Co-dependency with Clients - building a great product ≠ great product success

Digital Transformation Lab - Best of Practitioner Research - Jun 2021 - Barry Magee I'm an experienced senior business leader focused on how data-driven transformation creates organisational value with deep experience in sales, marketing, strategy, operations, and change management. I’m a recognized industry-leading specialist and academic on effective and systemic innovation using data and analytics to build competitive advantage and tangible results. https://www.linkedin.com/in/barrymagee/

10 Tips for Product Prioritization Moshe Mikanovsky, co-host of the Product for Product Podcast Presented at the ProductCamp On>Line 2021 unconference

Find out how marketing operations tools can help you to drive your business forward with big data strategies. In the age of data driven marketing a new superhero is emerging - the super CIO or IT manager. This new superhero is stepping up and rising to the challenge posed by big data - pushing into the realms of strategic planning and driving excellence throughout their company. It is this new superhero’s mission to prioritise data management, create cohesive and flowing data channels and transform the customer experience through a data strategy that also aligns to the business’ needs.

Learn how you can drive your business forward with confidence by making decisions based on actionable insights gained from organizational data in real-time.

This document discusses how machine learning can be used to predict customer lifetime value (LTV) for players in free-to-play games. It explains that accurate LTV prediction is challenging due to high variance between players and lack of long-term data, but can help optimize marketing budgets. The document outlines approaches for defining an ML prediction problem, training useful models, and communicating results to stakeholders in a way that demonstrates business value.

eGain provides analytics tools to help companies optimize their omnichannel customer experiences. Their analytics suite includes tools for voice, digital, knowledge, and journey analytics. The tools provide insights across channels to help businesses identify opportunities to improve processes, apply targeted interventions, and measure the impact of changes through continuous optimization driven by analytics. eGain's analytics are designed to provide enterprises with cross-channel, relevant, actionable insights for empowered decision-making to improve customer engagement.

This document discusses how digital transformation and artificial intelligence can empower all agents to handle all customer calls through knowledge solutions. It outlines the challenges of outdated customer service systems not keeping up with customers' needs and expectations. The solution proposed uses AI to provide an assisted knowledge layer that allows all agents to access necessary information to serve customers well without extensive training. Case studies show this approach reducing agent training time by 50-65% and improving customer satisfaction scores.

This document discusses capabilities for building future-ready operations including making marketing hyper-personalized, scalable, and seamless through omnichannel data aggregation and AI/ML powered insights. It provides key capabilities in areas like content services, digital marketing, and media growth services that can increase productivity, conversions, and efficiency while reducing costs. The overall goal is to leverage data and technology to amplify content, build customer relationships, and optimize media performance.

This document presents a visit score recipe developed by John Glinski at Vanguard to measure the success and ROI of a company's B2B web experience. It describes developing an engagement score by tracking key metrics like logins, tool usage, article views, and lead forms to quantify user engagement. The steps include determining key actions and success goals with stakeholders, mapping out the logic, implementing tracking in Adobe, analyzing results, and sharing recommendations. Examples of implementation for different departments like marketing, sales, IT are provided. The process is meant to test solutions, improve based on data, and continuously measure progress.