Marketing Analytics in Telecom: The Shift to the Cloud and Google Cloud Platform
While the shift to digital-first engagement models has created a wealth of information on customer interactions, CMOs, and senior business leaders are struggling to gain insights needed to drive business decisions and outcomes. Luckily, cloud platforms can unlock the potential to transform the way telecom brands leverage marketing analytics to drive business outcomes.
Many organizations today are sitting on a massive trove of customer data yet struggle to make out the forest for the trees. “An irony of having too much data is that you often have too little information,” explain Carl F. Mela and Christine Moorman in an article for Harvard Business Review. The article elaborates on the need for firms to decide what to do with the data first and then which data they need to do it, as opposed to simply collecting the data and hoping for a great result. And as we know, hope is no substitute for a good strategy.
Marketing analytics is one such treasure trove of data that can be particularly critical for telecom companies given their highly complex ecosystem and convoluted consumer journey. The way customers utilize connectivity and consume their products is changing rapidly, and a robust marketing analytics capability can help ensure a company stays ahead of the curve.
Contrary to popular belief, marketing analytics is not just about marketing and not just a solution for marketers. Marketing analytics is a powerful tool brands can use to solve a wide variety of business challenges ranging from traditional marketing use cases, like reviewing campaign results and gaining a deeper understanding of customers, to addressing broader business challenges that include understanding product performance or evaluating the efficiency of operations and execution of work.
Despite having access to an unprecedented amount of information, many brands struggle to unlock the value of marketing analytics or find real-world applications that move the needle on business outcomes. More is not always more, and simply having a sea of data does not data-driven one make… Gartner reports close to half of CMOs are unable to measure marketing ROI, tarnishing the perceived value of marketing analytics at large.
Marketing Analytics in Telecom
In the telecom industry, leveraging marketing analytics to drive business outcomes has proven especially challenging. The nature of the customer journey seen through the lens of top-of-funnel activities, which tend to skew heavily digital, contrasts greatly with activities associated with buying, picking up, and activating a mobile device, which usually takes place on-premise. Add to the mix burgeoning products offerings across OTT streaming services, coupled with evolving customer preferences, and many brands are struggling to collect and make sense out of a mountain of unstructured data.
Telecom brands have struggled to adapt to the new reality of the need for a customer-centric approach — a trend accelerated greatly by COVID-19 and the overwhelming shift to digital-first or digital-only engagement models. Suffice it to say, the traditional telecom customer journey model of “Learn Buy Get Use Pay Service” (“LBGUPS” in industry parlance) may need a refresh. “LBGUPS was born to be operational, ensuring the customer remained top of mind as the North Star guiding every decision and step of the process” explains Sam Andrews, Managing Director of Slalom Strategy and industry veteran. “With the reality of a new omnichannel consumer, the customer journey is more like a figure-eight blurring all of the lines between what used to be distinct activities.”
To evolve, telecom executives will need access to a wide breadth of analytics to decode the consumer journey and identify areas of opportunity improvement. The good news is that Google Cloud Platform (GCP) can transform the way brands look at marketing, offering an extensible ‘platform’ organizations can use to merge data from disparate sources, apply predictive/prescriptive models, and plug into operational systems used to power contemporary consumer experiences.
The Quest to Become “Data Driven”
According to a Gartner study conducted last year, only 54% of marketing decisions are being influenced by marketing analytics — despite years of investment in marketing analytics solutions — not to mention a consensus among leaders that their organizations need to gain more valuable marketing data in an overall effort to become more “data driven.”
Lack of demonstrable results has, by and large, led to apathy and stagnation. In 2019, for example, marketing analytics tied with market research and competitive insights as the top priority for CMOs. Today, by contrast, marketing analytics has plunged to fourth place, accounting for just 11% of the total marketing budget, a survey of 400 CMOs reported because the “results have failed to live up to the expectations.”
One can connect the tribulations of marketing analytics teams to the broader effort of brands seeking to become more “data driven” in the way they operate. If you speak with industry executives, for example, they will tell you they are striving to make their organizations more reliant on data to make decisions. Tech company (and Slalom partner) Tableau recently reported upwards of 83% of CEOs want their organization to be more data-driven.
Looking beyond the hyperbole of tech vendors with a stake in the game, being data-driven means being able to establish the value of data so it can be infused into strategy and decision-making. In other words, putting data to use within enterprise planning and execution. In this scenario, data isn’t simply a database or a technology — it’s a strategic asset that can be used for answering business questions and delivering value to the enterprise.
MarTech Stacks and a Sea of Data
Pushed to achieve data-driven status, CMOs of course make investments. And because growth in data collection is often driven primarily by tech investments, in recent years firms have gone through immense efforts to build a MarTech ecosystem capable of gathering, organizing, and making sense of hitherto unimaginable amounts of first-party data. These efforts have resulted in an unprecedented amount of information that is currently piling up on servers and in stacks, in applications and in platforms, both on-premise and, increasingly, in the cloud.
Marketing Analytics Strategy
So, what can executives do to mature their capabilities in marketing analytics to drive more impact across the organization? Understanding the scope of marketing analytics is a great place to start. Broadly speaking there are four categories of insight that marketers use to evaluate marketing performance:
Campaign Intelligence: insight into campaign performance over time, including Above-the-Line (ATL/Branding) and Below-the-Line (BTL/Direct) metrics. KPIs often include Reach, Frequency and GRP (Gross Rating Point) for ATL, and Engagement, Click-through-Rates (CTRs), Impressions, Conversions, Cost-per-Click (CPC), and Cost-per-Acquisition (CPC) for BTL.
Customer Intelligence: insight into customers, both individually and in the aggregate, across the customer journey. This category includes both sentiment and behavioral indices, including Reach, Frequency & Monetary value (RFM), purchase intent, Average Order Value (AOV), and of course Customer Satisfaction (CSAT).
Financial Intelligence: analytics that provides insight on marketing spend and performance of the marketing unit. Popular KPIs include roll-up marketing budgets, planned versus actual marketing spend, and Return on Marketing Investment (RoMI).
Operational Intelligence: this category drills down into the performance of marketing operations with productivity KPIs such as rounds of QA, error rates, and time/speed to market. Production metrics often include total hours worked, total production costs, efficiency gains, and so on.
By focusing on strategy first, organizations consider what insights various internal stakeholders need to do their jobs and make decisions. These needs can then be mapped to levers (see table that follows) one can pull to present information, which, in turn, can be used to identify what data sources are required to build the underlying data foundation.
Constructing an Analytics Value Tree (AVT) is the best first step as it provides a cohesive roadmap for the rest of the process and forces alignment on outcomes from the start. The AVT then serves as a reference through the process of setting up a powerful, effective marketing analytics framework and reporting layer… Analytics Value Trees are an effective tool to help plan out an analytics strategy using a top-down approach, starting with a high-level framework and drilling down into business outcomes, levers, and KPIs, which can be distilled into specific analytics products, or dashboards, that can be produced.
Here’s an example of a Value Tree Slalom built out for a client. In this instance, the client was most interested in understanding the performance of marketing operations, hence the table’s emphasis on core productivity and production metrics such as “capacity of resources,” “error rates,” “speed to market,” etc. The overall measurement framework, appearing on the left-hand side of the table, reflects the production process within the organization, going top to bottom. A different example more focused on customer experience would most likely use a different measurement framework or structure, for example, the customer journey stages, as opposed to content production steps.
The Shift to the Cloud and GCP
While the emergence of cloud hosting has been a tremendous boon to marketers in terms of providing a powerful environment to support marketing analytics, anyone who has been working in the field can attest to the difficulty in getting a clear picture of the totality of customer interactions over time, or what we today call Customer 360. The emergence of Customer Data Platforms, or CDPs, has gained steam in recent years specifically to address this need. A recent article published by my colleagues at Slalom introduces the CDP and discusses its merits.
Due to the disparate nature of data being collected by most telecom brands which still rely heavily on traditional channels (brick-and-mortar for sales and contact center for service), gaining an accurate, up-to-date view of the customer can be especially challenging. Clickstream data, usually owned and managed by marketing, contains a wealth of information of consumer behavior. Telecom marketers can leverage clickstream data to understand a wide breadth of consumer behaviors including identifying who is visiting their websites or digital properties, evaluating email campaign performance through opens, clicks, etc.
As parts of the customer journey have migrated to the cloud with the emergence of SaaS platforms, activating customer data has become increasingly difficult, not easier. A big reason for this difficulty is integration, because of the unstructured nature of the data. “Data collected by different systems is disjointed, lacking variables to match the data, and using different coding schemes,” explains Mela and Moorman. Processing the data in a timely manner is also an issue, as “vast amounts of information can overwhelm processing power and algorithms.”
Recent advances in cloud technology have also unlocked tremendous value for telecom executives. Data sets and applications have begun to migrate to the cloud in earnest, providing brands an extensible, easily accessible platform they can use to merge data from disparate sources, apply predictive/prescriptive models, and plug into various operational systems.
Google Cloud Platform (GCP) offers a compelling solution. Because GCP has native integration with the Google Marketing Platform and commonly used CRM systems, for example, Salesforce, it’s much easier to integrate datasets and pipelines from Ads, Google Analytics, and CRM to glean an omnichannel view of the consumer, which is easily accessible via the cloud.
Information from additional sources, such as operational data from work management solutions like Workfront or BrandMaker, or financial management data from tools like Allocadia, can also be piped in to provide a clear picture of overall marketing performance.
With new tools and capabilities available, change is happening across the industry. “Telecoms are adjusting their LBGUPS model to account for the blurring lines while retaining the customer as their North Star, a challenge they’ve already begun to tackle,” explains Andrews. “I’m excited to see how leading telecom brands evolve their traditional customer journey model in coming years.”
Who Telcoms use for their Customer360 is a critical choice. There are a plethora of options available today. Invariably, avoiding vendor-lock-in and partnering with future-looking partners are two core necessities. How does GCP compare to these requirements?
Google has reaffirmed their commitment to open-source technologies that accelerate digital transformation. Their approach is one of interoperability, flexibility, and choice. As 5G and edge solutions move more into the mainstream, Google has invested heavily in curating an open ecosystem of partners.
Being able to ingest this data for customer analytics is critical, and demonstrates Google’s advantage for both marketing analytics and telecom customers.
To find out more, download our article on customer data strategy.
Rio Longacre is an executive with two decades of leadership and experience in the digital space across strategy consulting, technology services, data, and media planning. He’s a frequent thought leader and subject matter expert in customer experience management, digital business transformation, and marketing/advertising technology. Reach him at firstname.lastname@example.org.