The following three tips are based on our published research in a book chapter. The chapter details are provided later.
The number of followers of influencers is overrated. Businesses need to understand that Number of followers is not the only factor defining an influencer. Micro-influencers with fewer followers may lead to higher sales than established influencers because consumers are well aware of the difference between sponsored and organic content.
Consumers notice when an expert in the industry shares an opinion, even if this expert does not have a large popular following across social media platforms. Therefore, businesses need to consider using experts for their influencer marketing.
Brands that engage an influencer who is not well-matched with the brand image can cause more damage than brands realize. So, it is very important for businesses to match the influencer with their brand.
To know more about these tips, please read my following published chapter-
Hasan, Rajibul; Billa, Malvika (2021) ‘Influencers’ Impact on Business Model Innovation in the Luxury Personal Goods Industry’ In Business Model Innovation. United Kingdom: Routledge. [DOI][Details]
Through social media, consumers can interact with Key Opinion Leader (KOL), publish User-Generated Content (UGC), and view products recommended by their network.
Key Opinion Leaders (KOL) on social media can influence a consumer’s purchasing decision process, the network community is an essential means of improving the learners’ cognitive level, and the learners’ implicit knowledge plays an essential role in this cognitive process.
Understanding these social media attributes and their influence on consumers can help marketers better comprehend social media’s influence on consumer behavior and adapt their strategies to attract consumers. The following Figure demonstrates Social Influences on Social Media and Its Influence on Consumer Behaviors.
To know more details, please visit my following research publication-
Hasan, Rajibul; Teng, Yulin; Shams, Riad (2021) ‘The Impact of Social Media on Business Model Innovation’ In: Business Model Innovation. United Kingdom: Routledge. [DOI][Details]
Is it possible to Text mine the tweets to understand peoples’ feelings towards a brand?
Yes, it is possible to understand what type of feelings people are expressing regarding any brand from the tweets of Twitter. I am going to demonstrate the example of Ben & Jerry’s below.
We have collected 6000 tweets from Twitter that use the keyword Ben & Jerry’s. the following picture is the word cloud result ( larger font size represents words more frequently used in this word cloud), which shows us what people are talking about Ben & Jerry’s.
We are going to demonstrate in the following picture what type of feelings consumers are expressing on Twitter related to Ben & Jerry’s.
From the above results of text mining, we can understand that people are expressing more joy and surprise feelings related to Ben & Jerry’s brand and it represents a good reputation for the brand.
Measuring public opinion was always about extrapolating from surveys and hoping the small sample you selected was representative of the general public. Today, individual public opinion is ripe for the taking on Facebook, YouTube, Instagram, Twitter, Reddit, WhatsApp, and whatever new product review or augmented-reality platform pops up next.
With the advancement of Artificial Intelligence, we can now analyze texts using different types of software.
For one of my research projects, We have analyzed 40,000 Amazon Reviews of Smart Wearable Devices like Fit Bit, Samsung Gear 2, and 3. The Following output provides us an idea, what people are talking about your brands or products. It may be difficult to go through 40,000 reviews because of a large volume of unstructured data. With the help of Automatic Content Analysis, you can easily understand what is important to your customers.
A picture can tell thousands of words. Can we analyze images posted on social media and review platforms? Yes, we can. With the advancement of the Artificial Intelligence area, researchers can use deep learning to understand images. We have used deep learning to Investigate the Digital Behaviour of consumers.
In this research, 4,000 photos of nine Chinese restaurants posted on Tripadvisor’s website were analyzed using image recognition via Inception V3 and Google’s deep learning network; this revealed 12 hierarchical image clusters. Two examples of these clusters are provided below-
Cluster Name: Atmospheric cluster This cluster of images is generated through the help of deep learning.
Cluster Name: Decorations using lamps in Restaurants This cluster of images is generated through the help of deep learning.
The above clusters provided us an idea of what type contents consumers are posting on social media. These will also help restaurants to formulate different strategies such as improving the atmospheric environment and decorating using lamps. Competitors can also learn about their competitors by analyzing the images posted by consumers on online platforms.
To know more about this research, please read my following article-
The following Figures represent consumers’ travel behavior on Social Media (SM). This can be used as a tool to formulate your social media strategies.
To know more details, read my following co-authored article-
ABDUNUROVA, A., USPANOVA, M., HASAN, R., SURAPBERGENOVA, Z., & KUDAIBERGENOV, N. (2020). Pre-Purchasing and Post-Purchasing Travel Behavior on Social Media: The Case of Kazakhstan. Journal of Environmental Management and Tourism, 11(6), 1475-1488.