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Machine Learning

How Machine Learning Is Changing Influencer Marketing

Nastel Technologies®
March 9, 2021

Influencer marketing has grown significantly due to the pervasive use of social media platforms in promoting products and services.

In 2019 the practice reached $6.5 billion and is projected to reach $15 billion by 2022. Marketing today is all about algorithms, data and analytics to gain a targeted audience rather than the traditional spray-and-pray approach. The major success factor is figuring out how influencer marketing can become more effective by targeting the right audience to increase customer engagement.

Technological advancements such as machine learning (ML), natural processing languages (NLPs) and artificial intelligence (AI) are changing how brands enhance influencer marketing. ML tech is assisting organizations in three areas: Creating relevant copy to reach the intended audience, identifying the right content creators for various marketing segments and recommending impactful workflow processes. As the digital space continues expanding, ML is a prerequisite for leveraging the actual benefits of influencer marketing.

Solving influencer marketing problems

Powering influencer marketing systems using machine learning is a welcome change to solve the issue of fake followers, likes and engagement. Such systems are vital since they evaluate more than just an influencer’s potential in successful influencer marketing campaigns based on monthly or annual performance. Since ML-enabled influencer marketing systems learn from all of the influencer’s posts and activities, they can understand when brands may experience decreased or increased marketing influence. Organizations can use these insights to adjust their marketing incentives accordingly.

Another common challenge in influencer marketing is the increased use of videos and images in marketing campaigns. The emergence and continued growth of picture messaging platforms, such as Tumblr and Instagram, has seen most influencers use pictures and videos extensively. However, most of the posted images lack identifying hashtags or text, making it difficult to track and verify their authenticity. The only method a brand can accurately identify image-based influencer marketing posts is by analyzing the images’ content.

Machine learning makes this possible through its abilities in image recognition. ML-powered influencer marketing systems and software tools use machine vision image recognition to identify places, objects, writing, and people within images, enabling marketers to identify and sort pictures more quickly. Furthermore, machine vision assists marketers with analyzing millions of images in seconds to identify pre-determined characteristics at scale. The elements could be images with a specific brand logo, product, or other sophisticated features like people in the pictures. ML computer vision, therefore, permits marketers to determine relevant social influencers and understand how the audience’s posts and interactions relate to the brand in question.

The last major problem is the challenging process of identifying the right influencers. Influencer marketing requires an influencer whose persona matches that of the brand. A Rakuten marketing survey drawing 200 UK marketers directly involved in influencer marketing campaigns found that 38% were unable to determine whether their influencing activities led to increased sales. 86% also admitted that they were not sure of the basis influencers used to charge for a marketing campaign.

Machine learning addresses the issue since it often returns quantifiable results, thus making the influencer marketing processes highly successful. True ML-enabled influencer marketing platforms move beyond filtering fake users and image processing to analyze influencer content created in the past years. The ML-based analysis allows companies to understand the brands and topics that various influencers are talking about, as well as their aesthetic styles, the effectiveness of the content in driving user engagements, product sales, traffic and audience sentiments.

Influencer marketing

Many companies believe that influencers have a higher engagement rate with the brands and products they market on their social media posts. Despite this, the model faces numerous risks since it exposes brands to higher scrutiny levels. That being said, a brand can only achieve customer trust by providing honest feedback on various products. Influencers play a crucial role in earning consumer trust since they take the brand’s storytelling and creativity beyond mere commercials.

Influencer marketing is still nascent, as most brands have not yet identified best practices for identifying, retaining, compensating, rewarding and onboarding influencers. Usually, many brands engage individual influencers or influencer marketing agencies for their marketing needs. The ready availability of influencers, including whether those have truthful or fake engagements and influencing history, limits most brands in choosing the best influencer marketing campaigns. It is also challenging to identify the right influencers based on the size of their network, as it is impossible to tell whether this network is original.

A robust machine learning framework that assists brands in identifying the right influencers, as well as onboarding, compensating, and rewarding them, is therefore necessary. The framework usually consists of an end-to-end machine learning algorithm containing various social media key performance indicators (KPIs) used to determine an influencer index score.

KPIs for ML-powered influencer marketing

Various KPIs in an ML-enabled system are transforming influencer marketing. These include engagement rate, target audience, segment expertise, content freshness and quality, online influencer presence and channels.

  1. Engagement rate: This considers factors like potential reach, post reach, likes, shares, amplification rate, vitality rate, average engagement rate, and audience growth rate. A machine learning algorithm mines the engagement activities from an influencer’s past posts to determine the engagement score. The higher the score, the higher the engagement rate, and the higher the possibility that an influencer can assist brands in exceeding their marketing needs.
  2. Target audience: Various brands have specific follower goals. As such, an influencer index score on the desired audience is essential to ensure the marketing campaigns target the correct market segments. In this regard, adopting machine learning in influencer marketing assists brands in measuring influencer metrics such as gender, fashion, region, interests, age and followers. It also measures the reach of all followers. Machine learning is hence changing influencer marketing by ensuring that brands connect only with those influencers with the desired impacts on the targeted audiences.
  3. Segment expertise: While influencers may cover a whole segment in one post, they can influence on a single sub-segment. Establishing an influencer’s expertise is an essential factor that all brands must consider. For example, the market segment may contain sub-segments like food and beverages, footwear, apparel, and tobacco; an influencer in that market segment may be an expert in marketing food and drinks only. A machine learning framework contains text mining capabilities which it leverages to extract profound insights past influencer posts in order to determine the expertise index score. Brands can use ML-powered influencer marketing tools to identify an influencer’s niche expertise and ensure they get the right influencer for a specific product.
  4. Content freshness and quality: A machine learning algorithm uses image recognition strategies, such as machine vision, to parse through an influencer’s previous video, image, and text posts. The aim is to analyze the post frequency, determine content freshness and quality, and identify the best practices employed in the content creation. It assists brands to spot influencers capable of driving brand marketing campaigns for longer durations, while simultaneously maintaining the content freshness and quality.
  5. Influencer online presence: Companies that invest in influencer marketing as the preferred marketing method rely on influencers that command a strong online presence. Using machine learning tools assists brands to periodically rate an influencer based on their posts’ traffic and activity. They can quickly determine influencers with a strong online presence to ensure advertisements reach the largest audience possible.
  6. Channels: Social media platforms empower customers to interact with businesses on many touch points. However, influencers with high activity rates on one channel may not succeed as much in another. Machine learning algorithms permit brands to use channel parameters to calculate influencer engagements. Brands can hence identify those influencer channels which are most appropriate for channeling their marketing needs.

This article originally appeared on, to read the full article and see the images, click here.

Nastel Technologies helps companies achieve flawless delivery of digital services powered by middleware. Nastel delivers Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate. To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s Navigator X fuses:

    • Advanced predictive anomaly detection, Bayesian Classification, and other machine learning algorithms
    • Raw information handling and analytics speed
    • End-to-end business transaction tracking that spans technologies, tiers, and organizations
    • Intuitive, easy-to-use data visualizations and dashboards

Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.


The Nastel i2M Platform provides:


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