Showing top 0 results 0 results found
Showing top 0 results 0 results found
Artificial Intelligence, the technology of the future, is already here.
Whether it’s Spotify recommendations, Google Ads, or conversations with Alexa and Siri, AI is ubiquitous. And the same AI can supercharge your company's sales. Do you want to forecast more accurately? Identify new sales opportunities? Boost productivity? Give your customers the best experience? AI gives you the tools to do all this while driving growth and profitability.
Firms have already started adopting advanced AI technology in their sales operations, knowing its transforming potential. More than half of high-performing sales teams globally have started using AI tools in their functioning, according to Salesforce. And the adoption is only going to accelerate in the coming years.
What makes AI so crucial for sales? Why are so many businesses ready to get on the AI bandwagon?
We’ll look at the basics of AI and its use cases. We’ll learn how businesses can leverage AI in sales and read about examples of companies using the technology. Finally, we’ll consider how companies can integrate AI into their sales operations.
What is artificial intelligence?
AI is nothing but teaching machines to perform tasks like humans are taught (probably better than we’re taught).
For example, it can be an AI-powered computer vision tool that automatically identifies objects and people in your Instagram picture. Or an algorithm Netflix uses to analyze your watch history and other data such as location, age, and gender to recommend movies matching your taste.
These applications employ AI techniques such as machine learning (ML), computer vision, and natural language processing (NLP) to learn from data and use it to mimic human intelligence.
Let’s look at two significant subsets of AI that make AI what it is. Knowing this will also help us identify and understand the type of AI technology we can use in our sales toolkit.
Machine learning enables a computer to learn from experience just like we do. The experience for the computer comes from the datasets we provide. ML algorithms analyze and identify patterns, and learn from the dataset to make predictions. It progressively improves on its learnings with every new finding it encounters.
A well-known example of everyday ML is a spam filter code in your email account. In sales, you can use ML to analyze massive data from customers or the sales channels like websites or apps and find patterns and insights. Finding patterns and insights into customer behavior can help you make intelligent business decisions.
Natural language processing
Using NLP, computers can learn to read, understand, and reproduce human language. The NLP codes help computers convert human speech into the machine language of 0s and 1s it can understand. Alexa, Siri, and Google assistant are all built on NLP. Speech recognition, language translation, and text data collection are also based on NLP. The chatbots you can find on apps and websites are an excellent example of NLP in a sales setting.
Apart from ML and NLP, there are other advanced AI technologies like deep learning, neural networks, and computer vision. The progression in these AI technologies, along with ML and NLP, opens new avenues for businesses to use AI-powered systems in their operations.
Take computer vision. It helps computers see and understand objects just like we do. Now consider using computer vision with NLP for chatbots or virtual assistants. A user could talk with a chatbot and send a picture of what they are looking for, which the chatbot can identify and locate. This tech could vastly improve customer experience and also increase conversion rates.
Beware: not all AI is AI
At this stage, it’s critical to remember that not everything marketed as AI-powered software is genuinely artificial intelligence. There is plenty of sales automation software that computerizes repetitive tasks but does not use AI tech.
The key is to distinguish between both. While automation tells machines what to do, AI teaches machines to learn how to do things.
Why is AI crucial for sales?
If it’s written that AI can do everything under the sun in the sales funnel and drastically improve sales performance, it is hyperbole. But AI can assist sales managers and reps in many tasks. From predictive analytics, sales forecasting, and prospecting to analyzing customer behavior for personalizing interactions and lead nurturing, the role of AI in sales is immense. It empowers sales reps to make data-driven decisions that deliver high value.
When AI is used correctly, sales can close bigger deals faster, shortening the sales cycles.
According to McKinsey, firms using AI in sales increased leads and appointments by more than 50%, cut costs by 40% to 60%, and call time by 60%-70%.
Imagine the value it creates for salespeople. They won’t be bogged down with data entry and other administrative tasks. They won’t exhaust their time on unqualified leads and struggle with ways to interact with customers. Instead, they’ll cultivate meaningful, profitable customer relationships and close deals.
Almost two-thirds of firms already employing AI believe that they have the edge over their competition.
The good news is that it's not too late for any organization that has not yet used AI in sales to catch up.
Does your sales process need a boost?
AI for sales: use cases
Consider a typical sales funnel of seven stages - prospecting, preparation/pre-approach, approach, presentations, objections, closing, and follow-up. Here’s how the sales team can use AI in all seven stages.
The sales team does two significant jobs in this stage - lead generation and prospect scoring.
Both are time-consuming and tedious jobs with little returns, and more than 40% of salespeople consider prospecting the most challenging part of the sales process. AI can be the perfect wingman to the sales teams in these critical tasks.
A robust AI system can identify customers' buying patterns to find prospects that are a good match. In this way, it can capture anonymous buying signals from the internet. Such a tool can also quickly scrap all the publicly available information about prospects from the internet, be it LinkedIn or other professional networks or social media like Facebook or random websites. It can automatically create customer profiles that the sales teams can use. And thanks to ML, the system can also easily segment customers that both sales and marketing teams can use.
Lead scoring and prioritization
Throw away the guesswork in deciding which prospects you want to pursue. Determining which prospects are likely to buy is easy with AI’s predictive algorithms. It can identify patterns of customer buying behavior and recommend leads to prioritize, saving time and energy for sales reps.
Case in point
Dell uses AI-powered software from Lattice Engines to identify and qualify leads. The system analyzes the purchasing patterns of customers that buy Dell products and matches them with the information on prospects it gathered from the web to pinpoint high-quality leads. As a result productivity and efficiency of the sales team using the software almost doubled.
We can also employ NLP to extract keywords, sentiments, and emotions from data we have on customers and perform sentiment analysis to predict the probability of their making a purchase.
For example, imagine the system learns that users who visit a website three times within two weeks are 40% more likely to buy something. With these cues, sales teams can take steps to convert browsers to buyers with personalized offers and optimized websites. Reps can use these indicators to brainstorm strategies for cross-selling or upselling.
Preparation and approach
Once we identify priority customers with lead scoring, the sales team can use AI in several ways in the following stages of preparation and approach. We can automate and personalize communications to prospects using AI tools. When we hear back from our potential clients, we can use that info to target the right customers at the right time with the right message.
Sales automation is pretty common these days. Many organizations have automated the routine tasks around making the sales approach, such as emailing inbound and outbound leads, scheduling calls, sending follow-up emails, and promoting content.
AI-driven personalization can take such automation a step further.
An AI assistant or intelligent bot can engage in a two-way conversation with a prospect with little to no human intervention and nurture the lead. The AI bot does the work of analyzing customer data and learning specific cues about prospects' behavior. The sales team relaxes and waits for the bot to share its insights when it’s time for human interaction. This automation frees sales professionals from low-level sales activities and allows them to spend their time on value-add contributions.
A sophisticated AI tool creates and delivers highly tailored content for leads based on their profiles and previously viewed content. It can also identify patterns in best-performing promotional content and offer real-time recommendations about messages and channels customers like the most, boosting the chances of sales.
It also removes a lot of guesswork and friction in communication for sales professionals.
Moreover, eight out of 10 customers are more likely to buy a product or service when companies offer a personalized experience.
An excellent example of personalization is Amazon's "you might like" recommendations we see when we add something to our Amazon Cart.
Case in point
Ingersoll Rand uses AI to automate and personalize dynamic content based on their customers’ profiles, locations, and weather conditions. Customers who have recently been through a flood get automatic emails about how to check their heating systems after bad weather. This automation and personalization substantially increased Ingersoll's campaign's open rate, click-through rates, and conversions, ultimately driving engagement and loyalty among leads and customers.
When the sales reps talk with prospects about their product, they can use AI tools to optimize their presentation to the customer. AI-driven presentation coaches give real-time feedback on your presentation to course-correct as you go.
If the rep is going too fast with a sales presentation, the AI coach will prompt them to take a minute and slow down. This can significantly improve sales pitches.
Another exciting use of AI is conversational intelligence. AI tools like this can analyze a salesperson's call and meeting scripts to examine them and provide insights on behaviors that lead to better conversions. Sales leaders and managers use these features to train their sales reps.
An AI system with ML and NLP can capture verbal and non-verbal cues in communication with prospects and run real-time analyses to determine the sentiment of potential clients as they are in conversation. Sales reps can use these insights to decide on the next best action.
For example, you’re on a call with a prospect, and you get a cue card from the AI tool running in the background -
"This prospect is ready to buy; offer this customized deal," or
"The prospect is doubtful; share this case study and working video" or
"The prospect is considering a competitor's product; talk about the pain points of a competitor."
Imagine how helpful such a tool could be to your salespeople. Sales managers and leaders can also use such a tool for ramping up a bunch of new hires.
Case in point
Manufacturing company Honeywell uses conversational AI tools in their sales kit to engage customers and coach their sales reps based on reports from calls and meetings. Using AI-enabled sales tools increased not only its customer encounters but also gave an estimated topline benefit of $100 million a year.
Objections and closing
While sales reps have to rely mostly on their judgment, interpersonal relations, and experience at this stage of the sales funnel, AI tools can augment the process. Like AI collects and provides customer intelligence, it also gathers up-to-date intelligence on competitors. When facing customer objections, the sales team can use this intel to build its arsenal of competitive battle cards.
Another common use of AI in this stage is price optimization for customers. An ML-powered system can efficiently study large sets of historical price data and give the best number for different customers.
Case in point
Retail giants such as Amazon, Walmart, Target, and companies like Uber and Airbnb use AI-powered dynamic pricing tools to offer the best price for their customers. AirBnB's dynamic pricing tool boosted its revenue by about 9% for its host.
Thanks to CRM tools, most order processing has already been automated. Now, AI systems can study the CRM data and prompt sellers when they see an opportunity to cross-sell or upsell to buyers.
Case in point
When you’re buying something on Amazon, the company uses a machine-learning algorithm to provide their “Customers also bought” feature.
Hyatt Hotel’s predictive analytics use ML algorithms to study customers' historical data. The AI-powered system automatically prompts the hotel's desk agent to ask the customers if they want an upgrade or more information about offers, creating chances for cross-selling and upselling. The hotel chain reported a significant increase in average room revenue with this system.
Other areas where sales leaders and managers can use AI include sales forecasting using predictive analytics and spotting demand surges with ML and NLP algorithms that can crawl the internet.
It’s crucial to note that AI augments the sales process but doesn’t replace sales reps. Most firms that have integrated AI into their sales team plan to hire more sales professionals to get the maximum potential from AI and human intelligence.
If you've read this far, you’re gearing up to add AI tools to your sales operations.
Here’s how to prep for a successful transition:
- Align your AI strategy with business and sales goals
It’s essential to devise your AI strategy for your business and your sales team to reboot your sales organization with AI successfully. Have a clear understanding of your business's financial goals and needs before planning your AI strategy.
Set realistic objectives and action items about what you want to achieve with AI. Consider these questions when you are strategizing your AI plan for sales:
- What is your current sales model, and where does it need help?
- Where and how do you want to use AI in your sales funnel?
- What is the goal of using AI tools or platforms?
Plan your AI strategy around the essential sales processes to gain insights and results.
2. Build tech and data
Choose your AI platform and tools based on your business needs.
Before selecting your AI platform or tool for your sales organization:
- evaluate your in-house tech capabilities,
- examine your existing sales tech and CRM tools,
- see the kind of data your sales team has - whether it’s integrated into a system or fragmented,
- assess the datasets you need for a particular AI tool.
If you have fragmented data across systems and use different sales and CRM tools, it’s best to get a unified tech stack with a common data model. It will help make AI adoption easier.
Also, consider offerings from your current sales enablement tools provider.
- Do your current tools have or support AI tech? Are your current sales tech vendors investing in AI?
- Can you get an upgraded version of your existing CRM tools with AI functionalities?
Before choosing your AI tools it’s crucial to consider these questions to avoid needless hassles during and after adoption.
There are plenty of AI tools on the market you can leverage with your in-house tools to get the best outcome. It’s okay to experiment with a few tools before finding the right one for your team. But make sure not to be waylaid by a cool-sounding AI tool that would be of no use to your business. Too much AI could also spoil your game.
Always remember your goals and needs to employ the tools accordingly.
3. Train people
More often than not, a lack of knowledge among employees about AI can derail businesses' quick adoption of AI tools. So before implementing your AI strategy, get your sales team on board. Sales managers and leaders need to train their sales professionals and support staff on the basics of AI.
They need to know how to use AI tools and interpret AI-generated data and insights to leverage it effectively. Only if they understand it can they identify any anomalies or inconsistencies.
Also, think about potential changes using AI might bring to the nature of the day-to-day work of your sales team. Upskill resources accordingly.
Finally, start executing your AI strategy. Please don't shy away from scaling it across teams as you eke out small wins. If a particular AI tool doesn't work well for your team, don't stop experimenting with other AI tools. Companies that complete the last mile of execution taste massive wins despite a few roadblocks and failures at the start.
Now that you are ready to use AI to increase sales and boost your bottom line, here are a few AI tools that you can consider.