WHAT IS PREDICTIVE SALES ANALYTICS AND HOW CAN IT HELP BOOST SALES?

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Predictive sales analytics have been and are still one of the most preferred approaches to achieving great success in business endeavors.

It offers businesses the opportunity to make decisions that affect the outcome of their sales based on customer data collected and analyzed.

These days, companies seek technologies that will help optimize costs, optimize profits, and manage time.

Predictive sales analytics, aside from being able to do all the things and more mentioned above, can also be designed specifically to meet the goals and objectives of businesses.

It could be fed with fresh data consistently to make the output very reliable and accurate. Further in this article, we'll discuss the importance of predictive sales analytics in boosting sales for businesses.

We'll also see some companies that put predictive sales analytics to work and how to implement it to help boost sales. Without further ado, let's get started with what predictive sales analytics is.

What is Predictive Sales Analytics

Predictive sales analytics refers to a data-driven approach that makes use of historical sales data, customer behaviour, and some other factors to determine what will happen with sales in the future.

It involves the use of advanced statistical methods and complex computer algorithms to analyze patterns and how data correlate.

Understanding this pattern will help companies make very informed decisions about what sales strategy to develop and implement that will help boost their sales.

Why Is Predictive Sales Analytics Important for Boosting Sales?

The overall sales effort of a sales body that comprises sales enablement managers, sales managers, sales representatives, and other sales team members finds predictive sales analytics of great importance, and here are four reasons why:

1. To Make Accurate Sales Forecasting

One of the reasons why predictive sales analytics is important to businesses and boosts sales is that, with it, accurate predictions of sales trends can be made.

This can influence the plans businesses make for their inventory, resources, sales and marketing strategies, etc., which are necessary for boosting their sales and financial metrics.

2. Optimization of Inventory Management

With accurate predictive sales analytics, it becomes easy to achieve optimized inventory control.

This means that there will be just the right amount of products available in stock that'll satisfy customer demands and not too much that some products get damaged or expire.

3. Personalized Customer Engagement

Another reason why predictive sales analytics is relevant to businesses is that it provides the data that's used to understand customer behaviour and preferences.

This information gathered informs businesses to use precise sales and marketing strategies to engage their customers on a personal basis. This will help businesses boost their sales and optimize their profits.

4. Higher Conversion Rates

With predictive sales analytics, lead generation specialists will know what to do and how best to generate and nurture leads until they are converted into actual sales.

The more personalized marketing strategies are for these leads, especially with products that address their pain points, the higher the conversion rates will be, which translates to more sales.

Predictive Sales Analytics Examples

What we'll be discussing here are three practical examples of companies in their industries that have succeeded in boosting sales through predictive sales analytics. They include:

1. Amazon's Product Recommendations

Amazon makes use of predictive sales analytics to recommend different products to potential customers who surf the internet based on their browsing and purchase histories.

They study and analyze what products users have been searching for online to recommend other products they could display that will attract them.

By constantly studying customer behaviour and preferences, Amazon has been able to upsell and cross-sell to customers, thereby boosting their sales and revenue.

2. HubSpot's Lead Scoring

HubSpot's customer relationship management system makes use of predictive sales analytics to assign scores and points to leads based on their engagement and how likely they are to become customers.

Its machine-learning algorithm is able to study the behaviour of customers, and based on the data it gathers, it can determine those leads that are most likely to be converted.

These scores from the sales analytics help HubSpot prioritize those leads that can actually be converted to sales. This saves time, resources, and efforts that would have been spent on leads with a lower conversion rate.

3. Coca-Cola's Demand Forecasting

Coca-Cola is a globally recognized beverage company that understands the needs of its very large customer base and knows how to satisfy them.

They are able to achieve this through demand forecasting using a predictive sales analytics model.

They study customer behaviour, sales records in the past, patterns, and even weather data that'll help them meet customers demands.

This has helped Coca-Cola for many years to be right on time with the beverage drink to satisfy their customers globally.

How to Implement Predictive Sales Analytics for Boosting Sales

To implement predictive sales analytics and to ensure it's effective for boosting sales in your business, you can follow these seven stages:

1. Preparation and Collection of Data

In the first stage, you prepare your clearly defined objectives and goals, which will serve as a road map to what you hope to achieve with predictive sales analytics.

Once you've done that, you then engage in data collection, including customer interactions, historical sales records, marketing campaigns, and website traffic data.

These details will guide you into the next stage of effective implementation.

2. Understanding and Preprocessing Data

In this second stage, you'll need to go through the data, ensuring to remove duplicate copies and complete missing values to help you fully understand the data relevant to achieving your goals.

This process is called data preprocessing. After this, you analyze the preprocessed data to reveal patterns and insights that can inform you about the predictive model to use.

3. Model Preparation and Training Data

The third stage comprises three basic steps: selecting the right feature for your model, model selection, and data splitting.

The features you choose to make use of in your model should have a relatively strong impact on sales prediction.

After picking the right and suitable features, the next thing to do is choose the machine learning algorithms that form your predictive model.

This will be based on the characteristics of the data you collected and preprocessed and the type of predictive tasks it will be handling.

The features you chose must fit well with the specific predictive model you've chosen. The third step in this stage is data splitting. Here, you split your data into three sets: training, validation, and testing.

By splitting the data, you can effectively train, adjust, and evaluate your model until it meets the requirements of its purpose.

4. Model Training and Evaluation

In this fourth stage, you'll actively train, adjust, and evaluate the predictive model you've chosen.

In model training, you simply input your training data sets into the algorithm to make it learn the patterns and correlations between the features you've chosen and possible sales outcomes you could have.

Next, you adjust the different hyperparameters of the predictive model to ensure they all fit well and that its performance is optimized.

Finally, you evaluate the predictive sales model to validate its reliability and accuracy by using evaluation metrics like precision, recall, RMSE, accuracy, etc.

5. Deployment and Integration

In this stage, your trained predictive sales model is ready to be deployed for work. This is done by integrating the model into your sales decision-making processes to make sales predictions in real time.

6. Monitoring and Collaboration

After you've integrated your trained model into your operational systems, you need to continuously monitor it and ensure it's retrained with the most recent data available to ensure its accuracy over time.

As often as you do that, also ensure to collaborate with cross-functional teams and stakeholders to ensure that the trained model brings value and aligns with their business goals.

7. Insights and Continuous Improvement

In this last stage, you'll need to turn predictive insights into actionable recommendations for relating with customers, developing sales strategies, and different marketing efforts and campaigns that can help boost sales.

Also, train other staff on how to use the predictive sales analytics model and interpret the outputs they get.

In addition to staff training, create a system for receiving feedback that'll help you make improvements to the predictive model.

All these you do to ensure you implement predictive sales analytics necessary to boost sales in your business or organization.

Conclusion

Predictive sales analytics will remain one of the most time-saving, cost-optimizing, and data-driven approaches for boosting sales in the business landscape.

Although other components like the sales enablement manager, sales team, marketing strategies, lead generation specialist, etc. aid in boosting sales, sales analytics is the data premise that aids all others.

This article also covered the seven stages that you can follow to implement predictive sales analytics.

Follow them or seek out an expert who can develop a model that works for your business, implement it, and also train your employees on how to use the output from the algorithm to boost sales.