Data science helps uncover actionable data insights to enhance your sales revenue and competitiveness. Data insights is one of the primary ingredients for next-generation sales growth.
Using data science, especially predictive analytics for data-driven sales decisions, B2B companies can achieve up to 25% sales growth.
Data science platforms ensure your sales operations efficiency through effective and precise sales forecasting and targeting. It helps 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 like logistics, production, inventory management, and human resource planning.
For example, inventory management depends on the purchase of raw materials and stocking of finished goods that relies on sales forecasting. Precision forecasting leads to better decision-making and smooth operations.
Having the ability to identify your best leads and prospects accurately is revolutionary to sales and marketing professionals. Besides, you can also precisely know when and how to communicate with your prospects. It becomes a great tool to drive exponential productivity.
Importance of Data Science in Sales Forecasting
Machine learning (ML) and artificial intelligence (AI) are the foundation of sales predictive analytics. ML processes historical data to understand future patterns while AI creates a discerning human capability.
It can use available data on customer behavior, market, etc., to predict future changes for sales. Precision sales prediction is possible by using vast data sets and models to query patterns amongst sales variables.
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. The model will have to be retrained if it doesn’t meet the criteria. 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. If you want to know what customers want and why; you’ll need to employ technologies to analyze their sentiment.
Sentiment analysis helps divide your entire customer or user base into several segments, like:-
- The happiest consumers;
- Customers with the biggest 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 for a set period. Through CRM data, ML algorithms are applied to uncover trends and patterns in customers’ behavior, contact, and purchases of quitting shoppers.
Client churn can be caused by a variety of factors, including price, limited customer experience, etc.
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 step up your cross-selling strategy. Your company can use data analytics to predict how effectively its cross-sell strategies will function.
Also, they can uncover significant sales factors like popular products, key-value items, and high-demand products that can influence the 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. Hence, it can aid in the creation of discounted packs of items.
Product Pricing Optimization
Setting the correct pricing is one of the most challenging undertakings of all time. The pricing should be acceptable to both sellers and purchasers. But, it takes a lot of effort to strike this balance. Several pricing systems can be applied for equilibrium price setting.
Data science is driving a robust and improved pricing mechanism. The programs also consider fluctuating price levels based on demand changes.
Also, they factor in inventory and manufacturing costs to create various pricing models best fit 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 a host of variables and goals.
For example, Uber uses a dynamic pricing model running on ML to forecast and determine the pricing based on demand, and traffic jam characteristics, amongst 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 aid in the automation of customer interactions and reduce time spent resolving issues. They are available round the clock, thus ensuring reliability.
Not to forget the increasing 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 be used to 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.
Digital data has become an integral part of both online and offline marketing. Marketing data analysis is no longer an up and coming practice and is now considered the industry standard. Customer data is being collected in mass, but not every business knows what to do with data. When analyzed and parsed properly, marketing data can provide invaluable insights and opportunities that can improve your business. Companies can track almost everything customers do online; every click informs a customer profile that businesses can use to better market their products or services. Data analysis can greatly impact and inform the decisions a business makes making it an important tool for conducting business in the increasingly digital modern world.
Customer Data
Customer data is the most common type of data businesses collect. Customer data can include customer names, phone numbers, addresses, purchase history, gender, age, and other demographic information. Customers voluntarily post most of their information across the Internet, social media platforms are the largest customer data treasure troves, making it easy for businesses to scoop up and store the information in a database. If a customer purchases an item or makes an account with a specific business, all of the customer, account, and transaction data is also collected. Most businesses have a large collection of customer data, but how is that data used?
Demographic Data-Based Personalization
If you have ever received a marketing email from a business, you may have seen personalization in action. Companies will commonly slot customer’s names into text fields so it appears as if the email was personally sent to that customer. Research by Experian found that personalized marketing emails had a 29% higher open rate and 41% higher click-through rates proving that personalization is effective. Some customers have stated that they are less likely to or refuse to do business with companies that don’t take the time to personalize their marketing materials. Using your customer database to fill in text fields is the easiest version of personalization, but there are other ways you can tailor your marketing content.
Customer data will frequently tell you where a customer or a group of customers live making it easy to alter your copy to reflect different climates. Sending an ad for winter clothes to a customer who lives in a cold climate will be more effective than sending the same customer an ad for beach attire. Time zones are another geographic factor to consider; sending a blast email at the same time across multiple time zones is ineffective. When one customer is off work and available to check their email, a different customer might be in the middle of their workday. By staggering your email blasts by time zone you can ensure your email arrives at the ideal time for every customer.
Behavioral Data-Based Personalization
You can also personalize the ads you send a customer based on their transaction and browsing history. If a customer has recently abandoned a shopping cart, send them an email reminding them of what specific items are still in their cart and highlighting special promotions that might affect their transaction total.
Customer transaction history can also tell you what items a customer would likely purchase next. If a customer bought a high-end sit/stand desk they may be more inclined to buy a treadmill desk in the future. Matching the price and category of items a customer has already bought to similar items is key to making your suggested items appropriate and effective. You also must factor in the number of items a customer has purchased. Offering a customer who has only bought single-user licenses of software will likely never buy enterprise-level software.
What Worked and What Didn’t
Data analysis can provide a clear understanding of what efforts benefited the company and which initiatives did not perform. Without data to objectively determine if a new process worked, success or failure could be blamed on any random element. Acting on an assumption without the data to back it up is reckless and could lead to disastrous consequences.
By tracking the performance of various ads sent out through social media, email, and mailers your business can determine what advertising method is the most effective. Were more emails opened than before because of the new ad or was it a coincidence? Did the social media campaign directly result in more sales? Did customers use the mailer promotion code?
Data analysis combined with customer feedback gives marketers a clear picture of what is working and what should be changed. Understanding what worked and what didn’t gives businesses the confidence to scrap ineffective methods and focus on already proven tactics. This saves businesses time and money by not wasting resources on ads that won’t yield the desired results.
Data analysis is especially important when using CRM systems such as SalesForce. When you export salesforce data you can find yourself leveraging the data in many ways.
Changing Future
Data analysis is an important tool for markets, but technology is slowly altering how marketers collect data. Over 30% of online users are now using a Virtual Private Network (VPN) which hides their identity and data. VPNs can make reliable data harder to collect, but new technological solutions are looking to solve the problem. Cross-device https://masterra.com/ ID tracking lets marketers track a user across multiple devices and combine data from different devices to create a well-rounded user profile.
Marketing data analysis is standard practice for modern businesses. Data can provide critical insights and highlight opportunities. Use data to personalize ads for your customers and to determine what marketing efforts were successful.