31 January 2022
How to measure the value of data science in business?
Sales must generate value for the customer. Value in business means something very tangible that could be a return on investment. Investing in an analytical project increases sales or reduces costs. As a result, the company generates higher profits.
As part of our projects, we generate value in several ways:
We acquire more clients
Projects such as funnel analytics, customer behavior modeling and lead prioritization help attract more customers, thereby increasing sales.
Increase Life Time Value
Projects such as behavioral segmentation, churn forecasting, and recommendation engines increase the average amount a customer spends on purchases (for example, per transaction or over a period of time).
Projects such as optimization of inventory levels and logistics, scenario simulations are aimed at reducing costs – for example by improving the throughput from the warehouse to the distribution network.
Text mining and data visualization reduce time spent on performing routine tasks, such as handling customer requests, compiling KPIs (key indicators and performance statistics) from the entire company.
The exact meaning of each category depends on the nature of the organization.
Projects focused on customer acquisition and Life Value (LTV) increase revenue. Analyses focused on saving time and costs increase profitability, directly or indirectly, by improving efficiency.
Within each of the above areas, there are specific indicators to measure the value generated by an analytical project.
10 most popular measures of the value of analytical projects
1. Funnel analysis
Funnel analysis explores click data on web pages to identify areas with high bounce rates or low customer engagement. The solution is A / B testing and making site changes based on that.
2. Lead prioritization
Models may indicate leads to which we address our sales activities. We carry out activities for the leads indicated by the model and for the part that was not indicated. We measure the value by comparing the difference in conversion between leads according to the model’s indications and without.
3. Behavioral segmentation
Segmenting clients according to behavioral information enables personalized messages that increase the positive reaction of the client. We personalize content based on behavioral data. To measure the value, we leave the control group to which messages are directed without personalization. We measure the increase in engagement on the basis of behavioral analysis and compare it with the control group.
4. Churn predictions
By using machine learning, we predict which customers are most likely to stop using a product or a service. We send offers to encourage continued use of the service to customers who are highly prone to churn. We compare the LTV of the group indicated by the model versus the LTV of the control group (for which no marketing activities were carried out).
5. Recommendation engine
By selling products in various combinations, we analyze the data to find well-correlated packages. We display the recommended products through the website. We measure the increase in engagement (uplift) on the basis of recommendations and compare it with the results of the control group.
6. Inventory optimization
We forecast the demand in order to maintain the optimal level of inventories. Forecasts reduce storage costs, improve the efficiency of logistics processes. We test a new strategy based on forecasts for a selected group of products.
7. Logistics optimization
Routing algorithms can determine the optimal placement of shipping hubs and allocate individual shipments in the most efficient way. We test an optimized strategy on the basis of back testing in order to test the new concept. We measure the reduction of transport costs.
8. Scenario simulations
We are building a digital twin of business activity. This allows us to accurately simulate future scenarios to optimize performance and strategy. We test on historical data (back testing). We measure the reduction of alternative costs.
9. Text mining
Text mining algorithms allow you to automatically classify and respond to incoming e-mail messages. We automatically classify and respond to incoming e-mails according to the text mining algorithm. We measure the time and accuracy of the classification and compare it with the control group.
An engaging, functional set of interactive dashboards can significantly reduce the time spent creating ad hoc reports. For the most frequent requests, we create a set of dashboards to allow users to self-service. We measure the reduction of time between the order and the generation of the desired report.
The value of data science lies in the use of data and analytical methods that lead to increased sales or cost reduction. Once implemented, they can be used multiple times, generating a constant return on investment.