How Data Teams Can Leverage Operational Analytics for Faster Actions?

We live in a world where data is piling up at an alarming rate – businesses are collecting increasing volumes of data from their surroundings like customers, products, marketplace, warehouses, and many more.

Companies nowadays must make informed decisions on their business’s minute-to-minute behavior. The days of waiting for weeks between progress reports, which were manually created and then sent to recipients for semi-manual scrutiny, are long gone. Even nighttime reports issued are frequently outdated when assessed the next day. Bottom line: the earlier you can make decisions, the faster you can give value to your internal and external stakeholders.

The transition from batch to real-time data processing has significantly grown during the last decade. The end result? New platforms and frameworks that enable real-time data processing and insights have emerged while retaining the benefits of tried-and-true frameworks.

Because of the cheaper cost of cloud-hosted resources (such as servers and databases), and the broad availability of well-maintained and well-documented open-source frameworks, both businesses and consumers are able to move quickly and intelligently, and this shift has gained traction in the last few years.

Today, different teams within data-driven companies use operational analytics for faster actions. Relying on high-quality data from different sources and leveraging advanced data technology to support real-time insights, operational analytics equips your teams with a holistic view of your business operations. It helps them to detect and address business inefficiencies quickly – expediting decision-making and ultimately improving customer experience.

Table of Contents

• What is Operational Analytics?
  • The Emergence of Operational Analytics
  • Operational Analytics vs Traditional Analytics: What’s the Difference?
  • A Practical Example of Using Operational Analytics
• Using Operational Analytics for Faster Actions
• How is Operational Analytics Used in Data Teams
  • Operational Analytics for Faster Actions in Marketing
  • Operational Analytics for Faster Actions in Sales
  • Operational Analytics for Faster Actions in Customer Success
• Faster and Easier Way to Operationalize Your Data: Reverse ETL
• How to Operationalize Your Data Using Hevo Activate?
• Final Thoughts

What is Operational Analytics?

Operational analytics is the process of developing optimal and realistic recommendations in real-time based on trusted, highly-accessible data from your warehouse.

Operational analytics gives you an overview of what’s going on in your business. Marketing campaigns, product enhancement, demand forecasting, supply chain management, and asset usage are all examples of business use cases where operational analytics can be employed.

Operational analytics offers more data transparency, allowing you to make better decisions. By deploying operational analytics in your organization, you can take certain advantages:

• Instead of depending just on weekly, quarterly, or annual reports to enhance your business, you’re operationalizing your data to take instant steps daily.
• You may respond to customer behavior in real-time.
• Inefficiencies can be identified and can be improved as they occur. 

The Emergence of Operational Analytics

Streaming data tools have helped to reinvent how businesses process extensive data in terms of intake, transformation, and integration across new data platform systems and services. They have also contributed to a framework for quick analytics processing on a combination of historical and new, near-real-time data, which is becoming known as operational analytics.

By offering a picture of the most up-to-date information relevant to day-to-day company tasks, operational analytics may assist drive everyday business operations and enhance revenues, all while generating a competitive edge for the organization.

Today, operational analytics isn’t only for tech-savvy companies like Amazon, Uber, and Apple. Because of real-time data, every organization that uses data may make more data-driven decisions. Consider any reseller that wishes to increase its profit margin. The information acquired at one of his points of sale may be utilized to manage inventories and distribute extra units to nearby establishments running promotions.

According to research by IDC, approximately 30 percent of the Global Datasphere will be real-time by 2025 due to the injection of data into our commercial operations and personal streams of life.

Operational Analytics vs Traditional Analytics: What’s the Difference?

Analytics is concerned with bringing together and displaying various data types to give a complete picture of what’s going on in the organization and allow new capabilities. This information is often provided in a Business Intelligence tool or dashboard and regularly shared with the rest of the business (weekly, monthly, quarterly, etc.). Traditional analytics is solely concerned with giving a high-level overview of many KPIs for strategic decisions and day-to-day operations.

Moreover, as observed in many cases, analytics has always been a batch-oriented activity. The data is collected from many sources and stored in a data warehouse such as Google BigQuery or AWS. Batch operations are started regularly to process data and create insights. The findings of these jobs are later made available to business users for them to make better business decisions. This actually delays the time it takes to make proactive decisions and seize opportunities or prevent problems before they happen.

Operational analytics focuses on evaluating data in real-time or near real-time. It helps your data and business users get ahead of curve and “do things” immediately. 

For example, an email is sent when a consumer registers or makes a purchase. Another example may be enhancing CRM data with product usage data to offer sales and marketing teams real-time insights. Operational analytics aims to sync data amongst systems to interact with users, charge consumers, inform teams, and so on. On the other hand, traditional analytics is frequently viewed as one of several “destinations” for the operational data flow.

In essence, operations represent the activities taken to exploit data in real-time, whereas analytics describes the business choices produced based on the data in a dashboard.

A Practical Example of Using Operational Analytics

Businesses can leverage operational analytics for faster actions and to improve the overall experience of their customers. In operational analytics, data is absorbed from many sources and processed in real-time. The analytical results are then utilized to initiate actions promptly. 

Consider a vast retail chain that sells its merchandise online. Before clients use their credit cards, the organization wants to examine their behavior in real-time and respond fast to any abnormal activity or suspected fraudulent purchases. If a credit card transaction appears suspicious, the organization can deny the payment immediately, rather than wait until the next day or week to determine if it was genuinely fraudulent. This would save the company a significant amount of money.

Using Operational Analytics for Faster Actions

The practice of leveraging data and faster analytics to improve organization performance is known as operational analytics. For data-driven organizations, it’s not simply a nice-to-have; it’s a must-have.

In our opinion, the following are the key reasons for implementing operations analytics in your business:

• Making data usable
• Making data accessible to all
• Ensuring that data is reliable
• To simplify data sharing and collaboration for diverse teams

Once those objectives are met, employing operational analytics will allow you to boost earnings, lower expenses, reduce risks, and remain ahead of the competition. To compete effectively in today’s corporate environment, you require operational analytics. 

Here are the reasons why you should use operational analytics for faster actions:

• Increased Operational Efficiency: Companies may improve operational efficiency by streamlining operations and enhancing quality control with operational analytics.
• Improved Client Experience: Businesses may utilize operational data to learn more about their consumers, predict demand for services and goods, and deliver better customer service.
• Better Decision-making and Business Intelligence: Operational data gives an objective perspective of your business’s state, allowing you to make better decisions about managing your firm successfully.
• Reduced Cost and Risk: Using operational analytics for risk management enables you to detect possible hazards before they become issues that cost your organization money and harm your reputation with customers and other stakeholders.

How is Operational Analytics Used in Data Teams?

Operational analytics assists data teams in becoming better strategic partners to operations teams. It helps them gain a seat at the table they deserve by empowering their business teammates rather than simply doing less of the work they despise (e.g., creating and maintaining custom integrations).

The following are the advantages of operational analytics for data teams:

• Allows data teams to spend less time on data integration and more time on high-value tasks such as co-creating and activating data models.
• Helps data teams in better promoting their team talents and establishing themselves as an essential internal partner.
• Allows data teams to provide data to consumers by granting them access to the data they require within their particular scope.
• Assists data teams in providing reliable and accurate data to everyone with minimal friction.
• Ensures data teams have complete stack monitoring of data models from beginning to end.

As a result, data team capabilities are increasingly in demand inside any company. Operational analytics guarantees that your company’s data teams are fully capable of doing high-value-added work that can assist any business operational unit.

Operational Analytics for Faster Actions in Marketing

Marketing is one of the fields in which operational analytics may be used. Operational analytics has grown in popularity in marketing since it assists marketers in optimizing their spending and marketing efforts.

Marketing teams utilize operational analytics for a variety of purposes, including:

• Optimizing Campaigns in Real-time: Marketing campaign managers should have a procedure in place that allows them to see how the campaign is performing on an hourly or daily basis. This will enable them to make changes to maximize their marketing efforts and boost conversions. If a digital ad performs well, it can be widely pushed or reused for usage in other media. If an ad isn’t working as planned, the campaign manager can identify and remove it before spending too much money on it.
• Examining Current Efforts: Marketers can measure their return on investment (ROI) for individual marketing initiatives, and then utilize that data to improve the total ROI for future campaigns.
• Segmenting Audiences: Marketers can segment their audiences based on a variety of characteristics, behavioral signals, and activities taken along the customer journey.

The best thing is that marketing teams no longer have to wait for engineering to supply the data required to begin campaigns. Instead, they may use a test & learn strategy to explore and iterate swiftly.

How Compare Club Increases Lifetime Value Using Reverse ETL

Compare Club is a comparison service provider that assists customers in making faster and more informed financial decisions. Compare Club compares plans from various providers and provides solutions tailored to each individual’s needs. 

Their business departments like sales, marketing, and support had no simple method to access information; the data team was frequently tasked with developing custom interfaces to marketing systems like Facebook Ads, Google Ads, Salesforce, and so on. On the other hand, these bespoke integrations might take up to three weeks to construct and were extremely fragile and difficult to manage.

By using reverse ETL, Compare Club was able to:

• Achieve a reduction of 9.5% in cost per inquiry by creating look-alike audiences for specific client categories.
• Increase Customer Lifetime Value (CLTV), and accomplish higher retention levels, and loyalty by becoming customer-centric.
• Reduce time to transfer data to ad networks from weeks to minutes.

Operational Analytics for Faster Actions in Sales

The sales team’s role is to increase revenue by growing their client base. To do so, they must be able to identify and prioritize prospects who are most likely to become customers. The process of converting those prospects into consumers must subsequently be optimized.

This necessitates access to data that will assist them in identifying high-value prospects, qualifying them as leads, and closing the deal.

Operational analytics assists sales teams in meeting these goals by giving real-time access to important performance indicators and analysis and visualization tools. It helps them in:

• Forecasting Sales: Using historical sales data, sales teams can forecast results based on present sales behavior.
• Sales Funnels: A funnel report may be used by sales managers to assess conversion rates between different phases of the sales pipeline.
• Customer Tracking: Customer tracking reports are used by sales teams to monitor their performance and evaluate the efficacy of their marketing strategies.
• Freemium Model Optimization: Better target clients who are more likely to subscribe to the premium plan, and tailor their sales experience.

How GoSite Increases Merchant Sales By Operationalizing Data

GoSite is a cloud-based software suite that assists small companies in staying in touch with their consumers. The GoSite team had difficulty deploying data-driven advertisements via HubSpot and maintaining a consistent brand experience for their clients.

GoSite used reverse ETL to transfer their financial data to HubSpot, which helped them boost awareness of GoSite’s financial offerings and resulted in a 42 percent increase in merchant gross processing volume.

Operational Analytics for Faster Actions in Customer Success

Operational analytics is used in customer success for the following purposes:

• To discover and develop effective practices for different client profiles and compare Key Performance Indicators (KPIs).
• Follow the consumer from the original contract until the renewal.
• Understand customers’ health using customer score systems, allowing Customer Success Managers (CSMs) to prioritize clients based on the ones that are most likely to churn and proactively interact with them before churn happens.
• Monitor and notify important events like contract renewals and upsell possibilities that CSMs must act on quickly to avoid lapses or missing out on new income opportunities with existing clients.
• Create hierarchical models to rank tickets according to account type.
• Reduce the time it takes to respond to typical support issues from days to minutes.

How Bold Penguin Reduced its Support Team’s Response Time to 30 Minutes by Operationalizing Data

Bold Penguin is an insurtech firm that provides services to commercial insurance carriers and agents. They provide a quotation platform in conjunction with a commercial insurance market to assist brokers in matching firms with “the proper quote in record speed.” They were spending too much time sending data to the data warehouse. They were also facing some more challenges:

• Maintaining custom scripts was time-consuming and diverted valuable BI resources away from other projects.
• Business users typically have to request updates to acquire the most up-to-date analytics in their preferred tool.
• Teams were held down by manually processing data, such as identifying client categories in Intercom.

Using reverse ETL, they achieved the following:

• Faster support response time for partner issues; time reduced from days to minutes.
• Allow business analysts who are not part of the BI team to build up their own data connections.
• Automated marketing segmentation to provide targeted efforts.

Faster and Easier Way to Operationalize Your Data: Reverse ETL

Reverse-ETL replicates data from your data warehouse to operational tools such as Salesforce, Mixpanel, Zendesk, Marketo, etc., that your teams use daily. Reverse ETL enables organizations to do more complicated analytics than what they can do with BI tools alone.

You may use reverse ETL tools such as Hevo Activate to sync your fundamental business metrics, such as Customer Acquisition Cost, Lifetime Value, and Health Score, to all your downstream applications, bringing data into every team’s everyday operations.

Reverse ETL is perhaps the most significant phase in a modern data stack and is required for data-driven success. A reverse ETL tool will allow your data teams to leverage operational analytics for faster actions.

Let’s look at some of the benefits of operationalizing your data through reverse ETL:

• Better Decision Making: Businesses that embrace operational analytics can make the required changes to processes and workflows in real-time or close to it, allowing them to boost profitability and minimize waste. This would also aid them in promptly detecting and responding to faults and inefficiencies.
• Enhanced Customer Experience: Businesses that use operational analytics can deliver better customer service by responding to business issues in real-time.
• Improved Productivity: Businesses can use operational analytics to simplify their operations by identifying inefficiencies in their processes and making the required changes promptly.

How to Operationalize Your Data Using Hevo Activate?

Hevo Activate syncs data from your data warehouse to your favorite business software, such as Salesforce, Marketo, Mixpanel, Mailchimp, or any other destination. The data warehouse tables may contain information imported from various sources, such as SaaS applications such as Facebook, Instagram, LinkedIn, etc., webhook sources such as SendGrid, etc.

Using Hevo Activate, you can create SQL queries to view your fragmented data in a unified way, which you can then modify for use with your CRM application. Hevo Activate offers custom data synchronization to select from a variety of frequencies and schedule an Activation or perform it in real-time. Our reverse ETL solution can automatically convert field types of the synchronized data during the mapping action. 

Using Hevo Activate, you can operationalize your data in 3 easy steps. These steps are as follows:

• Step 1: Connect data warehouse and destination application
• Step 2: Write a query to get relevant data
• Step 3: Map the output to the destination object

If you utilize Hevo pipelines to load your data from numerous sources to the destination warehouse, then with Hevo and Hevo Activate working together, you can create a bi-directional data pipeline for your data-driven organizations. 

Let us look at some of the key features offered by Hevo Activate:

• Real-Time Data Replication: With its excellent integration with numerous data sources, Hevo Activate enables you to transfer data rapidly and efficiently. This guarantees that bandwidth is used efficiently on both ends.
• Secure: Hevo Activate’s fault-tolerant design ensures that data is handled safely and consistently, with no data loss.
• On-Demand Sync: Hevo Activate allows customers to resume the sync or run sync immediately to perform data sync on demand.
• Intelligent Data Type Conversion: During the mapping action, Hevo Activate automatically converts the field types of the synced data.
• Data Transformation: Hevo Activate provides a straightforward interface for perfecting, modifying, and enriching the data you want to upload.

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Related:

• How to Improve Customer Experience with Operational Analytics?
• What is Data Activation? 4 Steps to Making Better Decisions
• How to Keep Salesforce Customer Data in Sync with Multiple Sources?
• How to Build 360 View of Customer Data for Your Sales Team?

Final Thoughts

With all the data in the warehouse, companies are trying to get relevant insights and make better decisions. To discover new opportunities, minimize attrition, and provide better customer service, go-to-market teams want actionable data they can rely on. Reverse ETL delivers that data from the warehouse to the hands of such teams for real-time data-driven decision-making.

Reverse ETL syncs data warehouse insights to downstream applications so your organization can leverage operational analytics and perform faster actions. Sending your customers’ data from the data warehouse to Salesforce, for example, will assist you in tailoring your marketing efforts, targeting your leads, and identifying trends that you would not have been able to uncover with CRM data alone.

You can use reverse ETL platforms such as Hevo Activate to operationalize your data and take real-time data-driven decisions to offer the best-in-class experience to your customers. Using Hevo is relatively easy and assists your data and business teams in extracting data from diverse sources and synchronizing insights from your Single Source of Truth (SSOT) to downstream SaaS applications. This, in turn, enhances customer experience through real-time analytics and assists you in cultivating a healthy and efficient data-driven atmosphere in your firm.