|Kayla Rodriguez and Sally Slater co-authored this article.|
PR and marketing professionals notoriously don’t like numbers. We’re creatives, storytellers and above all else, words people. Calculus, we hardly knew ye.
But with the advent of analytics and artificial intelligence, numbers are about to become your new best friends. The days of decision by gut instinct are behind us. Data reigns supreme, replete with quantifiable evidence that can prove or disprove our theories, measure program impact, gauge audience interests and behaviors and uncover opportunities to optimize performance. The communications strategies of the future will increasingly be one part art, one part data science.
Where analytics has gained the most traction in the industry to date is measurement and reporting. Demonstrating the ROI of PR and marketing has long been an uphill battle, and as reporting requests grow more frequent and more elaborate, collecting and collating the data can eat up a sizable chunk of retainer hours. Analytics can streamline reporting efforts and provide better metrics that resonate with business leaders outside the marketing function. Relational databases allow us to build dashboards with KPIs tailored to different stakeholders, and data visualizations help us relay the numbers in the most compelling way. And instead of using ad value equivalency, we can use attribution analytics to tie earned media directly to the sales funnel.
|This article is featured in O'Dwyer's May '21 PR Firm Rankings Magazine
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However, reporting efficiencies are just the tip of the iceberg. Here are some of the ways analytics and AI are transforming the communications landscape:
Analytics and AI can accelerate unwieldy competitor audits by automating data collection processes to quickly generate competitive benchmarks and insights on performance. Analyzing earned share of voice with tools like Cision and Meltwater is standard fare at this point. You may be less familiar with SimilarWeb, which lets you spy on your competitors’ website traffic (ethically!), or Newswhip, which not only measures social engagement on your competitors’ content, but it also predicts which competitor content will perform best next.
The holy grail of insights, audience analytics can be used to get a 360-degree view of your customers and the influencers who shape their opinions and decision-making. There are plenty of social listening platforms out there that will give you surface-level intelligence on broad market categories or topics of conversation. But to truly understand what your target audiences care about, who they respect, how they consume content and when you need to go deeper. We refer to this as audience architecture, the structural elements that shape human behaviors and outcomes. With machine learning techniques, you can identify and track hyper-targeted audiences to construct a dynamic view of those structural elements and use those insights as the foundation of everything that you do.
Content and media mix optimization
By leveraging analytics to map where key audiences go online to read and share content, you can more efficiently allocate media spend to those channels and outlets. Not only can you drive more website visits, you can drive more of the right visitors to your website. This applies to search strategy and keyword spend as well. By understanding the language your audiences use when they ask peers questions online about products and services, you can tailor your SEO strategy to match their language and optimize your web content to address gaps in knowledge.
ABM and sales team enablement
Analytics can also be used for strategic buyer intelligence to support an account-based marketing strategy or empower sales teams with insights into the needs and business objectives of their sales targets. A corporate issues report, paired with an audit of a key decision-maker's public social persona, paints a broader picture of where their priorities and pain points lie. Messaging, content and sales collateral can be personalized based on analysis of specific high-value audience segments.
The good news is that with self-service analytics platforms and a team of data scientists, communicators still don’t have to do the math. However, data isn’t insight, and the numbers alone don’t tell a story. To get the most value out of your data initiatives, consider these three best practices:
Build the right data foundation. Bad data in equals bad data out. To find meaningful insights, you need to draw from the right data sources—and those data sources need to be accurate, complete, relevant, reliable, timely—and most importantly, accessible. Figure out what first-party data sources (the data you collect directly), second-party data (first-party data from other entities) and third-party data (open databases or aggregated from other data owners) you have available to you, and which best fit your needs. Marrying first-party with third-party data can be particularly effective.
Break down silos. The project team should be multidisciplinary, integrating technical data skills with communications expertise and industry experience. Data teams can’t work in silos and need to collaborate directly with account leads and clients to thoroughly understand the applications of the research and realities of the market. Expect an iterative process and communicate frequently to avoid gaps in understanding.
Start with specific questions and hypotheses to test. Hogwarts isn’t real, and data scientists aren’t wizards. You can’t expect them to pull rabbits out of hats unless you give some guidance on the breed and color you’re looking for. The best analytics initiatives aren’t open-ended; they’re designed to find answers to specific questions or prove hypotheses true or false. The challenging part is knowing what questions to ask. To formulate the right ones, we begin with these thought starters: What are your marketing or PR goals? Who are you trying to reach? What do you already know about your key audiences? What types of insights would be most helpful? What themes do you want to learn more about?
Hypotheses don’t need to be PhD-level theses. The only prerequisite is curiosity. For example, you might postulate that your audience prefers short-form to long-form content. Analytics can validate—or invalidate—your theory.
The most critical determinant of analytics value is the extent to which the insights uncovered are actionable. You might discover that 55 percent of your customer base prefers dogs to cats, but what can you actually do with that information? (Unless you’re in the business of pets.) In our view, analytics is only successful when it leads to concrete changes that drive better outcomes. AI can accelerate information gathering, but it’s on communicators to turn insight into action.
Kayla Rodriguez is Senior Director of Analytics and Sally Slater is Head of Innovation at The Bliss Group.