When building a data pool the first and most important question should always be asked: How can my company turn this data into profit? It’s no secret that data is a valuable asset but many companies still fall short when it comes to exploiting this huge potential. They blindly gather more and more data and just store it when constant analysis of this data will drive their business forward.
Data Science drives profitability
Now you can easily solve this problem by using Data Science as a catalyst to transform your raw data gathered from various sources into actionable profit-making insights. The resulting high-quality leads (potential customers) and business decision-making enable inbound and outbound growth, more sales and, ultimately, more revenue.
AI Technologies help by combining, comparing and evaluating data from different sources with a previously set target in mind. This input consists of internal key performance indicators, external criteria (such as weather data or traffic volume) and non-structured data such as images, audio and/or video files from social networks. The software analyzes this data and then recommends actions and creates forecasting models that can identify changes in customer behavior and market trends and can highlight potential opportunities.
Exploiting the business value of Data Science
Now we come to the key question: How can this be done? Creating value from data comes down to three key areas that, when combined, will catapult the company forward and drive an effective strategy for reaching new levels of business growth. This is where a highly intelligent tool like paretos steps in.
1. Generating customer growth
Acquiring a new customer is at least five times more expensive than retaining an existing one. Unsurprisingly, this forms the basis for data-driven customer interaction.
- Data Science can be used to refine customer profiles, making marketing campaigns much more precise and effective. By using customer behavior as an indicator, customer analytics tools can help companies identify customers who are ready to buy more and those who want to switch and use a competitor. The goal of data-driven customer analytics is to create a unified, accurate view of a company’s customer base that can be used as a basis for making decisions about attracting and retaining future customers. Also, high-value customers can be identified and proactive ways of interacting with them can be suggested.
- Studies have shown that customers who are targeted with personalized emails spend 38% more and 70% of customers who are retargeted can be persuaded to stay loyal to your company.
- Loyal customers are essential to any business and Data Science can be used to prevent churn. Reference cases from McKinsey show that in a period of between one and three years, a decrease in churn of 10% could be recorded. Companies that use churn models to identify specific negative points have the opportunity to optimize their customers’ journeys and address problems long before they lead to an increased risk of churn.
2. Increasing sales
Use cases of Data Science to increase sales typically help companies improve their customer-centric activities in the areas of pricing, cross-selling, upselling and advertising optimization.
- paretos allows companies to acquire valuable insights on which factors effect sales. For example SNOCKS, the e-commerce company, utilized AI-based price adjustments – which take into account all influences from traffic flow to climate temperatures by using a data-driven, demand-oriented approach – and was able to increase its order volume by 30% within just a few weeks.
- In marketing, AI-based Data Science tools can analyze past customer behavior and score leads, driving the company to greater sales efficiency. A potential customer leaves data on a website that can be used to gain important insights into consumer behavior and then a predictive model can be built from this information. Companies using artificial intelligence to evaluate leads have been proven to be able to make 50% more appointments and reduce calling time by 60%.
- For airlines focusing on price optimization, analysis has shown that the nearer customers can get to their desired departure date, the more they are willing to pay – even up to 50% more. This can also be applied to other industries by examining customer reactions to price changes using sentiment analysis, which can identify customers’ reactions to a specific event on social media.
- Based on more than 100 reference cases in terms of top line use cases (i.e. revenue and gross sales), McKinsey has found that data-driven insights and decisions have an impact on growth by up to 2% in the areas of assortment optimization, cross-selling and upselling, pricing, inventory optimization and shelf stocking.
3. Reducing costs
The use of Data Science has a significant impact on reducing costs. Data-driven insights used for optimizing internal processes hold immense cost-saving potential.
- Over a period of one to three years, McKinsey studied the impact of Data Science on net profit and concluded that when looking at bottom line use cases, which refer to the number below the bottom line of the income statement, potential savings in expenses are hidden, including: reduction of call center costs by 20% to 50% with predictive maintenance, up to 10% of marketing costs with higher effectiveness of expenses, up to 30% of inventory costs by improved demand planning and up to 30% of logistics costs by optimization of supply chains.
- A leading European parcel delivery company modernized its manual resource planning with the help of paretos’ automated forecast analysis. After just seven months, forecasting accuracy was increased by 10% and that resulted in significant savings in personnel and fleet costs.
- In banking, time series analytics can be used to predict when customers are likely to be late with payments, thus making it possible to prevent breaches of contract and defaults with a timely offer of support.
- Similarly, fraud prevention is an important area for the use of data-driven analytics.
4. Data Science and Decision Intelligence
The possibilities of using Data Science to create more profitable business are almost endless. The problem of how to make the transition to a smart business model based on data-driven processes and decisions is addressed by the young discipline of Decision Intelligence. This takes data science processes to a new level by enabling traditional decision-making processes to be combined with advanced technologies such as AI, machine learning and data queries in everyday language (NLQ). Because of AI-based decision intelligence platform, paretos, companies without data science knowledge are now able to perform complex data analyses that would previously have required analysts or data scientists.
So where does this leave modern companies?
The challenge of acquiring technology is the first, and most important, step for a modern company to fully exploit their potential but, as McKinsey pointed out when it published its research results (see above), these impact values stated can only be achieved if the company is willing to invest and manage change. This includes breaking down silos in the company, removing data and interpretation ownership from IT departments and opening it up to the entire organization. All too often, data scientists are detached from corporate decision-making and the decision-makers usually lack access to analysis results and, most importantly, the knowledge to interpret them. Data-driven decision-making processes can only be effectively unleashed through collaboration of all personnel involved in the process.
Operational tips to address this challenge:
- It begins with deciding which types of information would benefit your business.
- The focus should be on the top three use cases that are easiest or fastest to implement, or those that have the most significant business impact.
- There should be no more “data science projects” – Data Science should be embedded in every project and application.
- There should be no focus on IT talent – the real changes need to happen across the entire workforce. Employees must be offered support to increase their analytics quotient (AQ) and, thus, their Data Science competence (e.g. using a decision intelligence tool).
- The transformation should be approached pragmatically and iteratively so that results can be tested quickly and the parameters can be adjusted flexibly, especially at the beginning.
Excursus: Which role does data sharing play?
It is here that we find the reasons for integrating Data Science into the enterprise. Data sharing breaks down silos and tears down barriers that stand in the way of efficient utilization of raw data. The old hierarchical thinking and the knee-jerk reaction to protect important data from access by “unauthorized” people still seems to be in place in many companies which leads to massive limitations. If different departments need the same information but cannot access them, it slows down productivity and the efficiency of the entire company. Data sharing avoids redundancy, unlocks valuable data assets, promotes transparency and increases employee collaboration and productivity – and not just within your own company! Data sharing in the B2B sector opens up entirely new possibilities to everyone.
The best-known example of this is the Global Positioning System (GPS) developed by the U.S. Government in the 1970s whose data still forms the basis of many innovative business areas today. The data sharing economy across organizational and even industry boundaries is a framework for companies to share data with partners, manufacturers, suppliers and other third parties that are part of the supply chain and business process. Taking into account privacy, regulations, competitiveness and other constraints, the B2B sharing model can help improve productivity, efficiency and decision-making within one’s own organization.