How Predictive Models are Changing the Landscape of B2B Marketing

How Predictive Models are Changing the Landscape of B2B Marketing

You don’t need to be a certified data scientist to understand that data is playing a crucial role in B2B sales and marketing. Today, the world belongs to big data. For B2B corporations and multinational companies, data is a way to peek into the future. But for all these developments, you’ll find one underlying theme – using data to predict future demands.

When you look at various predictive models, it’s hard to isolate a particular business activity. It’s all about driving positive changes for businesses across departments, especially B2B sales and marketing. 

Today, predictive analytics is everywhere – From targeted advertising to eCommerce product recommendations, it is delivering significant results in improving the organization’s overall marketing efficiency.

What are Predictive Models in B2B Marketing

For B2B sales, predictive analytics is the use of data, algorithms, and machine learning technology to identify the likelihood of future sales based on historical purchases. The goal is to go beyond knowing what has happened and provide an assessment of prospects that are likely to become your customers. Predictive models not only gather information about past sales outcomes but use the data to understand things yet to happen. 

Predictive analysis is not a new thing for the business world. It’s been around for years, but it’s been impractical in large-scale settings due to inadequate data gathering mechanisms. Over the past couple of years, access to relevant data, new software, and hardware platforms have come into play. Predictive models are playing an active role in B2B sales and marketing as businesses are looking to go beyond the basics.  

But the evolution of predictive models hasn’t been smooth. Many key B2B decision-makers still rely on their gut feeling over data and analytics. The biggest challenge of implementing predictive analytics isn’t technology but the general attitude and confusion about its role in key decision-making.

What makes predictive modeling work is the volume of data that feeds into it. In today’s digital world, the data volume is increasing, not only in-depth but also in breadth. B2B businesses that leverage new data sets are more productive and profitable.

How Predictive Models are Changing B2B Selling

The role of predictive models in B2B marketing and sales is critical and can provide massive benefits if implemented correctly. With the amount of data available at hand, predictive models are taking the guesswork out of sales, increasing reliability and accuracy. B2B business owners are always looking out to improve their sales process for maximum efficiency.

Let us take a look at how predictive analytics is changing B2B marketing and sales

Improved Lead Generation and Scoring

Analytics has the capability of improving all the aspects of your business and lead generation is no different. B2B companies are leveraging rich data sets to identify where their customers are, who they are and how they would like to be approached.

Predictive analytics is helping businesses leverage historical sales data to predict which prospects are likely to become their customers. Lead scoring algorithms are being introduced for better lead scoring mechanisms. The algorithms combine consumer behavior with external data to create a complex buyer persona.

Companies are also using predictive models to predict which leads are more likely to close and which industry can benefit most from their services.

In the top-of-the-funnel strategy companies used AI-enabled bots that leverage natural language processing to automate lead generation techniques.

Better Sales Forecasting

Proper sales forecasting is extremely important in the B2B sales process. Good forecasting keeps things moving smoothly for your team and discrepancies may lead to improper resource allocation.

With the correct predictive models, B2B sales organizations can make correct decisions with respect to sales resource allocation. When combined with customer behavior, seasonal changes, and other analytical methods, sales forecasting can become a powerful tool for B2B sales. These predictions can also lead to proper stock allotment and less customer churn.

Better Offer Planning

Traditionally, a B2B salesperson is involved in sales planning by relying on historical data. As time goes, salespeople change and the tactics are not updated. This results in poor resource allocation and ineffective offer planning.

When predictive models are introduced in the sales process, it leads to better resource allocation and updated sales strategies. With account-based marketing, your salespeople need to know exact details about the target account. Data analytics can reveal hidden traits among your most loyal customers and help curate offers that best convert the prospects.

Good Customer Lifetime Value   

Today’s sales is all about customer lifetime value. You may be able to sell your product to a customer one time by utilizing paid methods. But B2B companies today are looking for the lifetime value of a customer. When done manually, the salesperson misses a lot of opportunities for sales. As a result, many B2B companies are incorporating algorithms that suggest to salespeople which customers are likely to become loyal customers. This is extremely useful as it identifies cross-sell and upselling opportunities.

This approach can also help in customer retention as you can sense customer desires and ensure necessary action is taken.

Improved Pricing Strategy

Any marketer would admit that setting pricing strategies is always a hassle in the B2B world. The products are complex with a longer sales cycle. With data analytics, you can provide enhanced pricing transparency to your salesperson and your customers. Effective lead scoring algorithms will place the right information in front of your sales team – helping them navigate the proposal accordingly. 

Why Should B2B Businesses Use Predictive Analytics

Simply put, predictive models just work. They are useful in sales forecasting, creating marketing campaigns, understanding customer demographics, etc. They remove the guesswork that’s usually associated while starting out. Since algorithms are at work, you don’t have to spend time tweaking and making changes.

Here’s how your B2B business can benefit from predictive modeling: 

  1. Enhanced Productivity: You get access to data that is pre-analyzed, thereby taking the guesswork out, making your marketing and sales team proactive.
  2. Reduced Costs: Since you have up-to-date data, you can directly design your marketing campaigns, instead of allocating the budget for experimentation.
  3. Fewer Resources: Predictive modeling can help you decide the right amount of resource allocation. Hence you avoid misallocation and save on cost and time.
  4. Better Quality: Industries use predictive analytics to forecast demand and enhance their customer service and pricing strategies. Which in turn results in high customer retention.
  5.  Better Risk Management: Predictive modeling helps you prepare for upcoming changes in the market, nullifying any threats, leading to better risk management.
  6. Optimized Marketing: Predictive modeling quickly helps you understand consumer behavior and what trends they are interested in. This ensures you always retain your most profitable customers.
Conclusion

Predictive modeling is only going to get better with time. Businesses that’ll be soon adapt will beat their competition. There are a lot of developments happening with B2B technology, there are hundreds of factors at play. The only question is how will you use it to transform your business for good?

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