5 Ways Data Analytics Helps Your Business Grow
As big data goes on proliferating at full tilt, analytics is the linchpin that holds your business together.
However, there is no shortage of organizations that still refuse to adopt it. They hesitate as to why they have to go through all the hassle if their legacy software could manage to store and visualize their critical information. Locked in the spreadsheet world, these laggards make poor use, or worse, waste their valuable data as a consequence.
In this article, we will look at some of the typical use cases of data analytics and how it can encourage your growth.
Businesses are now able to tap into an unprecedented wealth of customer data.
It has become a fact of life that there would always be someone behind the screen spying everything we do online and capitalizing on us. The thing is. Millennials and Gen Z - the target customers of most businesses nowadays - do not seem to concern themselves over this unpleasant fact, as stat after stat has shown. These younger cohorts are willing to give out their information as long as their needs are satisfied, their experience is improved and their privacy is not violated.
Sounds like now are the golden days for marketers. Except there is a trade-off.
Customer expectations have become all-time heightening. According to a study by Salesforce¹, chances are your customers expect you to forecast what they would need even well before the needs are conceptualized.
So how could one anticipate these extreme demands, win customers, and get a leg up on the competition? The answer is to employ advanced data analytics services to make the most out of your customer data.
The use cases of data analytics are beyond enabling businesses to effectively collect information about their customers and portray these customers’ profiles. Leading organizations have taken steps forward and adopted predictive analytics.
When implemented properly, this approach helps to translate relevant data into reliable patterns that let you forecast future trends with confidence. In other words, you can discover what momentum shapes or alters your customer’s behaviors. Based on these insights, you could predict future demands more accurately.
Being able to foresee the once-unforeseen, you have grounds to make tweaks to your marketing messages or make improvements to the existing products and services. Or, well, develop a brand-new product that holds promise to satisfy a need that may loom in the future. All those hard works are to map out a strategy that allows you to promptly fulfill the ever-changing customer’s expectations.
What’s more? Customers are not just demanding, they are also very distinct from each other in terms of preferences.
With vast amounts of data acquired from multiple channels, both on and offline, you can segment your customers into different clusters based on preferences, purchasing habits, demographics, and such. Having these insights at your fingertips, you can better appeal to the customers by creating promotion messages that truly resonate and making products that truly meet their needs.
This brings us to personalization in customer experience.
While focusing on a group of like-minded customers has become a worn-out strategy that almost every organization uses, the ability to scale the segmentation down to the individual level is what gives you an upper hand against your competitors. Investing in advanced analytics, you can design a personalized customer journey that is tailored specifically to the personal needs and preferences of a given customer. Some prime examples are Netflix and Amazon.
Enhancing sales strategies
Unless you engage in analytics simply for analytics’ sake and let the valuable insights slip by, your sales team should be the one that reaps the most from this practice.
On the surface, data analytics helps to thoroughly evaluate your prospective clients and existing accounts. Yet it can do much more than that. The ultimate goal that you should aim for is getting a more granular, more nuanced view of your customers and the whole sales process.
With customer information being continuously gathered and analyzed, data analytics can explain why your customers choose you rather than a multitude of firms out there, which sales approach works and which does not, or how likely a given customer would churn out and what to do to prevent that from happening.
Building your sales strategies around fact-based data rather than resorting to conventional sales wisdom, it’s like you are put on a vantage ground, where you could have the deepest understanding of your prospects.
Because when a customer reaches out to you for a product or service, more than usual, they do not even know what their problems truly are. If you just give them what they ask for, not only do you fail to alleviate the customer’s pain points but you also blow many other sales opportunities.
Therefore, you need to start by gathering as much data about the customer as possible. What are they? Why do they ask for help? What do they truly need? What keeps them awake at night? So on and so forth.
From this starting point, using analytics to get a 360-degree view of the customer so that you can perfectly understand their needs and get them out of predicaments they are stuck in.
But it does not stop at merely giving a solution for the plights that the customer shows you. This is where data analytics opens up new up-selling and cross-selling opportunities.
Getting a handle on such insights, your next job is to probe the other underlying pain points that the customer fails to notice and suggest complementary products and services that can help them overcome these pains. This also is a great tactic to increase customer loyalty and retention simply because the more products or services a customer uses, the more they become dependent on your business.
Making better hiring decisions
And so the cliche goes - “employees are your greatest asset”, yet it may very well be true. Your people are those contributing to the lion’s share of your business’ gains (or downfall).
A survey conducted by McKinsey² suggested that attracting and retaining are cited as one of the most daunting challenges facing top executives today. Therefore, having a clear strategy for attracting and, better still, retaining talents should be a checkbox on your agenda.
Talent analytics (HR analytics or people analytics, whatever you would like to call it) refers to the use of data analytics in assessing people’s performance and potential in the workplace context. Talent analytics is far from a new term as forward-looking organizations have adopted it for years now, and seen it as a multi-purpose HR tool.
Recruiting based on data, without a doubt, leads to better hiring decisions. We, humans, are swayed by all kinds of biases - such as the halo effect or horn effect, and data analytics can help avoid or eliminate altogether these cognitive errors.
Another research brief by HBR³ showed that a simple algorithm may beat humans in 3 out of 4 cases. This holds true for any positions, including middle management and C-Suite. A disheartening fact signals that it is high time to switch to data analytics for better hiring decisions.
So, the next time, rather than going into an interview without preparing anything and leaving the candidate’s fate at the mercy of your gut feelings, here is what you should do: you need to accumulate everything you can about the candidate and let data analytics do its job, based on that, you can sap the influence that biases have on your judgments.
Nevertheless, we should not make light of human judgment because, at the end of the day, it is we who would work with the candidate, not the machine. A well-thought-out recruitment strategy embodying both the validity of hard data and the wonder of human intuition would work the best.
Upgrading financial management
Back then, Finance had to wrestle hard with the undersupply of inputs from other functions. To make matters worse, they also lacked the tools to collect and make use of these inputs. Due to these functional “silos”, Finance periodically produced an output - be it the financial plan or budget, that was barely relevant or aligned with the strategic aim of each function and of the organization at large. Hence, they were unable to adequately reflect the organization’s financial performance.
It is safe to say that the efficacy of Finance depends solely on how well businesses manipulate data. For this reason, poor data quality and poor use of data will inevitably handicap the planning of financial activities and the utilization of funds. At its extreme, this shortcoming could obscure the vision of the business.
One key benefit of analytics is in enabling every entity in the organization to speak the same data language and in creating a single version of the truth, which paves the way for data democratization.
If you could achieve this high level of data integrity and transparency, it would mean a transformation for your Finance function. Thanks to the sufficient aggregation and analysis of data, they would be able to synthesize a vast trove of financial data into a cohesive source, taking into account both discrete goals of each function and broad strategies of the whole organization.
Accordingly, they have the basis to produce an account of financial performance that is truly insightful. This means that they can crystallize the financial status, financial health, and financial standing of the organization. And identify market gaps by benchmarking these criteria against the competitors.
Other than reflecting internal and external changes in real-time, your Finance can also provide excellent visibility into the whole business. Other departments would have an idea of whether they are stimulating the business' growth (even though at face value, it seems like they are generating revenues) so that resources can be optimally allocated and future actions can be guided.
From an old-age practice of tabling past figures, financial management is now seen as an approach to facilitate organization-wide operations and performance.
Manufacturing is complex in nature. A high degree of variability, volatile customer demand, and error-prone humans - these factors together add up to this complexity. To stay on top of things, manufacturers need the ability to examine every aspect of their production and supply chain.
Complexity is inevitable. Yet the goal here is to mitigate it to an acceptable, controllable level with the right strategy. This, by all means, can only be achieved through the effective use of data and advanced analytics.
Recently, Honeywell Process Solutions and KRC Research have jointly conducted a study4 to describe the impact of data on manufacturing. One of the key takeaways is that 68% of respondents viewed analytics as a must in the manufacturing process.
Success in manufacturing boils down to making informed and opportune decisions. Employing data analytics, manufacturers at all times can have access to vital information on equipment, on the workforce, and on the supply chain operations. They can also narrow their focus down to each piece of equipment. These advantages help to uncover patterns across the ecosystem of production and manufacturing, giving them the ability to identify bottlenecks and dependencies. As a result, downtime, costs, and wastes can be reduced.
Since manufacturers can monitor and track the status of each segment around the clock, they would proactively schedule preventive and predictive maintenance. This means they can forecast how likely and when a given component breaks down so that appropriate corrective measures can be taken. These sorts of maintenance are key if businesses wish to detect faults, optimize resources, enhance productivity, improve customer satisfaction and avoid costly fines.
If you want to learn more about data analytics, read the report “Driving toward analytical ubiquity with embedded analytics” to discover how data analytics evolved over time, and which strategies top companies are using to approach data analytics.