8 Types of Data Brands Should Collect to Optimize the Use of Sentiment Analysis

Sentiment analysis is a powerful tool for understanding the interactions between customers and brands. It’s used to examine public opinion about products, events, brands and more. However, many companies are failing to collect necessary data to make full use of sentiment analysis tools that are available to them. This article discusses eight types of data that brands should be collecting to optimize sentiment analysis.

1) Metadata:

Most sentiment analysis tools allow you to gather metadata about the influencers in your industry. This data includes information like keywords used by specific people or organizations on Twitter and blogs. Metadata is important because it helps companies identify influential customers who are talking favourably (or unfavourably) about their brand.

2) Sentiment:

Sentiment refers to the feelings or opinions about specific products, services and more. A valuable aspect of sentiment analysis tools is their ability to quantitatively measure these feelings by identifying keywords in posts that indicate positive (or negative) emotions towards certain items. This type of insight helps brands understand what customers are saying to improve their marketing strategies accordingly.

Companies need to collect both quantitative and qualitative information about customer sentiments through surveys, focus groups and other methods for deeper insights into consumer feedback on different aspects of a brand’s offerings and overall experiences with it over time.

3) Reactions:

Reaction data refers to the specific emotions that customers feel about brands. These insights help companies better understand the underlying reasons why these feelings exist, which can then be used for more effective marketing strategies and customer service initiatives.

Reactions are also valuable because they provide context around sentiments. Moreover, identifying patterns in reactions over time helps identify what is working well instead of what isn’t so great; this allows companies to make changes accordingly.

4) Sentiment Over Time:

Sentiment overtime refers to how customer views change or evolve with respect to brands, products and more. This data is important because it helps companies uncover the reasons why sentiment changes. For example, if a brand’s sales are down, this could be due to poor product quality or low prices, among other things that have changed since customers last purchased from them.

5) Sentiment by Channel:

Sentiment by channel refers to the way customer sentiment changes over different types of media. For example, if a brand offers both online and offline experiences, it’s important that they track how customers feel about them in each environment because this could influence buying decisions. In addition, companies need to make sure their channels are consistent with one another, so there isn’t any confusion among customers when using multiple methods for interacting with brands.

Many consumer-facing businesses should also collect data on how much traction these sources receive compared to other advertising initiatives such as television ads or email campaigns. This information can help companies optimize budgets accordingly on what works best for certain products.

6) Sentiment by Language:

Language is important because many companies operate in multiple regions where different languages are the norm. In addition, it’s also necessary for brands to collect data on sentiment across various time zones since messages about them can be posted when they’re sleeping or working. This information helps inform what messages should be communicated so that customers aren’t being ignored at specific times of day depending on their location and schedule.

Using tools like Google Translate makes gathering language-specific sentiment easy by allowing companies to track posts from other countries in a native tongue.

7) Sentiment by Location:

Location is important because it can vary depending on the type of customer a brand has. For example, sentiment for fast food restaurants should be different in urban areas compared to rural locations since their demographics are likely very distinct.

This information helps brands better target customers who live where they operate and those who frequently visit which creates more interaction opportunities with them. By collecting this data over time, companies also understand how opinions change across regions and other geographical factors that influence buying behaviour accordingly.

8) Sentiment by Product:

Product sentiment refers to how customers feel about specific product offerings. This information is important because it helps companies get more detailed insights into what they should improve or change for brands to be more consistent with their customer expectations. For example, if a company’s shampoo isn’t selling well, then they might want to take this data and use it when creating new similar products, so brand loyalty follows over time.

Collecting product-specific sentiment also allows brands to provide coupons or discounts on related items, which could inspire future purchases from their consumers depending on their needs at certain points in time.