Episerver Personalization Ascend Europe 15 th November 2016
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1 Episerver Personalization Ascend Europe 15 th November 2016 David Bowen Head of Product Frank Lombos VP Sales Personalization Episerver
2 Episerver: A leader in omnichannel personalization Proprietary personalization technology, developed since 2007 Delivering personalization and recommendations to over 200 sites worldwide 28 out of the Top 100 retail sites in the UK using Episerver s personalization technology World s first 3 rd generation personalization technology $18 BN influenced in omnichannel revenue Dr. Andreas Weigend Ex Chief Scientist at Amazon Dr. Mike Baxter Ecommerce Expert Dr. Bruce D Ambrosio Cleverset, sold to ATG
3 Why the Combination Makes Sense
4 We have over 100 machine learning algorithms to build the recommendations stack 4 Abandoned Basket Attribute Similarity Regression Bought Bought BoughtBought Time Ranged CoBasketed Collective Match Bought Bought Collective Match View View Dynamic Selection Emerging New Product Emerging Product Experimental Inference Algorithm Flow Last Days Popular Last Days Popular By Conversion Rate Last Days Popular By Revenue Last Months Popular Brand Last Months Popular By Revenue Last Two Days Popular Last Weeks Popular Last Weeks Popular Cat Manual Recommend New Product Personalised Ensemble Personalised Meta Ensemble Popular Popular Basket Popular Product By Conversion Rate Popular Product By Revenue Popular Product By Revenue per Product View Popular Without Categories Pre Calculated Pre Calculated Popular Like Rating based Matrix Factorization Recently Bought Recently Viewed Recently Viewed With Filters SA BoughtBought SA Popular SA Popular Basket SA Popular Cat SA View Basket (Cat) SA ViewBasket SA ViewView SACoBasketed Search Product Search: Popular Search: Product-Product Smart Recommendation Engine T2 Experimental User Bought Bought User Bought Bought Previous Basket User Discount Previous View User Discount Previous Viewed Related User Emerging Previous Purchase User Emerging Previous View User New Previous Purchase User New Previous View User Popular Connected Category Previous Purchase User Popular Previous Basket User Popular Previous Purchase User Popular Previous View User Popular Previous View with Attribute Boosting View Basket ViewBasketExtended ViewBasketExtended2 ViewBasketExtended3 ViewBoughtExtended ViewBoughtExtended2 ViewBoughtExtended3 ViewView ViewViewHoliday
5 Peerius Methodology: Generating Personalisation 5 The Behavioral Engine Advanced algorithmic methods to best predict customer intention A merchandising mind-set: what the customer is CURRENTLY trying to do? Self learning through customer actions and reactions Your behaviour Behaviour of others Product attributes Opportunities to merchandise across multiple channels
6 Peerius Overview
7 GTM Strategy 3 Markets 2 Approaches
8 Some of our personalization customers
9 The ROI of personalization 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 In Episerver Perform, we ve found a technology that can engage every individual with unique recommendations, deliver real commercial value, and that is sufficiently flexible and scalable to grow with our brands as we build out our mobile, multi-channel, and e-commerce business. Simon Pritchard Ecommerce Director, Arcadia
10 The individualized customer journey Content personalization List personalization recommendations Triggers Product recommendations
11 Prospect Pitch <Costco Example> Episerver
12 Example Value Proposition <Costco> Episerver
13 The value of on-site recommendations Our aim is to serve recommendations on our clients sites from the start to end of the user journey. Quality recommendations should lead users to discover and engage with more products, and subsequently lift overall sales volumes. Site analysis On the website we specifically highlight the following pages as very suitable for recommendations: Homepage Super-Category Pages Category Product Pages Basket Page Search Error Page 404 Error Page
14 Home page We have found in general that between 10-15% of recommendation sales come from click through on this page. Strategies We propose creating personalized, dynamic recommendations these can be based on two types of customers. Returning Visitors: Abandoned items and Alternatives to them Recently Viewed Products Cross-Sells to Previous Purchases and Views Popular items from the categories the user has recently viewed New Products (Latest products added to catalogue) New Visitors: Popular Items from the most Popular categories Bestsellers items across all categories Popular & Bestsellers Items are based on: Cost of Sale Conversion Revenue Click Through Rate New & Emerging Items
15 Product page 81.9% of rec revenue Preferred purchase alternatives Same category co-purchases Same category recently viewed Co-baskets Similar attributes view/view
16 Basket page 9% click to purchase Strategies Items related to current basket contents from different categories Items of lower value to basket contents New products from a different category Emerging products based on different popular categories Bestselling items linked to browsing and basket contents Complementary items added items users can purchase that has a close relationship with the products in basket such as matching products, same brand from a different category Recommendations will change if a user adds, removes an item or returns back to the page.
17 Zero Search Results Page 22% click to purchase Where did customers actually go: View Basket Purchase Recently viewed with variety Popular items from users favorite categories Popular items from popular categories, variety promoted
18 Episerver Personalization Episerver
19 Episerver Personalization Build on Visitor Groups Engaged Customers Proven ROI Focus on strategy Episerver Advance Episerver Perform Episerver Find Episerver Insight
20 Thank You Episerver
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