• What are the top skills you need for digital marketing?

    What are the top skills you need for digital marketing?

    Hiring talent with analytics experience is emerging as a critical priority this year. By hiring marketers who can effectively analyze data and glean insights, organizations can stay ahead of the curve and make more informed decisions. This article explores the most sought-after skills in digital marketing and what they mean for marketing professionals and the industry. Top skills marketing leaders look for when hiring Up to 57% of marketing leaders prioritize analytics experience when hiring new talent, according to the State of Marketing 2023 report. As brands grow and become more data-driven, marketers who can effectively navigate and interpret data are highly valued. Other skills that marketing leaders are hiring for and prioritizing are: Social media management (12%) Copywriting (9%) Video production (7%) Graphic design (6%) Search engine optimization (6%) Google Ads (2%)  Although marketing analytics is specified, the ability to use data when managing social media communities, producing content and managing paid search marketing is also critical. This shift towards prioritizing analytics experience reflects a growing recognition of data’s vital role in marketing strategy and decision-making. This makes sense for a few reasons.  The need to demonstrate business value Companies are tightening budgets to weather the current economic storm. At the same time, CMOs have been demanding that their marketing and PR teams demonstrate ROI from their programs. This is a trend that I’ve seen over the last 5 to 7 years. Marketers were asked about their KPIs and how they plan to measure their programs’ performance in the same report, and 26% said that cost per acquisition/sale was the number one KPI, followed by: Social engagement (19%) Customer lifetime value (17%) Cost per impression (9%) Customer retention rate (9%) Cost per click (8%) Cost per lead (8%)  These data points clarify that marketing leaders prioritize metrics that prove value. Outside of social engagement, these KPIs are all aligned with financial metrics. Third-party cookie deprecation Google plans to phase out third-party cookies in Chrome by 2024. Aside from rethinking audience targeting and focusing on first-party data, marketers must up their analytics skills to use the data effectively and draw meaningful insights. Consumer privacy is also a significant consideration. Legislation, like the GDPR and CCPA, require companies to obtain explicit customer consent before collecting and using their data. Still today, 75% of marketers rely on third-party cookies. Dig deeper: Why we care about compliance in marketing Marketing budgets are on the rise This year, over 50% of marketing leaders plan to increase budgets, but just 14% will make substantial investments, according to the same report. This is likely due to the uncertain financial times that have characterized the last 12 months. However, despite these budget constraints, marketing leaders are still investing in data-driven strategies, such as: Investing in analytics tools. Hiring talent with analytics experience. Other initiatives to help them better understand their customers and engage them on a deeper level. The demand for analytics skills will likely remain strong as marketing teams continue leveraging data to improve customer experience, drive sales and maximize ROI. Per Gartner, almost 30% of the digital marketing budgets are being allocated to analytics across three functions:  Marketing data and analytics (9%) Customer analytics (8.8%) Marketing insights (8.3%) While each function serves different purposes, all require an in-depth knowledge of data and analytics. Marketing data and analytics is about performance Hiring marketers with an analytics background is necessary to measure marketing performance better. Marketers should be able to analyze data from various channels such as paid search, email, display ads and social media to identify opportunities for improvement and provide actionable insights. Knowledge of conversion rates, budget optimization, clickthrough rates and other performance metrics are critical. One mistake in reporting can result in millions of dollars of loss for brands. Typically, someone working within this function would review the data and provide actionable insights after the campaign has ended. Always-on customer analytics Customer analytics is the process of collecting, analyzing and interpreting data about customers to better understand their behavior, preferences and needs. This involves using data sources such as customer transactions, demographics, web and social media metrics and customer feedback to identify patterns that inform business decisions. In most cases, initiatives that require in-depth customer analysis using survey data happen quarterly or bi-annually. In large companies, this is usually outsourced to a research firm managed by an internal staff member with expertise in analytics. Bringing the outside in with marketing insights Marketing insights refer to the actionable knowledge gained from analyzing third-party marketing research from firms like Gartner, Forrester, Global Web Index, Kantar and Nielsen. These insights can help marketers and PR pros understand the current macro trends, consumer behavior and competitor activity in their industry. This might be similar to customer analytics, but it’s more focused on industry trends and macro-level insights. Again, this helps marketers plan their strategies and understand the broader industry landscape. Cultural trends and insights can also come in other ways. In the report, 31% of the marketers surveyed have designated cultural insights teams in-house. This approach is more expensive, given the cost of salaries and the current economic climate. But having an internal team can be beneficial in speed to insight and data ownership.  Invest in the right resources to drive marketing ROI Data and analytics are essential tools for modern marketers. Investing in the right talent, tools and processes helps you keep up with the competition. Building a team with different functions specializing in customer analytics, marketing insights and data and analytics is key to success. With the right talent and resources, brands can tap into valuable insights, drive revenue and maximize ROI.

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  • Adobe announces Firefly for AI-driven creative

    Adobe announces Firefly for AI-driven creative

    Adobe Summit 2023, Las Vegas “Our belief is that generative AI will enhance human ingenuity, not replace it,” said Shantanu Narayen introducing Adobe Firefly at Adobe Summit today. Firefly is a new group of generative AI models focused on creating images, video and text effects. Firefly uses generative AI with graphics tools like brushes, color gradients and video tools to speed up production and make it easier for creators to make high quality content. The videos and images projected on-screen during the two-hour Summit keynote had been generated by Firefly. Adobe’s generative AI. The generative AI announcement predictably stole the show although there were some other new product announcements. “Over time,” said Narayen, “AI will help us reimagine every aspect of marketing.” He could not resist adding that Adobe has incorporated AI in its creative products for well over a decade. The first Firefly tools are available today in beta. Narayen also emphasized that Adobe is seeking to protect human creators, both by developing a model for compensating for use of their work and by moving towards a global standard “Do not train” metadata tag that creators could use in an attempt to ward off AI infringements on their content. Summit also saw the launch of Sensei GenAI, natively embedded into the Adobe Experience Platform, although it was not immediately clear how this would enhance the platform’s existing Sensei AI capabilities. Again, the aim seems to be to allow users to work with generative AI capabilities within Adobe Experience Cloud rather than using independent tools and migrating results to Adobe. Why we care. In many ways it would be shocking if Adobe had not taken this route to incorporating generative AI into its platform. Although Adobe was one of the first marketing clouds, its roots are in iconic creative tools like Photoshop and Illustrator. The promise, of course, is that Adobe users will be able to reap the benefits of generative AI within the Adobe ecosystem, rather than have recourse to one of the many independent tools rushing to market. Dig deeper: Adobe CEO: Make the digital economy personal Adobe Product Analytics. If overshadowed by Firefly, Product Analytics was a significant announcement. The Adobe Experience Platform already includes, among its many components, Customer Journey Analytics. Product Analytics offers a similarly drillable dashboard by presenting product-related information such as user growth and engagement with features of products and trends. The aim is to align the product team with other teams working on aspects of customer experience. Adobe also announced Adobe Mix Modeler, a new dashboard giving a cross-channel view of campaign performance allowing real-time optimization of channel spend. Additional reporting by Chris Wood.

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  • Accuracy in digital analytics: What marketers need to know

    Accuracy in digital analytics: What marketers need to know

    There is a misconception that digital analytics reports are inaccurate. In reality, they are highly accurate in their own way, just not precise. The issue lies in users who don’t know what the analytics data means or how it is gathered. To make matters worse, different tools measure things differently but call them by the same name.  In this article, we’ll take a closer look at nuances in data measurement and how various analytics software are in action. Looking at nuances in data measurement   Digital analytics tools were never intended to work as accounting systems or sales registers. They were made to collect and quantify interactional user data into easily usable insights and reports. Over the years, these tools’ data collection methods have evolved. In turn, the way specific data points are measured also changed.  Let’s say you changed your tape measure from imperial (measuring in inches) to metric (measuring in centimeters). The length of a desk might be reported as 39.4 in one and 100 in the other. The length of the desk didn’t change, but how you measured it has.  Try switching between different analytic tools. Often, you’ll see that your numbers may be different, but trend lines remain similar. Each tool counts things slightly differently; the same issue frequently applies when upgrading software. At one point, unique users were counted by combining the total number of unique IP addresses that accessed a website in a given period. Eventually, organizations started using firewalls/proxy servers, requiring all internal users to access the internet with a single IP address. How unique IP addresses were counted didn’t change, but the count of unique users dropped dramatically. Counting of unique users evolved into using a combination of IP address, OS and browser (type and version), then the addition of a persistent cookie to better estimate unique users. Once again, no matter how you count unique users if the user cleared their cookies and cache or switched computers (office vs. home vs. phone), no analytics tool will have provided an exact number. Nowadays, tools take other factors into account when counting unique users.. Dig deeper: Data analytics: Your stack’s past and limitations How to think of your analytics data Your analytics software is imperfect because of many factors beyond its control. Users might be blocking cookies or other tracking methods. Internet blips might prevent data from reaching the data collection server. The best way to think of your analytics data is by viewing it as a poll of user activity. Everyone is familiar with polls at election times. A typical U.S. presidential election poll surveys approximately 10,000 people (or less) out of 150+ million eligible voters (0.006% of voters). This is why when news broadcasters report on the poll results, you hear something along the lines of “This data is accurate within 4 percentage points 4 out of 5 times.” This equates to it being off by more than 4 percentage points 20% of the time. When it comes to your digital analytics tools, most analytics professionals estimate the loss of data to be no more than 10% and most likely around 5%. How does this translate into data accuracy? If your site received 10,000 sessions in a reporting period but for various reasons, you could only capture data on 9,000 sessions, your data would be accurate within a margin of error of less than 1%, 99 times out 100.  In other words, 99 times out of 100, your data is accurate and 1 out of 100 times, it is off by more than 1%. Simply put, your data is accurate, but it is not perfect (precise) and will not match your sales records. Such data is more than accurate enough to determine which marketing efforts — SEO, paid ads, sponsored posts, social media marketing, email marketing, etc. — are working and even which ones drive traffic versus drive sales. Dig deeper: Don’t apply wishful thinking to your data Analytics in action While analytics data may be accurate, even being off a small percentage in precision can call your analysis into question. This is especially true when the difference between two data sources changes.  The key is to monitor the data and, where possible, compare it. If there is a sudden change in accuracy, you need to investigate. For example, was your website recently changed? Was this change properly tagged to capture the data? A client once added a pop-up to their Shopify account after an order was placed but before the thank you page was generated. Their analytics tool records sales only when the user receives the thank you page.  With the pop-up in place, the order still went through, but many users didn’t click through the messaging. As a result, a large percentage of sales were suddenly not being captured as no thank you page was generated. There wouldn’t have been an issue if the pop-up appeared after the thank you page. Below is an example of monitoring sales and orders between Shopify and Google Analytics 4 (GA4). We can see how much data is being lost because of various factors. Using Shopify’s analytics as a record of true sales and comparing it to data collected via GA4, we see the following: The daily variations in total revenue and orders varied from virtually 0% to nearly 13%. Overall, in these 24 days, GA4 reported 5.6% less revenue and 5.7% fewer orders. This data is accurate, especially when applied to marketing efforts to see what drove the user to the site to make the purchases.  Should this company use GA4 to report sales? 100% no! That’s what accounting software is for. If your organization demands even more accurate data, there are methods to push data directly to most analytics tools (server side). This avoids issues with user browsers and cookies.  While sales data may be more accurate, other soft measurement aspects of user interaction may drop (e.g., scroll tracking). This is a complex and time-consuming method to implement for most organizations.  You must ask yourself, “is this extra effort necessary just to capture another 2-5% of sales revenue in my analytics reports?” Understanding your analytics data Everyone needs to have faith in their analytics data. The key is ensuring your analytics software is installed and configured correctly. Understand that it can’t capture everything.  Your analytics software simply takes a poll with a sample size of over 90%. This makes the results highly accurate (on target), if not 100% precise (actual numbers).

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  • The Big Game brand lift results are in! by Digital Marketing Depot

    The Big Game brand lift results are in! by Digital Marketing Depot

    What did you think of the ads that ran during the Big Game? Did any stand out to you? If you’re looking for a more detailed evaluation of the ads, DISQO’s got you covered. Their data-driven Big Game report is the ultimate tool for marketers looking to evaluate brand performance and formulate best practices for their own campaigns. They’ve analyzed the top performers, key themes, and implications for future campaigns — all backed by real-world data. Visit Digital Marketing Depot to download Big Game Brand Lift today to get actionable insights and maximize your brand’s performance. Add MarTech to your Google News feed.     Related stories New on MarTech @media screen and (min-width: 800px) { #div-gpt-ad-6013980-7 { display: flex !important; justify-content: center !important; align-items: center !important; min-width:770px; min-height:260px; } } @media screen and (min-width: 1279px) { #div-gpt-ad-6013980-7 { display: flex !important; justify-content: center !important; align-items: center !important; min-width:800px!important; min-height:440px!important; } }

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  • How Haleon built social media intelligence in-house

    How Haleon built social media intelligence in-house

    Social media platforms are an important arena for consumers to talk about brands that affect their lives. That’s why Haleon assembled an in-house team to own social media for their many over-the-counter products. Haleon was created last year out of a joint venture between GSK’s and Pfizer’s consumer products, which include Advil, Excedrin, Robitussin, Tums and other household brands. The company assembled an in-house team to use social media intelligence, or “social intelligence” — tools and strategies to understand what customers are saying about brands and how to leverage that intelligence to boost marketing efforts. Dig deeper: Social media marketing guide for brands First, Haleon had to define social media intelligence. It can mean different things in different organizations, so it’s important for each business to establish goals and benefits derived from social intelligence operations. “Social intelligence is folding in all these different data sources and really trying to figure out what this data is actually going to do and what [it] tell us,” said Danny Gardner, analytics manager U.S. and North America social intelligence lead for Haleon, at The MarTech Conference. Gardner and his team consider social intelligence as a more sophisticated version of social media monitoring and listening. Instead of just tracking different topics that consumers are talking about on social platforms, social intelligence draws insights from this data and ties the insights to marketing actions. “Why does the business want to have social intelligence?” Gardner asked. “At its core, it’s insights. We’re able to act on this data and get to insights faster than any other team in the company.” Brands that gather social intelligence have access to consumer opinions about their own products and also the competition. They also gain feedback about marketing campaigns and can learn more about their target audience. Another benefit of social media intelligence is finding out where consumers say they are purchasing products. For Haleon, knowing if customers are talking about buying Advil at a Costco or through an online retailer helps the company develop an ecommerce strategy. If consumers are speaking negatively about a brand on social, knowing this can help the brand execute a crisis management strategy, said Gardner. Four social media intelligence categories Social media is a vast space, and listening to it intelligently means having clear categories or “buckets” for the data. Image: Haleon. Gardner and his team established four main buckets of data they wanted to gather through social channels. They wanted to analyze and gain insights from social conversions that related to their own portfolio of brands, competitor brands, broader topics related to using these products, and “macro and cultural” trends. “There are a lot of trends that go on and things that happen in society that we’ve realized our consumers care a lot more about than our brands, and rightfully so,” said Gardner. “And so we took it upon ourselves years ago to build this into our remit.” Building and scaling social media Although Haleon only went live as an organization in 2022, their marketing strategy, including their approach to social intelligence, has been years in the making. Here’s a timeline of the steps they took to implementing social intelligence tools and strategies. Image: Haleon. “There was this large discovery phase around what data is available, how can we get to it, what does data mining look like, what vendors exist and what are their capabilities,” Gardner explained. “It was actually a couple years before I was hired that they started building the case that, hey, we actually think we might be able to do this in-house.” Haleon also debated the pros and cons of building versus buying their solution, and eventually wound up settling on a suite of social intelligence tools developed by Meltwater. Piloting social media intelligence during the pandemic Just as Haleon was ready to test pilot some of their social media intelligence capabilities, the world changed. During the first years of the COVID-19 pandemic, many consumers upped their use of digital channels to purchase products and self-educate. “We came out of our 12 month pilot, and at the end of the tunnel was COVID-19,” said Gardner. “And so this definitely accelerated the demand and interest for what social listening was and really catapulted us into the limelight…Social media was kind of the go-to for questions [consumers] didn’t have answers to.” He added, “So at the time this is actually what inspired this macro trend tracking capability and we now know we can do this pretty well around our brands.” As a result, Haleon has a better understanding of how consumers feel about their roster of brands. And they can join the conversation on larger issues in a way that’s relevant to their customers. Register for The MarTech Conference here.

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