Any market researcher or brand expert knows that learning and understanding of target audience should be brands’ first priority for driving success. Small brands and large corporations are realizing a significant impact of their current referral’s voice on would-be customer perception of the brand. Moreover, user insights can provide a valuable source for building differentiation in the market. Hence, brands should focus all efforts on choosing the best tools for analyzing clients’ motivations, priorities, and the purchasers’ reactions.
Given today’s global reach of business, a target audience may be up to hundred thousands of people who vent their opinions on all possible domains available on the Internet. A-to-Z analysis of audience floating around dozens and hundreds of online platforms takes a great deal of time and effort. Then again, it gives no guarantees of holistic and accurate results owing to the current infoglut.
Anyway, the good news here is that we live in an age more than ever teeming with modern technologies leaked into all areas of our reality, including business. New artificial intelligence opportunities allow now to process a raft of information on the human-touch quality level. Thus, this article aims to reveal these opportunities.
Let’s delve into the innovative approaches to text analysis in marketing — Sentiment Analysis, Intent Analysis, and Contextual Semantic Search. More information about advanced ML techniques you can gain from this Serokell blog about artificial intelligence, which I am reading regularly with great pleasure.
Sentiment Analysis is natural language processing that analyzes emotional content incorporated into online messages and comments left by users about a particular product or service. This type of analysis undergoes keywords search and provides the texts grouped by the specified topic. Next, it identifies the attitudinal meaning of the texts and tracks whether it’s positive or negative. Thanks to Sentiment Analysis, companies now can look at the brand from a customer’s perspective and make some modifications to resolve the inconsistency between audience view and brand positioning. Unfortunately, the approach has a big drawback — it fails to cover all possible keywords related to a specific topic. It results in that some comments with non-obvious keywords may fall off of the analyst’s radar.
For example, for a supplier company, the keywords would be delivery time, delivery man, parcel location, post office, etc.
A brand selling sportswear would set the following parameters: sports apparel, new products, shop address, quality feedback, etc.
Any brand pursuing to study customers’ attitudes to its pricing policy would take price, fee, amount, money, dollars, euros, etc.
Another approach is the Intent Analysis. It extracts and reconstructs users’ intentions from a message or a comment. Given a large language variation capacity, we face again and again a wide array of word meaning interpretations resulting in misunderstandings. Thus, Intent Analysis seems to be an indispensable tool for upholding research integrity. Here the context matters: what are the aims a user pursues by texting this or that message in a particular case. Expressing an opinion, making a complaint, and providing feedback are typical examples of such intentions or contexts. Spam, email marketing campaigns, newsletters are integrant to research results, but these are not the subject to analysis. No research is needed for finding out these contexts make up a fair share of all content on the Internet, which is particularly problematic for carrying out unbiased analysis. Misinterpreted messages create a possibility of distortion of results. Therefore, you should ignore them.
Let’s take, for example, AbeBooks, a popular online book store. When querying AbeBooks on Facebook, you get various comments, including positive and negative reviews, questions about shipping costs, or intentions to buy its book. Besides these “human” comments, results show the store news, ads, promotions, and other irrelevant content.
Contextual Semantic Search or CSS, combined with Sentiment Analysis and Intent Analysis, makes it an upgraded and holistic approach. It handles the whole scope of online conversations. Next, it thoroughly selects those messages that include all possible semantically similar units. The chief distinction between Contextual Semantic Search and Sentiment Analysis is that it covers a copious amount of all conversations by using even less obvious keywords.
CSS considers each word of a message as a specific conceptual token within the relevant semantic field. This field incorporates many other tokens related to the same semantic meaning. The distance between these other tokens and the input one may range that demonstrates their high or low semantic correlation.
How to apply these marvelous CSS opportunities for delivering best practices and getting accurate data?
Here is a step-by-step guide for implementing the above methods when analyzing online conversations:
- Take fresh data from a brand’s official page on any social network, platform, news search system, etc.
- Choose a specific category word as input (for example, Delivery) for further text classification.
- With Sentiment Analysis, filter all the online conversations obtained by the chosen input word. Fix the results. Check whether user comments are positive or negative.
- Apply Contextual Semantic Search to expand the coverage of relevant keywords on the Delivery topic.
- Use Intent Analysis to analyze the contexts of online conversations. Filter out all irrelevant sources and contexts (email ads, spam, etc.).
- Discover the most relevant topics for online discussions. Analyze the shift in customers’ attitude toward the brand against the preliminary results of Sentiment Analysis (this stage often marks the fall in the percentage of positive comments).
- With data obtained in mind, home in on those business processes that require changes or improvements.
In recap:
- Understanding your audience is a key for identifying their attitude towards your brand and, subsequently, improving the company and providing the best customer experience.
- Use opportunities of the Sentiment Analysis, Contextual Semantic Search, and Intent Analysis linguistic methods to explore your customers, their intentions, and visions of your brand.
- Sentiment Analysis outlines online messages related to the specific topic and provides preliminary data on customer satisfaction.
- Mere Sentiment Analysis is insufficient for providing valuable insights — it fails to reach maximum data and underreports outcomes.
- New-sprung Contextual Semantic Search enhances Sentiment Analysis capabilities by covering non-obvious keywords.
- Intent Analysis specifies conversation context, filters out irrelevant comments and messages, and obtains unbiased and accurate data.
Today’s consumers catch on to the benefits and drawbacks of products and services offered by brands and then size them up. They no longer demand mere products or services. They look for a “friend” they can trust and who can provide them with the service they deserve.
If you are into becoming this best friend, then learn your audience’s wishes, fears, doubts, and dissatisfactions. Don’t hesitate to use new approaches and AI solutions in semantic analysis.
Embrace opportunities for Sentiment Analysis, Contextual Semantic Search, and Intent Analysis within your business. With the in-depth approach, you can avoid false statistics, walk in your audience’s shoes and discover the actual image of your brand. What if people around see your brand not the way you position it? It’s high time to establish, develop and promote your true identity!
