Mobile, AI Trends Impact E-mail Marketing

E-mail marketers can expect to ride some positive trends into 2020, according to e-mail pros. While continuing to top other channels in terms of ROI, e-mail marketing in 2019 also showed improved delivery rates, rising open rates and increasing click-through-rates, per UK-based Data and Marketing Association. Digital marketing platform Smart Insights recently turned to several experts to find top strategies to help leverage the positives this year. The impact of mobile usage on e-mail can't be underestimated. In 2019, mobile browsing (53% of traffic) surpassed desktop/tablet browsing (47% of traffic), notes Smart Insights. And while mobile still lags in terms of purchase revenue (32%) versus desktop/tablet (68%), it's growing fast, with a 23% year-over-year increase that has helped accelerate mobile optimization across digital channels. Data from Litmus shows that mobile devices also led in e-mail opens (41.9%) compared with desktop opens (18.2%) by Q1 of 2019. Coding e-mail creative for a mobile-viewing format is only the beginning. Changes to copy, images and overall layout can boost click-through by streamlining presentation to place the focus on quick engagement and CTA links, note experts. Concise copy with enough white space for easy reading is a first goal. Other tips include using image dimensions that are small enough to render well and placing images on the "first fold" to encourage scrolling. Using e-mail design with short copy not only leads to quick scanning but also allows the call-to-action (CTA) to be seen early and avoids excess scrolling that loses click-throughs. Finally, make sure that e-mails are rendering correctly across all types of devices, a testing option offered by most e-mail service providers. Artificial Intelligence (AI) is coming of age in marketing, including e-mail campaigns, with 87% of current AI users say they are planning on using it for sales forecasting and e-mail campaign enhancement, per Statista. Connext Digital foresees seven ways that AI will begin to influence e-mail marketing: 1) applying algorithms and data insights for predictive personalization; 2) analyzing demographics, purchasing patterns and online behavior for smarter segmentation; 3) automating workflow for e-mail triggering, tailored messaging and lead nurturing; 4) using natural language technology to find the best words for response optimization of subject lines and CTAs; 5) improving e-mail timing and frequency by specific recipient;6) A/B and multivariate testing to quickly identify trends and develop predictive results; and 7) developing data to enhance broader analyses, for example to predict potential churn. Privacy regulation is another factor for e-mail. Cold e-mailing, spamming and phishing have tainted e-mail’s reputation among consumers (and marketers), but recent privacy regulations, including the European Union's GDPR and California's CCPA, have spurred the e-mail marketing world to go beyond CAN SPAM to focus on privacy, compliance and subscriber trust. See our full website post at https://www.acculist.com/mobile-ai-highlight-e-mail-marketing-trends-for-2020/

Personalization, Privacy Drive 2020 Data Strategies

Multiple data issues face direct marketers in 2020, starting with data privacy. While many of our U.S. clients are not affected directly by the European Union's General Data Protection Act (GDPR), U.S.-based consumer-data privacy efforts have now resulted in the California Consumer Privacy Act (CCPA), with more legislation on the horizon. Meanwhile, omnichannel data personalization has become essential for targeted response and ROI. Companies face complicated decisions when combining first-party data collection, user-level data from the big digital platforms (Google, Facebook and Amazon) as well as second- and third-party data in ways that balance consumer privacy with smart (and customer-demanded) personalization. A post in AdExchanger by Briggs Davidson, a senior manager at Deloitte Consulting, outlined some key steps for coping. He advises starting with a focus on the customer in collecting, organizing, storing, and activating data across all silos that may need to meet data-privacy compliance. When it comes to first-party data, prepare to shift marketing strategies to ensure consumers have a reason to share their data, delivering value to build trust. Davidson predicts creation of data clean rooms, or a separate analysis space for combining first-party data with platform-level customer data under strict privacy controls. Regardless, marketers will need multidisciplinary teams as well as partner collaboration. Meanwhile, prepare for hyper-personalizaton to drive marketing, according to European digital platform firm Qualifio, which found that 83% of marketers say creating personalized content is one of their biggest challenges. Why? Because personalization now requires: 1) new tools to collect and analyze first-party data for compliance with data privacy regulations like GDPR and CCPA; 2) an omnichannel purchasing journey and analytics for a single customer view; 3) incorporation of new technologies such as voice search (50% of Google searches are expected to be voice searches in 2020); and 4) meeting rising customer standards for personalized promotion and service. In fact, 70% of the customers surveyed want an immediate response to their questions or complaints, which is fueling artificial intelligence (AI) and machine learning (ML) initiatives. This means data quality will be even more key. A Forrester Consulting July 2019 report revealed that while 82% of companies place a high priority on refining data quality, more than a quarter of all marketing campaigns were hurt by substandard data in the last 12 months. Plus, a majority of marketers launching artificial intelligence (AI)/machine learning (ML) initiatives (78%) say these projects have stalled—with data quality as one of the culprits for 96%—according to a new study from Dimensional Research. CMO Kristin Hambelton, of Marketing Evolution, urges marketers in a recent Forbes magazine post to take these basic steps for improved data quality: 1) prioritize data quality and create a comprehensive initiative on data verification, collection and cleansing policies; 2) define and verify high-quality data in terms timeliness, completeness, consistency, relevance, transparency, accuracy, and representativeness; 3) organize disparate data sources with unified marketing measurement for a holistic customer view. For more, see https://www.acculist.com/personalization-and-privacy-trends-highlight-need-for-data-strategy/