Data science helps uncover actionable data insights that enhance sales revenue and competitiveness. Data insights are one of the primary ingredients for next-generation sales growth.
Using data science and significant predictive analytics for data-driven sales decisions, B2B companies can achieve up to 25% sales growth.
Data science platforms ensure the efficiency of your sales operations through effective and precise sales forecasting and targeting. They also help identify hidden opportunities for competitive leverage.
This article will discuss how data science can boost your sales. Let’s dive in.
More Accurate Future Sales Prediction
Precision sales forecasting is critical to any organization. It influences other essential business processes, such as logistics, production, inventory management, and human resource planning.
For example, inventory management depends on purchasing raw materials and stocking finished goods, which relies on sales forecasting. Precision forecasting leads to better decision-making and smooth operations.
The ability to accurately identify your best leads and prospects is revolutionary to sales and marketing professionals. You can also precisely know when and how to communicate with your prospects, making it a great tool for driving exponential productivity.
Importance of Data Science in Sales Forecasting
Machine learning (ML) and artificial intelligence (AI) are the foundations of sales predictive analytics. ML processes historical data to understand future patterns, while AI creates discerning human capabilities.
It can use available data on customer behavior, market trends, etc., to predict future changes in sales. Precision sales prediction is possible by using vast data sets and models to query patterns amongst sales variables, often requiring an effective ETL pipeline to process and prepare this data.
But, creating quality ML tools that meet business needs and compliance can be challenging.
You have to develop a model, test, deploy, and track it to meet requirements. If it doesn’t meet the criteria, the model will have to be retrained. Machine learning model management solves this by offering logical flow and consistency. It allows your team to scale your ML models and meet your business needs effortlessly and accurately.
Upsell Your Happy Customers Using Sentiment Analysis
Customer emotional analysis is a technique for identifying attitudes from communication. Customer feedback is critical to any business. To know what customers want and why, you must employ technologies to analyze their sentiments.
Sentiment analysis helps divide your entire customer or user base into several segments, such as:-
- The happiest consumers;
- Customers with the most significant purchasing potential;
- Dissatisfied customers and
- Customers who are about to exit your brand.
By exploiting your happy and satisfied clientele and users, you can develop an upselling strategy to drive revenue.
Prevent Churn Rates
Customer churn is the number of customers who quit purchasing and consuming a brand. ML algorithms are applied to CRM data to uncover trends and patterns in customers’ behavior, contact, and purchases of quitting shoppers.
Various factors, including price, limited customer experience, etc can cause client churn.
To avoid or reduce customer churn;
- Attempt to focus on and consider your best clients,
- Offer timely feedback and communication,
- Give incentives; and
- Inquire about your customers’ opinions.
Enhance Cross-Selling
You can leverage your CRM and transactional data to develop your cross-selling strategy. Your company can use data analytics to predict how effectively its cross-sell strategies will function.
They can also uncover significant sales factors, such as popular products, key-value items, and high-demand products, that can influence sales revenue.
You can also utilize data science to deliver tailored cross-selling suggestions. It’ll indicate extra items that a customer might like to buy in addition to an item they’ve already purchased or are about to purchase.
As a result, data science can deliver realistic recommendations based on CRM and transaction data. These algorithms help determine whether goods can be sold or placed alongside each other in the catalog.
Package deals are often featured for cross-selling purposes, which can aid in the creation of discounted packs of items.
Product Pricing Optimization
Setting the correct pricing is one of the most challenging undertakings. The pricing should be acceptable to both sellers and purchasers. However, striking this balance takes a lot of effort. Several pricing systems can be applied to set equilibrium prices.
Data science is driving a robust and improved pricing mechanism. The programs also consider fluctuating price levels based on demand changes.
They also factor in inventory and manufacturing costs to create various pricing models that are best suited for a specified use case scenario.
Additionally, you can adjust these pricing models for certain client groups. Thus, it offers you price optimization capability to drive your marketing and sales based on various variables and goals.
For example, Uber uses a dynamic pricing model running on ML to forecast and determine pricing based on demand and traffic jam characteristics, among other variables.
Improves Customer Experience
Integrating machine learning-enabled chatbots into your digital marketing strategy – specifically, your website – can improve the user experience.
Using bots instead of salespeople appears to be the most exciting application of sales data analytics.
Chatbots automate customer interactions and reduce the time spent resolving issues. They are also available 24/7, thus ensuring reliability.
Not to mention the increasing number of voice searches driven by ML learning to enable understanding of human speech.
Sentiment analytical techniques help modern chatbots to analyze client messages better.
These models can also alter prices for specific client groupings. Thus, price optimization, reliability, and dependability enhance client satisfaction.
Improve Revenue Growth Through Marketing Automation
Embracing market automation can improve your sales productivity by over 10%. Approximately 80% of marketers feel that deploying market automation tools can improve sales revenue.
At the core of this marketing automation is ML. Machine-learning-based tools can decode and learn from trends and recommend actions based on history and previous experiences.
Also, they provide reliable analytics to assist you in developing plans and taking actions that deliver results.
Using advanced ML-based marketing automation tools, customer segmentation, selling to the right target, and conducting follow-ups become non-human duties.
Marketing automation tools learn and identify areas for improvement for marketing efforts after each deployment and operation.
Take Away
Data science is disrupting several sectors, including sales and marketing. It is an essential partner whether you want to boost sales or enhance your business processes.
You can use it to improve sales by:
- Fast, accurate sales forecasting;
- Driving superior customer experience;
- Enhancing your cross-selling and upselling strategies;
- Preventing more churn rates;
- Price and revenue optimization.
Now you get it. Data science should be at the core of your sales growth strategy.
