Brief

Generative AI Could Be Telcos’ Key to Winning Medium Enterprises

AI can help telcos move from “one-size-fits-all” to more personalized offerings—without busting their cost structure.

By Jani Kelloniemi, Herbert Blum, Danielle Stekelenburg, Glyn Truscott, and Anastasia Bournelli

First published on mayo 29, 2024
min read


Executive Summary Small- and medium-sized businesses account for about 30% of global enterprise spending on connectivity services and information and communications technology, and their spending in this category is growing around 8% annually, according to Bain analysis of IDC data. But most telcos are failing to capitalize because they treat these businesses as a monolith, leading to ill-suited go-to-market strategies and poor customer experiences. Generative AI could help telcos deliver more relevant offerings and experiences to these customers at scale, fostering loyalty and unlocking significant growth opportunities. It’s hard to imagine a business overlooking or underinvesting in a market that has about 400 million potential customers worldwide, an 8% annual revenue growth rate, wide profit margins, and relatively manageable service costs. And yet, that’s the case for many telecommunications companies when it comes to their small- and medium-sized business customers. It’s a big opportunity. Small- and medium-sized enterprises (SMEs) account for about 30% of global enterprise spending on connectivity services and information and communications technology, and their spending in these areas is growing about 8% per year, according to Bain analysis of IDC data. Furthermore, SMEs generally cost less for telcos to serve than large enterprises (see Figure 1). Figure 1 When served effectively, small- and medium-sized businesses are 1.5 times more profitable for telcos than large enterprise customers But many incumbent telcos are failing to capitalize on their established SME customer base, which should lend itself to upselling and cross-selling. One signal: Telcos’ customer advocacy scores are lowest among this segment. Telcos’ aggregate SME Net Promoter Score^SM is 9 percentage points lower than their NPS^® for large enterprise customers, and 2 percentage points below their consumer NPS. What’s going wrong? The crux of it is that despite the heterogeneity of this segment, telcos have treated SMEs like a monolith. This has created significant problems, particularly in three areas: Customer segmentation. Telcos over-rely on segmenting customers by size (number of employees), a reductive categorization that results in product offerings or value propositions that aren’t relevant to many customers. Go-to-market. Telcos frequently apply generic strategies and marketing messages that fail to recognize the unique needs and dynamics of individual businesses. Customer experience. Too often, telcos deploy “one-size-fits-all” solutions that result in a poor customer experience across sales, service activation, and usage. Telco executives are well aware of these challenges but have had difficulties addressing them at scale. However, the emergence of generative artificial intelligence presents a unique opportunity to change all of this. Telcos that effectively wield this powerful new technology can forge deeper, more meaningful, and ultimately, more valuable relationships with SMEs. Importantly, they can accomplish this without significant increases in cost and complexity. Applying AI in sales and marketing is quickly becoming a differentiator for leading telcos. According to a recent Bain survey of senior executives from 59 telcos worldwide, about 60% of industry leaders (those in the top quartile of revenue growth rate in 2023) say they’re increasing productivity by empowering sales reps with tools that leverage AI, compared with just 17% of laggards (those in the bottom quartile of revenue growth). Percentage of telcos making it a top priority to empower sales reps with better tools that leverage AI 60 Leaders 17 Laggards Furthermore, leading telcos that have deployed generative AI at scale report an average weekly time savings of 28% for their sales and marketing operations, according to our survey. Let’s unpack how generative AI can resolve the three issues above for telcos’ medium-sized business customers, which for the purposes of this article we’re defining broadly as companies that have 50 to 1,000 employees. Customer segmentation Traditional segmentation approaches, often rudimentary and size-based, have been unable to capture the complex needs and dynamics of medium-sized businesses. Think of how different the connectivity and technology requirements of a 75-person software company in Helsinki are from an 800-employee retailer with outlets located throughout Southeast Asia. Nevertheless, it would be too time-consuming and costly for telco staff to manually compile data on millions of medium-sized businesses to create a full view of each one. By harnessing the power of AI-driven analytics, telcos can now analyze vast arrays of unstructured data—from online behavior to social media patterns and beyond—to segment their medium-sized customers with unprecedented precision, speed, and scale. This hyperpersonalized segmentation enables telcos to identify and understand the nuanced needs, challenges, and preferences of each customer, paving the way for targeted and effective customer activation initiatives. Go-to-market strategy Many telco executives recognize that their sales model for medium-sized businesses is broken. They’re hampered not only by undifferentiated go-to-market approaches that often don’t resonate with individual medium-sized businesses, but also a lack of ability to sense and then serve the consultative moment—which is the key to selling more without incurring significant additional costs. It’s a capability that telcos have successfully honed for their large enterprise customers, but not for medium-sized businesses. Marrying predictive analytics with generative AI content creation has the potential to transform this process, leveraging nuanced customer profiles to craft go-to-market strategies relevant to each target. By dynamically adjusting messages, channels, and offerings based on real-time data, telcos can now engage in timely, meaningful dialogues with medium-sized businesses. Importantly, this doesn’t mean creating new products or bespoke offerings. Rather, telcos can flexibly configure their existing products and services in a thoughtful package that feels custom-fit to the customer’s unique requirements. For example, a retailer may need robust connectivity solutions for point-of-sale systems at its stores, including fixed connectivity supported by mobile backup. Using insights gathered through AI analysis, telcos can deliver the desired solution with minimal complexity and costs. Some telcos are already seeing impressive results applying these hyperpersonalization techniques at scale with consumers. By using machine learning models to analyze internal customer data, consumer psychographics survey results, and market insights, one telco was able to break down its consumer base into more than 60 microsegments. It previously had identified just four broad segments. The detailed microsegments are more easily understandable and actionable, enabling the telco to develop more relevant offerings and messaging based on individual behavioral profiles. Generative AI can then produce draft advertising copy in minutes, enabling the telco to create customized creative content at speed and scale. As part of this initiative, the telco identified 30 new potential sales and marketing campaigns. The company estimates its five highest-priority campaigns could increase its revenue from consumers by the equivalent of about 3% of total annual revenue, based on early results. Its first two pilot programs delivered increases in email openings and click-through rates, and sales conversions more than doubled. Customer experience Standard solutions plastered over unique problems have led to frustration, churn, and a tarnished reputation for telcos. But now, generative AI makes it feasible for telcos to create cost-effective experiences that make it feel like they actually know each business customer. By analyzing data across the customer life cycle, from first contact through sales to support, generative AI can help telcos’ customer support staff personalize interactions at every touchpoint, anticipate needs, and preemptively offer solutions. For a broader look at how AI is being applied across telecommunications, an industry overview is also available. Navigating adoption challenges In our experience working with clients worldwide, emerging generative AI leaders across sectors are forming a comprehensive strategy that addresses several key areas. First, they ensure the implementation of ethical practices and data security measures to protect customer data. They also evaluate their existing technology systems for necessary upgrades and plan their integration with AI technology in stages. This minimizes disruption and avoids the complexities that bespoke solutions may cause with incompatible back-end systems. Leading telcos recognize it’s important not to overlook the human element of AI. They try to foster a culture that values data-driven decisionmaking, and they equip employees with the training and tools needed for effective AI utilization. This includes knowing when and how to best apply AI. Furthermore, these companies are adjusting employee incentive structures to reward AI-driven achievements while also setting clear, measurable objectives tied to AI deployment and its impact on serving customers efficiently. Emerging leaders recognize that this new approach demands thoughtful change management and operational adjustments, especially in how sales teams engage with both direct and indirect partners using AI tools. The goal is to ensure these organizational transitions are sustainable. Resetting the relationship Generative AI offers a future where each interaction between telcos and their medium-sized business customers is deeply personalized, highly relevant, and immensely valuable. Telcos that can quickly navigate generative AI’s challenges have a chance to redefine their relationship with medium-sized businesses, fostering loyalty and unlocking significant growth opportunities.

Executive Summary

Small- and medium-sized businesses account for about 30% of global enterprise spending on connectivity services and information and communications technology, and their spending in this category is growing around 8% annually, according to Bain analysis of IDC data.
But most telcos are failing to capitalize because they treat these businesses as a monolith, leading to ill-suited go-to-market strategies and poor customer experiences.
Generative AI could help telcos deliver more relevant offerings and experiences to these customers at scale, fostering loyalty and unlocking significant growth opportunities.

It’s hard to imagine a business overlooking or underinvesting in a market that has about 400 million potential customers worldwide, an 8% annual revenue growth rate, wide profit margins, and relatively manageable service costs. And yet, that’s the case for many telecommunications companies when it comes to their small- and medium-sized business customers.

It’s a big opportunity. Small- and medium-sized enterprises (SMEs) account for about 30% of global enterprise spending on connectivity services and information and communications technology, and their spending in these areas is growing about 8% per year, according to Bain analysis of IDC data.

Furthermore, SMEs generally cost less for telcos to serve than large enterprises (see Figure 1).

When served effectively, small- and medium-sized businesses are 1.5 times more profitable for telcos than large enterprise customers

But many incumbent telcos are failing to capitalize on their established SME customer base, which should lend itself to upselling and cross-selling. One signal: Telcos’ customer advocacy scores are lowest among this segment. Telcos’ aggregate SME Net Promoter Score^SM is 9 percentage points lower than their NPS^® for large enterprise customers, and 2 percentage points below their consumer NPS.

What’s going wrong? The crux of it is that despite the heterogeneity of this segment, telcos have treated SMEs like a monolith. This has created significant problems, particularly in three areas:

Customer segmentation. Telcos over-rely on segmenting customers by size (number of employees), a reductive categorization that results in product offerings or value propositions that aren’t relevant to many customers.
Go-to-market. Telcos frequently apply generic strategies and marketing messages that fail to recognize the unique needs and dynamics of individual businesses.
Customer experience. Too often, telcos deploy “one-size-fits-all” solutions that result in a poor customer experience across sales, service activation, and usage.

Telco executives are well aware of these challenges but have had difficulties addressing them at scale. However, the emergence of generative artificial intelligence presents a unique opportunity to change all of this. Telcos that effectively wield this powerful new technology can forge deeper, more meaningful, and ultimately, more valuable relationships with SMEs. Importantly, they can accomplish this without significant increases in cost and complexity.

Applying AI in sales and marketing is quickly becoming a differentiator for leading telcos. According to a recent Bain survey of senior executives from 59 telcos worldwide, about 60% of industry leaders (those in the top quartile of revenue growth rate in 2023) say they’re increasing productivity by empowering sales reps with tools that leverage AI, compared with just 17% of laggards (those in the bottom quartile of revenue growth).

Percentage of telcos making it a top priority to empower sales reps with better tools that leverage AI

Leaders

Laggards

Furthermore, leading telcos that have deployed generative AI at scale report an average weekly time savings of 28% for their sales and marketing operations, according to our survey.

Let’s unpack how generative AI can resolve the three issues above for telcos’ medium-sized business customers, which for the purposes of this article we’re defining broadly as companies that have 50 to 1,000 employees.

Customer segmentation

Traditional segmentation approaches, often rudimentary and size-based, have been unable to capture the complex needs and dynamics of medium-sized businesses. Think of how different the connectivity and technology requirements of a 75-person software company in Helsinki are from an 800-employee retailer with outlets located throughout Southeast Asia. Nevertheless, it would be too time-consuming and costly for telco staff to manually compile data on millions of medium-sized businesses to create a full view of each one.

By harnessing the power of AI-driven analytics, telcos can now analyze vast arrays of unstructured data—from online behavior to social media patterns and beyond—to segment their medium-sized customers with unprecedented precision, speed, and scale. This hyperpersonalized segmentation enables telcos to identify and understand the nuanced needs, challenges, and preferences of each customer, paving the way for targeted and effective customer activation initiatives.

Go-to-market strategy

Many telco executives recognize that their sales model for medium-sized businesses is broken. They’re hampered not only by undifferentiated go-to-market approaches that often don’t resonate with individual medium-sized businesses, but also a lack of ability to sense and then serve the consultative moment—which is the key to selling more without incurring significant additional costs. It’s a capability that telcos have successfully honed for their large enterprise customers, but not for medium-sized businesses.

Marrying predictive analytics with generative AI content creation has the potential to transform this process, leveraging nuanced customer profiles to craft go-to-market strategies relevant to each target. By dynamically adjusting messages, channels, and offerings based on real-time data, telcos can now engage in timely, meaningful dialogues with medium-sized businesses.

Importantly, this doesn’t mean creating new products or bespoke offerings. Rather, telcos can flexibly configure their existing products and services in a thoughtful package that feels custom-fit to the customer’s unique requirements. For example, a retailer may need robust connectivity solutions for point-of-sale systems at its stores, including fixed connectivity supported by mobile backup. Using insights gathered through AI analysis, telcos can deliver the desired solution with minimal complexity and costs.

Some telcos are already seeing impressive results applying these hyperpersonalization techniques at scale with consumers. By using machine learning models to analyze internal customer data, consumer psychographics survey results, and market insights, one telco was able to break down its consumer base into more than 60 microsegments. It previously had identified just four broad segments. The detailed microsegments are more easily understandable and actionable, enabling the telco to develop more relevant offerings and messaging based on individual behavioral profiles. Generative AI can then produce draft advertising copy in minutes, enabling the telco to create customized creative content at speed and scale.

As part of this initiative, the telco identified 30 new potential sales and marketing campaigns. The company estimates its five highest-priority campaigns could increase its revenue from consumers by the equivalent of about 3% of total annual revenue, based on early results. Its first two pilot programs delivered increases in email openings and click-through rates, and sales conversions more than doubled.

Customer experience

Standard solutions plastered over unique problems have led to frustration, churn, and a tarnished reputation for telcos. But now, generative AI makes it feasible for telcos to create cost-effective experiences that make it feel like they actually know each business customer.

By analyzing data across the customer life cycle, from first contact through sales to support, generative AI can help telcos’ customer support staff personalize interactions at every touchpoint, anticipate needs, and preemptively offer solutions.

For a broader look at how AI is being applied across telecommunications, an industry overview is also available.

Navigating adoption challenges

In our experience working with clients worldwide, emerging generative AI leaders across sectors are forming a comprehensive strategy that addresses several key areas. First, they ensure the implementation of ethical practices and data security measures to protect customer data. They also evaluate their existing technology systems for necessary upgrades and plan their integration with AI technology in stages. This minimizes disruption and avoids the complexities that bespoke solutions may cause with incompatible back-end systems.

Leading telcos recognize it’s important not to overlook the human element of AI. They try to foster a culture that values data-driven decisionmaking, and they equip employees with the training and tools needed for effective AI utilization. This includes knowing when and how to best apply AI. Furthermore, these companies are adjusting employee incentive structures to reward AI-driven achievements while also setting clear, measurable objectives tied to AI deployment and its impact on serving customers efficiently.

Emerging leaders recognize that this new approach demands thoughtful change management and operational adjustments, especially in how sales teams engage with both direct and indirect partners using AI tools. The goal is to ensure these organizational transitions are sustainable.

Resetting the relationship

Generative AI offers a future where each interaction between telcos and their medium-sized business customers is deeply personalized, highly relevant, and immensely valuable. Telcos that can quickly navigate generative AI’s challenges have a chance to redefine their relationship with medium-sized businesses, fostering loyalty and unlocking significant growth opportunities.