The thing with analytics is that - it’s not about how you gather data, it’s more about the structure, the tools you use for analysis, and the problems you look to solve.
Delivering the right insights to the right people at the right time in a way that informs their decision-making to drive better business outcomes is what data analytics is about.
The time for simple experimentation with companies is over. And most businesses already understand this.
Despite this, businesses are not able to capture the real value of data.
The reason: while they’re eking out small gains from a few use cases, they’re failing to embed analytics into all areas of the organization.
Nevertheless, getting to that point is not easy.
Why do businesses need analytical data?
Data analyses aggregate customer interactions and extensive customer information across brands and channels, enabling the company’s analytics teams to target offers to customers at a microsegment level.
Let’s take marketing, for example, businesses can deliver personalized content through their website, emails, and digital ads.
Advanced analytics can transform performance, boost revenue, increase profits, and improve consumer satisfaction and retention.
However, adopting analytics across all lines of business and functions requires a clear, coordinated strategy and focused investment.
#The Right Strategy
You may have set your business goals for 2021, but have you considered the tools and resources required to achieve scalable growth?
You can accelerate your growth and chart a course for long-term success by making good use of data.
Internal processes are the most common factor in businesses' inability to scale successfully. Whether it's faulty internal processes or a lack of employees, data can help you identify practices that are preventing you from meeting your growth objectives.
Data is essential for gaining insights about your customers — what they think, like, and want from your products or services.
To get the most out of the analytics investments, organizations must integrate analytics into the company's critical strategic areas, such as customer experience.
For example, the Starbucks mobile app utilizes reinforcement learning technology to provide personalized recommendations to app users.
These recommendations are based on a wide range of data points, including local store inventory, previous orders, current popular selections, and even weather or time of day.
With 18.9 million active members, this data-driven resource now accounts for 17% of Starbucks' sales.
#Cut out the fluff
The issue for many entrepreneurs is that they believe they must increase spending in order to increase revenue. Unfortunately, this eventually results in profits remaining stagnant — or even declining.
A deep dive into your data can help you identify wasted expenses or ways to increase profit without requiring a large financial investment.
Cutting wasteful spending will significantly improve your company's cash flow as it grows. This will ensure your company's financial stability during this critical transition.
#Innovation is critical
While culture and data quality are important in developing a solid data strategy, platform innovation is critical in ensuring that strategy's long-term viability.
Vodafone is a prime example of a company that is capitalizing on platform innovation.
Accenture assisted the company in developing Intelligent Care, a solution that uses analytics to direct customers to the best channel for their specific needs.
Improving Vodafone's ability to predict why customers contacted them allowed them to be more proactive, saving the customer from having to make the call in the first place.
Inbound calls were down 1.5 million less than a year after its launch, while digital channel use increased by 26 percent.
Turn insights into outcomes
Organizations must make analytics extremely user-friendly and customized for making decisions. This requires a combination of the right technical tools (for example, API-enabled middleware) and support tools such as intuitive dashboards, recommendation engines, and mobile apps.
Companies must incorporate analytics-based decision-making into their corporate culture, fostering an environment in which employees view analytics as a necessary tool that challenges conventional thinking and augments their judgment.
The massive amount of data gathered from its more than 58 million subscribers creates Netflix's recommendation algorithm, which has proven to be quite successful in predicting what people will watch.
This gives the company an advantage over competitors such as Disney and other streaming services such as Hulu.
When Amazon makes purchasing recommendations, it appears that it knows you better than you know yourself.
Those personalized messages are the result of data analytics, which tells the retailing giant when you make purchases, how you rate them, and what other customers with similar purchasing habits are buying.
With over 6,000 properties in 122 countries, the hotel operator believes that data analytics is the key to remaining competitive.
It enables the company to identify specific "types" of customers, such as those interested in fitness and wellness or those who enjoy immersing themselves in a foreign culture and then promote the properties and amenities that match their interests.
The company collects data to increase current consumption and upsell new products, resulting in a more efficient operation that reduces costs and increases profits.
As customers share their thoughts on the product via social media, phone, or email, the company is able to adjust its approach and better align with consumer interests and demands.
Most businesses begin their analytics journey with data; they assess what they have and determine where it can be applied. That approach, by definition, will limit the impact of analytics. Companies should work in the opposite direction to achieve analytics at scale.
They should begin by identifying the decision-making processes that could be improved to generate additional value in the context of the company's business strategy and then work backward to determine what type of data insights are needed to influence these decisions and how the company can provide them.
The stakes are high, but it’s all worth it.