Facing increased pressure to achieve growth and better margins, telecommunications companies have traditionally concentrated their analytics efforts on their higher-profile B2C business, specifically on identifying new sources of value in this segment. Yet the B2B segment, which can account for up to one-third of total revenues and boasts higher margins than B2C, represents a relatively untapped opportunity. Our research suggests operators that harness the potential of advanced analytics can capture substantial additional revenues through customer retention and acquisition while reducing operating costs.
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However, companies must first tailor their B2B analytics playbook to the unique characteristics of the segment. McKinsey’s Analytics Quotient survey revealed the gap that exists between B2B and B2C analytics capabilities in high-tech and telco companies (Exhibit 1). B2B often lags behind B2C across the dimensions critical for capturing the impact of analytics at scale: analytics strategy, culture, analytics organization, data, models and tools, and value assurance.
The good news: telcos that excel in applying analytics to their B2B business can capture significant upside. Our analysis of a portfolio of use cases suggests that operators can use digital and analytics to generate incremental revenues of 5 to 15 percent and reduce costs by 15 to 35 percent (Exhibit 2).
There are substantial differences between segments
Operators typically organize their B2B units under different lines of businesses based on various factors such as customer size, order value, transaction volume, contract duration, and buying behavior. These groupings may include small, midmarket, and enterprise segments. Vertically oriented units tend to be focused on public sector, local health, and education as well as retail and wholesale segments.
The differences between B2B segments require a customized analytics approach that involves prioritizing specific use cases for each one. For example, small-business customers often closely resemble B2C segments in nature—that is, they are more transactional and have shorter sales cycles. As a result, telcos are more likely to use standard offers, pricing, and terms. By contrast, the enterprise space has fewer, much larger customers with six- to nine-month sales cycles and highly customized offers and contracts. There are several other critical differences to keep in mind:
Product complexity and diversity. B2B is characterized by much greater product diversity and complexity. Purchases typically include a larger, more diverse set of value-added services than on the consumer side—for example, a variety of managed services (such as security and VoIP), direct or partnered offerings in the cloud and hosting space, professional services, and support tiers.
Channels and routes to market. B2B channels often include both direct and indirect feet-on-the-street sales channels as well as those often more closely associated with consumer segments, such as call centers and retail stores. Achieving better performance at scale in this complex environment requires operators to think through the broad change management needed to incorporate analytics into their operations earlier in the process to ensure the best return on investment.
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Data scarcity and unique data requirements. In our experience, data availability and timeliness are bigger issues in B2B segments than in B2C. For one global operator, a large variety of data was readily available for B2C wireless products three to five days after the end of the month. For essentially the same offering on the B2B side, however, limited historical data and recent updates were not ready until two to eight weeks after the end of the month. Therefore, B2B operators have had to work around these limitations to make initial progress.
Decision-maker variation. Decision makers across B2B can vary by segment. In small firms, it's common for a single manager to oversee purchasing decisions for a single location. In midmarket and enterprise customers, such buying decisions are often more concentrated centrally. A telco’s analytics approach as well as the execution playbook needs to be tailored to the unique buying patterns for each segment.
The B2B analytics playbook
Once operators have identified the unique characteristics of B2B, they must then adapt their analytics approach accordingly. Successful operators do so along three dimensions:
1. Adopt a segment-driven view of the opportunity
Given the stark differences between B2B segments, operators cannot take a one-size-fits-all approach. For smaller customer segments, leading telcos are homing in on more “B2C-like” opportunities in retention, acquisition, or cross-selling. Pricing can be addressed by analyzing different plans to optimize the value proposition for customers while maximizing value for the telco. For enterprise customers, however, leading telcos are increasingly using advanced analytics to target new opportunities. These efforts could include enhancing sales productivity, streamlining contract negotiations, and predicting deal values and likelihood to win to optimize sales strategies. Exhibit 3 illustrates a typical heat map of use cases, prioritized based on distinct needs of each segment.
One global integrated operator, for example, used analytics in its small-business segment to determine customer lifetime value in an effort to increase acquisition, cross-selling, retention, and migration. However, as the telco moved to the midmarket and enterprise segments, it prioritized use cases based on customer buying behavior. While the focus was still on enhancing customer life-cycle value, the selected use cases were calibrated to support upselling opportunities, including optimizing sales representatives and using dynamic deal scoring and share-of-wallet analytics.
2. Prioritize data sets on usage, service history, and product performance
B2B customers typically place a premium on service levels, uptime, reliability, and customer satisfaction metrics, especially given the portfolio of products they buy. Any degradation in service levels affects two categories of stakeholders—the businesses and their end customers. Ideally, B2B analytics efforts must prioritize and incorporate all data sets on usage, service history, and product performance versus a telco’s competitors. This data should be aggregated across all customer engagement channels—chat, email, inbound calls, and field sales notes—since insights gleaned from these data sets show up as higher propensity features in predicting the likelihood of a customer to stay, buy more, or join as a new customer.
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Telcos should also aggregate this service history across products, since a disruption in one service can affect a customer’s decision to purchase that company’s other products. For example, at one North American operator, poor broadband speed compared with its competitors’ speeds led to higher churn rates on not only its broadband offering but also its wireless portfolio, since customers attributed their poor experience to the company in general. After applying analytics and discovering the cause of the exodus, the operator created a marketing campaign across all zip codes in which its speeds trailed those of competitors, touting its better wireless service and performance and incorporating promotions on broadband prices. These efforts led to a significant reduction in churn.
In addition to prioritizing different internal data sets, operators need to invest in finding unique sources of external data to generate additional insights on their customers. This approach has already shown promise in the B2C segment, where an ecosystem of new partners—including software companies, identity-management companies, and social-media platforms—has enabled operators to create a consumer-behavior data platform. An analogous ecosystem for B2B is still only emerging, especially given the complexity of data aggregation in B2B, where a large portion of SMEs may go out of business in their first few years of operation. Operators should undertake a structured assessment of the data that external vendors (such as Dun & Bradstreet, Lattice, and ZoomInfo) can provide on their B2B customers or prospects, even if just at a zip-code level.
3. Use data to improve decision making at the point of sale
Upmarket B2B segments often require human interaction facilitated by field and inside sales—both internal and partner-led. That said, person-to-person contact increases channel complexity and necessitates broad change management, which can include agile and automated campaign delivery through digital channels and call centers. Leading B2B operators often invest in broader change management and capability building that can span in-person field training and capability building. They also invest in the realignment of sales roles and channel mix, a performance-management system with leading and lagging indicators integrated into regular reviews, an incentive system that rewards the right behavior and investments in new technologies, and customer-relationship-management systems that make data and analytics available to agents in real time and are integrated into sales-force workflows.
One leading operator launched a comprehensive sales training program in parallel with an analytics transformation to both train and get feedback from field representatives on how to use data to guide decision making at the point of sale. As part of this effort, it equipped frontline reps with the necessary tools to compare prices at point of sale for renewals and acquisitions, increasing booking value by 5 percent and minimizing unnecessary variation in discounting.
Mounting margin pressures exacerbated by required investments in capital expenditures and other areas of the business, such as content and advertising, means that telcos cannot afford to leave any stone unturned in their quest for profitable growth. B2B analytics programs can be a rich source of revenues and margins if digital and analytics are applied systematically. However, a lift-and-shift approach will not suffice. Operators need to understand the unique context and challenges of B2B and design their analytics strategies accordingly.
