Some things will always be a part of a marketer’s job description: Connecting with your audience. Optimizing campaigns. Demonstrating value on behalf of your brand. That said, how marketers do this most effectively is ever-changing. At this year’s MarTech Conference, marketers from around the globe gathered to discuss lessons learned from 2019 and more importantly, how to stand out in the year ahead. So, read on for the must-know marketing trends for the end of the year and beyond in 2020.
The Rise of Marketing Data Directors
Understanding marketing ROI has been a huge issue across many industries. After all, how can a team definitively say what led to a campaign’s success (or lack thereof). Was it the list? Was it the time of day? Was it the channel? And when a team struggles to prove its value, executives, in turn, struggle to see it. Marketing data directors are changing this once and for all.
Directors of marketing analytics sit at the intersection of IT, marketing, and analytics. While their roles cover everything from AI implementation to data hygiene, their core functionality always comes down to this: developing a measurable framework to understand growth metrics & marketing spend. Or, in other words, to master and own the analytical understanding of customer behavior from a marketing perspective. Powerhouse companies like Tinder and legacy agencies such as Ogilvy have already started hiring, and this role is only expected to rise in the coming year.
Making Personalization A Reality With AI & Ml
In a worldwide survey, 94% of companies agree that personalization is critical for future marketing success. Why? Just look at how consumers feel about it: 63 percent of consumers expect personalization as a standard of service and believe they are recognized as an individual when sent special offers. Between fragmented data and technology limitations, personalization has been notoriously difficult to achieve at scale. In 2020, however, machine learning will become more accessible making it easier than ever to create custom experiences.
In marketing, the bulk of AI investment (60%) has gone to machine learning. Machine learning is when a computer is able to find patterns within large amounts of data in order to improve or optimize for a specific task. Why does this matter for personalization? Well, machines can recognize if people from certain areas are more likely to respond to a specific offer. Or, by analyzing which types of special offers have previously received a high response, can predict which may resonate in the future. The hard part is figuring out the best way to fit ML into your overall strategy. Should you use a platform? Should you have a team build something custom? Lineate’s AI data scientists are alway happy to chat if your company isn’t sure where to start.
Thriving in the Data Privacy Era
On May 25, 2018 Europe’s GDPR law shook the world. This law was the biggest of its kind to give consumers power over their own data. Specifically, the law requires that companies make it easy for consumers to view, access, alter, or delete their data from companies at any time. At first, this scared marketers. Now, 60% of companies are embracing GDPR as an opportunity to improve privacy, security & data management for new business models.
Similarly, U.S. companies are now preparing for the CCPA. Like many marketers did in Europe, U.S. marketers are taking a hard look at their data: how it’s collected, why they need it, and where it’s stored. We’re not saying data audits are easy, but they’re necessary. Especially when the long-term result is building trust with customers and in some cases, avoiding massive CCPA fines. For specific tips on building trust in the CCPA era, be sure to check out our ebook on navigating the new law as a marketer.
Agile Marketing Adoption Is Increasing
The word “agile” is thrown around in the tech industry a lot. Recently, it’s been popping up in MarTech, too. But what does it mean? Agile marketing breaks down team silos in favor of focusing on high value projects collectively. The result? Different areas of expertise are shared throughout the duration of a project—ensuring the ability to improve the results over time (rather than waiting until the end of a project’s completion for feedback. In short, it is an approach that encourages collaboration and regular iteration to drive better results.
According to a 2018 survey by Kapost, 37% of businesses have already adopted agile marketing, and another 50% said they haven’t yet become agile, but expect to be soon. As agile marketing becomes more prevalent, a larger problem will be addressed: data silos. For teams to truly be collaborative, they need access to the same baseline information. Before integrating your teams, make sure to explore strategies (like data orchestration) to merge your data so teams who are collaborating don’t duplicate their efforts or reach out to potential customers who have opted-out.
Customer Centricity Drives Loyalty
Customer centricity is when a company provides its customers with a positive experience before and after a sale to ensure long-term retention. That said, creating impactful positive customer experiences has never been more challenging. With so many brands now competing for attention, it makes sense that 88% of CEOs are concerned about customer loyalty. Ultimately, a customer-centric mindset can make or break this concern.
For customers, a good experience means personalization. It means not getting the same message over and over. It means predicting needs so your customers don’t have to think when they shop. Just click. Data silos play a big role in ensuring customers get the right message, at the right time. Plus, having a single place to view all of your data ensures an easier way to monitor customer struggles in real-time (and respond to them just as quickly). Beyond data silos, improving customer service is a huge part of customer centricity–and ML can help.
Dutch airline KLM, in fact, is using ML to field customer service requests via social media, often without the intervention of a live agent. KLM says its team of 250 social media service agents have about 30,000 conversations a week, double the volume of the year before. Customers contact KLM through Twitter, Facebook, and WhatsApp. Most conversations consist of five to six questions. KLM built an ML platform that analyzed customers’ questions and returned suggested responses that shows up on the screen of a customer service agent. The agent could accept the answer, reject it, or send a personalized response. Because their customer service process uses machine learning, it’s gotten more accurate over time, allowing KLM to automate the most common questions and save the tough stuff for human agents.
2020 is almost here, and brands must act fast to keep up with customer expectations for the new year. From adopting machine learning to improving customer centricity, it all comes down to data. How do you organize it, how do you clean it up, and how you use it to reach customers in a meaningful way. Lineate specializes in building platforms and machine learning algorithms that make complex data problems simple. Contact us now to see how we can help your team get ahead of its 2020 data goals.