“Spending money in the right places to reach the right customers at the right time,” for one utility, began with a catalyst of reducing call center demand and spread to include a multitude of other use cases within a few short months after realizing the power of their customer data. In this article, we’re showcasing how one BlastPoint partner is utilizing its customer data to reduce operating costs and increase CSAT across its customer base.
Our partner, a utility company serving roughly 500k customers across multiple states, faces many of the same challenges as others when it comes to their customers.
Externally, their customers deal with challenges such as inflation, disruptions due to global warming events, and technological advancements – such as EVs or energy efficiency product upgrades. Internally, because customer data is fragmented across various departments – such as call centers, customer success, billing and collections, and more – they struggled to connect customers with relevant programs that could assist them through external events.
With attitudinal, behavioral, and demographic insights from BlastPoint’s Customer Intelligence Platform, our partner – including teams from customer success, marketing & communications, customer programs, and analytics department – gained a 360-degree view of their customers; allowing them to utilize resources to effectively and efficiently meet customers where they are at.
Not only did they increase enrollment in internal programs like budget billing or autopay; but also external ones like LIHEAP or LIHWAP. This enabled them to reduce overall operating costs by lowering call center demand while increasing CSAT scores across the board. In this article, we’ll explore our partner’s journey within the first few months of generating these, and other, insights.
The Catalyst: Reducing Call Center Demand
We began our journey by integrating a wide array of our partner’s customer records – including payment and collections data, call center and contact records, and various program engagement analytics – with household-level demographic and behavioral data. We use this enriched data to develop a model predicting each household’s propensity to call our partner’s call center.
After each customer’s propensity score is identified, BlastPoint’s data revealed interesting insights. Users were able to segment high-propensity callers by delinquency status, energy usage, outages, engagement with the call center, web, and more.
Next, teams can analyze these segments and find targeted ways to divert callers from the call center. For example, our partner found a high proportion of callers are likely to pay via IVR, are less green than average, and are more likely to watch daytime TV. In response to those insights, marketing teams can consider directing ad spending toward TV or radio ads call center representatives can ask customers to enroll in E-billing when they call in, and the utility can consider upgrading IVR technologies to divert customers toward self-service solutions.
Data finds a higher percentage of callers who were not actively receiving assistance – and have never received it, despite their income eligibility.
Following this initial initiative, our partner was able to understand program saturation statistics, compare good fit versus poor fit profiling, and gather program personas and channel insights. We’ll show you how the platform extended to these use cases while simultaneously reducing call center demand.
How do we integrate your data? Check out this quick guide on how BlastPoint makes data onboarding easy for your organization.
1. Grow Customer Assistance Program Awareness
Many customers are unaware of the payment assistance programs available to them through their utility. Knowing this, our partner leveraged BlastPoint’s Customer Intelligence Platform to segment customers into two categories: those who know and those who don’t.
Education was crucial to reaching their goal of increasing awareness, engagement, and enrollment-in available customer assistance programs.
“While we cannot directly enroll customers in assistance programs ourselves, we can educate our customers about the options available to them.” – Customer Programs Team Member
From BlastPoint’s Platform, our partner built a successful education campaign targeting customers in their region who met LIHEAP (Low Income Home Energy Assistance Program) eligibility criteria. Customer segments were broken down based on specific categories, such as those new to LIHEAP, those familiar with it, and seniors who were LIHEAP-eligible.
By segmenting the audience into specific categories, marketing and communication teams are able to craft campaign materials that resonate with each target segment – focusing primarily on those most in need and reaching them through the channels they are most likely to respond to.
Our partner experienced impressive results. From a single awareness campaign, targeting low-income customers that qualify for payment assistance programs, 16% of customers applied for a payment arrangement option, applications for payment extensions exceeded 25%, and 12% viewed community resource options available on our partner’s website.
2. Identify Customers ‘Best-Fit’ for Program Initiatives
Our partner’s analytics team utilized BlastPoint’s data and guidance to build machine learning propensity models for their payment program initiatives. These models output a list of attributes, or key drivers, as indicators of a customer’s likeness to engage or enroll in targeted program initiatives and are used to calculate program saturation, potential program uplift, and ROI estimates.
Data cleaning is extremely important when building these models. This is because, in order for models to predict the future, they have to learn from past data. As part of BlastPoint’s onboarding process, we clean, extract, append, and analyze data to meet the objective at-hand. Learn more from our blog, ‘Messy’ Data? Don’t worry! We’ve Seen Worse.
For example, if the key driver low home square footage indicates customers are less likely to enroll in autopay, marketing teams can eliminate this attribute from their outreach campaign and target customers with high to highest home square footage – as this segment is more likely to enroll.
Histograms demonstrate the distribution of heavy Twitter usage for three segments.
Furthermore, key drivers also indicate a program’s level of saturation, enabling teams to pinpoint segments with untapped potential for enrollment. For instance, if a key driver such as heavy Twitter users suggests low saturation, teams can leverage this opportunity and launch a targeted Twitter Ad campaign engaging that segment of customers with messaging to enroll.
Age of account, whether or not the customer has an email account on file, home market value, social media usage, and which social channels they prefer, are some of the many key features that propensity modeling accounts for.
Through propensity modeling, marketing teams can micro-target segments, deliver targeted messaging via their preferred channels, and eliminate inefficient “spray-and-pray” campaigns. By employing this method, teams can optimize their budget and set achievable program goals for engagements.
3. Meet Customers Where They Are
The Utility Burden Index (UDI) reflects the utility burdens of customers based on income and energy consumption, then maps it to the region based on those scores. Mapping gives business users the ability to visually compare higher and lower concentrations of UDI scores within a region.
Our partner’s data revealed high UDI scores were densely populated within their urban core. Data revealed this segment as lower income and harder-to-reach customers. With regionally specific mapping features, their marketing team can target billboard ad placements with payment assistance messaging in high-traffic locations for optimal reach and engagement.
Another interesting insight revealed higher UDI scores correlated with first-time home buyers. This prompted teams to launch a series of two educational campaigns targeting this segment. The first focuses on payment assistance programs, while the second provides information on home appliance and energy efficiency technology upgrades.
Using BlastPoint’s AI-powered technology to build residential call center segments can help understand your customer behaviors and direct customers away from your call center – so your teams can optimize low-touch customer engagement campaigns. When your business understands who customers are – at a household level – your teams can provide customers with the right information, at the right time, through the right channel to ensure overall customer satisfaction.