Using AI in sales makes your business smarter.
If yours is like most companies, you are drowning in data from customer relationship management (CRM) systems, enterprise resource planning (ERP) platforms, market research, customer interaction, marketing automation, customer data, and other sources.
You have the raw data, but how do you turn that data into intelligence that can help drive sales and improve the customer experience?
Using artificial intelligence, your marketing team and salespeople can create predictive models that tell you more about your customers' behavior, market trends, and new sales opportunities.
Predictive analytics is an incredibly powerful sales tool that uses historical data, statistical algorithms, and machine learning to anticipate outcomes based on historical patterns.
This is certainly not a new discipline (bookies have been doing it for years), but the increasing volume of available data and the growing sophistication of artificial intelligence models are making predictive marketing and sales analytics more accurate and more valuable for improving sales.
To harness AI for sales, you have to start by filtering oceans of data. Big data techniques can help you sift through the information available, but rather than stirring the ocean for tidbits, you want to determine where and how to gather the right digital marketing and sales data.
Automation is a great tool for data gathering, storing normalized data from reliable sources such as CRM and other tools in the sales tech stack. Automated data is generally better-quality data, and it is certainly less expensive to collect.
By gathering the right data and ensuring that it is structured and interconnected, you can use machine learning and AI to reveal trends, improve every marketing campaign, and highlight customer opportunities.
We have found that AI and predictive analytics play an especially useful role in four areas across sales and the sales enablement tasks performed by every marketer.
1. More Accurate Lead Scoring
Sales prospecting is more precise with AI. Social selling generates more inbound queries for information, but it is difficult to determine which of those prospects are ready to make a buying decision.
With predictive analytics, you can assimilate data such as corporate background, demographics, and more to create well-defined buyer personas. Using similar customer profiles already stored in the database, it’s easier to make comparisons and identify the characteristics that point to a likely sale.
More importantly, you can use analytics to predict when to close sales. Analytics can reveal customer need as it relates to your product or service and even create a likely timeline to a final close. The more accurate the predictive profile, the higher the close rate.
2. Guiding Sales Prospects
Personalization has become a vital part of the customer’s sales journey and predictive analytics can help trigger responses to queries. By applying machine learning, specific steps can be programmed to automate lead nurturing and qualification, providing a customized experience and even handling objections until the sales rep is ready to step in.
Predictive analytics can also be used to monitor progress and help identify when the prospect is ready to buy. How you approach a prospect is as important as knowing when, and data gathering and analytics can improve sales pitches, showing which subject lines and topics work best.
Analytics can also help create the right sales offer based on company size, budget, and need.
3. Reducing Churn and Upselling
It's common knowledge that the cost to retain a current customer is a fraction of the cost to acquire a new customer. More companies are restructuring to take advantage of recurring revenue, and so customer retention and expansion is crucial. Predictive analytics can tell you a great deal about customer satisfaction.
For example, it can show if the customer is underutilizing your service, having issues with support, or looking for new features. Tracking activity can reveal when you may be ready to lose customers.
You can also create predictive models to identify customers who are ready to buy more. How they use a product or service, or activity such as seeking out more data sheets, blog posts, and website videos could be an indicator that they need additional products.
4. Improving Sales Management
Predictive analytics have also proved to be an invaluable tool for sales management, not only improving sales forecasting but adding insight into structuring sales teams:
- More accurate sales quotas—Incorporating factors in addition to sales rep performance makes it possible to create more accurate and more aggressive sales quotas that are still achievable.
- Optimizing sales territories—Whether you are selling by territories or verticals, analytics can help you align sales expertise and resources with territories to deliver optimal results.
- Realigning sales policies and compensation—Predictive analytics can also point to new selling models, including revenue distribution. For example, Microsoft restructured its sales compensation by rewarding levels of service consumption rather than straight commissions.
- Revising staffing—Predictive analytics can point to likely changes in staffing demands in the coming quarter or year. Because it takes time to find new sales resources, anticipating demand can guide hiring strategies, providing enough time for hiring and training to optimize sales productivity.
- Projecting the impact of product changes—New products, releases, features, and pricing models will have a direct impact on sales. Predictive analytics can help uncover the potential impact of product changes and minimize the impact on sales.
Though predictive analytics are clearly a powerful tool, finding the most effective way to implement them is still a challenge. You could hire programmers to develop your own predictive analytics models, but more businesses are finding it more cost-effective to outsource sales analytics and predictive modeling.
However you approach it, predictive analytics can transform the way you approach sales, giving you greater accuracy, more control, and increased revenue.
Diane is the Director of Performance and Analytics at MarketStar. Diane uses quality actionable insights through automation, AI and advanced predictive modeling.