Data analysis and data science are popular terms today, but the direct advantages of using them in business are still unclear for many people. If you’re a business owner, you need to know what you stand to gain from incorporating analytics into your company.
And if you’re a data analyst, you need to show exactly how you bring value to the table — it could make the difference between a good career, and a great one.
So, what are the tangible benefits of data analytics? Here are 9 examples.
1. Seeing Company Performance in Real-Time, Not Just on an Accrual Basis
Company leaders are used to seeing company finances on an accrual basis — that is, waiting for accountants to close each month’s books and display data on the income statement and balance sheet.
There are two disadvantages to this setup:
1) decision makers have to wait until the end of the month to see how the company is performing, and 2) they only see sales and cost figures for transactions that have been delivered, or fulfilled.
In other words, the accrual basis means decision makers miss out on valuable transactional information, otherwise known as deferred accounts. Deferred transactions are simply ones where money is exchanged but the service or product is not yet delivered.
For example, you may have been paid for a huge shipment of watches, but until the watches are actually sent, you won’t see them as part of revenue.
Most companies have a semi-effective solution for this: the cash flow statement. While a cash flow statement neutralizes the impact of deferred and accrued transactions by only showing the cash that comes into and leaves the bank account, it does not show this information in the profit & loss format, which is how decision makers interpret performance and profitability.
But data scientists and their databases are much more flexible than company accounts. All digital transactions are entered into a data warehouse on a real-time basis. Thus, one advantage of having data scientists in your company is that they can show you a profit and loss statement at any given time, and on a transactional basis. This will enable you to make decisions quickly and in an informed manner.
One drawback, however, is that if the company does not have digital sales (for any number of reasons, such as clients are stuck in their paper-based systems), then transactions must be entered by hand.
This manual work is not much faster than the accrual basis, and it isn’t a long-term solution. The good news most companies are already making the switch to digital, which means you should keep in mind…
2. Uncover Specific Details about Customer Product Satisfaction
Companies face the fundamental challenge of making products that their customers need and want. As technology and competition evolve, so too do the needs and wants of market segments.
This continuous progress means business leaders need to constantly ensure their customers are happy with the product.
And understanding customer satisfaction is not as simple as tracking revenues, since customers may have previously purchased from you simply because there was no alternative. Once a competitor comes along, you could end up watching your customers walk away, with little way of knowing why they were unhappy.
Data analytics can actively determine how happy your customers are using statistical models and surveys. There’s no single way to approach collecting this information; instead, analysts have come up with a multitude of approaches, including retention analysis, customer satisfaction analysis, and cohort analysis.
(By the way, if you’re interested in this concept, the simplest notion behind all of these analyses is called clustering. You can learn more about data clusters here.)
In short, companies can ensure their longevity and relevance by incorporating…
3. Identify Which Activities Should Have the Full Attention of Company Leadership
Companies, especially young ones, struggle to prioritize goals. Should they prioritize customer acquisition up front, or cash flow? Should they favor product iterations, or stick with the original product concept?
While many executives believe they understand company priorities, countless examples show that ignorance of the market and new technologies, as well as getting comfortable with internal stability, can lead to failure.
In other words, executives need help understanding what the most important issues are, and they need this on a real-time basis. Data analytics, when executed correctly, monitors both internal processes and external risks.
Examples of internal monitoring could be transactional financial data, manufacturing output, or departmental productivity. External risks, on the other hand, are slightly more complex.
The field of data analytics that takes care of external activities is called market intelligence. It uses macroeconomic indicators such as disposable income or GDP to project general economic demand, as well as competitor analysis to investigate upcoming technologies and competitors that could steal market share from the company.
Using both internal and external monitoring, data analytics helps company leaders decipher what’s most important to the company.
Companies that use data analytics benefit from…
4. Pinpoint Customers With Peculiar Promotional Campaign Sensitivities
Promotions are based on trail-and-error. In fact, there are not many ways to judge the efficacy of a single promotion in advance — you just have to do it. The first step in a company promotional strategy is to test different types to see which works best.
But companies that don’t emphasize data analytics usually make the mistake of testing without tracking. The real value of testing efforts lies in data collection. When you track the purchasing habits of individual customers in response to promotions, it allows you to develop a database, and thereafter perform cluster analysis.
Cluster analysis allows you to identify customer segments that prefer, for example, discounts, or BOGO offers, as well as product-based promotions. Once you know who wants what in these clusters, you can re-target them and upsell your products.
For example, a wine company may find that some of its customers prefer promotions on mid-tier wines, whereas a smaller portion have a tendency to buy top shelf wine in bulk when there’s a good price. With the leverage effect, you can end up making a lot of money.
Companies that collect promotional data benefit from…
5. Identify Which Employees Are Most Likely to Get Promoted, & Which Deserve It
When a company reaches a certain size, its employees become its largest expense. That’s because retaining talent is key to generating cash. This is why companies take a strong interest in understanding who their highest performers are and how to push them up the ladder.
Unfortunately, many great employees don’t get promoted, and they watch under-qualified colleagues pass them in the hierarchy. There are many explanations for why this happens, but the obvious factor is human bias.
Data analytics can not only predict which employees will get promoted, but also provide data-based performance metrics to help companies make impartial promotional decisions.
You can think of the technology behind the predictive as a decision tree: If one condition is met, then two others are possible; when one of those two are met, then only one result is possible.
In other words, it’s probability analysis. A very common and effective version of probability analysis in companies is called Naïve Bayes, which we discuss in multiple articles under free data content at AnalystAnswers.com.
Moreover, HR departments in many companies are already implementing performance tracking technologies. For example, a sales department might track sales rep conversion rates, total income, and return on investment of salary.
A development department might track how quickly IT Operations teams close tickets and turn out features, and in competitive companies may track the cash-generation potential of developed features (although the latter is under intense scrutiny due to the inability to eliminate the impact of external variables on feature cash generation).
Companies can win and retain strong employees by using…
6. Exploit the Seasonal Evolution of Supplier Costs
Many companies establish long-standing relationships with suppliers that they can trust, but is this really the best way to minimize costs?
Depending on the industry, companies can use data analytics to take advantage of seasonal prices and incorporate a sense of real-time competition between suppliers.
In fact, this is the way real-time bidding works for online advertising. Supply and demand of relevant ad positions generate the optimal price point.
But in a world where big suppliers can only provide price estimates after investigating the unique needs of the buyer, a buyer who’s under the gun to produce for an increasing demand in his/her business has very little leverage in these negotiations — he just doesn’t have the time. That’s where data comes into play.
Data analytics can be used to track the historical price that suppliers request and set up a knowledge base outlining which periods are the least expensive. Once analysts establish this information for all possible suppliers, they discover when it’s the best time for a company to buy from one supplier versus from another based on their seasonal price changes.
Companies can improve their profitability by exploiting…
7. Optimize Cash Flow By Only Purchasing The Absolute Minimum Cost of Sales
Seasonality is just one side of the coin when it comes to suppliers, and it’s most important when you’re working with manual orders.
Once you can place requests digitally on multiple supplier platforms, you can let a price optimization technology decide on a real-time basis which suppliers make the best offer. That way you know you’re getting the best deal without having to worry about it.
In other words, good data procurement techniques can enable buyers to pit two suppliers against each other and compete for your business.
So, remember the…
8. Optimize Cash Flow by Aligning Late Payers and Demanding Suppliers
One of the biggest obstacles companies face is proper cash flow management. It’s easy to get focused on growing a business and forgetting how to keep it afloat and cash positive. For example, if you know one of your clients regularly pays you late, and you know that one of your suppliers has a very strict on-time payment policy, then you should try to avoid having these two deadlines at the same time.
This is why experienced treasury professionals develop techniques for prolonging the time required to make payments to suppliers, and minimizing the time allowed for credit sales to be paid in full.
But not all companies have treasury professionals, and in many cases they don’t need them enough to support the additional administrative salary. The good news is that with simple data analytics, they don’t need to pay for an employee to optimize cash flow by aligning payers and suppliers.
Cash flow projection of upcoming cash receipts and disbursements is a very simple and common function of data analytics. The challenge, however, is that very few legacy accounting systems help you go a step further to align late payers and demanding suppliers.
This is much easier said than done, but good data analysts will build the infrastructures to automate this for you.
Businesses that use data analytics capitalize on…
9. Show Investors Awesome Dashboards to Raise Capital
As we’ve seen in this article, a common theme with data analytics is visibility. Data provides extremely detailed information that would otherwise be inaccessible. This brings us to the last item on the list: dashboards for investors.
The more data you collect, the more you are able to communicate to your investors. And when it comes to showing the potential your company has in order to get them to give you money, information is key.
While this may seem obvious, the explosion of non-financial metrics for investors is a relatively new concept. It wasn’t until the 21st century when technology companies took the helm of the business world that data analytics and detailed communication became so important to secure financing.
So, if not for themselves, business owners should make use of data analytics to secure financing for their companies by keeping in mind…