How FinText Benchmarks Investment content
- Investment marketers lack hard data on which marketing content will deliver leads and inflows.
- Text Analytics is a useful NLP concept, bridging between algorithms and meaningful questions.
- FinText uses Text Analytics to benchmark the content investors read.
- The outcomes point to specific improvements on topics, style and content formulation.
While marketers know content marketing can be effective, they lack hard data to decide which content works.
But this information is urgently needed: All too easily companies can sink money into creation processes that deliver content no one wants to read.
Meanwhile, the active-management industry has seen its market transform in the past decade. The rise of platforms and passive investing played a part, as did the surge in available investment products. Overall, it results in ongoing outflows and fee compression.
Clients have moved online. Content marketing has become an integral part of the lead generation and sales process. The pandemic has only intensified this trend.
Concept: Text Analytics
Machines can understand textual content better than ever before. Applied Natural Language Processing (NLP) is at an inflection point, following breathtaking progress over the past decade.
The field of NLP is rich with tools and techniques; but these often concern specific tasks, unlike how humans ingest and reason about written content.
Text Analytics (also known as Text Mining) bridges between the high-level information needs and algorithmic techniques. It breaks questions concerning texts to smaller sub-tasks, and later integrates the outcomes.
With clients now expecting a strong digital presence, content becomes the new business card. But which content is worth investing in?
The explosion in information online changed how people read. Behavioural studies show that, when readers come across new content, their attention is governed by trust. Time and attention are scarce – but it’s trust that determines how they’re spent.
FinText uses text analytics to build audience trust.
We break apart the articles investors read, and the conversations they have online, to offer data-led, actionable insights on the content investors will find trustworthy and appealing.
Text Analytics at the service of investment marketers
Real engagement is driven by trust. Digital readers now pick-n-mix their information sources, creating their own private content bubble. WHAT you talk about must be new and valuable to your audience. HOW you talk about it needs to mirror the language your readers enjoy.
Text analytics allows us to profile investment managers’ content, for trust-building tactics on both fronts.
Take language-mirroring: the chart below shows benchmarks the content published by seven investment managers over an entire quarter by complexity.
We look at the percentage of “big words” – words containing four syllables or more. Two lines are added for reference: how complex are financial news stories published over the same period (often on the same topic!) and how complex are investor conversations over the period (discussing the same market trends!).
An asset manager can gain an edge just by making its content easier to read. Improving texts on text analytics measures increases their appeal. See the different it makes to a LinkedIn post of a large wealth manager:
Benchmarking also reveals what makes content needlessly complex, with actionable insights on avoiding complexity pitfalls.
Complexity is just one angle for probing content. See the case studies below for other benchmarking types.