Why Business Data is the Key to Unlocking Growth in 2022

An explanation of why business data is essential for unlocking growth.

By Kelly Kirwan

What do you buy to prepare for a hurricane? Back in 2004, Walmart dug into its sales data to find out. They found that people in Florida bought more strawberry Pop-Tarts and beer than usual right before a storm.

It may sound strange, but thanks to Walmart’s willingness to discover the facts, consumers found what they wanted on the retailer’s shelves. And Walmart avoided a supply shortage and made profits. 

That’s a prime example of why business data is important for growth – it reveals your blind spots and gives you a factual, objective basis for making business decisions. With the past few years marked by volatility, data analysis gives business leaders much-needed clarity and confidence, by revealing both small details and big-picture trends. 

What is business data?

Business data is any information generated and recorded by a business in the running of its operations. All business processes in a company generate data, whether they’re focused internally (e.g., admin, finance), externally (e.g., procurement, customer service), or both (e.g., HR, legal). Records of organizational structure, production output, payroll, ad campaigns, customer complaints, and expense claims are all examples of business data. 

Companies capture such information digitally by inputting data manually into a spreadsheet/CRM or automating the data ingestion process using tools like customer data platforms and data integration software.

Why is data so important for businesses?

Businesses need data to:

  • Identify trends based on historical performance. When you examine past months, quarters, or years, you discover overarching trends, which can help you set metrics and a benchmark for future performance. 

  • Examine the effect of events and actions on business outcomes. You do this by running regression analysis on past events and by observing the outcomes of A/B tests. 

  • Gauge current states. Real-time data shows you the present conditions of your business, such as the productivity of machines in your factory, the health of your finances, and customer response to an ongoing promotion. 

  • Investigate problems. Data can pinpoint the possible causes of a myriad of problems, such as production delays, increased customer complaints, and high employee turnover.

  • Predict outcomes. All the analyses mentioned above help you form intelligent estimates or projections of the future. Your predictions can be granular, such as when a certain machine is likely to break down, or broad, such as revenue projections for the year.

4 types of business data

There are as many types of business data as there are types of business processes. Let’s look closely at how data contributes to business growth by homing in on four examples.

Transactional data

Transactional data tells you about the purchases your customers made: when they took place, in which physical store or digital channel, how much they cost, and what items or services were bought. You obtain transactional data from point-of-sale (POS) systems, receipts, online payment platforms, e-commerce sites, and invoicing software.

When you analyze transactional data, you discover:

  • Trends in customer demand — so you can prepare for peak periods and predict what types of products your buyers will want next.

  • What items are often bought together — so you can grow basket sizes by recommending related items and improve margins by strategically pricing complementary products.

  • Differences in the performance of various channels (e.g., brick-and-mortar store, branded online shop, e-commerce platform, shoppable social media) — so you can adjust your inventory and promotional strategies based on each channel’s sales trends.

Product usage & event data

When we talk about product usage and event data, we mean the usage of apps, websites, or platforms. This type of data tells you what people do when they use your product, how frequently and how long they do so, and what features they use most and least often. Data sources include software usage logs, product analytics platforms, customer data platforms (CDPs), heatmaps, and session recording software.

When you analyze product usage and event data, you discover:

  • Bugs and poor user experience — so you can identify potential causes of churn, fix the problems, and improve customer retention.

  • The ‘aha moment’ in the user journey — so you can refine the usage and onboarding experience to drive such moments, thus improving stickiness and boosting free-trial conversions.

  • Which users are most engaged — so you can spot cohorts of users with potentially high customer lifetime value and allot marketing resources to winning them over.

First-party customer data

First-party customer data refers to information you collect from your customers with their consent. It’s personal data that customers give you – such as their names and contact information – as well as data on their actions on your website, app, and communication channels. You get such data from site and app analytics tools, forms, user profiles, surveys, and the messages your customers send you.

When you analyze first-party customer data, you discover:

  • When and how customers interact with your business — so you can illustrate the customer journey and create personalized customer engagement campaigns based on those journeys.

  • Similarities among groups of customers — so you can identify and define customer segments, thus helping you plan highly targeted marketing campaigns.

  • How different customers respond to your marketing campaigns — so you can keep using the channels, messaging, and timing that work for each customer and stop the activities that don’t reap results.

First-party customer data is crucial for gaining unique insights and complying with privacy regulations. Learn more about first-party data in our ebook, Cookies, compliance, and customer data: Navigating the future of data privacy.

Supply chain & vendor data

Supply chain and vendor data tell you where your products are in the supply chain, what conditions they are in, and who is responsible for them at any given moment. You get this data from IoT sensors, location-tracking devices, barcode or QR code scans, customs logs, shipping documents, forms, logistical permits, and vendor CRMs or databases.

When you analyze supply chain and vendor data, you discover:

  • Details of your products’ journey from manufacture to delivery — so you gain historical and real-time visibility into your supply chain and can keep track of your goods.

  • Where bottlenecks happen — so you can identify steps in the supply chain that you can optimize, automate, or change.

  • Logistical and supply trends — so you can predict delays and disruptions.

How businesses can get the most out of their data with a CDP

Analysts talk about the difference between data and information: one is a collection of facts, and the other is organized and comes with meaningful context. To transform data into information that will support business growth, you first need to clean, centralize, and structure it. One of the best ways to do so is by using a customer data platform (CDP) like Segment.

A CDP brings together customer data from multiple sources in one central database. There, the data is cleaned and validated. Using identity resolution software, a CDP then unifies the disparate data into profiles that tell you about the customer’s journey across your different business channels. Marketers use these profiles to identify customer segments and personalize campaigns. And as we found in our survey of 3,500 businesses, personalization unlocks growth – it drives repeat purchases, encourages larger spend, and boosts customer loyalty.

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Frequently asked questions

You collect data through online activity logs and tracking (with users’ consent and within privacy limits), observation, and first-party sourcing (surveys, forms, direct messages).

Transactional, supply chain, and marketing data are different types of business data.

Businesses use data to identify trends, examine cause and effect, gauge current states, investigate problems, and predict outcomes. Data analysts use business analytics software, customer data platforms, and analytics tools powered by artificial intelligence to organize and analyze datasets.

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