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Turbocharging CRM Decisions with Predictive Analytics

Maria Singson & John Kariotis, Mastech InfoTrellis

To be predictive of anything, you have to have good metrics. For metrics to be good, they must be relevant to results that matter. In terms of customer journeys, results that matter is often oversimplified as the customer either Loves You or Leaves You. In a metrics continuum, these are but the endpoints of the good old Net Promoter Score (NPS). Over time, NPS has been thought of as too high level, and measured too archaically with an annual survey, checking in on the customer once a year or once a quarter at most. Much of how the customer felt about their experiences with a brand is missed in-between measurements, and even with such a survey, a superb questionnaire design is often a challenge. The customer’s experiences, therefore, have gone uncaptured. In other words, wasted opportunities for significant CRM data.

From a customer service perspective, there is just as much knowledge leakage. Traditionally (and ironically), the customer’s unhappiness does not make it on the service desk’s radar until it is too late. This is because service centers have traditionally cared about quantity/volume and time as their main KPIs. Their definition of performance is nowhere near the customer’s experience. Rather than overhauling their metrics to keep a pulse on the customer’s experience, they’ve tacked on the once a year NPS survey and pray that there’s a correlation in retrospect. Such customer service teams could not compete in today’s “Know Me” instant gratification customer culture. Doing more and faster is all about quantity, while “Know Me” screams quality. If anything, quantity is inversely correlated with quality: more and faster service leads to transactional experience.

Nobody wants a transactional relationship

No company would ever boast of having a transactional CRM. Whether in business or personal life, being transactional is associated with untrustworthiness, haphazard, unaccountability, lacking strategy, lack of real or long-term value, and in some cases, even disrespect. To customers, companies who feel transactional simply do not care to get to know them. As companies strive for brand advocacy and loyalty, trust and dependability take center stage.

Does this mean companies must be reactive to customer demands at all times? At all times, yes. Reactive, not quite:

  • It is still suboptimal to hire a ton of customer service reps and then wait for customers to complain; it means they expect complaints to be rolling in, but not investigating why
  • It is still suboptimal to answer every call; if call times are logged, passing the customer around to decrease average handling time seems like a good result
Predictive Analytics

A conversation on metrics is a data readiness assessment

What’s missing is the right setup to capture metrics that are more relevant to customer experience so that companies can be more responsive. And when they do respond, it is laser-like to the customer’s specific needs at the time, but conscious of the bigger picture. To be able to drill down to the immediate need at hand, and still know the customer’s profile and recent interactions in general enables the company’s predictive abilities. To get to this state, they must leverage all customer data points to paint a full picture of who a customer is and the lifetime value they bring to the company.

When we partner with clients in their Digital CRM Transformation, we do everything from drafting a vision statement and a strategic roadmap of their business goals to executing on the analytics and AI enablement necessary to get to such goals. With changes abound, making sure that the metrics in place measure the right things is also essential; therefore, we do a bit of metrics design and innovation in the beginning as well. That’s not to suggest that the company abandons its incumbent metrics. In fact, it’s important to keep them and show how things will change, since there may be some important decisions being made off of them, such as how to allocate resources and leads in the company’s business as usual way of doing things.

Ready, set, get Predictive!

To become more proactive in their CRM, companies focus all resources, not just people, but also their data, infrastructure, and designs to be less responsive to customer demands, rather predictive of their needs. They do this by executing the BAE system:

  1. Blend. Combine not only customer data related to issues, but also their order data, product data, and marketing insights
  2. Analyze. Consider all channel interactions to identify patterns where trends can be further evaluated
  3. Engage. Apply engagement rules based on their data to monitor and trigger customer insights ahead of the curve

By leveraging customer-related data, companies can gain better insight to develop the most impactful strategies to take the right actions and focus on customers’ problems you can predict. With the right information and right data, you should be able to predict customer issues, and understand their behavior better.

Turbocharging CRM decision with Analytics

Turning a leaf on Analytics

AmberLeaf has spent the last few years working with our clients to build relationship-based models using data for what we called Predictive Service. With such rule-based consultancy, we enabled clients to shift focus to checking things that might go wrong, monitor for behaviors that they weren’t aware of, and derive further insight into the health of their customer base. In this framework, clients used their data to show early signs that a customer might be struggling, therefore creating opportunities to reach out proactively to see how they are doing. The added CX-SX analytics to this enables true CRM transformation, where companies can proactively look inward and improve their service excellence in ways that are most beneficial to customer experience. In this way, better CX metrics are born, making significant contribution to overall customer excellence.

With Amberleaf now being part of Mastech InfoTrellis, our Analytics capabilities just got better, and our Predictive Service now offers statistical and AI versions. The consultancy is still as tailored and detailed as before; it’s only now enhanced so that there is more analytics supporting every recommendation we give. The predictive power of our solutions now can be stamped with reliability and replicability measures, not to mention confidence bands on how accurate our predictions are. Whereas before, we had no way of producing granular scoring systems that can prioritize customers and prospects, now we can score every record, can estimate their likelihood to behave or react a certain way, given the right messaging, and can even find lookalikes of the most desirable customers. Our Predictive Service is now a full-blown Analytics Service, housing more than Predictive capabilities to include Prescriptive Analytics, classification, and metrics innovation. This is equivalent to turbocharging CRM for our clients, as now the service is end-to-end, with a way to segment and score customers so that their behaviors are prioritized according to some expected gains.

Even the way we look at data is enhanced

Transforming from the older way of having service focused KPIs just grasping for correlation with surveyed CSAT data, now, with our Analytics Services, the way we structure data is also improved to accommodate the completeness in metrics and CRM capabilities that we offer. By leveraging different ways to build graph databases, we enable our clients to:

  • Review and react quickly to changing customer dynamics
  • Forecast and plan product needs, service levels, and work force management demands
  • Drive product and service decisions based on insights that customers aren’t saying in a survey

With a graph database, coupled with graph analytics, the relationships in a CRM system just got a lot more mine-able, with deep learning analytics within reach. The insights afforded by such an infrastructure allows our clients to be more strategic and confident in their CRM decisions and initiatives.

To close, a full Analytics Service model moves to build the enterprise’s business around the data. It builds on the enterprise’s traditional operations-related reporting and moves towards the goal of bringing the voice of the customer directly into its daily business decisions. In today’s data and analytics landscape, it is virtually a moral obligation for the enterprise to use the right data responsibly at all times in serving the best experience to its customers. We firmly believe this, as it simply makes good business sense. Delighted customers = savings in operational costs = competitive, repeatable, self-learning CX engine = a true differentiator.

Mastech InfoTrellis Data Science Practice and Analytics Center of Excellence

Our team of data scientists hails academically and professionally from diverse backgrounds, allowing them to derive best practices across domains and design the Analytics Center of Excellence (ACE) that best fits specific client requirements.

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We Architect Enterprise Intelligence

At Mastech InfoTrellis we work to expose the entire corpus of enterprise data and leverage it with state of the art techniques from Decision & Data Science to accelerate enterprise learning. We would love to talk with you about it.

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Author

Maria Singson

Maria brings 20+ years of experience as a technical and business leader of analytics-driven Centers of Excellence (CoE) for clients aiming to be strategic and culture-conscious in their digital transformation. In her past roles as Leader of Innovation Analytics at Dun & Bradstreet, CEO of twoMS.co, and Chief Science Officer at Genpact, Maria established CoEs that helped companies realize value in their data, and reimagine their risk decisioning and sales and marketing analytics. She is also the founder of multiple startups in analytics and retail, where she leverages AI/ML to create economic opportunities for disadvantaged women and benefit disabled children. In her spare time, Maria teaches AI strategy and metrics for organizations to gauge and forecast their AI adoption at Rutgers Business School for Executive Education. Her human performance-centric approach to AI readiness and transformation is rooted in her Ph.D. in Cognitive Science (UC Irvine), and bachelor’s degree in Psychology (University of Southern California).

John Kariotis

John has over 20 years of experience delivering innovative, proven, CX-based solutions that solve critical business challenges. He aligns across several disciplines that span Marketing, Sales, Field Service, and Customer Data Management.

From acquisition, retention to care, John manages the support and services needed to maximize CX solutions enables organizations to drive real business value and results. He is focused on driving positive outcomes for clients by reorganizing Marketing, Sales, and Service Operations by building out Centers of Excellence, breaking down the barriers of cross-divisional resources, and establishing CX platforms for channel support agility to predict and respond to customer demands.

Through his leadership, he was presented the Oracle Excellence Award as the CX Global Partner of the Year and is a three-time award winner of the North America Partner of the Year for Customer Service. His efforts were recognized for demonstrating an outstanding and innovative solution based on Oracle CX products that create value for customers and generate new business potential.

About

Mastech InfoTrellis partners with enterprises to help them achieve their business objectives by leveraging the power of data to derive deep, analytical insights about their business and its operations. We accelerate business velocity, minimize costs, and drastically improve corporate resiliency through personalized, process-oriented programs, consisting of strategy, data management (including master data management), business intelligence and reporting, data engineering, predictive analytics, and advanced analytics. Part of the NYSE-listed, $193.6M, digital transformation IT services company, Mastech Digital; we drive businesses forward around the world, with offices spread across the US, Canada, India, Singapore, UK, and Ireland.