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Using Data Analytics for Strategic Decision Making

The current global business landscape has matured into one where a lot of split-second decisions have to be made – and they have to be right. These decisions culminate into the bigger picture of where the brand goes too, underlining their importance even further.

One such decision can be left to gut thinking. Maybe two, or three in extremely lucky cases. Beyond that, though, guesswork and feelings alone will not cut it.

This is where quality data-driven decision making takes over.

Fortune 500 companies and the best performers on the S&P Index are always collecting one data or the other. If this is common to them all, it means that the data is playing a very crucial role in keeping them where they are today. Such thinking alone, though, is where many other businesses get it wrong. While it is one thing to have the data, it is yet another thing to use the data right.

How to Win with Data

The past decade alone saw a lot of changes that affected how business is done globally. Some of the notable ones that come to mind include changes in how consumer data can be collected and used, improvement in consumer experience, and consumer demand for transparency.

The rise of digital also pushed a lot of naturally traditional companies to make the move to go online. Given, not all companies succeeded at that, but that only showed why they should have been better prepared in the first place. We also detailed how some of the top companies succeeding in digital transformation today failed initially. Within the past 150 days alone, the coronavirus pandemic has shaped the business space in new ways. Stringent measures towards flattening the curve have led to lockdown initiatives and a lot of changes in how business goals are met.

What this shows us is that the next hour is not promised in business. What is promised is now – and the data that you have on hand to shape the future.

Choosing to live in the present alone has not done well for companies in the past. Here, we have the likes of Nokia and Blackberry failing to jump on the Android and/ or touchscreen train early enough and getting phased in the aftermath. We are sure that the data was there. The numbers were also there to have informed the right move. But what went wrong?

Below, we share insights into how the top players leverage data analytics for the best possible outcomes.

1 Collect Relevant Data

Executives need to have the right kind of data to make relevant decisions. This makes the collection of relevant data the most important part of the data analytics process.

A common mistake to avoid here is to collect a kind of data because every other person is doing so. While businesses might share similarities, they will often have core concepts that make them stand apart from the competition. Once your core message/ product is determined, a better path to what data to collect is suddenly illuminated.

Your data needs could be an internal product survey, external consumer review, market analysis, etc. Know what you want – then reach out to us to help you model the proper data collection system for improved success.

2 Eliminate Bias

The financial markets are an interesting place. Via the use of charts, candlesticks, and analyzing trends, savvy market traders can see where the price of a commodity could be heading.

However, there are two main kinds of biases in the market – bearish and bullish.

Logic should play a huge role in making data-based decisions. Unfortunately, most of the ‘logical’ work that we do is subconscious, making it difficult to identify bias in them when it does happen. This is why some traders will keep seeing a possibility for bearish/ bullish reversal even when the opposite is what is in front of them.

Quite frankly, bias remains the single biggest obstacle in the way of a decision-maker. Awareness of this bias is a recognizable first step to beating it. Furthermore, collaborations are important, helping decision-makers to check bias in others while they do the same in themselves.

With such systems in place, it is easier to identify the data and trends for what they are – not what we want them to be.

3 Identify Consumer Patterns

At the height of its powers, Yahoo seemed to be unstoppable. No one even thought Google could come close – neither in the search engine game nor in the emailing game. So huge was the gulf between both companies that Yahoo tried to acquire Google twice.

So, what changed?

Google was building a system to last in the long run. They saw what the consumers wanted in a search engine. This led to the search engine that not only checked the title of books and webpages for them but crawled all the content for relevance too.

Google was also building an infrastructure that will ultimately allow consumers to enjoy more features from it in the future. Yahoo was content with adding everything at once rather than waiting to see the consumer trends to emerge later.

That is where they lost: on consumer trends. Today, Google’s market valuation is over $1 trillion. In contrast, Yahoo hit its highest market valuation in the year 2000 at $125 billion. You can make this work for you also.

A technology-based business can check where its customers are buying a lot of its products from. Some buyers prefer to be able to walk in-store to check out what a piece can do before they put money on it. Thus, no amount of money spent on online walkthroughs will mean much. Identifying such a trend will help marketers to allocate their spending budgets better.

4 Boosting Performance

Data analytics can help to identify the preferences of the consumers and better cater to that. While such moves are made externally, data should also be leveraged to boost internal performance and productivity.

The HR department should not only be tasked with recruiting the staff but keeping track of relevant staff data and performance metrics too. This enables the HR department to monitor which crop of staff is better suited to management roles, which skills are common in top players for different departments and more.

In a world where every company is jostling to hire the best talents, such data analytics will help to identity the ideal candidate – and not only on paper. Managers also benefit from using data to understand how better to allocate costs and other resources to different projects. The same data trend can be extended to the measurement of key performance indices, understanding what processes need to be maintained, discarded, or improved upon.

Speaking of cost savings, the top companies are utilizing top-notch data analytics to save costs on the supply chain. The importance of the supply chain is in how it could work positively or impact the company negatively too. When negative, such a supply chain could wipe out all the profits that companies are making on their products.

For example, companies like Microsoft have already shut down ALL of its official retail stores. The company will instead focus on its digital channels to get products out to the consumers.

The coronavirus pandemic has given them a chance to evaluate how well they can thrive on the digital model only, and the data shows some tremendous results. The better news about this is that staff will not be laid off. They will, instead, be trained to serve consumers in other capacities.

5 Advanced Risk Management

Financial institutions are a force to be reckoned with regards to predicting and managing risk effectively with data analytics.

Leveraging credit card scores to determine if a person would pay back their loans faster, or not default on a mortgage, is fast becoming old school. It works, no doubt, but it is no longer enough to make a valuable decision. Drawing data from other sources such as government databases and loyalty customer card information, they can make a better decision regards the consumers.

The success of that model informs decision-makers of why they should never rely on a single data source. There are a lot of datasets available to the average executive, but there is the tendency to favor one over the other.

That would not be an issue if a single dashboard can be built to aggregate the data from different sources into the same file. This way, the relevant information is presented neatly, allowing for inferences to be drawn from different pointers in the same breath.

With such data, precisions can be made based on what works and what will fail. The failing aspects can easily be tweaked and plugged for a better chance at success. Even if such an approach fails later, it would be a part of a learning process.

That is much better than going back to make the same mistake that others have made on your behalf.

6 Expanding Numbers Scope

Proper data collection shows you not only what might be lacking, but what works also. Sadly, many companies will only focus on bettering the things that do not work instead of also improving on the ones that work. You have struck gold already, so why not keep digging?

Your data might show you how your content marketing brought in 75% more leads, but only reached 20% of your target population. This does not mean to improve only on reaching more of your target population. It also means looking at the strategies which have caused the 75% boost so that they can be applied to other forms of content.

Ignoring this approach is why a lot of businesses keep going the trial-and-error way when they should be successful already.

A common mistake to avoid here is to want to find a one-size-fits-all model. The same strategy that boosts your lead generation might not necessarily translate into reaching more of your desired demographic too. To make things work, isolate these goals, and work on them differently. That way, you can start winning on two sides.

In the end, both wins tie into the common goal of pushing the brand forward.

7 Map Out Consequences

In discussing the importance of data, it is easier to forget that every decision comes with a consequence to it too.

Airbnb decided to stop all marketing activities for the year due to the coronavirus pandemic. This will help save the travel company a massive $800 million, but at what cost?

When the economy picks up again, they will have to understand the market anew, launch a series of tests to know what works and… basically, start from scratch again. That is not to mention the marketing talent that they will have to lay off. We do not need to reinforce how important it is to hire the right talent these days.

Microsoft is also having to deal with the consequences of its decisions. Brands like Apple make a lot of added revenue by having experience stores all around where people can come in to fawn over the products in real-time. Microsoft will lose that kind of closeness with the market.

But then, what is the bigger goal? Is it worth it?

Blackberry saw many people flocking to the Android and iOS devices. Given the form factor and functionality of the latter devices, the sweeping change in market sentiments is not too surprising. However, the Canadian company chose to ignore that data and went with their physical keyboard-totting, fairly-limited devices. By the time they saw the consequences of that decision was too high and switched to making Android OS-supported devices too, it was too late for them.

In all of these cases, one thing is evident: never make a data-driven decision without first identifying the consequences. If they seem too grave, that might be the sign to opt-out.

8 Revisit Data Frequently

The consistent winners do not check the datasheet once and never look back. Executives and decision-makers need to be in constant contact with their data sources to keep making better decisions in the long run.

In line with that, success/ failure reporting is also very important. For every data-based decision, track the level of success or failure from the implementation. See what went wrong – either with the data collection process itself, how the results were reported, its implementation, etc. That will inform the logical base of how data is seen and used for the good of the company.

How Well Is Your Company Using Data?

Are you collecting data right? If so, how well are you using such data?

Poor results from data-based decisions could be an underlying problem from executives or the data collection process itself. Reach out to us today to get valuable insights on how to improve your percentage success with data sourcing and implementation to move your company forward.

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