Sales Robots: How AI is Taking Over Sales

A recent study predicts that robots will take over 73 million jobs in the U.S. by 2030

These predictions can be troubling for many workers, but less for sales professionals. Artificial Intelligence (AI) technology can help companies analyze customer data and provide meaningful insights from data. These insights are then used to make sales predictions, personalized product recommendations, and important sales moves.

How is AI Taking Over Sales?

Taking over does not mean that AI robots will take sales jobs from people. Taking over means that AI is becoming a necessity for retailers. AI can help salespeople gain an advantage over their competitors by collecting and analyzing customer data. This analysis can help you better your customers. For example, you can discover how customers feel about your company, and whether this perception aligns with their core values.

In addition, customers expect businesses to be available at all times, via multiple channels. A company may lose money if a customer doesn’t get an immediate response when considering buying something. Approximately 64% of consumers expect to get real-time interaction from brands. This is why retailers use chatbots. Chatbot technology improves the interaction between customers and salespeople, thus enhancing customer experience. Chatbots also improve operational efficiency by reducing the cost of customer service.

How AI Assists Sales?

Sales reps usually don’t have enough information on potential customers. For example, more than 80% of sales reps who have sufficient market intelligence claim that it helps them do their job more effectively. However, only 51% of sales reps think that they have enough market intelligence on prospects and customers. The numbers are even more indicative when it comes to predicting customer behavior.

In summary, most sales reps think that this information is valuable. This is exactly the type of info AI can provide. Companies need to first collect this data through different tools. AI systems can then analyze this data to provide meaningful insight about customers. Sales reps can use these insights to create more tailored sales processes. A process that considers the unique preferences of each customer.

AI can help sales reps do a much better job by providing valuable data and feedback. In fact, the highest performing sales teams are 2.3 times more likely to use AI-based selling techniques.

5 Use Cases of AI in Sales

AI can analyze customer data, organize it, sort it, and provide insights. Retailers can use this data to understand their customers and their market. This approach can help with sales forecasting, lead generation, and enhance productivity.

Personalized Recommendations

Personalized content that suits any particular user is the most important consequence of AI-guided sales. In fact, 57% of consumers are willing to share personal information in exchange for personalized offers or discounts. In addition, 52% of consumers will share personal data to get product recommendations that meet their needs.

An efficient AI algorithm that creates personalized recommendations can be beneficial for any one-to-one marketing and sales strategy. One-to-one marketing is a strategy that focuses on individualized customer experience. Personalization can improve customer loyalty and have a high return on investment.

This strategy helps people buy exactly what they want, even if they were not actively looking for it. For example, almost 35% of Amazon sales come from product recommendations. AI will only become better at recommending special offers, because these algorithms learn from feedback data.

Lead Scoring and Prioritization

Traditional lead prioritization and scoring process is based on incomplete information and gut instinct. AI algorithms can collect historical information about a customer, together with social media posts and previous interactions with salespeople. The algorithm then ranks these leads according to their probability to convert. AI can bring a level of standardization and logic that people just can’t match.

Sales Recommendations

Planning and creating a sales process can help your sales team convert more leads and close more deals. AI systems can recommend sales actions based on your goals and insights from the analyzed data. These recommendations include advice on who to target next, how to price a deal, or which consumer to target first with cross-sells or upsells. This targeted guidance enables sales reps to free up bandwidth to close deals, instead of discussing what to do next.

Productivity and Performance Enhancement

Sales pipelines usually involve a lot of manual and repetitive tasks that distract you from higher-value tasks. AI systems can analyze your historical decision-making data to automate tasks like meeting scheduling, calendar management, or sales pipeline assessment. Task automation can help you focus more on closing deals and nurturing leads.

Upselling and Cross-Selling

Upselling is the procedure of encouraging existing customers to buy more expensive items with an upgrade or premium. Cross-selling invites customers to purchase complementary or related items. Cross-selling and upselling can provide maximum value to customers and increase revenue without the recurring cost of marketing.

AI algorithms can identify which of your existing clients are more likely to purchase an upgraded version of their current product, or which customers are most likely to buy a new product offering. The end result is an increase in revenue and a reduction in marketing costs.

Conclusion

The usage of AI technology in sales can benefit both customers and salespeople. AI can provide personalized offers that increase customer loyalty, score and prioritize leads, make sales recommendations, and automate repetitive tasks. As a result, retailers get more useful information about their customers, compared to traditional sales pipelines. This information can help them grow their business by adding a personal touch to sales and providing better user experience.


About the Author

Limor Maayan-Wainstein is a senior technical writer with 10 years of experience writing about cybersecurity, big data, cloud computing, web development, and more. She is the winner of the STC Cross-European Technical Communication Award (2008) and a regular contributor to technology publications.

Featured image: ©Bigstock

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