ecommerce

How Do Consumers Respond to Gender-based Pricing?

  • MSI

Markets are growing increasingly transparent. While one of the easiest ways to segment customers is by gender, pricing differences based on this factor are becoming more apparent and harder to justify. Women perceive such pricing discrimination for comparable product as unfair. This reduces purchase intent and their liking of and appreciation for the brand, while men tend to rationalize such differences as justified by product attributes (e.g., dry cleaning a “blouse” versus a “dress shirt”).

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Brands Should Communicate Positive Reviews to Customers Post-Purchase

  • JOURNAL OF ADVERTISING RESEARCH

Can reading online customer reviews (OCRs) influence the opinion of a consumer who has already experienced a product? The answer is yes, new research finds. It turns out, consumers are far more persuaded by online customer reviews than previously thought, according to this research. The findings have significant implications in how best to communicate with customers post-purchase.

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How Speech VS. Text Disclosure Affects Privacy Expectations

  • MSI

Even though consumers “opt-in” to sharing their personal data at the initial point of disclosure, they continue to maintain certain expectations on how their data will be used. According to this study, this is especially true when such data is shared online through voice rather than text. To meet these concerns, managers should adapt data capture, storage and use policies to meet evolving concerns.

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Covid-19 Impact by Category: An Age-Period-Cohort Analysis

  • MSI

Lockdowns and other widespread Covid-19 disruptions caused significant changes in buyer behaviors. For instance, it was a major catalyst in shifting buying behavior from in-store to online. With the pandemic receding, how can we know which trends may persist and for which categories, and which might drop off? Since all consumers were affected, no untreated experiment control group exists. However, one strategy could prove fruitful, age-period-cohort (APC) analyses. APCs offer an alternative way to analyze changes in consumer behavior before and after the pandemic.

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TV Deconstructed: Latest Findings from DASH

The ARF Universe Study of Device and Account Sharing (DASH) is a nationally projectable enumeration study of consumer behavior in TV and digital media. DASH records in granular detail how US households connect to and consume TV, use digital devices, and interact with and share streaming media and ecommerce accounts. Launched in 2021, this syndicated study is designed to serve as an industry standard truth set for insight and data calibration. The report just released by the ARF highlights findings from the first wave of DASH 2022. Data and findings from the full 2022 data set will be available in January 2023.

Ad Revenue Trend

This chart from Visual Capitalist shows how just three companies now make up 50% of U.S. ad revenues. 

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Performance Pressures?

A survey among marketers conducted for the WARC Marketer’s Toolkit 2023, shows that almost half plan to increase their investment in performance, while less than a third plan increased branding spend. However, that is only part of the story. Read more »

Evidence-Based Research for Effective Marketing

On November 9, 2022, industry leaders joined us to share highlights from EffWorks Global 2022 — a week-long celebration of the best new thinking and evidence-based decision-making research for marketing effectiveness. Topics of discussion included: marketing in the post-Covid economy, effective advertising in unprecedented times, the value of Share of Voice/Share of Attention/Share of Search in terms of effectiveness and commercial decision making, and more.

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How to Flag Fake Reviews without Targeting Legitimate Ones

  • MSI

Online reviews can have a substantial impact on the consumer decision-making process. That makes them crucial to firms and online marketplaces. Some sellers are sometimes tempted to manipulate ratings to give their products fake, positive reviews. As a result, it’s important for such marketplaces, like Amazon, to eliminate fake reviews. Some methods use metadata, review characteristics, text or images to remove such reviews. This may also flag legitimate ones, however. Researchers in this study found a method using the network structure of reviews to reveal fakes without wrongly targeting authentic ones.

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