Machine Learning in Ecommerce

Companies that use the power of AI and data to drive more relevant engagement drive outsized business impact including 65% greater engagement in their target accounts, 106% higher pipeline and 3x higher ROI on marketing spend. A pioneer in the IT Operations market, BMC enables companies to simplify their IT environment and drive business agility - counting 92 of the Forbes Global 100 companies as their customers. At SiriusDecisions Summit 2019, BMC discussed how they created the single source of marketing truth to drive segmentation and targeting for their 1:1 omni channel programs.

The document discusses integrating web marketing and CRM data to better understand customer conversions and attribution. It describes identifying conversions like admissions in a university's CRM from digital marketing campaigns. It also discusses mapping customer records between analytics and CRM systems to analyze multi-touch attribution and conversion paths over time. Finally, it proposes using data integration tools like Stitch to pull ecommerce and other data into a centralized warehouse for combined reporting and analytics.

Presented by Dave Chaffey, CEO and Co-Founder, Smart Insights Better understanding and acting on the holistic customer journey is paramount to success for today’s marketers. Reaching that goal, however, has become increasingly complicated due to an explosion of disparate technologies and fragmented data sources. How can marketers collect and stitch together the customer data they need, and then act upon that information to drive results? Join digital marketing expert Dave Chaffey as he discusses the latest trends, innovations and best practices for driving more timely and relevant interactions across touch points. Dave will discuss the opportunities and challenges for real-time personalization; give examples of what leading brands are doing today; and talk about how more and more brands are turning to the customer data platform (CDP) and other core technologies to accelerate their initiatives.

Companies that use the power of AI and data to drive more relevant engagement drive outsized business impact including 65% greater engagement in their target accounts, 106% higher pipeline and 3x higher ROI on marketing spend. A leader provider of news and information-based tools for professionals, Thomson Reuters sought to shift their go to market from being a product-centric to a customer-centric approach in order to further distinguish themselves from the competition. At SiriusDecisions Summit 2019, Thomson Reuters talked about how they empowered their marketing teams with a centralized segmentation platform powered by AI to successfully execute personalized customer journeys at scale.

Scott Brinker, Co-Founder and CTO, ion interactive, and blogger (chiefmartec.com) Marketing has become thoroughly entangled in software. But this is a good thing! It dramatically changes the leverage that marketers can bring to their craft. But to succeed, marketing management must evolve to harness the dynamics of a digital world instead of fighting them. Scott’s presentation, based on his new book, Hacking Marketing, will inspire you with a fascinating collection of ideas from the world of software development — agile management, models of innovation and scalability, and strategies for taming the explosive growth of complexity — that marketers can adapt to thrive in this new environment.

Today, it’s big data, machine learning, and artificial intelligence (AI) that have taken the spotlight as the new tools of highly effective marketing teams. The results are highly personalized, real-time consumer “experiences” that are significantly lower in cost than traditional high-expenditure campaigns. With these tools, every single interaction a prospect or consumer has with a product, whether through a website, email, or social interaction, is tracked and recorded for future optimization. Machine learning algorithms can collect this data in real time, and immediately personalize experiences unique to each visitor, eliminating the need for static profiles based on outdated or grouped data sets. With this newfound wealth of data, and efficient processes in place, marketing teams can focus on identifying strategies to effectively use this technology to optimize operations and output. Without a well-planned strategy, machine learning can simply become a cog inside a big machine, and AI can become just another wasted expenditure instead of a highly advanced resource. This is not the time or place to jump into processes without considering goals, so marketers need to take the time to contemplate the ideal outcomes and plan accordingly.

The document discusses the results of a study analyzing the effectiveness of "all-in-one" marketing automation platforms. The study found that these platforms provided limited benefits for increasing website traffic or keyword rankings. Specifically, smaller businesses saw more traffic benefits than larger businesses, but overall increases were modest. The document concludes that additional tools may be needed beyond automation alone to drive greater brand awareness and search engine visibility.

This document discusses making web analytics more customer-centric. It argues that current tools focus too much on simple metrics like bounce rate and conversion rate that do not capture customer relationships and behavior over time. The document advocates focusing on metrics like annual retention rates, new customer counts, customer lifetime value, and how customers migrate between channels over time. It provides an example analysis of how paid search is better for acquiring but not retaining customers long-term. The analysis joined order data, campaign data, and traffic sources to understand customer channel preferences and profitability. The goal is to optimize marketing spend based on expected long-term customer revenue rather than short-term conversions.

Née en 2009 aux Etats-Unis et leader mondial du Tag Management, Ensighten est un nouvel entrant sur le marché français. Son approche technologique radicalement différente – et parfois destabilisante – a suscité un vif intérêt en France depuis l’ouverture du bureau parisien en 2015. Une tendance qui se confirme plus généralement sur la zone EMEA. Ian Woolley, General Manager EMEA, expliquera la vision stratégique d’Ensighten et présentera les grands enjeux auxquels Ensighten répond en Europe.

The document discusses how predictive analytics can be used to improve marketing and sales performance. It describes how leading companies are using account-level data and predictive attributes to more accurately score leads and identify those most likely to convert. Specific examples are provided of attributes that can increase conversion likelihood for certain industries. The presentation advocates that marketing organizations embrace these predictive techniques to target prospects selectively and find their unique "trigger" to improve results.

This document discusses risks in retail and how to manage them. It contains the following key points: 1. Retail has become more complex with the rise of online shopping and expectations of fast delivery and easy returns, creating new risks around inventory, fraud, and fulfillment. 2. Managing risks effectively requires integrating store inventory and fulfillment into the online order process through approaches like ship-from-store, in-store pickup, and multi-node fulfillment networks. 3. Adopting modern order management systems is important to gain visibility and control over the fulfillment process and meet rising consumer expectations. Fraud management also requires balancing machine learning and human review to approve orders while minimizing losses.

Learn more about Thinkspeed and how it provides crowd sourced market intelligence through its innovative research platform and professional community in this short overview.

This document discusses how to measure "ecommerce domination" and owning more of the total addressable market (TAM). It explains that the TAM is the overall revenue opportunity available if 100% market share was achieved. To measure ecommerce domination, you compare your company's year-over-year growth to the growth in the TAM. The document cautions that year-over-year growth does not necessarily mean growing the TAM. It provides an example of estimating annual incremental revenue from a new initiative. Finally, it advises comparing your company's performance to search demand trends to better understand how you are performing relative to the market.

This document summarizes a presentation on digital marketing analytics and automation. It discusses defining website, email, and other digital analytics; using data to understand customer locations and marketing effectiveness; tracking digital behavior; differentiating prospects; using CRM systems; and how marketing automation can help achieve goals by personalizing content and increasing ROI. The overall message is that digital strategies should be data-driven and analytics-focused to continuously improve performance.

Performance Marketing is going through dark times. The only chance? Staying in your customers’ consideration set and inspiring your audience in the long run! Dr. Florian Heinemann, founding partner of Project A Ventures, stresses this topic and explains why the No. 1 priority of every CMO should be CRM.

This document discusses how machine learning can be used in ecommerce applications such as email marketing, cross-selling, personalized recommendations, and abandoned cart emails. It explains that the process involves collecting customer data on purchases, views, likes, and wishlists, training a model with this data, and then making predictions. An example is given of a wine company that saw a 45% increase in session length, 22% increase in conversion rate, and 37% increase in average order size after implementing personalized recommendations and similar product suggestions based on machine learning.

This document discusses how machine learning and data science can be applied to ecommerce and retail. It outlines several machine learning problems that are relevant, such as search ranking, typeahead, spell correction, cold start recommendations, and inventory management. It also discusses the large data volumes and processing needs, as well as hardware requirements, for deploying machine learning solutions at large retailers.

This document discusses how machine learning can be applied to ecommerce and retail applications. It outlines several problems that ML can address, including search ranking, typeahead, spell correction, cold start recommendations, left-hand navigation, query understanding, related searches, product discovery, image similarity, voice search, attribute extraction, user modeling, title generation, and inventory management. It also provides context on data sizes, user behaviors, and the need for models to have fast prediction speeds and work within memory constraints in a production setting.

Ralf Herbrich gave a presentation on machine learning at Amazon. Some key points included: - He discussed his background and experience in machine learning from 1992 to the present. - The presentation provided an overview of machine learning including definitions from computer science and statistics perspectives, the history of machine learning, and examples of machine learning applications at Amazon like forecasting, machine translation, and visual systems.

A brief overview of Machine Learning and its associated tasks from a high level. This presentation discusses key concepts without the maths.The more mathematically inclined are referred to Bishops book on Pattern Recognition and Machine Learning.

2015 presentation on deep learning for the enterprise by Skymind, the commercial support arm of Deeplearning4j.

Одна из наиболее часто возникающих задач в бизнес-аналитике для компаний — это предсказание оттока клиентов. Ведь если заранее знать, что клиент собирается уйти к конкуренту, его можно попытаться остановить. Задача будет рассмотрена на примере прогнозирования оттока игроков из World of Tanks.

In this presentation, learn how an end-to-end smart application can be built in the AWS cloud. We will demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We will then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We will walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you will learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud. Presented by: Guy Ernest, Principal Business Development Manager, Amazon Web Services Customer Guest: Pim Vernooij, Partner, Lab Digital

There’s a lot of noise about big data and cutting edge algorithms optimisations. Returning to the basics, this presentation shows you might not need as much data as you think to get real world benefits. Learn about machine learning in ecommerce, PredictionIO and how we used off the shelf, well implemented algorithms to get a 71% increase in revenue with an online wine retailer.

I had the pleasure to speak at the DD Summit in SF this fall on the topic of building Customer Engagements teams from the ground up leveraging disruptive technologies like Big Data, Machine Learning and the right mix of MarTech platforms.

The document discusses how retailers can harness customer data through a customer data platform (CDP) to personalize customer experiences. It outlines that CDPs can help overcome data silos, provide a unified 360-degree view of customers, and put customer data to work driving revenue through better understanding customers. Specific benefits mentioned include collecting first-party data directly, avoiding data silos, unifying cross-channel execution, and getting to know customers better. Use cases are provided showing how machine learning models in a CDP can improve customer engagement and spending.

Customers are the lifeblood of your business. But in today’s glut of communications overload, its more challenging than ever to get your message through. At the same time, customers are getting more savvy, fragmented and unpredictable.