Suchergebnisse

Brand tracking, CRM programs, trade shows, online behavior tracking, satisfaction studies ... mounds of marketing metrics are generated across touchpoints and channels. For marketers drowning in data, dashboard analytics bring order to sprawling numbers, and accurate answers to questions about what's working and what's not. These web-based tools gather, synthesize, and visually display essential data in real time, directly connecting marketing with performance. With its rigorous methodology, wealth of case studies, and step-by-step instructions, this substantial guidebook explains how to set up.

Abstract
Marketing accountability is essential for sustained organic growth, but the challenges to it loom large. The major steps in truly accountable marketing include defining the right results, using the right metrics and finally acting upon the collected insights. To identify the right metrics one has to start with defining the right results: What is the informed decision that needs to be made? But getting data-based answers to key questions is only half the battle. Actually acting upon it is the other half, and often companies are reluctant to change. To create momentum, marketing and finance need to pull together, and the selected metrics need to be useful to both mind-sets. Other proven ways to overcome resistance to data-based recommendations include moving to the proposed optimal allocation gradually and demonstrating the real-word gains through field experiments. When companies succeed in establishing truly accountable marketing, they improve and simplify recurring and quantifiable decisions, which leaves them more time to scan the environment for new opportunities and allows them to take smarter risks.

Abstract
In the 2016 presidential election, the vast majority of available polls showed a comfortable lead for Hillary Clinton throughout the whole race, but in the end, she lost. Campaign managers could have known better, if they had had a closer look at other data sources and variables that – like polls – show voter engagement and preferences. In the political arena, donations, media coverage, social media followership, engagement and sentiment may similarly indicate how well a candidate is doing, and most of these variables are available for free.
Validating the bigger picture with alternative data sources is not limited to politics. The latest marketing research shows that online-consumer-behavior metrics can enrich, and sometimes replace, traditional funnel metrics. Trusting a single 'silver bullet' metric does not just lead to surprises, it can also mislead managerial decision-making. Econometric models can help disentangle a complex web of dynamic interactions and show immediate and lagged effects of marketing or political events.

Abstract
An effective dashboard integrates data, processes and viewpoints to show what happened, why it happened and what could happen with the right remedial action. It serves as a communication tool for what is important to the organization and helps to align all parties to the right objectives. It also serves as an aid in decision-making and goal attainment and the pathway to get there. The dashboards of the future need to be dynamic and constantly adapting to changing market conditions. They should include the long-term effects of marketing spending. Artificial intelligence has already made great strides in automating parts of dashboard development and can write out human language stories based on statistical evidence. AI can generate graphs and allow dashboard users to verbalize hypotheses to be tested in follow-up research.

Abstract
Vector auto regression (VAR) is a form of econometric modeling that is receiving increased attention in marketing research applications. It is used to observe whether potentially relevant indicators have a real impact on sales or success factors. Compared with correlation, regression and conjoint techniques, VAR models are superior because they are able to show the impact of changes over time on the basis of real business data. The research shows how VAR models are applied in different marketing settings. VAR models can filter relevant metrics from a whole set of potentially relevant performance indicators and quantify the sales impact of each variable. They further observe lead and lag effects that cannot be tracked when measurement is conducted at a single point in time. Modeling can be performed on competitive brands as well. VAR models also make it possible to test whether the same success factors that drive a category also drive the sales of each of the brands in that category.

Abstract
Interview with Neil Hoyne, Chief Measurement Strategist at Google
Dashboards are a common tool for managers to monitor a company's performance, and since the COVID-19 pandemic they have gained popularity among even broader audiences. But what is the real use of these dashboards? Is it just performance art or is it a tool that provides managers with the information they need? It may be slightly astonishing that Google employee Neil Hoyne is no fan of dashboards, but he believes they can be toxic when taken out of context. In this interview, he explains his skepticism of monitoring the same KPIs quarter after quarter and suggests different ways to make dashboards more strategically useful to companies. In his view, dashboards should inspire questions and curiosity, reflect market context and align toward specific business initiatives. He also suggests a more professional use of data and favors the scientific inquiry of the relationship between marketing measures and business outcomes.

Abstract
In our augmented world, many decision situations are designed by smart technologies. Artificial intelligence helps reduce information overload, filter relevant information and limit an otherwise overwhelming abundance of choices. While such algorithms make our lives more convenient, they also fulfill various organizational objectives that users may not be aware of and that may not be in their best interest. We do not know whether algorithms truly optimize the benefits of their users or rather the return on investment of a company. They are not only designed for convenience but also to be addictive, and this opens the doors for manipulation. Therefore, augmented decision making undermines the freedom of choice. To limit the threats of augmented decisions and enable humans to be critical towards the outcomes of artificial intelligence–driven recommendations, everybody should develop "algorithmic literacy." It involves a basic understanding of artificial intelligence and how algorithms work in the background. Algorithmic literacy also requires that users understand the role and value of the personal data they sacrifice in exchange for decision augmentation.

As customer-brand engagement progressively shifts to digital domains, understanding social media effects in branding has become a vital issue. Social media effectiveness is especially important for the US retail sector due to intense competition among retailers for consumer attention and engagement on digital channels. Yet, the research on the effectiveness of social media in the retail industry remains sparse. Thus, the purpose of this paper is to investigate how social media affects US retailers' customer-based brand equity (CBBE) which is an important indicator of brand success. Using a dataset of 15,717 retailer-day observations, the authors empirically test the dynamics between owned and earned social media and CBBE using panel vector autoregression (PVAR). The authors find strong impacts of owned and earned social media on CBBE across the board. However, they find that owned social media harms CBBE of retailers dealing in hedonic and high involvement products. Whereas owned social media helps general retailers in building CBBE, it reduces CBBE of specialty retailers.