Apr 19, 2024

Here are 6 reasons why companies are relying on AI to boost their sales !

Do you still tend to waste more resources on time killer prospects? AI is gaining a lot of traction due to the fact that this technology can automate & enhance much of the sales process.

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Do you still tend to waste more resources on time killer prospects? Have you ever felt like you’re spinning your wheels in terms of lead generation and reaching no where? You’ve hired clever people, collected and analyzed data, and are encouraging your team to push their limits. However, converting a call into a lead and then turning that lead into a sale still remains an irritating puzzle for several lead generators, sales force, and managers.

Artificial intelligence (AI) has been gaining a lot of traction in the last couple of years and more companies are seeing the value in using it for their sales strategy.

This is due to the fact that this technology can automate and enhance much of the sales process. As a result, sales representatives can concentrate on what really matters: closing the sale.

Salesforce also discovered that high-performing teams are 4.9X more likely to use AI than low-performing teams. It’s because AI is providing practical significant results to the sales teams by bestowing superpowers on them, with many real-world use cases and tools already in use.

From using AI to engage with customers more efficiently to using machine learning for competitive analysis, here are five reasons companies are using AI in their sales strategy.

1. Short-Lived Trends And Innovations Are Taking Over

With the continuous evolution of technology and the speed at which new trends emerge, companies must always be evolving with it.

There has been a significant shift towards digital transformation and having a digital footprint means a company can be accessed through many different channels. In order to remain competitive, companies need to adopt new ways of interacting with their customers. Artificial intelligence can play a crucial role in enabling companies to become more agile and evolve with the times.

AI’s predictive analytics capabilities enable sales teams to anticipate and align with emerging trends, staying ahead of the curve. By analyzing vast amounts of data, AI identifies patterns and consumer preferences, empowering sales professionals to fine-tune their strategies and tailor offerings that resonate with the current market pulse.

Coca-Cola employs AI to predict demand fluctuations based on factors like weather, events, and social media trends. This has led to a 12% improvement in inventory turnover.

Generative technology, an offshoot of AI, empowers the sales department with novel avenues for engagement. From automated content creation to personalized communication, generative technology crafts tailored messages that engage customers on a deeper level. This adaptive approach not only enhances customer satisfaction but also positions the company as a trend-savvy player that understands and capitalizes on the fleeting nature of market trends. Amazon’s recommendation engine, powered by AI algorithms, drives 35% of the company’s total sales. It suggests products based on customers’ browsing and purchasing history.

2. Customer-centricity Is the Name of the Game

AI can be used to gain more understanding of what is important to customers and what they look for in a company. AI allows you to gather large amounts of data quickly and easily. You can use this data to create actionable steps to enhance your product and customer service process. You can, for example, process a large number of customer messages if you have automated text analysis.

A study by McKinsey found that companies that leverage customer behavior data for predictive analytics outperform their peers by 85% in terms of sales growth and more than 25% in gross margin.

Generative AI has emerged as a potent tool for mapping the intricate landscape of customer experiences. It enables companies to create tailor-made content, responses, and interactions that cater to individual preferences. By crafting experiences that feel personal and genuine, companies are solidifying their customer relationships and nurturing brand advocacy.

The AI revolution has paved the way for hyper-personalization, enabling companies to treat each customer as an individual with unique desires. AI-powered recommendation systems and dynamic content generation enhance customer interactions, driving engagement, conversion rates, and ultimately, sales.

The customer journey is being redefined through AI-driven chatbots, virtual assistants, and predictive analytics that guide customers seamlessly towards their goals. This enhances satisfaction, loyalty, and retention.

3. The Need For Faster Decision-Making

Decision-making is one of the most critical aspects of a project, and it can be extremely time-consuming. With so many moving parts and uncertainties, it can be hard to find the right people, negotiate the right terms, and make the right judgement calls.

AI has been around for long enough for businesses to understand the value in applying it to their decision-making process. Instead of depending on human intuition alone, AI can be used to analyse and interpret information, identify potential problems, and make better decisions faster.

AI algorithms can analyze market data, competitor pricing, and customer behavior to optimize pricing strategies in real-time. For instance, companies like Uber and Amazon use AI to dynamically adjust prices based on demand and supply conditions.

Levels of decision-making-

Assistance in making decisions:

Humans can make accurate decisions with the help of forecasting, diagnostic, or descriptive analytics. The advantage comes from the combination of human intelligence and data-driven insights. When rational thinking and expertise are combined, they have the potential to make the most of AI in business.

AI aids sales teams by analyzing customer data, market trends, and competitor insights. This empowers sales professionals to approach customers with tailored solutions and targeted offers.

Decision enhancement:

With augmented intelligence comes symmetry. It is a two-way process. The aim is for machines to learn from human input. Humans in turn base their decision accuracy on intelligent information.

AI-driven predictive analytics assist in forecasting sales, identifying high-value prospects, and optimizing pricing strategies. Sales decisions become more refined, informed, and aligned with market dynamics.

Automation of decision-making:

Decision automation, like decision augmentation, is based on prescriptive analytics. Its scalability, speed, and consistency in decision-making benefits humans.

In sales, AI can autonomously manage routine tasks such as lead qualification, follow-ups, and even email outreach. This allows sales teams to focus on building relationships and strategizing for higher-level interactions. Amazon’s warehouse robots utilize AI to autonomously manage inventory, track products, and optimize order fulfillment, leading to more efficient operations.

Teva and Hoka use MakerSights to make AI-based product decisions. MarkerSight’s AI-driven technology provides Teva and Hoka with a rational decision framework for each product creation process step. It also aids them in identifying strategic opportunities and anticipating issues and complications.

4. Identifying High-quality Leads

Gone are the days of manual lead sifting and guesswork. AI-driven algorithms process vast amounts of data to discern patterns, behaviors, and indicators that signify potential high-quality leads. This data-driven approach not only saves time but ensures that every sales effort is focused on prospects with the highest likelihood of conversion.

One of the biggest challenges in any sales rep’s day is reaching beyond their normal contacts to find prospective buyers. A crucial step in the lead generation process is to filter out the bad leads from the good ones. AI can play a big role in identifying high-quality leads by using various marketing and prospecting techniques including email mining, content analysis, website traffic, and social media listening.

Research by Gartner suggests that companies that deploy AI for lead management can increase conversion rates by 30%.

Leadspace, a B2B predictive analytics platform, uses AI to analyze customer data and predict the likelihood of a lead converting into a sale. This enables sales teams to focus their efforts on high-potential leads, increasing their chances of success.

The advantage of having a diverse set of leads is that it maximises the chance of closing a sale. Companies that rely on a single technique, such as social media or telemarketing, tend to miss out on a lot of business because they limit their lead generation strategy and only focus on one or two methods.

5. Optimizes price

Sales representatives no longer have to assume what price will help them close a deal thanks to AI. To determine a suggested price, machine-learning technology analyses all sales data about customers, along with location, size, and previous successful deals.

AI algorithms can analyze market data, competitor pricing, and customer behavior to optimize pricing strategies in real-time. For instance, companies like Uber and Amazon use AI to dynamically adjust prices based on demand and supply conditions.

Gone are the days of static pricing models. AI revolutionizes pricing strategies by analyzing vast data sets, market trends, and customer behaviors. This data-driven approach allows companies to optimize prices dynamically, aligning them with market demand and customer preferences.

Pricing is personalised and time-sensitive for a specific customer, increasing the likelihood of winning the deal. AI also protects corporate margins by integrating pre-approved discount guardrails.

AI-equipped companies can respond to market shifts in real-time, leading to more competitive prices and improved customer satisfaction.

This not only shortens the sales cycle, but it also reduces the ramp-up time for new sales reps, allowing them to start selling right away without worrying about pricing themselves out of a deal or offering too large a discount. Optimal pricing enhances the customer experience by lowering the back-and-forth negotiations

6. Improved coaching

When sales managers are aware about pipeline health, they no longer have to waste time questioning sales agents about the status of deals. A sales manager can easily provide effective coaching to help sales reps enhance and drive better sales outcomes with precise, objective information generated by AI. Personalized training from sales managers can help a sales rep develop their talent, increase productivity, and seamlessly integrate sales processes with the user journey.

AI not only provides sales managers with real-time visibility into the pipeline health but also generates objective, data-driven insights.

Salesforce’s Einstein Analytics: With AI-driven insights, sales managers gain visibility into deal progress, enabling them to provide timely and targeted coaching to improve sales reps’ strategies and performance.

If you are in this dynamic field of sales, now is the time to get started. A little delay can cost you your precious time, capital and manpower. Fluid AI helps you to rejuvenate your business with AI, reach out to us, will help you right from the scratch!

Decision pointsOpen-Source LLMClose-Source LLM
AccessibilityThe code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation.The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer.
CustomizationLLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques.Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer.
Community & DevelopmentBenefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements.Development is controlled by the owning company, with limited external contributions.
SupportSupport may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance.Typically comes with dedicated support from the developer, offering professional assistance and guidance.
CostGenerally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance.May involve licensing fees, pay-per-use models or require cloud-based access with associated costs.
Transparency & BiasGreater transparency as the training data and methods are open to scrutiny, potentially reducing bias.Limited transparency makes it harder to identify and address potential biases within the model.
IPCode and potentially training data are publicly accessible, can be used as a foundation for building new models.Code and training data are considered trade secrets, no external contributions
SecurityTraining data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the communityThe codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment
ScalabilityUsers might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resourcesCompanies often have access to significant resources for training and scaling their models and can be offered as cloud-based services
Deployment & Integration ComplexityOffers greater flexibility for customization and integration into specific workflows but often requires more technical knowledgeTypically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor.
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