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How telcos can drive digital transformation with advanced data analytics

(Image credit: Image Credit: Chombosan / Shutterstock)

Telecoms is a tough market where success depends on keeping up with evolving customer needs, technologies, and competitors. Most businesses recognize the need for constant adaptation; that’s why optimizing digital models was listed among the top industry priorities for 2020. Ensuring consistent transformation, however, isn’t necessarily simple.

Over the last few years, companies have used a variety of tools to extract as much data as possible from every available source, in a bid to build big data strategies that will help them boost revenue, reduce churn, and stay dynamic. But the majority are still hampered by back-end systems and decision-supporting mechanisms that are heavily rooted in legacy tech, making it hard to implement a customer-centric and data-driven approach. And these issues are only compounded by the tendency to keep data in silos, shortages of in-house data talent, and clunky internal procedures.

So, while they have plenty of information — huge stores of historic customer data, as well as a continuous flow of data from online and offline channels — many are missing the means to convert it into accessible and usable insight.

Clearly, what’s needed is better analytical ability. Telcos must focus on leveraging advanced analytics and improving their understanding of how it can be used to power business success, starting with smarter data unification.

Making siloed data a thing of the past

The first step to realizing data potential is removing divisions. As part of their data collation efforts, many telco marketers are already evaluating customer and campaign activity at a fundamental level; be that through sporadic checks on the results of advertising across different social media channels or assessment of post-campaign reports. But this limited and often isolated information doesn’t tally with the habits or demands of channel-agnostic customers.

Interaction is ever varying for customers, they may choose to engage with companies via countless digital or real-world avenues; websites, call centers, emails, physical store visits, social media platforms, and more. For telcos, this creates multiple issues. The endless influx of fragmented data makes it hard to understand customer needs and identify which mediums or tactics drive the greatest responses. At the same time, customers expect all interactions to be part of a streamlined and relevant experience.

The best solution to these challenges is bringing data together into a unified multi-channel view; and that is also where smart marketing analytics comes in. By integrating and harmonizing disparate data, sophisticated tools can create a single pool of valuable insight that paves the way for efficient cross-channel analysis and like-for-like comparisons.

A prime example of what this looks like is the case of Vodafone. The telco giant’s Italian digital marketing division realized their siloed data practices were fueling difficulties, especially with linking crucial prospect data from various online and offline sources. To overcome this issue, they needed a self-service way to create an end-to-end, single source of truth for digital marketing, call centers, and sales activity; and sophisticated analytics was the perfect fit. Using intelligent tech to generate unified business-ready data and run granular assessment, the Vodafone team revolutionized their insight generation and reporting, gaining faster access to vital insight and reducing waste by 75 percent.

Identifying the right path to results

Once telcos have their holistic data foundation in place, step two is establishing how they can use it to improve their understanding and performance. A good starting point is tapping artificially intelligent (AI) tech to rapidly analyze overall activity and pinpoint which actions are needed to optimize varied metrics and key performance indicators (KPIs); from subscriber acquisition cost and average return per user (ARPU) to net promoter scores (NPS) and return on ad spend (ROAS).

Take NPS scores such as customer satisfaction and lifetime value (LTV). By running in-depth analysis of existing customer preferences and purchases, telcos can match them with the product bundles most suited to their requirements — increasing the chances of lasting happiness and loyalty. Additionally, the ability of AI-based analysis to run quick and detailed assessment at scale can help telcos overcome issues with problematic metrics, such as ROAS. For example, running in-depth analysis of click-through rates (CTRs) by keyword will help to zero in on specific advertising impact; providing the insight needed to determine where focus and investment should be directed to bolster returns.

And that’s not all. When advertising data is merged with other information — including bid strategy, ad placement and time of day, and customer data such as site interaction, browsing, search terms, and location — media managers can also use comprehensive analytics to recognize wider trends and conversion opportunities across the multi-channel spectrum.

Predictive analytics comes of age

Next up is enhancing ongoing agility. Telcos must harness the power of analytics to stay ahead of competitors and in tune with real-time customer requirements by persistently identifying what buyers will want and delivering it at the right moment. In short: they need to create more personalized and relevant messaging with predictive analytics.

Achieving this isn’t as complicated as it seems. As most industry marketers know, finding the ideal mix for each customer and channel has traditionally been a difficult task; especially with data generally spread across many spreadsheets and reports. Predictive analysis tools, however, enable telcos to quickly obtain the answers they need using a blend of data modeling, mining, and machine learning (ML). By taking on the heavy lifting of large-scale data evaluation — including behavioral pattern analysis — these platforms can provide telcos with insight that allows them to create customized journeys for a segment that has an impact at multiple levels.

For example, they can create predictive analytics algorithms that collect data about a specific customer or prospect to anticipate what they are likely to buy and automatically recommend tailored product packages. Or, from an advertising perspective, combining predictive analysis with business rules can define the next best message for customer prospects, in line with their tastes, favored channels, and position in the purchase journey.

The additional advantage being that by continuously using predictive assessment to test new techniques and fine-tune interactions, machines will gain a deeper understanding of what works for each customer that enables them to increase precision and conversions over time.

Preventing key triggers of churn

No matter how streamlined predictive personalization may be, it can’t guard against every possible cause of customer dissatisfaction. By making maximum use of analytics, companies can ensure issues are quickly detected and addressed; in fact, previous research by McKinsey shows that when telcos take an insight-fueled approach, they can reduce churn by up to 15 percent. The final pillar of analytics they, therefore, need to master is leveraging analytics to prevent customer loss.

There are many ways analysis can assist organizations in mitigating customer frustration. For instance, implementing micro-segmentation — which groups together and evaluates individuals with similar interests and attributes — can ensure packages are marketed and sold to the right people. Analysis of service usage levels can also help spot signs that packages aren’t aligning with customer needs, such as customers who frequently exceed their data or call limits, and preemptively offer more appropriate bundles.

Alternatively, evaluating historical data to isolate factors that often lead to churn — such as mis-sold packages or particular broadband speed fluctuations — will enable telcos to act before irritations turn into contract-ending problems. For instance, companies can spot and meet training needs at certain call centers to better equip service agents with the skills required to identify the best fit for each customer or use micro-segments to accurately target and engage individuals who are reaching the point of high churn risk.

In a sector where rivalry is fierce and customer demands are forever changing, continual transformation isn’t just important; it’s vital to ensure lasting survival. Fortunately, most telcos are already on the right track. Across the industry, companies have recognized the value of data for enriching their customer knowledge and steering smarter decisions. Now they must progress to the next level of data-powered efficiency. By taking advantage of advanced analytical abilities offered by specialized vendors to join-up their existing data and unlock the critical insights it contains, telcos can minimize churn while gaining a greater ability to improve communication, ROI, and the overall customer experience.

Alex Igelsböck, CEO, Adverity