7 Major Data Science Use Cases In Sales Rakesh Reddy April 28, 2020

7 Major Data Science Use Cases In Sales

7 Major Data Science Use Cases In Sales
Enterprises are embracing data science and analytics across various functions to leverage data to reduce costs, improve efficiency and drive revenue. With respect to sales, there are numerous use cases where data analytics can be applied. And many more use cases are fast emerging. According to a survey by McKinsey of over 1,000 sales organizations around the world, it was found that 53 percent of the companies that are “high performing” rate themselves as effective users of analytics. Data has now become the building blocks across all industries and is vital for sales leaders to run their operations effectively, focus on viable strategies, generate leads, enhance customer experience and uncover hidden opportunities.

The 7 Major Use Cases of Data Science In Sales

1. Predicting Sales

Predicting sales is of immense importance to organisations as its effects trickle down to critical business processes like inventory management, logistics, production and manpower planning. For instance, buying raw material and maintaining finished goods inventory is fundamentally driven by sales forecast. Accurately predicting sales helps organisations to make better decisions and ensure the smooth running of processes. Sales forecasting algorithms use a large amount of diverse data to look for patterns and relationships among various factors that affect sales under changing circumstances, thus predicting sales with a high level of accuracy.

2. Improve lead generation

Analytics has proven to be a great tool to improve lead generation and automate pre sales processes. Companies are leveraging a vast resource of data to identify the right customers at the right time. Enterprises use a wide array of historical data to get a holistic picture of their prospective sales and many companies are pushing the limit by deploying lead-scoring algorithms which are fuelled by granular and segmented data about each of their prospects. A complete 360-degree view of the customer is generated by combining in-house customer data and external data from news reports and social media posts. These algorithms guide sales strategies by predicting the factors that are pivotal to lead conversion . According to a report by McKinsey, big-data analytics can be used to predict leads that are most likely to close which is useful in planning the allocation of resources to improve lead conversion rate. By employing intelligent automation into the insight generation process, companies are seeing a significant leap in their ability to identify promising prospects and zero in on the right moment to target them. Enterprises are testing AI-enabled agents powered by predictive analytics and natural language processing to automate pre sales activities and early lead-generation activities.

3. Analyzing customer sentiment

Sentiment analysis proves useful in understanding the feedback by customers. It employs AI to discern the emotions conveyed by customers and the semantics of the conversation. This is beneficial for businesses to understand how customers perceive their brand. Sentiment analysis uses text mining algorithms to extract insights from social media websites, blogs or review sites. Automated sentiment analysis tools can be useful in extracting real-time actionable insights.

4. Better Cross-selling and Up-selling

With data analytics, companies can have an understanding of how their upsell and cross-sell strategies will perform well beforehand and also identify important sales parameters like key value items, key value categories, popular products and high demand products that can affect the sales bottom line. Data science is also used to provide personalized cross-selling recommendations — which suggest additional products that a customer would desire to buy along with an item already bought or intended to buy.

5. Improving CLV

Although identifying the loyal and appropriate set of customers is an easily achievable task, predicting time of customer attrition and the behavioural changes of customers which greatly affect the CLV, is rather a tricky one. With data science, companies can now drill down the root causes for such a shift in customer trends and behaviour. Using data to build CLV models, companies can derive the dependencies of variables affecting customer relationships and predict the future sales and actions. CLV modeling helps companies to learn about viable marketing channels and campaigns, identify areas for cutting costs, build retention strategies, formulate sales pitch and plan inventory with the right mix of products. Mitigating the risk of customers leaving for a competitor and engaging them successfully demands identifying the signs of customer dissatisfaction well before they take action. Pattern-recognition skills of machine learning algorithms are best suited to address this problem.

6. Setting the right price

Deal analytics provides a foresight on prices and allows sellers to arrive at viable trade-offs and business deals during negotiations. While B2B sellers have traditionally relied on their experience to make decisions on pricing, purchasing teams have managed to gain the upper hand by deploying sophisticated pricing tools, putting the sales teams in the back seat. Dynamic deal scoring has levelled the playing field by equipping sales representatives with relevant information on deals well beforehand. With data science tools sales representatives can now identify similar purchases and appropriate  information on deals to make a well guided sale. Another challenge that sales teams face is setting an ideal price for new products or solutions, especially those that do not have a similar product for comparison in the market or when the market conditions drastically change. Companies are deploying dynamic-pricing engines that coalesce real-time market and competitor data with sales strategies to derive optimum prices.

7. Churn Prevention

While it is important for sales players to predict customers’ purchasing, understanding the trend of customer churn or attrition is of equal importance to improve business. Machine learning algorithms sift through the company’s CRM data to find consistencies among the customers who have stopped buying. These algorithms find patterns in attrited customers’ behaviour, communication and ordering which helps companies to understand the reasons for attrition and predict customers who can stop buying. These insights are a valuable feedback for companies to improve their business and control customer churn.


Whether it’s to improve customer experience or reduce churn or generate leads, modern sales leaders need data to stay competitive. Across industries and functions, it is the adoption of big data analytics that is differentiating winners from the rest. If you’d like to learn more about this topic, please feel free to get in touch with one of our big data consultants for a personalised consultation.

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