Artificial intelligence is moving away from the realm of science fiction and futuristic technology.
AI software can be applied to various areas of your business, from customer service to sales and marketing. In this post, we’ll take a look at some of the ways you can use Artificial intelligence to increase your sales numbers and improve your company’s bottom line.
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Artificial Intelligence Solutions can automatically qualify leads and refine your sales funnel:
Machines still can’t beat the salesmanship of humans. They excel in their ability to process huge amounts of data. This means that Artificial intelligence software is a very efficient tool for spotting trends and patterns in customer data. You can automatically score leads more accurately and less subjectively than with manual scoring.
Artificial Intelligence lead scoring allows sales teams to focus on their most valuable leads and return less qualified leads to the marketing team for further nurturing through the sales funnel. Using sales software that uses predictive analysis to qualify leads means that sales and marketing teams have more time to engage with prospects and create effective marketing campaigns. No need to spend valuable staff resources analyzing data and manually qualifying leads and identifying the best prospects.
Predict cross-sell and up-sell:
Just as Artificial intelligence is effective at identifying the leads that are most likely to convert, it can also predict cross-sales and up-sales as to how a sales team is most likely to be successful for each individual lead. Chances are.
Applications of machine learning software can analyze data from past interactions with a prospect and past sales with other customers to predict the products and services that will be most attractive to each prospect.
When it comes to cross-selling it takes the guesswork out of sales teams and reduces the risk of annoying existing customers by overselling products and services they don’t care about. Cross-selling and up-selling can make a lot of money – you have a 60-70% chance of selling to an existing customer, while only a 5-20% chance to a potential new customer. However, many software development companies fail to take full advantage of the cross-selling opportunity due to a lack of understanding of customer needs. Artificial intelligence helps fill this knowledge gap, squeezing more sales out of each prospect.
Optimize your marketing messages:
How do you know if your website copy, emails, display ads, sponsored search listings, social media posts, and other marketing messages are effective? The truth is, you don’t know at least until you can start collecting data and do some A/B testing to optimize your campaigns.
There are best practices to follow when it comes to writing sales copy and of course, designing marketing communications. But these guidelines are general and may or may not apply to your unique industry or audience.
How do you know if your Facebook ad will sell more than an image of a man or a woman? You can make a good guess based on what you know about your audience, or you can split the test into two different versions of your ad. Split A/B testing will confirm what you need to know, but it takes a lot of time and effort. Did you notice that a picture of a blonde girl works better with a brunette? Great, but now it’s time to try a different headline – you can see how it turns out to be a never-ending task.
Fortunately for today’s marketers, there’s another option:
Over time, your marketing messages will automatically be optimized for the version that is most likely to result in a sale.
And what’s more, it doesn’t mean that one version is best for all users. The Applications of Artificial Intelligence is smart enough to analyze all the data and optimize it in real-time for different types of leads based on demographics, past interactions with brands, and many other factors. This type of custom customization is only possible if supported by AI-powered software.
Better sales management:
Not only to improve sales forecasting but also to add insight into the structure of sales teams, predictive analytics has proven to be an invaluable tool for sales management:
More accurate sales quotas – Incorporating factors other than sales rep performance makes it possible to create more accurate and aggressive sales quotas that are still achievable.
Sales Area Optimization – Whether you are selling by region or vertical, analytics can help you align sales expertise and resources across regions to deliver optimal results.
Realignment of sales and compensation policies:
Predictive analysis can also target new sales models, including revenue distribution. For example, Microsoft restructured its sales compensation by rewarding service consumption levels instead of direct commissions.
Staffing Review:
Predictive analytics can indicate potential changes in staffing demand in the coming quarter or year. Because it takes time to find new sales resources, anticipating demand can guide hiring strategies, allowing enough time for hiring and training to optimize sales productivity.
Project the impact of product changes:
Predictive analysis can help uncover the potential impact of product changes and reduce the impact on sales.
Although predictive analysis is clearly a powerful tool, finding the most efficient way to implement it remains a challenge. You can hire programmers to develop your own predictive analytics models, but more companies find it more profitable to outsource sales analysis and predictive modeling.