Getting Started in Big Data-Fueled E-Commerce


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Presentation to Outdoor Industry at the European Outdoor Summit, 17 October 2013 in Stockholm.
Abstract from the event program:
"Everywhere you turn these days there is a story on the promise of Big Data. Fact is, there is a wave of innovation in Big Data technologies under way that will affect our business. But are we really having a clear idea on how to use it to create new business?
Here in Europe, we're selling against Amazon, Google and other algorithmic commpetitors using spreadsheets and other manual methods. Nobody is talking about concrete use-cases or generating any new business value from Big Data. Until now.... Listen to Jason Radisson, in charge of a complex and forward looking initiative from Sport Scheck to grasp the huge untapped potential of tomorrow's e-commerce consumers."

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  • The main challenge is, most companies can’t staff this capability alone and suppliers aren’t set up yet to help. By the time they are it may be too late. Change hurdles are non-trivial.
  • Getting Started in Big Data-Fueled E-Commerce

    1. 1. Getting Started in Big Data Fueled E-Commerce European Outdoor Summit Stockholm, 17 October 2013 This document contains confidential material and ideas proprietary to Echtzeit GmbH. This document may not be reproduced in any form or by any means or disclosed to others or used for purposes other than for this discussion. It may not be disclosed to any third party even for the purposes of evaluation, except as expressly authorized by Echtzeit GmbH in advance for each case. This document is intended to be delivered orally and does not represent a complete record of that discussion.
    2. 2. E-commerce is entering another wave of disruption, Big Data is a driver Universalists and market-places USP 1. Same-day delivery Food/Non-Food Price, broad selection, convenience 2. Big data Category specialists Sporting Goods Curation: i.e., deep selection, specific expertise and service HABA Brands Pharmacy Cosmetics Brands Outdoor Fitness Brands Electronics DIY 3. Proliferation of incubators Furnishings Brands Artisan Flash Ethnic Organics Fresh OEMs and niche retailers Electronics & Media Video & TV Grocery Home & Garden Garden Clothing, Shoes & Accessories Merchandize and business model innovation in the long-tail 1. With push to same-day delivery aggregators and grocery players will take on the universalists and market-places in 2014-15. Everyone will be looking to the long-tail items to subsidize increasing fulfillment costs. 2. Big Data advantages universalists who can aggregate long-tail demand. They’ll use it to move into curation. 3. Increasing disruption to traditional consumer businesses drives demand for innovation, leading to a spike in e-commerce incubation activities. Lots of venture funding chasing similar concepts (project a, 7 ventures, rocket internet, REWE ventures, etc.) > Everyone, especially category specialists and brands, will need to step up as gross margins get squeezed, even as OPEX rises 1
    3. 3. We define Big Data as smart applications that create a sustainable competitive advantage in e-commerce Desired business outcome (e.g., 7d Marketing ROI) Big Data: • Is the umbrella term for a class of business applications that learn from millions of interactions and automatically adjust to customer intent and market context • Key Big Data apps in e-commerce: 1) SEM/O, 2) Loyalty & Onsite Merchandizing, 3) Dynamic Pricing/Offers, 4) Inventory & Fulfillment • Creates a break-away competitive advantage as more visits = more split testing volume = smarter systems & teams = more demand/visits (…) 2. ‘Cold-start’ phase and initial semi-automatic optimizations 3. ‚Hill-climbing‘ phase of a Big Data application implementation 1. Manual processes and ‘gut’ decisions time © Echtzeit GmbH 2013, all rights reserved 2
    4. 4. Valuation of the Big Data opportunity in e-commerce is straightforward, the mechanics are well understood Percent of total online sales driven by run-time applications in algorithmic merchandizing and customer marketing, in %** 35 7-30 2-3 Assume ecommerce business of €400 to 500M p.a. in turnover and 10% incremental sales (lift) for early-stage Big Data implementation Potential for €4050M in incremental sales for an midsize e-commerce division * 12-15 100+ 2-3 Number of loyalty/merch strategies in portfolio *eBay range from average business day (7%) to peak holiday shopping season day, such as Cyber Monday (30%) ** To convert percent of total online sales (PTOS) to lift use LIFT = PTOS/ (1-PTOS) Source: Amazon numbers published in HBS case ‘eBay Inc. and’ from 3 April 2012; eBa, SportScheck estimated © Echtzeit GmbH 2013, all rights reserved 3
    5. 5. But, there are several challenges with executing a Big Data strategy in Europe … motivated us to found a new company Incumbent perspective in DACH Companies need Big Data applications and processes and can’t readily build/buy them 5. Open-source systems require specific data-science and engineering skills. EU has yet to build talent pool 4. Legacy infrastructure scales expensively and slowly (2-3 year cycles). Can’t keep up with data-volumes or open-source innovation 3. For data-privacy, time-zone and cultural reasons, it is easier to do business with local partners, rather than Silicon Valley startups 2. Marketers have a tougher time employing playbooks and hillclimbing strategies (less traffic and MVT knowhow) 1. Big Data applications require automating business processes and an IT product-focus at board level. Change-resistance is a factor © Echtzeit GmbH 2013, all rights reserved 4
    6. 6. Establishing a fact-basis early-on helps to validate the opportunity and clear out internal change hurdles Our agile implementation model 1. Establish facts and quantify latent opportunity (2-3 months) 2. Pilot the application and playbook of strategies 3. Deploy application at scale (4-6 months) (4-6 months) Build application and playbook of successful strategies Run  Audits of customer base and item catalog performance  Initial playbook of algorithmic ‘strategies’  Optional: overhaul of metrics, descriptive segmentations (e.g., CLV, psychographics)  Piloting algorithmic strategies with Tiger Team. Develop the application  MVT reporting, including ROI and other causal metrics  Implement performance management process d/w/m  Deploy the application with initial champion portfolio  Realign resources on testing challengers  Automate causal performance reporting Quantify opportunity gap and establish fact basis for change Demonstrate effectiveness of Big Data approach vs. business as usual Achieve scale via automation. Realign processes. Continually improve © Echtzeit GmbH 2013, all rights reserved 5
    7. 7. Focus your digital marketing efforts first on the frequency upside sweet spot. This is where you can drive ROI at scale 14d Lift in % Response in % 250% 25% 200% 20% 150% 15% 100% 10% 50% 5% 0% 0% b. ONE PURCHASE c. TWO OR 3 PURCHASES d. FOUR TO 11 PURCHASES e. 12 TO 49 PURCHASES f. 50 TO 149 PURCHASES g. 150 TO 349 PURCHASES h. MORE THAN 350 MerchLift 120% 83% 58% 22% 4% 2% -16% LoyaltyLift 205% 129% 73% 45% 26% 38% 140% MerchResponse 2% 4% 6% 11% 17% 20% 22% LoyaltyResponse 3% 5% 8% 14% 20% 20% 16% • In general, your actions will be most effective in the sweet spot of frequency* upside. • Specifically, your strategies will speak to discrete opportunities in loyalty and merchandizing. For this category, there should be about 30-40 maximum. * Recency R is an accelerator, M monitization is almost a constant for a given consumer’s wallet © Echtzeit GmbH 2013, all rights reserved 6
    8. 8. We believe a Tiger Team drawing from Business, Data Science and Infrastructure is best Organizational model for building and implementing any Big Data application in e-commerce Business • Own the results • Generate and prioritize hypotheses (‘challenger’ strategies) to maximize long-run returns from the Big Data portfolio 4-5 from Business Planning and Campaign Ops Application Development Infrastructure Operations 8-10 Engineers* 2-3 Engineers • Build and maintain the application plus the algorithms and data that power it • Build and maintain APIs • Generate datasets for BI • Build and maintain scalable infrastructure (run-time and backhaul) at 5-9s uptime • Deployment of applications and updates * New engineers typically from either Computer or Data Science track and will need to be trained on any gaps during first year © Echtzeit GmbH 2013, all rights reserved 7
    9. 9. At SportScheck we built a recommendations application to mitigate cart abandonment in real-time as a first step Challenge • SportScheck is Germany’s leading sporting goods retailer with ca. €500m in revenues, 60M online visits, and 20M visits p.a. to its 16 physical stores. • The online business is growing steadily at some 5-10% p.a. But compared to Amazon, with 2030% CAGR in EU, there is a significant opportunity gap. • SportScheck’s customer marketing, merchandizing and post-sales processes are otherwise largely manual. Approach • We selected a white-space business opportunity, mitigating cart abandonment (ca. 50% incidence with no pre-existing treatment), as first use-case, and worked in a cross-functional Tiger Team. • Listening began in May and the system went live in early July. • We implemented our pixel, began logging clickstream data and training our models. • We went live with a minimal implementation (2-3 simple real-time strategies) on the homepage. © Echtzeit GmbH 2013, all rights reserved CASE STUDY Results • It's early days and the work shows great promise. We are generating a couple percent lift in conversion-rate & sales • We are implementing several enhancements which will livetest in Q4 (additional algos, offers and placements). Each improvement will generate 50100 basis points in incremental conversion. 8
    10. 10. Takeaways (by page number) APPENDIX 1. The point of Big Data in e-commerce is to unlock the Long Tail and enable competition on price, selection and convenience USPs. Current industry dynamics and competitive forces – for example, same-day delivery, Big Data, proliferation of incubators -- are such that the middle market will continue to be squeezed. 2. Big Data is the key enabler for category specialists to compete in this and the next wave of e-commerce. The way to see it is as a set of smart systems that learn from interactions with millions of customers and automate your core business processes. There are four classes of applications: 1) SEM/-O, 2) Loyalty & Onsite Merchandizing 3) Dynamic Pricing/Offers, 4) Inventory/Fulfillment 3. There is a huge opportunity in getting this right: initially a 10% improvement in top-line. 4. The main challenge is, most companies can’t staff this capability alone and suppliers aren’t set up yet to help. By the time they are it may be too late. Change hurdles are non-trivial. 5. Best practice is an agile and interdisciplinary ‘Tiger Team’ approach for getting started in Big Data fueled ecommerce. First you audit, then you pilot, last you scale/automate. 6. In general, a great high-ROI first target for your first Big Data pilots as ‘frequency upside’ segment. SEM/-O fills this bucket with high potentials and specific strategies are selected from loyalty, pricing, merchandizing, fulfillment, etc. in real time to migrate these customers to higher frequency levels and keep them there. 7. Team should be staffed with a triad of a) business, b) application development and c) infrastructure engineering. Most important hire is the application development engineering lead. Locate this team where ever s/he has best access to raw talent. A more cautious approach is to first rent/buy another company’s work, thus establishing a baseline for how much value a given Big Data application can add at your company. Negotiate a minimum performance level with any application provider to ensure self-funding. 8. We’re having success at SportScheck, where each ‘strategy’ equals a 1% improvement in site revenue © Echtzeit GmbH 2013, all rights reserved 9
    11. 11. Thanks! © Echtzeit GmbH 2013, all rights reserved 10
    12. 12. Frontends It’s possible to incorporate an open-source Big Data platform into a corporate IT landscape APPENDIX Enterprise CRM • Unica, Aprimo • Sugar • Salesforce … • • • Enterprise BI Microstrategy Cognos, BO Tableau … Live Web-shops E-Mail Mobile Apps SEM / Ads Social Agents/ Call Centers Data sources Production File Real-Time System Applications Service Bus Merch/Re co Loyalty & Offers Pricing Optimizer SEM SEO Customer Authentication Item/ Customer tables BI Data Cubes/ MDX Run-Time Applications (e.g., Couch, Hbase) Core DWH (e.g., MPP-database on commodity hardware) Backhaul processing (MapReduce, Mahout, job management framework) Hadoop File System (HDFS) AppDev • • • • • Maven Hive R PIG Mahout ETL (Runtime & bulk) Reference / Master Data Click-Stream Monitoring Social Media © Echtzeit GmbH 2013, all rights reserved Machine (Server) Logs Marketing Outcomes On-Device collecting Offline Channel Data Billing & Payments 11
    13. 13. Bio APPENDIX • I’m based in Munich and founded Echtzeit (means ‘real-time’ in German) GmbH about a year ago to build Big Data applications for several of Germany’s largest consumer companies, including SportScheck, on the open-source Hadoop technologies. • My first paid Big Data job was as a teenager, programming text-mining algorithms (e.g., classification, similarity) in Opposition Research for the winning side in the 1990 Massachusetts gubernatorial race. • Based on my Tiger Team work on the eBay turnaround and as a McKinsey consultant I’m also frequently an advisor on digital transformations, bridging Silicon Valley technological innovation and European corporate culture of my clients. © Echtzeit GmbH 2013, all rights reserved 12