Case Study: Cutting through in the era of big noise
Marketing has a ‘lagging indicator’ problem.
The behavioural and survey data that appears on many marketing dashboards is measured after the point of inflection when events are set in motion. By the time it’s a behaviour or NPS score, it’s too late. These measurements shed very little light on how people actually think, or on the drivers of future behaviour and outcomes.
The most valuable early signals are hiding in plain sight - in the expressed language of customers, stakeholders, patients and employees, as captured through social media, reviews, and open-ended feedback. Unfortunately, standard techniques of text analysis and Natural Language Processing lack context, and are based on fragmented and outdated models which have little in common with how people think.
The untapped opportunity? To get in front of behavior, taking action based on the earliest narrative signals to more proactively drive performance. Unstructured language is flowing in and around your brand every day, loaded with the critical performance drivers: drivers that are hard to detect though behavioral analysis, NPS surveys, and brand trackers.
Enter the new field of Narrative Analytics, which helps marketers make a shift away from identifying and understanding lagging indicators toward taking action based on leading indicators instead: helping you get ‘cut through’ in the era of big noise…
In an increasingly cluttered landscape, brands need new ways to detect - and go - where the ‘narrative traction’ is. It starts with hearing: How do you understand the wider context in ways that neither social media monitoring nor generative AI can reveal?
Understand the full narrative landscape - simply:
What are your competitors and customers actually saying in the first place? The first step is an interactive map of the outbound Tweets of three major US banks, along with their investment arms: Wells Fargo, Citi, and JP Morgan Chase. High level narratives like “corporate citizenship” break into subnarratives like “women winning,” “racial wealth gap,” and “supporting the arts”. The map is interactive, with each dot corresponding to a tweet which can be read by hovering with your cursor. Is it more intuitive than what you are using now?
Hit areas of high ‘narrative traction’ to cut through:
Based on the clusters in the above map, you can see which narrative spaces generate the highest proportion of likes or shares. It varies by brand, so you may find that what gets traction for you is different versus what gets traction for your competitor. Would this help you to improve your earned media performance?
Set direction for prompt engineering:
The attached map shows how prompt engineering can miss the mark. In our prompt testing, ChatGPT generated a lot of tweets about gender-based inclusion, but none about racial inclusion. Comparing the map to the generated results make it very easy to gaps like this. Random prompt engineering won’t provide this clarity. While your competitors are flailing by ‘experimenting’ with prompts, you will be far better informed and productive in the application of generative AI, by starting with narrative analytics. How smart would that be?
Better correlate trends with outcomes:
As Jeff Bezos said, ‘your brand is what people say about you when you aren’t in the room’. Narrative analysis makes it easy to track that grassroots reality, because the ‘categories’ that appear in a map are never pre-set by an algorithm, model, or researcher. They are unique to that dataset, built from the true conversation. (something that isn’t usually true of your brand tracker or social listening approach, wherein most of what you see is based on pre-assumed categories or guesswork). Tracking the clusters in the map over time, we have found that narrative analytics correlates with business outcomes between +40% and +80% better than traditional approaches. Would that help you to explain the value of the brand to others in the business?
The best part is speed and scale: you can be doing all of the above within about 3 weeks, simply and easily. Phrasia is the only company making this type of narrative analysis simple and scalable, and it works in more than 60 languages simultaneously.
More about Phrasia:
Phrasia is the world’s leading Narrative analytics solution. Built on large language models (LLMs) and deep learning, narrative analytics is only fast, scalable way to turn the views that customers express - in their own words - into quantified understanding of emotional and experiential drivers.
Established in 2019 during the early phase of LLMs, Phrasia was co-founded by Ben Gaff and Jeff Bradley. Ben, a Cambridge-educated data scientist, has collaborated with prominent companies like Sky, Experian, and Aviva, and is adept at aligning complex analytical methodologies with business needs. Jeff, a linguist from Northwestern, has a proven track record in leveraging narratives for business growth with companies such as Leo Burnett, P&G, and Aviva. He held prominent roles at Aviva, where he collaborated with Ben to explore the synergy of language and data.