In a study involving asset managers by Accenture, an overwhelming majority of 98% agreed that data analytics is the industry’s highest priority.
Any investor would tell you that data is the industry’s most , chiefly responsible for setting one apart from the competition.
Although, it is not just the finance industry that revels in the wild success brought on by data analytics.
Pretty much every industry leverages data to drive growth.
Sales, for example, a fundamental part of any business, has been revolutionized by data analytics.
According to a study by McKinsey, the use of data led to a nearly twofold rise in sales and profit. Its use, they concluded, is undoubtedly the single biggest difference between the best and the rest.
Why is that?
Let’s find out.
What data analytics?
Knowledge is power. And that is precisely the logic at the heart of data-driven growth.
Data analytics is the combined practice of collecting, sorting, and ultimately analyzing information to gain knowledge to make better, more accurate decisions.
Businesses make decisions every day, some big and expensive, some small and inexpensive.
What data analytics offers is accuracy and scalability.
Businesses can get those decisions right, taking fewer risks and getting more bang for their buck.
And they can do it at scale.
But how does that accuracy drive sales?
How data analytics increases sales
Your sales force makes critical decisions every working hour —
- Identifying and acquiring prospects
- Personalizing sales communication
- Up-selling and cross-selling products
- Retaining customers, to name a few
The truth is, without a plan, it is hard to identify and acquire prospects, let alone do the rest.
Now, businesses can develop a plan based on instinct, often misidentifying prospects, wasting resources, and taking other costly risks.
Or they can act upon insights gleaned from sales analytics.
Sales analytics is what you get when you apply the principles of data analytics to sales.
Instead of relying on instinct, sales analytics enables businesses to make decisions based on evidence.
The evidence is obtained in the form of insights learned by collecting and analyzing sales, marketing, customer, and other relevant data.
It should now be clearer how data analytics increases sales and profit by twofold.
Identifying and acquiring prospects: Sales analytics uses customer data collected from past transactions, marketing engagement, website or app tracking, and other methods to identify customer needs and pain points. The insights also inform product development.
Personalizing sales communication: Customer and sales data also allows your sales force to understand your customers in greater depth, and if done thoroughly, at the individual scale. As a result, they can create communication unique to a demographic, making it more personal and meaningful.
Up-selling and cross-selling products: When businesses analyze sales data, they look for patterns or correlations between two or more products with the goal of anticipating future needs. Hence, instead of bundling together unrelated products, data-driven up/cross-selling is a win-win for both: businesses drive sales while customers are offered products that genuinely complement each other. That is value for all.
Retaining customers: And that brings us to retention or customer loyalty. When customers’ needs are heard, communication and service are personalized, and they are offered just what they need, customers are more likely to be loyal than when they are treated just like anyone else.
Data analytics offers an incredible competitive advantage.
And that advantage leads to more sales, profit, and customers, which produces more data, which further extends your advantage, which further expands your data set, and so on.
Data analytics turns your business into a flywheel. It turns it into an avalanche.
The challenges to data analytics
The Accenture study also reported that nearly 75% of the participants believed that data-related skills are in most demand.
A lack of data skills is a symptom of data illiteracy, arguably the biggest challenge to adopting data analytics solutions.
While data is available wherever we look, only a few can meaning from it.
That is because, well, data analysis is awfully hard. It demands expertise in statistical analysis, modeling, and, most often, coding, among other advanced disciplines.
As is with any knowledge-based profession, businesses ought to invest more in knowledge than in means.
In this case, businesses should invest more in talented analysts than in cutting-edge technologies that enable analysis.
Because while data analytics undoubtedly has the potential to drive sales, its insights are only as good as the analyst who procures them.
What good can one make of data analytics if one doesn’t understand the data itself?