Data Analytics to Drive Successful Business Outcomes

Authored by: Team AKAVEIL

Data-based or data-backed decisions are the only type of informed decisions business leaders should make. Otherwise, it is just an assumption or a hunch. Data measurement is the means to quantify and track the progress made on targets and goals and by extension, the means to improve the plans to reach those set goals.

But to say data is everything is a common misconception. Data cannot single-handedly give answers that your business needs.

This is where contextual decision-making comes in. Successful businesses identify the context and intent by answering the ‘what’ and ‘why’ behind the data. This is where data analysis sits. Accomplishing data analysis requires businesses to first collect data based on their goals and strategies.

Here are a few questions to know if your business is doing it right:

• Does your team have the competencies to analyze, interpret and understand its meaning?
• Is your business benefiting from the information currently being captured?
• Is the information overwhelming?

The fundamental question here is if your business is leveraging the data collected to create real outcomes and impact.

In this article, you’ll understand how data analytics can amplify your business and the different ways to collect enriching data.

Analytics and outcomes

Businesses that use big data saw a 10% decrease in overall costs. With data analytics as a central resource, you can maximize conversions, reduce additional business costs and understand customers better. Here’s what data can achieve for you.

• Data analytics helps in making better business decisions and improving business productivity.
• Data analytics gives your business direction by dissecting micro-processes like understanding the customer’s problems and providing solutions and future insights to prevent risks.
• Data analytics helps businesses understand customer intent and gives product recommendations based on customers’ preferences.

Measuring data to drive intelligent decision-making

We’ve established that data is important. But the real question lies in identifying the right data.

We’ll illustrate this with an example.

Consider you offer a SaaS project management platform. You want to understand what leads customers to conversion and to replicate that to scale your business.

The following are some questions you want your data to answer:

1. What is your ideal customer profile?
2. Is the current signup process working?
3. Does the customer onboarding need improvement?
4. Does your product require new features?
5. What is your unique selling point?

Here are some data points to capture.

• What is the demographic of the users?
• What are the geographies of active customers?
• What features are the current users paying for?
• Will adding a new feature add enough value for free users to upgrade to the paid version?
• What are the difficulties and bottlenecks users currently face?
• How did the users hear about your offering?
• What were the problems users faced during sign-up and onboarding?

This information can then be used to improve and refine user acquisition. The strategies that can be employed to do so depend on the market, business, and resource constraints.

Collecting data purposefully

More data is not better data. Capturing hoards of user information does not always lead to better or informed decisions. Sometimes, it adds noise to the signals you are looking for. This calls for some understanding of the types of data and means of collecting it.

There are two different types of data: primary and secondary.

Primary data: Data collected by researching customer’s problems, choices and preferences at an organizational level is called primary data.

Secondary data: Using existing data generated by other organizations is called secondary data.

When the goal is to accelerate business growth, the focus is generally on generating primary data. Primary data can be further divided into two categories:

• Quantitative
• Qualitative

Quantitative data is based on numbers and statistics, meaning this data is measurable.

For example, customer ratings, the number of subscriptions, or the number of monthly active customers are all quantitative.

Qualitative data usually helps in answering the “why” behind the quantitative data and forming the foundation for the data strategy. Such data is based on detailed statements and opinions.

For example, the conversation a customer had with the customer support team, the feedback a customer gave about your product, a social media post a customer wrote about your company.

Both quantitative and qualitative data play an important role in data analytics. Data analytics circles around quantitative data as it creates the foundation of your data strategy.

Here are a few ways businesses collect quantitative data to create a data-based strategy:

Surveys

Surveys are a means to reach and understand customers directly. They can be used to gather both types of data: quantitative and qualitative.

These are usually questionnaires with targeted questions you want to know from the customers. They can be distributed through a page on the website or third-party tools like Google Forms, Microsoft Forms or Survey Monkey. Alternatively, surveys can also be offline by connecting with customers over email or call and asking them the listed questions.

Online tracking

Your website and application are a great resource to collect customer data. When a user visits a website page or takes some action, this information can be collected and analyzed.

• How many people visited the website in a month?
• How long did the visitor stay on the website?
• What kind of content are people reading on the website?
• What did the visitor click on the website?

By tracking the customer data on the website, you can understand customer patterns like the type of content customers consume on the website, and what triggers them to take a certain action. These patterns help in improving and strategizing your systems.

For example, when a customer withdraws from filling the information on the contact us page, it shows the shortfalls in the conversion process.

Transactional data tracking

Transactional data is the revenue-based data collected when customers buy a product or service. This data can show where your business stands in terms of revenue goals.

Transactional data can be as follows:

• Sales your business made in a month
• Subscriptions bought
• Upgrades made on current plans
• Purchase frequency of users
• Revenue from recurring customers

This data can be analyzed to retarget lost users and upsell and cross-sell to existing customers. Transactional data can also be a good indicator to know if the strategies are working.

Social media monitoring