Predictive Analytics in Action

Grounding process in a real-world example is one of the best ways to understand a new capability. Our recent series on predictive analytics shared a good deal of information on the why and the how of predictive’s benefits for PR. Our VP of Marketing Tech, Chris Penn, even detailed a theoretical example of how predictive analytics would be applied to his own personal efforts. This post will detail a live example of a predictive model used recently in a SHIFT new business effort.

Like most agencies, we conducted research ahead of our initial input call. Alongside a review of prospect and competitive websites/social channels, we also reviewed the larger conversation landscape. This included both earned media and paid/organic keyword research. This level of research educated us on market nuances, and served as a counterpoint to potential prospect messaging biases/gaps. Most clients by nature are self-focused in their messaging. By combining our traditional research with predictive analytics, we plotted new earned and content strategies to help this client stand out.

Step One: Choosing the Right Terms

Our prospect touts themselves as a leader in programmatic advertising. Through keyword research we identified video advertising as an emerging theme. However, cost per click is relatively high and there are multiple competitors chasing this term. A predictive analytics model on “video advertising” helped us see where the market conversation, and likely the buyers, are heading.

Step Two: Choosing the Data Source

During our input call, the prospect stated video advertising is an emerging trend in their mind. We wanted to know if this term was predicted to see an uptick in search, which would signal increasing market interest and buyer intent. We also wanted to identify seasonality to help plan organic, earned and paid strategies. We collected and visualized multiple years of search data through Tableau to uncover specific trend points for further analysis.

predictive models

Step 3: Data Analysis and Recommendations

Our visualization showed some clear areas to further explore with the pitch team, including specific trade shows or industry events that might correlate to the spikes. After matching industry aspects with a more general understanding of buyer intents and cycles, we drafted recommendations to maximize both organic and paid strategies.

Week of September 23, 2017

  • An initial peak in search sees the market conversation continue above normal levels through the fall. Consistent content, earned media and targeted ad spend should be planned through early December.

Week of December 3, 2017

  • The start of a significant dip as brands shift into EOY budgeting and planning-mode through the holidays. The company should consider shifting ad spend to retargeting/remarketing efforts to capture latent sales opportunities. Resources should also shift to developing content for an early 2018 push. The company should begin long lead pitching to secure coverage during the mid-January through May search wave.

Week of January 13, 2018

  • Brands are back from the holiday season and are looking to drive Q1 sales. Deploying the content developed in December and ramping ad spend will position the company up to ride the upcoming search wave through the spring.

Week of May 8, 2018

  • Search volume starts to taper off and dips significantly over the spring through summer. Maintenance content and social media posts, along with earned media on trends and lessons learned from 1H 2018 will position the company near the top of search results ahead of the fall push.

Using predictive analytics this way yields specific recommendations that help a PR team truly partner with their internal clients. These insights keep programs focused on tactics that will help support sales and drive higher brand awareness and consideration.

Derek Lyons
SVP

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Posted on October 12, 2017 in Data-Driven PR, Future of Marketing, Marketing, Marketing Technology, Predictive Analytics

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