Data-Driven Decision Making To Improve Business Outcomes

Data has become the predominant operational asset in businesses like capital, factory, and machinery.

Traditional decision-making practices have become ineffective as the business environment has become more complex and digital disruption has occurred. Executive leaders must rethink their decision-making processes to execute favorable and data-driven decisions.

Data in the right hands can change the course of business operations that can directly link to the expected outcome. For that, leaders need to find the innate value of data, use it intelligently to make informed decisions, and establish a data-driven culture in the entire organization.

91% of companies say data-driven decision-making plays a massive role in their business growth, and 57% of companies say data is the base for their business decisions. This is a positive indication that many organizations started to realize the value of data and how to use it rightly to fuel business growth.

(Source: https://bi-survey.com/business-decisions-gut-feel)

But, as per a survey conducted by Deloitte in 2019, many leaders are not confident to say they are 'data-driven,' and 67% pointed out that they are neither comfortable using or accessing data! Why?

Because data never sleeps, it populates break-neck pace since humanity creates 2.5 quintillion bytes of data daily! So it gives companies hard times collecting the correct data they want, analyzing it, and interpreting it to convert them into actionable decisions.

Rethink the Contribution of Data and Analytics in Effective Decision Making

Data-driven decision making (DDDM) or information-based decision making is a method or strategy that puts data at the core of all business decisions.

Simply put, in this decision-making, all historical data will be put together to find the trends to make decisions for the future based on what worked in the past, instead of making decisions based on gut instincts.

In simple words, "do more of what worked and less of what may or may not work."

To make effective decisions, leaders should sit together and brainstorm what needs to be done to achieve each goal, how to get the relevant data and analytics, and who is accountable for each action to achieve an outcome.

In this entire decision-making process, only data and analytics can improve the quality and effectiveness of each decision. Because being aware of the actual performance of your products and how it is perceived by customers from pre-purchase to post-purchase experience matters in reframing decisions every time to get the business to perform better in time.

Here, the notion is not to reframe the decisions with data but instead to reframe the thought process of leaders to make effective and fruitful decisions to achieve desired outcomes.

Hence, nurturing a data-driven decision-making culture is essential to sustain in today's market because traditional decision-making methods are gradually getting outdated because of failing to achieve the outcomes.

4 Key Types of Data Analytics for Effective Decision Making

Data analytics is a broad arena. There are 4 types of data analytics companies use for analytical decision making.

1. Descriptive Analytics

This analytics can answer the question - What happened? This type of analytics is the simplest form of data analytics. The rest of the analytics are built based on this analytics. Here, we pull trends from raw data and describe what happened in the past and currently happening with the company, its products, and its customers' experience.

For example, Imagine you are a chocolate brand, and when you analyze your data, you notice a hike in sales of certain chocolates. The descriptive analytics can say, "There is a boost in the sale of chocolates in February."

2. Diagnostic Analytics

Diagnostic analytics is one step forward from descriptive analytics. This type of analytics will help to figure out the root of organizational concerns. Here, we ask the logical question - Why did this happen?

This type will analyze the correlation between multiple data points and track the co-existing trends to find relationships to understand why a specific trend happened.

When considering the previous example, in diagnostic analytics, we will get to know, "The boost in sales can be seen more in milk chocolate bars. Male and female customers aged 16 - 27 mainly purchase this chocolate, and the sales boost because of Valentine's day gift giving in February".

3. Predictive Analytics

As the name suggests, predictive analytics looks at the future and predicts what trends and events will happen in the future as per previous data. This analytics mainly answers the question - What might happen in the future?

Here, analyzing the past or historical data with the current industry trend will help predict future events and trends your company has to face.

For instance, by gathering ample data about the chocolate sales of the past few years in February, you can expect the same sales of chocolate gifts in next February's valentine's season. So the next time, you can come up with more exciting offers of chocolates specifically curated for gifting to attract more customers.

Hence, predictive analytics allows your company to create strategies and decisions to work out when similar scenarios occur in the future.

4. Prescriptive Analytics

Prescriptive analytics is the most crucial analytics that can contribute to data-driven decision-making. This analytics can answer the question - What should we do next?

Prescriptive analytics considers all the possible scenarios that can happen in the future and recommends the possible and effective actionable.

For example, to boost the sales of valentine's day season, your company can introduce special gift hampers and customizable chocolates. And do an A/B test with 2 ads for gift hampers and customizable chocolates. Analyzing the result will help you understand how to capitalize on this seasonal spike in sales and what target customers prefer. If they prefer gift hampers, you can start a marketing campaign in January to stretch the spike to one month before!

5 Steps to Become a Data-Driven Decision Making Organization

"Being a data-driven organization means culturally treating data as a strategic asset and then building capabilities to put that asset to use not just for big decisions but also for everyday action on the frontline." - Ishit Vachhrajani, Amazon Web Services (AWS).

To become data-driven, begin with a plan of action that describes how you will capture essential data and interpret and analyze them to create business decisions.

There are five steps in the entire process.

1. Prioritize Your Data Initiatives

As the first step, leaders should look into their main business goals at the core while making each decision and ask themselves, 'what goals do we need to improve?'.

Always start with the most crucial goal with high priority when making decisions.

For example, assume that you want to increase the sales of your luxury bags in the USA. Here, the main goal with high priority is "generate more sales of luxury bags." But, in the research, you found that 80% of luxury bag consumers are in New York, but less than 20% are in Michigan and Florida.

Then your priority goal will be "increase sales in Michigan and Florida." After deciding on the goal, you need data to support the goals.

2. Filter the Relevant Data

After identifying the issue, find the solution by collecting 'relevant data.' For that, organizations need to figure out where they will get the data.

It can be from BI platforms, IoT sensors & equipment, unstructured text, surveillance footage, social listening tools, CRM software, security logs, customer feedback, etc.

Check if the required data are available internally. If they are unavailable, find out what data you miss, if these missing data got stuck in silos, and what is opaque in capturing those data. If no data is available internally, use 3rd-party tools as companies can take the external organizational data and publicly available data to create big-picture insights.

Going back to the previous example, you can ask your customers what made them buy your luxury bag over competitors' products. Using collected data from them, you can curate messages or ad campaigns for targeted customers in Michigan and Florida.

Also, even if your goal here is not about customer acquisition, you can dig data to know what makes Michigan and Florida customers churn. Check at which stage the customers in Michigan and Florida drop-offs from your online stores.

3. Draw Conclusions through Deep Analysis of Data

In this stage, check your historical data and gather the trends and patterns.

Considering the previous example of customer drop-offs, you may see more drop-offs happen in the checkout stage. And, using the data collected, you can make improvements such as adding more payment methods, including 'cash on delivery' and some other credit and debit card options that were previously missing there.

In the entire process, you might see that,

1) Adding these additional payment options has increased purchases in both MI and FL.

2) Listing the available payment methods on the product page improved engagement.

Here, you can conclude that adding additional payment methods is a safe bet because it will make an impact. So, your decision based on the data is a big success!

For more clarity, let's compare the non-data-driven decision you might take here. Instead of looking at the historical data, you might have tried to improve the checkout and loading speed and added more discounts on the product page, assuming that the pricing was the barrier to customer checkouts. You implement the change without valid reason and don't see any impact. Then you think checkout is not the core reason, and something else is the problem. You may amplify your ad frequencies and go further steps based on guessing.

See the difference between these 2 decisions. Now, think how your business would be if you took each decision based on data!

4. Strategy Planning

The decisions you derived through previous steps can put work in this step. Establish the objectives by identifying what needs to be done, why we need to do it, who is responsible for a specific action, and what outcome we want.

Furthermore, set clear milestones to check if we are on track. And avoid setting vague goals like 'be more productive, 'increase sales,' and unclear deadlines like 'hit the target by 2025'.

For example, a clearly defined goal will be - "John will set up Bank of America and Capital One cards on the checkout page within 3 weeks to increase 20% retention."

5. Measuring Success

Finally, this is the last step. Put it simply, check if you could achieve the outcome you needed.

Measure the outcome with the historical data you laser-focused on the first step with the outcome you achieved. This includes checking with the goals you set to know if they all are syncing up with the outcome. Check the outcome with the benchmark you previously set and ask questions such as -

What are the changes?
How did the decisions derived from data analytics impact the business?

If everything is syncing up and you succeeded, then congrats! If not, no worries, it was the decision that didn't work.

Go back to the decision you took and see if it was the correct data you focused on and the right initiative you prioritized. If you drifted on the right path, but it seems you spent a long time getting there, refine a few elements in your decisions. Also, give required training to employees if needed.

Data Driven Decision Making - Linked, Adaptable and Consistent

It is found that 65% of decisions we make nowadays are more complex because of involving more stakeholders in decision making and different choices. Also, according to a report by AdWeek, approximately half of the insights of 2020 were irrelevant. Why? Because of the unprecedented pandemic!

We don't know when the next hurricane or pandemic will hit, and those unexpected problems will force us to change the decisions we take with several steps. Sometimes, data-driven decision making is unsustainable since we can't predict these factors or calamities.

But, to reengineer the decisions to deal with uncertainty and complexity, the analytical decision making process should be Linked, Adaptable, and Consistent.

Furthermore, many business benefits like scalability, speed, and accuracy can be achieved with linked, adaptable, and consistent decision-making.

1. Linked

A strategic decision can't create and execute within a single team or department. Hence the decision should be linked and covered on all organizational levels in a network structure. The data and insights should flow across the organizations without any silos and boundaries. Only then can the decisions be flexible enough to cope with uncertainties.

2. Adaptable

Decision alternatives should be established and evaluated with context sensitivity. Because if a particular situation changes, the business should be adaptable to the changes and be able to go forward seamlessly.

3. Consistent

Companies should be responsive to grab new opportunities and bend in downfalls. Based on the situations, the companies need to make certain decisions spontaneously. Decision-making is never a process that happens once a year; instead, it is a consistent and continuous process in any business.

Conclusion

Businesses would encounter many situations which are inevitable and uncertain. Data-driven decision making gives strength to organizations to cope with these certain and uncertain scenarios. To make effective data-driven decisions, you must go through different stages with the data, refine it to insights, and make a good decision to yield the final outcome. The data analysis also comes with multiple phases as there you will understand how your brand performs in the market and how customers perceive it with data. To make the company flexible to uncertainties, those decisions should be linked, adaptable and consistent.

Data-Driven Decision Making To Improve Business Outcomes

Do you know what your customers really want?

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