needs at any given time. Again most sites and search solutions don’t address this. In fact they’re not able to address it because… [next slide] …it’s incredibly hard to do.
- When we talk about segmentation We say “this is my universe” Some may be male, some may be female New visitors Returning visitors Some people will have shopped before/not shopped before Then you can start to segment up or divide up your customers But the reality is, it doesn’t look like this:
- It doesn’t even look like this
- The reality is; you have hundreds of thousands or millions of visitors hitting your website They all have different needs and wants Those change throughout the visit Fundamentally, the problem with segmentation ceiling is that ultimately “how many is the right number of segments”? 5, 50, 500, 5000? It becomes (click) hard, almost impossible to maintain And what’s worse is (click) you’re going to miss all these opportunities There will be people that you’re putting into specific buckets or specific groups where you aren’t addressing their intent with visiting the site
- The reality is; you have hundreds of thousands or millions of visitors hitting your website They all have different needs and wants Those change throughout the visit Fundamentally, the problem with segmentation ceiling is that ultimately “how many is the right number of segments”? 5, 50, 500, 5000? It becomes (click) hard, almost impossible to maintain And what’s worse is (click) you’re going to miss all these opportunities There will be people that you’re putting into specific buckets or specific groups where you aren’t addressing their intent with visiting the site
- We’re striving to make you more efficient by helping with some of the day-to-day marketing challenges…
- The solution is always-on marketing, engaging with visitors based on the consideration stage that they are in and the interest they have. Applying the SEE, THINK, DO framework, you cannot push DO stuff to someone in the SEE stage and companies do that all the time. But you can nudge someone once a week or a month with great SEE content that they are interested in. People are not idiots. They see your BUY NOW or REQUEST A DEMO button, but most folks that interact with you have other intentions and if you are customer centric you stop shoving DO stuff down their throat and try figuring out their intent and serving them something that’s relevant based on consideration stage and interest, so they can complete their task. If you enable them to do that with a good experience, you have a pretty good chance of getting them to buy from you down the road.
- Filter, group and segment your audience with multi-dimensional filtering, based on their historical interactions
- Filter, group and segment your audience with multi-dimensional filtering, based on their historical interactions https://www.mni.com/images/micro-targeting.jpg?crc=176708655
- Filter, group and segment your audience with multi-dimensional filtering, based on their historical interactions https://www.mni.com/images/micro-targeting.jpg?crc=176708655
- build segments and discover interests in certain product categories among your assortments Picture: http://www.dynamicpeople.nl/wp-content/uploads/2014/01/over-ons.png
- build segments and discover interests in certain product categories among your assortments Picture: http://www.dynamicpeople.nl/wp-content/uploads/2014/01/over-ons.png
- anything you want with the data Accessing and enriching the data to build on top of the segmenting and BI that Insight delivers.
- But I much prefer to think of it as…
- But I much prefer to think of it as…
- Walkthrough – Advance Machine Learning What data capture used to look like…. Website tracking only – disconnect – Silos Then Mobile sites Then Mobile apps Today Retail environment - Store Beacons/Wifi Contact Center
- Walkthrough – Advance Machine Learning IBM recently said that 90% of the worlds data was created in the 2 years The Platform and suite of over 100 algorithms processes and sorts this data. Building relationships between individuals, products and content to determine: Whats valuable? Where is the opportunity?
- .
- .
- .