The business sector is currently immersed in an avalanche of data. There are multiple studies, reports, analyses and blog posts that talk about the importance of data and the need to define a business strategy based on it, however, this is relatively new for most companies.
Although many companies are still trying to understand why they need to invest in data analysts and define a business strategy based on them. The number of organizations that base their business strategy on data analysis to create a competitive advantage are growing at an average of more than 30% annually and are expected to reach $1.8 trillion in profits by 2021.
Unlocking data
Unlike traditional enterprises, data-driven organizations do not grow linearly, but exponentially. To do this, we only have to observe the dramatic growth of companies like Amazon and Google, which have built their business models around the exploration and exploitation of information.
What these companies have in common is a data-centric approach that goes beyond operational excellence. This requires that you bring data and analysis to the forefront of everyday business processes, and to think beyond the silos and even the company’s own walls (literally and metaphorically) to build meaningful collaborations.
The potential of data analytics to achieve greater positive impact remains great for many companies. Approximately six out of ten companies make the most decisions based on feel and experience, rather than data and information. And the 40% of the best companies make decisions based on sensations or visceral experiences.
There isn’t a single right path to becoming a data-driven company, but there are common features and best practices shared by those who do well.
Quality information is the secret to the success of companies that manage to increase their conversion rates, increase their sales and break customer loyalty records and this is confirmed by some data:
- By 2020, the customer experience will outperform the price and product as the key differentiator of the brands.
- U.S. companies lose approximately $83 billion in sales a year due to poor customer experience caused by inefficient data management.
- The 91% of dissatisfied customers will not do business with a brand that did not meet their expectations.
- On average, loyal customers are worth about 10 times the value of their first order.
- 80% of customer data will be wasted due to immaturity in data management.
The obstacles
Once the importance of good data management has been demonstrated, what is preventing organizations from widely adopting the analyses? According to the latest surveys, the lack of knowledge to implement effective data analysis and the lack of internal skills in the line of business are the main barriers to becoming a data-driven company at 100%.
These hurdle are just a few of the ones that the figure of the data analyst can help you get around. For example, analysts can help “level” their skill set and can be more proactive in doing a good job and communicating their value to their managers and the business. They can investigate more to unravel case studies on how other organizations addressed similar business problems through analysis.
On the other hand, senior managers can promote or require greater data exchange and designate clearer ownership and management of data, such as the appointment of an analysis manager or data manager.
All this makes clear is the importance of investing resources so that employees have the necessary skills. And it is that, more than a simple calculation of numbers, a special sensitivity is needed for data science.
It is not an easy task to find people who understand the information, while understanding the functioning of the company and having a deep technological knowledge.
Due to the overabundance of data solutions, many companies today are struggling to find a good option for their specific case. Identifying and successfully implementing the most appropriate technologies remains one of the main challenges on the road to data-driven greatness.
Success stories
Decision-making after data analysis can work for an organization of any size, from multinational giants to family businesses, provided there is a commitment to the principles of the method.
Large technology companies have been pioneers and have perfected this methodology mainly because they have a unique combination of analytical minds, technical expertise and open culture that is conducive to these actions. However, it’s not just them that have a chance.
The social network by antonomasia discovered from the beginning that the democratization of access to data, that is, its widespread availability, allowed the company to be much more agile and responsive to market changes and product development.
In an example of the impact of this approach, Facebook observed how many people started using a feature through which they could ask a friend to remove them from a photo, even though they would then leave that request when they realized they had to write a message to your friend explaining why.
Facebook analysts found that if they automatically filled in a sample message, the number of users completing the request increased to 60% from 20%. That information led to the decision to turn the autocomplete message into an official part of the tool.
More traditional companies have also learned to harness the power of analytics. For example, Southwest Airlines discovered that it could use analytics to save airplane fuel.
The company also found that it could determine which airport gates were open to receive aircraft so that customers had to spend as little time waiting as possible.
Sometimes the results of the data analysis are a little less glamorous, but they still come as benefits for a business. For example, Walmart used product purchase data from areas where hurricanes had caused damage to find out what people were buying when they were supplying before a storm.
The company wanted to use predictive analytics to determine how to supply stores before future storms. They found that, in addition to staples and lanterns, stores were in high demand for unexpected items. Sales of strawberry tarts increased sevenfold as a tasty non-perishable product that requires no cooking, and beer was the best-selling product.
The U.S. retailer began shipping trucks loaded with these items to stores in areas where hurricanes were forecast and sales were on the rise.
The city of Boston, Massachusetts, has adopted data-driven management with a program called CityScore. The initiative consists of an online dashboard that shows the government’s performance in 24 key areas in relation to its objectives, such as responding to emergency calls and collecting trash.
Sensors automatically record much of the data, and city workers record information in mobile apps when they complete an activity. The system makes the problems obvious and helps ensure that the city allocates resources where they will have the greatest impact for citizens.
UPS wanted to become the best delivery service by adding value to the speed at which you receive your order. In their analysis, they found that they could achieve this goal by reducing the number of possible left turns on the driver’s delivery route.
As a result, between 2004 and 2012, they saved 100 million gallons of gas and reduced their carbon emissions by 100,000 metric tons. This also saved them 83 million euros in inactive minutes, which cost 22 million euros in labour costs each year.
It is a payroll, human resources and employee benefits company that serves mainly small businesses. They lost 25% of their customer base each year and decided to analyze and build predictive models to detect high-risk customers.
His strategy was to develop a year-end retention program aimed at customers who were more likely to leave by providing free payrolls and loyalty discounts. This reduced your customer loss rate by 7%.
This is just a small selection of companies that have adopted a data-driven business strategy to maximize profits and efficiency. Although most are large corporations, it is not a path intended only for them, but a guide to follow for companies of all sizes.
One of the reasons these companies have grown and continue to grow is because they are actively exploring how to become more efficient thanks to data.
Conclusions
For most companies, data and analytics are mostly important in driving operational excellence and fostering better customer relationships.
Innovative organizations use new technologies and ideas to go one step further and reconsider their entire strategy, or even create new and creative business models.
For example, governments can provide better services to citizens, hospitals can care for patients beyond the walls of clinics, and companies can build data-driven platforms that invite more customers and partners to create together new products and services and unlock new sources of value.
