Rich Insights

Rich Insights:
Brand Perception Inputs and Outputs

This post explains the types of activities and information that commercial team users are currently using (or training clients to use) to approximate answers for Brand Perception analyses.

Notes

  • In general, it would be helpful/beneficial for users (internal and external) to know what these tools are actually doing. For instance, the Company_high operator can be helpful for reducing result set to focus on a particular company, but it’s not clear exactly what is happening on the backend when you’re using it, which makes it hard to explain and use definitively (without trial and error).
  • 75% of the time this user is also looking at competitors in the same topic space they’re exploring for their own brand.
  • Adding a topic in addition to the name of the brand (e.g. “Apple” + “technology” vs. just “Apple") would likely yield better analysis results, but many of the Rosa users who are doing this by themselves are probably just using their brand name as the input.
  • Some of these users are also using Quid as their primary media monitoring tool.
 

 

Goals and Current Methods

Inputs (Search)

Find stories that are actually about my brand (narrowing, filtering out noise).

PROBLEMs:

Just because a brand is mentioned in an article, that doesn’t necessarily mean that the article is about that brand. In the case of Brand Perception, it’s important that the article is actually about the chosen company/brand.

CURRENT METHODS:

  1. (Search) Company operator
    1. Use this the most frequently because it reduces fewer articles than Company_high
    2. What is the difference (technically) between this and Company_high?
  2. (Search) Company_high operator
    1. Best for companies with high volume (Walmart, ATT, etc.) because it greatly reduces number of results, so might not be applicable for companies with fewer mentions
    2. What does this actually do? Does it essentially say “only include if Company is mentioned X number of times”?
  3. (Search) Title operator
    1. Used to specify that the name of the company occurs in the title of the articleBest for companies with high volume (Walmart, ATT, etc.) because it greatly reduces number of results, so might not be applicable for companies with fewer mentions
  4. (Search) Near operator / Abhishek’s “Proximity Operator Builder” Excel tool
    1. This is used for noise removal, but it’s more broad than using Company operator.
    2. Used primarily when doing a trend analysis. When “millennials” and “laundry” are within X# of words of each other, this can help you tell the article is more about this. 
    3. It’s used mostly when you’re looking at 2 things together: topic + topic, company + topic (e.g. “Apple" + “technology”)

 

Eliminating certain types of articles (narrowing, filtering out noise).

CURRENT METHOD:

  • (Search) use specific string of terms in their query:  AND NOT (stocks OR shares OR earning* OR “Market report” OR “Marketresearch” OR “research reports” OR “financial results” OR “analyst reports” OR “research and markets” OR “market forecast” OR “industry forecast”)
  • “NOT”-ing the Finance topic in the search topic aggregations might not make sense for financial institutions, like Citibank or Wells Fargo, etc.

1. Earnings Reports


CURRENT METHOD:

  • (Search) use specific string of terms in their query (??)

2. Press Releases


CURRENT METHOD:

  • (Search) use specific string of terms in their query (??)

3. Industry Projections

 

Outputs (Viz)

General Overview

CURRENT METHOD:

  • (Viz) Default network view
  • Rename
  • Identify most important/relevant clusters (How? What does this mean?)

Goal:

Get a general understanding of topics of conversation and identify the most important topics of conversation.

 

Find and Understand Key Entities

CURRENT METHOD:

  • (Viz) Search and tag companies
    • The bar chart is only primary mention and you lose a bunch of coverage because of this and incorrectly disambiguated entities

Goal:

Understand where key companies (e.g. my competitors) show up in the conversation.


CURRENT METHOD:

  • (Viz) Search and tag people
    • The bar chart is only primary mention and you lose a bunch of coverage because of this and incorrectly disambiguated entities

Goal:

Understand where key people (e.g. my company’s CEO) show up in the conversation.


CURRENT METHOD:

  • (Viz) Search and tag sources
    • The bar chart is only primary mention and you lose a bunch of coverage because of this and incorrectly disambiguated entities

Goal: 

Understand where key sources (e.g. sources or reporters I have a relationship with) show up in the conversation.


CURRENT METHOD:

  • (Viz) Look at Primary Mention bar chart to identify (then search and tag the important ones)

Goal: 

Identify unknown key companies (e.g. competitors).


CURRENT METHOD:

  • (Viz) Look at Primary Mention bar chart to identify (then search and tag the important ones)
    • Especially important for corporate comms group, they want to see where CEO is coming up

Goal: 

Identify unknown key people (e.g. my company’s CEO).

 

Understand Perception

CURRENT METHOD:

  • (Viz) Use network colored by sentiment to isolate negative articles. Tag the articles and put them in a bar chart to see which clusters they show up in.
  • (Viz) Put isolated negative articles in a scatterplot to understand if there’s a specific article or cluster that’s driving conversation
  • (Viz) Put isolated negative articles in a timeline to see the distribution and how far in the past/recent the article/cluster-in-question occurred

Goal:

Understand the most negative topics of conversation.


CURRENT METHOD:

  • (Viz) Use network colored by sentiment to isolate positive articles. Tag the articles and put them in a bar chart to see which clusters they show up in
  • (Viz) Put isolated positive articles in a scatterplot to understand if there’s a specific article or cluster that’s driving conversation
  • (Viz) Put isolated positive articles in a timeline to see the distribution and how far in the past/recent the article/cluster-in-question occurred

Goal: 

Understand the most positive topics of conversation.


CURRENT METHOD:

  • (Viz) Use a Social Sharing x Published Count scatterplot; pick out and investigate context surrounding clusters in the upper left
    • "For example, a class action lawsuit might be 4% of the convo, but it’s highly shared."

Goal: 

Understand what articles/topics are important to the general public, specifically content opportunities and potential risk areas.


  • Is this actually important to Brand Perception?

Goal: 

Understand what articles/topics are important to the media. (?)


CURRENT METHOD:

  • (Viz) Look at Primary Mention, Sources bar chart to identify (then search and tag the important ones)
    • [Rosas] will often have personal relationships with reporters and are interested in understanding who’s saying what.
  • (Viz) Use a network colored by cluster to look at the key themes surrounding a tagged group of “source” articles. Dig into articles to understand themes.
  • (Viz) Use a network colored by sentiment to look at whether the coverage surrounding a tagged group of “source” articles is positive, negative, or neutral
  • (Viz) Use a bar chart by cluster to understand volume of coverage broken down by topic for a tagged group of “source” articles
  • (Viz) Use a social sharing vs. published count scatterplot to understand which of the source’s articles are getting social media traction

Goal: 

Understand which sources are reporting on my brand most frequently and why.

 

Understand Specific Topic(s) or Interest Area(s)

CURRENT METHOD:

  • (Search) Adding key terms into the query?
  • (Viz) Search for a specific term or terms and tag it/them together as one group (e.g. “organic,” “organic food,” “organic foods,” “free-range,” etc. all searched and group together to approximate the concept of “organic foods”)
    • If they have specific messages they want to push, they want to see how often those terms are coming up. For example, whether, where, and how the concept of “organic foods” in appearing in conjunction with their brand.

Goal:

Understand whether, where, and how topics I care about are appearing in the larger conversation around my brand.