The Social Salesperson

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Researchers at IIT Kharagpur have developed a new marketing model for influencing sales on social media. While customer reviews play a critical factor in online sales through social media, the researchers have developed an advanced model to identify influencers who could have more influence on potential buyers based on opinions and social ties on a popular social networking platform.

Social media is one of the most popular emerging strategies today with a fundamental goal of increasing sales. According to a study, about 91% of retail brands use multiple social media channels with 81% of SMEs using at least one social platform. The global revenue of social media sales is expected to grow to Euro 39 billion in 2019 as per a report by the business analytics firm Statista. However, on social media, people are more likely to adopt a product recommended received from their acquaintances or based on product reviews.

“We already know that comments on social media affect potential buyers. We have considered the personal valuation of the adapters based on their comments. Initially, we have segregated the adopters and the influencers based on their valuation and the threshold value to become an influencer. This helped us to categorize the users and strengthen their influence on adopters. In our second model, the peers’ connections are considered to influence a user. Additionally, we considered all the users who have purchased the product earlier, as the influencers other than just the potential buyers who are considered as users highly connected in the network,” explained Prof. M K Tiwari who led the research.

The study identified the different set of users based on their level of the tendency towards the product. This helps to segregate the adopters by whom they will be getting influenced instead of using all the buyers to influence all the adopters.

Thereafter the research group targeted the influencers by offering the product either for free or at a discounted price depending on their possibility to diffuse the information and influence their neighbours effectively for revenue maximization.

This finding provides the required benefits for marketers regarding the future of advertising and targeting customers in social networks. Marketers know that following traditional methods to motivate consumers in any social network might not always be effective. If marketers motivate any informal member of social networks without their knowledge by offering free or discounted products to initiate and launch any product related information, this can then be an effective strategy for social network advertisements. Also, the study showed that iterations in the product reviews by the influencers show a sudden increase in the number of people getting influenced. Such changes are to be estimated beforehand by the company and required steps to be taken in order to stay away from the lost sales.

“Here, we aim to increase the influence on people by offering the product for free to potential buyers who are capable of influencing more people and then the product is offered at an increasing price, i.e., decreasing discount rates and increasing the revenue as well as the growth of the influence among customers’ acquaintances,” he confirmed.

Computational experiments were conducted on real-world networks representing different scenarios with varying complexities and tested the effectiveness of these algorithms.

The research work was conducted under the research project EBusiness Center of Excellence (ECO) funded by the Ministry of Human Resource and Development (MHRD), Government of India under the scheme of Center for Training and Research in Frontier Areas of Science and Technology (FAST).

This work can be extended by implementing this algorithm on dynamic networks and budget and time constraints can be imposed for influencing. The results of this study shows that mixed influence model can be used to identify the potential users whom a company can target and also can decide the budget that can be spent on each category of such users based on their level of influencing others.

A future extension would be interesting by adding more social media futures such as likes and shares from sources like Twitter, Instagram, Pinterest etc.

Graphic Credit: Suman Sutradhar