Redefining Influencer Marketing with a MachineLearning Framework

Abstract

Cord-cutting and mobile ubiquity coupled with an

increasing distrust towards brand messaging are

fuelling the growth of influencer marketing led by

social media influencers including micro-

influencers. With followers between 1,000 and

10,000, micro-influencers have created a niche

owing to their relevant and reliable content and

commitment towards audience interests.

From being an industry dominated by handpicked

celebrities, the CPG industry is now proliferated

with a digitally empowered general populace.

Given the vast scope of the influencer marketing

field, we look at how a combination of machine

learning algorithms along with distinct social media

KPIs can help to identify authentic influencers.

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Influencer Marketing Driving

Transformation 4.0

As with every industry or business function, a sea change is

underway in the way businesses drive their marketing. Case in

point, while celebrities just deliver scripted lines in

commercials, influencers are believed to be more engaged with

the products they endorse on social media.

However, there are numerous risks involved in adopting the

influencer marketing model, as this endangers the brand to a

higher level of scrutiny. That said, what a brand achieves with

honest feedback is trust among its customers. Also influencers

can take the creative and storytelling beyond Commercials.

Influencer marketing as a strategy is still nascent and brands

are yet to identify best practices to reward, compensate and

onboard influencers.

Usually, CPG brands engage influencer marketing agencies to

execute their campaigns. Agencies possess a database of

influencers and this may present a challenge to brand

marketers as it limits their choice. Additionally, the process of

selection lacks transparency and governance.

Another method that brands have started to explore is the opt-

in influencer networks, where they can build long-term brand

advocacy by onboarding influencers who do not switch brands

frequently. However, this is not a flawless approach either; it's

often difficult to appoint an influencer based on their network

size as there is no way to tell if their network growth is organic.

A spate of fraudulent practices have made it difficult for brands

to measure the RoI on influencer marketing campaigns.

The need of the hour is a comprehensive machine learning

framework which helps brands right from onboarding

influencers to measuring campaign outcomes.

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Building a Robust Framework

An end-to-end comprehensive algorithm armed with several

social media KPIs is used to arrive at the influencer index

score. While campaign objectives determine the weight on each

KPI, the proposed framework leverages Natural Language

Processing (NLP) and image recognition techniques to validate

the legitimacy of the content curated by the influencer. Below

are some of the KPIs that should be considered before arriving

at the influencer index score.

Target Audience: Metrics like the number of followers, their

age, gender, interests, region, and also the reach of each

follower ensures that the brand connects only with influencers

who have the right impact on the intended target audience.

Engagement Rate: Consider factors such as audience growth

rate, post reach, potential reach, likes, comments, shares,

amplification rate, average engagement rate and virality rate.

The machine learning algorithm must mine the data from the

influencers' past posts and score the influencer on their

engagement.

Content Quality and Freshness: The algorithm must use

image recognition techniques to parse through past text,

image, and video posts of the influencer. It should also analyze

their best practices, post frequency, and determine the quality

of the content. It must spot influencers who can drive brand

campaigns over a longer duration.

Segment Expertise: An influencer can cover an entire

segment on their feed, but they will have more influence or

expertise on one sub-segment over others. Determining

influencers' niche expertise is a crucial factor for brands. For

instance, the CPG industry consists of several sub-segments

such as food and beverages (F&B), tobacco, apparel and

footwear, and an influencer in the CPG industry might be an

expert in only the F&B industry. The framework must have text

mining capabilities to extract deeper insights from the

influencer's posts to arrive at their expertise index score.

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Channels: The advent of social media has empowered

consumers to interact with brands across multiple touchpoints.

An influencer effective in one channel might not drive the same

level of engagement in the other. The algorithm must calculate

the engagement based on the parameters for each channel.

For instance, cosmetic brands can opt for an influencer who

has mastered YouTube makeup tutorials, whereas Instagram

might be appropriate for the apparel and footwear sub-

segment.

Influencer's Online Presence: Brands that invest heavily on

influencer marketing want influencers who have a strong online

presence. The algorithm must periodically rate the influencer

on the basis of their website activity and traffic.

Celebrity Index: As celebrities drive top-of-the-mind

awareness, consumers often associate celebrities with the

brands they promote. Thus, monitoring the online reputation of

celebrities is critical. The proposed framework must be able to

crawl through celebrities' online activities and alert brands

about any sensitive content.

Target Audience(20%)

Engagement Rate(20%)

Celebrity Index(20%)

Content Quality & Freshness

(10%)

Segment Expertise(10%)

Influencer’s Online Presence

(10%)

Channels(10%)

Influencer Index Score

Figure 1: Influencer Index Score

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Navigating the Influencer Marketing

Practice

Choosing the right influencer starts with the brand campaign

guidelines. The campaign brief must help the brand to identify

traits important to the brand which influencers must possess.

Brand managers can then use our proposed framework (fig 1)

to scans through influencers' repertoire and select profiles

based on their index score. (Index score appraises the

influencer's resonance to the brand campaign.)

The Influencer Index Score ensures that shortlisted profiles

have undergone a stringent validation process which examines

the influencers' posts by checking on parameters like

engagement pods, bot-generated comments, followers-to-

engagement ratio and also anomalies like an abrupt increase in

followers. This ascertains onboarding of only authentic

influencers.

Once the influencers are finalized, the brand establishes a

partnership with them through a contractual agreement. As the

execution of the influencer marketing campaign starts across

social media channels, the brand needs to measure business

outcomes and various strategic KPIs in real-time. An end-to-

end integrated framework (see figure 2) can help the brand on

this front too; it will enable them to execute all the activities

and optimize the marketing spend.

Campaign Strategy

Campaign BriefInfluencer Validation

Authentic

Influencer Blacklisted

Influencer On-Boarding/

Discovery

Fraudulent

Contract Management

Campaign Execution

Campaign Performance

Figure 2: Influencer Marketing Process

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Conclusion

Influencer marketing is a tricky issue; even marquee CPG

brands face challenges in identifying influencers who augur

well with their brands. Though social media channels such as

Twitter, Instagram, and Facebook have taken measures to

identify fake accounts and have passed a mandate to call out

sponsored posts, these issues are far from resolved. Our

proposed framework, equipped with distinct social media KPIs

helps brands to work with transparency and control the

process. Brands will no longer be restricted to select

influencers from an opaque repository. Consequently, they will

be able to build a lasting relationship with influencers that

promotes trust amongst consumers.

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All content / information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content / information contained here is correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in any form without prior written permission from TCS. Unauthorized use of the content / information appearing here may violate copyright, trademark and other applicable laws, and could result in criminal or civil penalties. © Copyright [2020], Tata Consultancy Services Limited. All Rights Reserved. Document ID CGEP006003

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About The Authors

Zakhir Hussain Sidickk

Zakhir Hussain Sidickk is a Senior

Consultant with TCS' Consumer

Packaged Goods (CPG) business

unit. With more than 25 years of

experience in the IT industry, he

currently leads Digital Innovation

in the unit's Digital team, and is

responsible for developing digital

marketing and SAP ERP-based

solutions for TCS' CPG clients.

Sidickk holds a Master's degree in

Business Administration from the

PSG College of Technology,

Coimbatore, India, and a

Bachelor's degree in Production

Engineering

Prashanth Ananda Venkatesan

Prashanth Ananda Venkatesan is a

Digital Evangelist with the Digital

Front Office team of TCS' CPG

business unit. He works with CPG

clients to understand how digital

marketing ecosystems are

evolving in the wake of emerging

technologies and changing

consumer preferences. Prashanth

has a Post Graduate Program in

Management from Great Lakes

Institute of Management, Chennai,

India.

Atish Dash

Atish Dash is a Digital Evangelist

with the Digital Front Office team

of TCS' CPG business unit. He is

responsible for the

conceptualization and

development of digital technology

solutions for the CPG industry.

Atish has a Master's degree in

Business Administration from

Xavier Institute of Management,

Bhubaneswar, India.

Contact

Visit the page on Consumer Packaged Goods www.tcs.com

Email: global.cpgsolutions@tcs.com

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