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How Operationalized Data Solves Customer Engagement Problems & Achieves Business Goals

image credit: Copyright BlastPoint 2020
Tomer Borenstein's picture
CTO, Co-Founder, BlastPoint

Tomer Borenstein is the CTO and Co-founder of BlastPoint. Leading operations and data science teams to serve the energy, retail and enterprise industries, Borenstein focuses on bringing the power...

  • Member since 2020
  • 7 items added with 9,387 views
  • Dec 2, 2020

This item is part of the Data Analytics & Intelligence - Winter 2020/21 SPECIAL ISSUE, click here for more

When you think about data, you might imagine a dizzying array of ones and zeroes, or spreadsheets filled with minute details. But as a data scientist and machine learning evangelist, I see data as a rich collection of tools and materials for solving real-world problems. 

Like the bricks, boards, screws and sheets of drywall you’d assemble to build a house, data can (and should) be architectured purposefully to create something of value that’s safe, reliable, familiar and useful for years to come. 

I call this process ‘operationalizing’ data, and anyone can do it if they want to make better, data-backed business decisions that solve customer engagement problems. That’s because operationalized data, especially when it’s infused with A.I., is fully functional, responsive, predictive and useful for fixing problems as they arise, even if you’re not a data guru, yourself. 

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Our utility partners use operationalized data to achieve long-term goals, like increasing customer engagement with company programs and services, generating new revenue streams and saving money. And they’re doing so quickly and efficiently, without having to lean too heavily on scarce IT resources. Here’s how:

Internal Data Inventory

Utility providers possess loads of internal data: payment history, energy usage, employee time, call center volume, grid distribution and so on. 

Before it’s operationalized, however, they need to find out whether it’s in tip-top shape to help them reach their goals, and whether they’ll need to integrate any outside data to get there. We often ask a few critical questions to get them started on taking an internal data inventory:

  • Is my data fragmented, with some bits collected by one department and other bits gathered by another?

  • Is it difficult to share one team’s data with another? 

  • Do employees use multiple methods to collect and record the data?

  • Do different departments use different systems to update the data over time? 

  • Is any important data missing?

If the answer to any of these is yes, it’s time to clean, organize and orchestrate your data. The goal is to make all of your internal information accessible to and understandable by everyone across the organization, so that regardless of any team’s unique goal, they can put the data to work quickly to get results.

Data Quality Over Data Quantity

Oceans of data are available to us from all over the world, including everything from how many dog owners live in one Census block to which consumers are most likely to respond to a Yellow Pages ad. 

But just because we can get that data doesn’t mean you need to have it. Unfortunately, sometimes people think more data will answer their problems. But more data isn’t going to magically begin generating revenue or increasing customer satisfaction on its own. What matters is what you do with the data that you already have. 

The data you just orchestrated and organized may be sufficient for solving your customer engagement problems quickly and efficiently. For example, you may be able to answer the following questions:

  • Which of my customers have missed two or more bill payments in the last six months?

  • How much electricity or gas are my customers using this month as compared to last year at this time?

  • How many customer calls have we received about bill payment options in the past 30 days?

Being able to answer those questions with the right data means you can solve problems like:

  • Knowing which customers to engage about enrolling in energy assistance programs.

  • Identifying which customers may be good candidates for smart thermostats.

  • Understanding whether now is a good time to invest in a new IVR system.

Enhance and Integrate

Once your internal data has answered as many questions as possible, you can begin to see where knowledge gaps exist and, therefore, determine what kinds of data you’d like to add into your current framework to achieve bigger goals.

Say a utility’s goal is to enter the EV market, but it doesn’t know where real opportunities exist. The utility has organized its internal data, knows who its existing commercial customers are, knows which commercial businesses aren’t buying electricity from them, and knows which of its residential customers own electric cars. 

What it does not know are which local commercial businesses are most likely to install electric vehicle charging on their property. External data can tell them that. With that knowledge, they can approach the right business owners, initiate conversations and discuss opportunities for partnerships that will drive EV infrastructure expansion.

Choosing the right external data at this juncture is critical. We’ve seen companies overspend on data that was irrelevant and unnecessary, so it’s important to pull in only what you need and weave it into your existing framework. 

Enriching Data with A.I.

Artificial intelligence-infused data means that a geek like me creates a complex math equation, sometimes known as a machine learning algorithm, and applies it to your data to generate answers. Following all necessary cybersecurity protocols, what gets generated are predictive models and propensity scores that utilities can use to understand things like:

  • How likely is Customer A to respond to email advertising?

  • Is Customer B more likely to respond to eco-friendly messaging or budget-conscious messaging when being contacted about enrolling in e-billing?

  • How likely is Customer C to pay their bill in full upon receiving a 10-day warning letter? 

With these insights, utilities can plan effective, targeted outreach campaigns. They know where to start engaging because the data has indicated where to focus resources first and what kinds of messaging will resonate most.

Launching into action with effective strategies, deriving results that fulfill your goals, applying what you know across the organization to generate new solutions means you have achieved operationalized data. 

You have architected it purposefully and carefully. You’ve ensured that it provides safety and privacy for all the functions of your business. You’ve built a structure that will be reliable for years to come, which can be added to and refreshed easily, whenever new data becomes available. Teams across your organization are able to apply your data to a variety of different scenarios and solve problems nimbly, allowing you to fulfill long-term goals, like increased customer engagement, revenue generation and cost savings. 

We expect to see more utilities working toward operationalizing their data in the near future, as it creates real, actionable value in so many ways. 

Matt Chester's picture
Matt Chester on Dec 2, 2020

But just because we can get that data doesn’t mean you need to have it.

Please, repeat this one ten more times-- make sure those in the back hear you! It's so important to recognize that more isn't necessarily better. And in utilities especially where data can grow exponentially, having a judicious eye towards what's important to collect and analyze is so important. 

Tomer Borenstein's picture
Tomer Borenstein on Dec 2, 2020

Totally, Matt. Before utilities go out and try to get more data, it’s important to understand what data they already have in-hand and make the most of it. Most utilities we work with sit on mountains and mountains of data, but end up never using most (or any) of it. Something BlastPoint helps utilities with is identifying what data they have, what data is needed to solve their problems and meet their goals, fill in the gaps, and put that data to work in a way that is quick and efficient, rather than spending years building data lakes that seldom get used (especially by business teams). Thanks for the comment!

Tomer Borenstein's picture
Thank Tomer for the Post!
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