Can Data Science Help Sales Managers Get Better Results?

Quantifying the behaviors that distinguish top sales representatives from less productive salespeople can assist managers in identifying abilities that need to be improved.

Traditionally, sales have been an intuition-driven career.

What exactly do I mean? Salespeople, on the other hand, undertake research on potential clients before engaging in dialogue with the best-fit prospects. This allows representatives to gauge each prospect’s interest and assess whether or not they are a good fit.

However, sales datasets have transformed the way salespeople think and feel in recent years – intuition is no longer in charge. For a clear reason, sales data has gained control.

McKinsey predicted that big data projects in the US public health system ‘may translate to $300 billion to $450 billion in decreased healthcare expenses, or 12 to 17 % of the $2.6 trillion benchmarks in US healthcare expenses.’ On the other side, poor data is predicted to cost US $3.1 trillion every year.

Businesses can survey huge numbers of individual transactions and sales rep contacts (emails, meetings, and phone conversations, for example) using machine learning to uncover trends in finished deals and give insights on which practices might help win deals and raise customer satisfaction levels. 

Such data-driven insights may then assist sales executives in steering and coaching their sales team in ways that create both top- as well as bottom-line development.

Data has now become the foundation of many businesses, and it is critical for sales executives to manage their operations efficiently, concentrate on viable tactics, create leads, improve customer experience, and unearth hidden possibilities.

How can Data Science Help Sales Managers Get Better Results?

Here are a few ways in which data science can help sales managers get better results.

1. Predict sales
2. Enhance lead generation
3. Better cross-selling
4. Prevent churn
5. Identify the right KPIs
6. Hire the right candidates

Read on to learn more

• Predict Sales

Predicting sales is crucial for businesses because its impacts ripple down to critical business activities such as inventory management, shipping, manufacturing, and labor planning. Purchasing raw materials and keeping finished goods inventories, for example, are primarily driven by sales estimates. 

Predicting sales accurately allows firms to make more informed decisions and ensure that operations function smoothly.

Sales territory management is a part of predicting sales. 

You can optimize sales territories using data science to eliminate any guesswork of sales. It helps minimize costs, create a balance, and maximize your profit while keeping everyone in and out of your organization happy. 

Sales forecasting algorithms seek patterns and linkages among many aspects that impact sales in a dynamic environment, allowing them to estimate sales with a high degree of accuracy.

• Enhance lead generation

Companies employ a wide range of historical data to create a comprehensive picture of their future sales. Many organizations are pushing the envelope by installing lead-scoring algorithms powered by detailed and segmented data about each of their customers. 

Integrating in-house client data with external data from news headlines and social media posts yields a comprehensive 360-degree view of the client.

These algorithms assist sales strategy by anticipating key aspects in lead conversion. Big-data analytics may be used to anticipate which leads are more willing to close, which is important in optimizing resource allocation to increase lead conversion rate.

Companies are witnessing a huge improvement in their capacity to discover good prospects. They target them at the appropriate time by incorporating intelligent automation into the insight creation process.

• Better cross-selling

Companies may use data analytics to predict how effectively their upsell and cross-sell tactics will function in advance. You can also discover significant sales factors such as key-value goods, key-value groups, successful products, and demanded products that can impact the sales bottom line. 

Data science is also utilized to deliver tailored cross-selling suggestions, which propose additional things that a consumer might like to buy in addition to an item already purchased or planned to purchase.

• Prevent churn

While it is crucial for salespeople to forecast client purchases, monitoring the pattern of customer turnover or attrition is equally critical for company improvement.

Machine learning algorithms trawl through the company’s CRM data in search of patterns among consumers who have ceased purchasing. These algorithms detect trends in attrited consumers’ behavior, communication, and order. It allows businesses to identify the causes of attrition and anticipate clients who may cease purchasing.

These insights provide crucial input for businesses looking to enhance their operations and reduce client turnover.

• Identify the right KPIs

Every organization must consider whether to base commissions and incentives on sales statistics, profitability, or some other criterion. A bad metric selection might lead to poor outcomes. 

Big data and analytics may assist in identifying KPIs that are most aligned with company goals and in defining granular measurements that can achieve desired outcomes. According to data, clients who remain for six months generally stay for a year.

As a result, the business revamped salespeople’s incentives around what experts term a sales persistency metric: the percentage of revenue that persists after a transaction for more than six months. This shifted agents’ focus from ‘hunting’ to ‘farming,’ with the goal of optimizing the onboarding process and preserving client relationships.

• Hire the right candidates

Reviewing resumes all day is a common occurrence in the life of a recruiter, but that is evolving thanks to big data. There is so much information about the talent available via social networks, corporate databases, and online recruitment websites. Data scientists can sift through all of these pieces of data to locate the applicants who best meet the goals of the firm.

Data science can enable your recruitment team to make quicker and more precise judgments by mining the massive amount of data that is now available, in-house analytics for forms and inquiries, and even complicated data-driven skill assessments and activities.

Wrapping Up

Data science may provide value to any organization that can effectively utilize its data.It is critical to any firm in any field, from information and analytics to procedures and hiring new people, to help senior staff make better-informed selections.

Modern sales executives want data to be competitive, whether it’s to improve customer experience, minimize churn, or create leads. Across sectors and functions, it is the use of big data analytics that distinguishes winners from losers.