Midterm DSMI PDF

### Strategic Focus in 2024 - Focus on Brand Management and Reach (Upper Funnel) - Focus on Marketing Operations (Execution), e.g. improvement of websites, E-Commerce, CRM-Systems, Data Analytics - Increase focus on Digitalization and Implementation of Marketing Tech - Increased...

### Strategic Focus in 2024 - Focus on Brand Management and Reach (Upper Funnel) - Focus on Marketing Operations (Execution), e.g. improvement of websites, E-Commerce, CRM-Systems, Data Analytics - Increase focus on Digitalization and Implementation of Marketing Tech - Increased Need for KPI Frameworks, Measurement (marketing Spend Optimization) and Data Models (Marketing Mix Modelling) - Focus on Sales and Performance Marketing (Lower Funnel) ### Most important topic areas in 2024 - AI -- Artificial Intelligence - Digital Marketing in total - Customer Experience Management - CRM Database & Management - Brand Strategy & Management ### Top Challenges &Barriers in 2024 - Collection, consolidation, quality of distributed customer data for a 260-degree view - Lack of understanding & knowledge of how AI will change business - Consistent customer experience across all touchpoints - Lack of integration of data science process into existing process (marketing planning, media buying, etc) - Recruitment of employees with relevant experience ### Different roles of marketing technologists in marketing operations according to Brinker(Figure Three) Ein Bild, das Text, Screenshot, Schrift enthält. Automatisch generierte Beschreibung **Internal vs. External Orientation:** The left side of the model (\"Internal Orientation\") focuses on roles that are more internally oriented, emphasizing management and analysis within the company.\ The right side (\"External Orientation\") displays roles that are more focused on direct market engagement and external customer orientation. **Brand/Demand Building vs. Technology Orientation:** The upper axis shows roles oriented toward Brand/Demand Building, which focus on developing and strengthening the brand as well as increasing demand.\ The lower axis shows roles with a Technology Orientation, which concentrate more on technical implementation and data analysis. - **Brand/Demand Builder** --- **MARKETERS** who are fluent with the use of martech in their work, focused on applying it in campaigns and programs to attract, engage, and retain customers. - **Operations Orchestrator --- MAESTROS** who design and manage the workflows, rules, reports, and [tech stacks](https://chiefmartec.com/2019/04/2019-martech-stackies-marketing-stack-awards/) that run the marketing department. Typical titles: *Marketing Manager, Growth Marketer* - **Analytics Architect --- MODELLERS** who dive deeper into the structure (and infrastructure) of data that marketing collects and uses for business/customer intelligence. Typical titles: *Marketing Analyst*, *Data Scientist*, *Data Engineer* - **Marketing Maker --- MAKERS** who build custom apps and digital experiences with code (and, increasingly, with [no-code tools](https://chiefmartec.com/2018/10/every-marketer-app-developer/)). Typical titles: *Marketing Engineer, Web/App Developer, Citizen Developer* ### Digital Transformation and its Effects on Business Models, Brands, and Customer interactions - identify instances where data have become more readily available and effects of Big Data Storage and Data Analytics, Data availability: Previously: High costs and delays in data collection. Data from digital sources (e.g. online purchases) was easier to access than non-digital data (e.g. cash payments in stores). Only structured data (e.g. addresses) was digitized, mostly for legal reasons (e.g. accounting). Today: Digitization makes data cheaper, faster and more widely available, even from unstructured sources (e.g. conversations with call centers). Data can be recorded and used in real time. Big data effects: Storage: Thanks to the cloud and distributed storage, large amounts of data can be stored cost-effectively. Analysis: Big data enables fine-grained analyses at customer, household and interaction level. Real-time reactions and comprehensive analyses are possible. AI: Artificial intelligence can be used to automatically identify patterns in large volumes of data and create personalized offers in real time. ### Digitization -- Digitalization- Digital Transformation Digitization: Conversion of analogue to digital data and automation of individual processes. Example: Scanning of documents. Digitalization: Integration of digital technologies to improve and optimize processes. Example: use of robots in production. Digital transformation: Changing the entire business model through digital innovations. Example: Uber, Netflix or Airbnb. Digitization is the first step towards digitalization, which in turn forms the basis for a far-reaching digital transformation. ### Consequences for Data and Data Management Data: Changes in Strategic Assumptions from the Analog to the Digital Age From TO ------------------------------------------------------------------------------------ ------------------------------------------------------------------- Data is expensive to generate in firm Challenge of data si storing and managing it Data is continuously generated everywhere Challenge of data is storing and managing it Challenge of data is turning it into valuable information Firms make use only of structured data (like excel) Unstructured data is increasingly usable and valuable (Live chat) Data is managed in operational silos Value of data is in connecting it across silos Data is a tool for optimizing processes Data is a key intangible asset for value creating (changes) ### The 5 Domains of Digital Transformation  ### 5 Network Behaviors and Strategies -- extremely important Network Behaviors -- What customers want Network Strategies -- to address network behaviors (how to react about it) - **Access**: to digital content and interactions, as rapidly, simply and flexibly as possible - Strategy: Be fast, easier, everywhere and always available for customers - **Engage**: with digital content that's interactive, sensory and relevant to their needs - Strategy: Offer valued content that customer will seek out, use and share with others - **Customize**: Customize their experience by selection and tailoring a range of info, products & services. - Strategy: Personalize your offerings to fit customers' needs or empower them to do so on their own. - **Connect**: with others by sharing views, ideas and experiences via text, images, links etc - Strategy: Be part of customers conversations by using social media - **Collaborate**: wit others on common goals and projects. - Strategy: Invite customer to help build your business toward a shared goal ### For Douglas: **1. Access** - **Omnichannel Experience:** Douglas offers customers fast access to products and services both offline in stores and online via their website and app. - **Mobile App and Website:** Customers can browse, order, and track products anytime and anywhere. Additionally, the **Click & Collect** option allows quick in-store pickup of online orders. **2. Engage** - **Personalized Product Recommendations:** Douglas provides personalized recommendations on their website and app based on customer behavior and preferences. - **Beauty Tutorials and Content:** On social media platforms and the website, Douglas offers interactive content like tutorials on beauty products tailored to customers\' needs. These contents can often be shared and encourage customers to engage with the brand. **3. Customize** - **Personalized Beauty Services:** Douglas provides individualized consultations with beauty experts in many stores, allowing customers to choose products based on their personal needs and skin types. - **Online Beauty Tests:** On Douglas' website, customers can take online tests to find products that match their skin type and preferences. **4. Connect** - **Active Presence on Social Media:** Douglas is active on platforms like Instagram, Facebook, and TikTok, regularly sharing product presentations, customer reviews, and stories. - **User-Generated Content:** Douglas encourages customers to share their own experiences through reviews, social media posts, or by using branded hashtags. **5. Collaborate** - **Customer Feedback and Co-Creation:** Douglas has platforms and campaigns where customers can share their opinions on new products and services. - **Community Initiatives:** For example, customers can participate in Douglas events or online workshops to share their beauty knowledge and experiences, and collaborate on projects together. ### Name D2c and sharing brands, describe a brand and its use of digital data D2C (direct-to-consumer) brands sell their products directly to the end consumer, without intermediaries. Exempels: Kylie cosmetics, Glossier,Fenty beauty by Rhianna. Use of digital data at Glossier: Sharing Brands enable the sharing or renting of products or services instead of ownership. Examples: Airbnb, Uber, Rent the Runway. Use of digital data at Airbnb ### Digital Transformation Roadmap 1. Digital Transformation Program: An overarching program to coordinate digital initiatives. 2. Customer & Marketing: Use of digital technologies to improve the customer experience and optimize marketing strategies. 3. Order, Fulfillment and Dispatch: Digital optimization of the order and delivery process to increase efficiency. 4. Products & Automation: Integration of automation and digital technologies in product development and production. 5. Data Analytics & Data Management: Collection and analysis of data to improve decision-making processes and efficiency. 6. Cloud services: Use of cloud technologies to make IT infrastructure more flexible and reduce costs. These fields are all interconnected and help to achieve a comprehensive digital transformation in the company. ### Customer Journey ( Stand-Up 1) 1\. Awareness Stage Paid Touchpoint: Earned touchpoint: Owned touchpoint: Digital touchpoint: Physical touchpoint ------------------ ------------------------------- ------------------- -------------------------------- ----------------------------------------------------- Google Ads Social media shares and likes Douglas website Online banners and display ads Outdoor advertising (e.g. posters, digital screens) 2\. Consideration Stage Paid Touchpoint: Earned touchpoint: Owned touchpoint: Digital touchpoint: Physical touchpoint --------------------------------- -------------------------------------- ------------------- -------------------------- ------------------------------ Retargeting ads on social media Customer reviews and recommendations Landing pages Virtual advice via video In-store advice from experts 3\. purchase stage Paid Touchpoint: Earned touchpoint: Owned touchpoint: Digital touchpoint: Physical touchpoint ---------------------------------- ---------------------- ---------------------------- --------------------- ---------------------------------- Promotion ads for special offers Influencer marketing e-commerce website and app mobile app in-store purchase (physical POS) 4\. post-purchase stage Paid Touchpoint: Earned touchpoint: Owned touchpoint: Digital touchpoint: Physical touchpoint ----------------------------------------- ------------------------------- ----------------------------------- ------------------------------ ---------------------- email marketing for post-purchase sales customer feedback and reviews Customer service website and FAQs customer service via chatbot Returns in the store 5\. loyalty stage Paid Touchpoint: Earned touchpoint: Owned touchpoint: Digital touchpoint: Physical touchpoint ------------------------------------------- -------------------------------------------- -------------------------- -------------------------- -------------------------------- Exclusive offers via paid email campaigns Social media engagement (comments, shares) Loyalty program platform Personalized newsletters Events and in-store promotions ### What is Data Strategy? - An enterprise-wide strategy that ensures the adequate protection, value, and utilization of corporate data assets available through harnessing data-related and data dependent capabilities - An effective data strategy (similarly to other corporate strategies) has the following properties: - Actionable - Relevant (contextual to the organization, not generic) - Evolutionary (needs continuous revisiting and update) - Integrated/connected 8 questions a data strategy should answer 1. What is the problem I need to solve? 2. What kind of data would help? 3. Where will I analyze it? 4. How will I analyze it? 5. How will be responsible? 6. How will I store and safeguard it? 7. How will it be shared across the team? 8. How will it be implemented into team´s working processes? ### Data Strategy and Business Strategy -- Some questions to consider according to Marr **Customers, Markets, and Competition** 1. **What are some of the key trends in our market?**\ This question helps identify important changes and developments in the market that could impact the business. 2. **How do we best price our products or services?\ **A strategic pricing policy is crucial to stay competitive and define the right value for the offering. 3. **How satisfied are our customers with our service?**\ Customer satisfaction is an indicator of long-term loyalty and the potential for repeat business. **Finance** 1. **How does our strategy generate money?**\ This question reveals how the company generates revenue, which is important for long-term financial planning and scalability. 2. **What are our key sales, revenue, and profit trends?**\ Understanding key financial trends in sales, revenue, and profit is essential for making informed business decisions. 3. **Where are our biggest cost-saving opportunities?**\ Identifying cost-saving opportunities is crucial to increase profitability and make better use of financial resources. **Internal Operations** 1. **What parts of our operations could we make more efficient?**\ Identifying areas of inefficiency is crucial for streamlining operations, cutting costs, and improving overall performance. 2. **How do we optimize our supply chain?**\ Optimizing the supply chain ensures that resources are used effectively, reducing delays, and cutting unnecessary costs while meeting customer demands. 3. **What are the key quality issues we are experiencing?**\ Addressing quality issues is essential to maintaining customer satisfaction, reducing rework or returns, and improving the overall product or service offering. **People (Employees)** 1. **What are our core competencies in the business?**\ Understanding the core competencies that make the business unique helps guide long-term strategic direction. 2. **What skills gaps exist in our business at the moment?**\ This question helps identify missing skills that are necessary for the growth and success of the business. 3. **How engaged are our employees?**\ Employee engagement is a key factor in productivity and long-term job satisfaction. These questions are essential for making strategic decisions and advancing the business in the areas of **customers, markets and competition**, **finance**, and **people**. ### Bernard Marr: Data Strategy (Video) In the video, Bernhard Marr discusses how to develop a data strategy. He emphasizes that businesses must align their data use with their overall business goals. Marr introduces four key areas: understanding customers, improving products and services, enhancing internal processes, and monetizing data. He outlines a step-by-step process for identifying strategic data use cases, setting goals, and ensuring necessary data governance and skills. He also suggests using templates for structuring data strategy and encourages identifying \"quick wins\" for immediate impact. In the video, Bernhard Marr discusses four key data use cases: 1. **Understanding Customers**: Using data to better understand customer behavior, preferences, and market trends. 2. **Improving Products and Services**: Leveraging data to create personalized, smarter products or services for customers. 3. **Optimizing Internal Processes**: Using data to improve operations, marketing, and production efficiency. 4. **Monetizing Data**: Transforming data into a marketable asset that can be sold or shared, such as in sectors like telecom or finance. how Douglas could apply the four data use cases: 1. **Understanding Customers**: Douglas can analyze customer purchase data to predict trends, personalize recommendations, and understand buying behavior to tailor offerings to different customer segments. 2. **Improving Products and Services**: Data can help Douglas offer personalized beauty products or experiences, like customized skincare routines based on customer data. 3. **Optimizing Internal Processes**: Using data to optimize inventory management, streamline supply chains, and improve customer service, making operations more efficient. 4. **Monetizing Data**: Douglas could sell aggregated, anonymized customer insights or collaborate with brands to offer targeted marketing services based on customer behavior data. Ein Bild, das Text, Screenshot, Karte Menü, Schrift enthält. Automatisch generierte Beschreibung ### Examples of Intelligent Products - Smart Phones - Smart TVs - Smart Homes - Fitness Trackers - Smart Insulin Pumps - AI powered medical scanner - Ai robots in production - Smart Factories ### Examples of Intelligent Services - Streaming (Netflix) - Social Media (FB) - Search Enginges (Google) - Food-Delivery Platforms (Foodora) - Digital Baning (Georg) - Payment and money transfers (Apple pay) - Ride Sharing Service (Uber) - Food-Saving platforms (TOgoodToGo) ### Mapping Use Cases Against Elements of Data Strategy: 5 Cross-cutting Goals **Data Requirements:** - Data availability: Ensuring data is accessible. - Internal-external: Data from internal vs. external sources. - New-existing: New vs. existing data. - Data diversity: Variety of data types and sources. - Structured-unstructured: Organized vs. unorganized data. **Data Governance:** - Data quality: Ensuring data accuracy and consistency. - Ethics: Fair and responsible data use. - Privacy: Protecting personal data. - Ownership: Clarifying who owns the data. - Access: Managing who can access data. - Security: Safeguarding data from unauthorized use. **Technology:** - Data collection: Tools and methods for gathering data. - Data storage: Systems for saving data securely. - Data processing: Analyzing and transforming data. - Data output: Presenting data for decision-making. **Skills and Capacity:** - Skill gaps: Identifying missing expertise. - Training requirements: Providing necessary training. - Insourcing: Using internal resources for data work. - Outsourcing: Hiring external experts or services. - Partnering: Collaborating with others for better data handling. **Implementation/Change Management:** - Ensuring that privacy, ownership, and security are maintained during the implementation of new data processes or systems. ### The Dark Side of Data Availability: Data Opportunism ### Data Temptations: 3 Behaviors to Avoid **Don't collect data just because you can**: Have a clear strategy for what data is needed and focus on maximizing capabilities (collection, analysis, reporting) for specific purposes. **Don't lose sight of the big picture**: Big data doesn't equal big insights. Align data capabilities with long-term business goals (3-5 years) and company KPIs. **Don't use data without context**: Use multiple data sources to get a complete picture. Combine quantitative and qualitative insights to make informed decisions. ### Digital Short-Termism and Digital Myopia  ### The Downsides of Digital Myopia and Digital Short-Termism - Risk of under-investment in non-digital communication and marketing initiatives wit long-term effect - Ris of investing in the wrong digital measures - Example: Missing OCT(Offline-Conversion Tracking) - Risk of neglecting long-term brand building and customer-life time value - Risk of ending up in a me-too position or being outspent and outperformed by larger players on digital - Risk of building data assets which just support the here and now - Risk of being Out-data ed by digitally native disruptors or bigger players - Ris of being made obsolete by or fully dependent on Bid DATA layers (Amazon, google, apple) ### Example: Offline-Conversion Tracking - OCT is the tracking of conversions triggered by online advertising but effectuated offline (purchases in store, visits to the dealerships that ultimately end in sales, registering for a service at the POS etc.). - Google, Meta, linkedin etc. all allow for automatic upload of offline conversions from the advertiser CDP (within 90 days). - Offline conversions need to be traced and imputed by hand, otherwise.. - their powerful algorithms overlook offline conversions and instead optimize targeting, messaging and timing towards customers who convert online even where these conversions are more costly or less profitable than offline conversions. - Case of Newspaper: OCT increased subscriber volume by 185% and decreased costs per subscription by 55% ### Data Opportunism versus Data Strategy Ein Bild, das Text, Screenshot, Schrift, Zahl enthält. Automatisch generierte Beschreibung ### The Concept of Digital Brand Differentiation Beyond copying what your competition is doing, make data a differentiator supporting Brand Core Values of your brand: - Acquire unique data, i.e., data which are different or better than the ones of competitors - Acquire data that offer a unique fit with unique complementary assets of your company (that is, things which only your company has). - Acquire unique capabilities to ingest, store, process, analyze, and activate these data. in order to - strengthen the brand's current strengths and reduce its severe weaknesses - leverage new opportunities offered by technological innovations and long-term social trends - build long-term differentiators for your brand through - improving customer services - developing new products and services that are unique, relevant, and believable. ### Digital Brand Differentiation / Define Brand Core Values which meet the following 6 criteria **Desirability criteria (customer perspective)** - Distinctive and superior - Relevant to the target group(s) - Believable and credible **Deliverability criteria (company perspective)** - Feasible and profitable - Communicability - Defensible/difficult to attack/sustainable ### To apply Digital Brand Differentiation to Douglas: **Desirability Criteria:** 1. **Distinctive and superior**: Highlight exclusive, premium beauty products and personalized services. 2. **Relevant to target group**: Focus on beauty-conscious consumers, offering tailored beauty experiences. 3. **Believable and credible**: Build trust with high-quality, authentic products and transparent practices. **Deliverability Criteria:** 1. **Feasible and profitable**: Leverage Douglas's strong retail and e-commerce presence to offer exclusive, profitable offerings. 2. **Communicability**: Use clear digital marketing to emphasize beauty expertise and customer care. 3. **Defensible**: Build customer loyalty and exclusive brand partnerships to maintain a competitive edge. Digital Differentiation - What assets make/made them different? - Brand core Values - Risk of becoming obsolete? - Weaknesses along digital and non-digital customer journey? - What new data would held them overcome weaknesses? - Which new data could create digital differentiation with new products and services? ### MediaMarkt Case: 1. **Assets that make them different**: - Wide product range in electronics. - Strong physical and online presence. - Expertise in customer service and post-purchase support. 2. **Brand Core Values**: - Innovation, customer-centricity, convenience, value for money. 3. **Risk of becoming obsolete?**: - Digital shift and competition from online giants like Amazon. - Need to adapt to changing consumer behaviors. 4. **Weaknesses along the customer journey**: - **Non-digital**: Outdated in-store experience and inconsistent customer service. - **Digital**: Less intuitive e-commerce and lack of personalized recommendations. 5. **New data to overcome weaknesses**: - **Customer behavior data** for personalized experiences. - **In-store feedback** to improve service. - **Product usage data** for better after-sales support. 6. **New data for digital differentiation**: - **AI-driven data** for personalized shopping and promotions. - **IoT data** to offer smart, connected product services. Vokabel Bedeutung --------- ----------------------------------- MarTech Marketing Technology D2C Direct to consumer OCT Offline-Conversion Tracking SQL Structured Query Language DAM Digital asset management MCCM Mulit-channel campaign Management UI User-Interface MDM Master data management CDP Customer data platform RTIM Real time interaction management CRM Customer relationship management CMR Customer Managed Relationships MQL Marketing qualified lead SQL Sales qualified lead ### The Customer Data conundrum: Taming Martech´s "Frankenstack" - Martech = Marketing Technology: „any digital platform or tool that helps marketers achieve their objectives" - Martech Stack: the collective of all Martech tools and platforms used by a company which are supposed to integrate and work together - „Frankenstack": humorous label coined by Scott Brinker for a dysfunctional, ill-fitting Martech Stack in a company where different „silos" (e.g., e-commerce, advertising, social media, service) have each implemented multiple tools and platforms of their own without long-term planning or co-ordination with other silos - Martech Industry: collective of all companies providing Martech, with popular illustrations by Luma Partners („LumaScape") and Scott Brinker („chiefmartech") ### Group Standup 3 -- E-Commerce Platform/Storefront (Enterprise) **1. What do apps in this category do?**\ Enterprise E-Commerce platforms help large retailers manage online stores with tools for product catalog, order processing, inventory, customer management, payment integration, and analytics. They support high-order volumes, data management, and international growth. **2. What data do they generate and require?**\ **Generated data** includes sales, customer profiles, inventory, behavioral data, and operational metrics.\ **Required data** includes product info, customer data (CRM), shipping updates, financial data (ERP), and marketing insights. **3. What's new since 2023?**\ Recent trends include AI-driven personalization, headless commerce, sustainability features, better omnichannel integration, and enhanced data privacy/security compliance. **4. Major players:**\ Key platforms include **Shopify Plus**, **Magento**, **Salesforce Commerce Cloud**, **BigCommerce**, **SAP Commerce Cloud**, and **IBM Digital Commerce**, all supporting complex, high-volume operations and integrations.