The rise of artificial intelligence has changed dramatically almost every sphere of our lives from industrial assets to marketing – and business is no exception. More and more companies are leveraging the power of machine learning and AI for sales to increase their productivity, save time spent on regular manual processes, reduce expenses, automate task management, make accurate forecasts, and consequently, sell more.
With artificial intelligence becoming smarter with each day, the venue of selling is shifting from the individual toward the machine, meaning that AI now can handle the entire pipeline without human intervention. Yes, the role of artificial intelligence in sales is immense and its evolution is fast-moving so that businesses have to adapt to this game-changer to stay afloat.
In this article, we will touch upon specific areas where AI algorithms can be used to help your business pick up momentum and assist your sales team close deals faster and sell more.
Taking Predictive Forecasting to The Next Level
In a highly-competitive business world, big data is one of the key drivers for companies to enhance their sustainability. When artificial intelligence and sales play together, this high-yielding team will allow enterprises to identify and find more leads, bring in-depth insights, as well as accelerate customer understanding by using personalized data collected over time.
In a nutshell, using AI software, sales teams can collect and process such data as the previous order history of customers, their interests, preferences, pages they view, products they buy, as well as items they might need along with the existing order. For instance, let’s assume that a product A and product B is a popular combo bought together by customers, and 75% of people who ordered the combination also purchased a product C along with it. With the help of AI-powered algorithms, you can easily analyze the remaining 25% of the prospect and suggest a product C as part of cross-sells.
More and more companies have started to implement the power of artificial intelligence in sales forecasting to gain useful insight that will help to delve into what happened and advise what can be done to improve a scenario.
Efficient Lead Scoring
Lead scoring is a robust model that helps both marketing and sales teams identify which prospects are potentially more valuable to the enterprise and its sales funnel. The power of predictive lead scoring enables sales reps to determine which parameters define a hot lead, create a strong scoring model based on your funnel, as well as to be flexible enough to adjust to an ever-changing market.
Hence, with artificial intelligence in sales and marketing, your managers can better judge how close a prospect is to make a purchase. They can easily look at where the lead is in the funnel so that employees can prioritize their tasks and time to focus on the right group of prospects, and consequently be more effective.
As such, Salesforce – one of the most popular CRMs – provides its users with Einstein Lead Scoring that adds the specialized field to leads. The Lead Score lets reps to sort out priorities by ranking prospects as per their similarities to prior converted leads.
However, configuring this Salesforce CRM feature can be a challenging task for an inexperienced team. So, in some cases, it is better to address Salesforce consulting services for support.
Artificial Intelligence And Sales Automation
With AI tools serving as virtual assistants, companies can release marketing and sales professionals from some routine tasks and free up time to handle more involved responsibilities by automating many processes via artificial intelligence. These tasks may include lead scoring and follow-ups, creating templates, forecasting, data entry, pipeline management, meeting scheduling, etc.
Such solutions can be integrated with existing CRM software or work alongside other programs, but some of them are also standalone AI-powered mobile apps.
The greatest part about AI in sales automation is that your staff can spend more time actually selling. According to a recent study, The average sales rep spends 63,5% of their working hours handling admin tasks including paperwork, data entry, internal meetings. Robust tools driven by machine learning will allow your team to focus on selling. Click To Tweet
Better Customer Experience with Chatbots
While AI in sales automation holds huge value in terms of time and cost savings, there is another area in which artificial intelligence promises greater returns – customer experience. It has been said that customer experience is a competitive driver of sustainable growth but at the same time a tremendous source of risk when falling. The technology has already transformed customer experience in many ways:
- By empowering self-service. The uprise of smart chatbots has eliminated the need to wait for an agent to help with an issue. The virtual assistants are able to answer customer questions, take or cancel an order, recommend a product using the data collected.
- By improving personalization. According to the stats, the majority of consumers will only engage with personalized marketing messages. As such, a growing number of companies are getting rid of cold emails and are turning to AI services to create marketing and sales campaigns targeted directly to each prospect.
- By providing visual, voice, and text engagements. For a long time, the inability to identify humans’ emotions and respond to them correspondingly has remained one of the greatest challenges of AI in sales management. But this confront has been overcome. For instance, the AI-driven tool Cogito provides its users with real-time conversation guidance and analytics. The software takes customer experience to the next level by identifying and analyzing non-verbal cues to give hints to sales agents on how to route communications, increase lifetime value, and achieve clients’ satisfaction. With data science consulting services, you can find out how to implement such a solution to your company’s processes.
Artificial Intelligence for Sales Leads: Building Lead Database
Apart from AI sales management, there is another area in which the power of innovative technologies can shine – list building. Creating an omnichannel strategy is essential for modern businesses as companies should not only work with existing leads but also constantly look for new ones. As such, organizations spend most of their time prospecting for the right people that would be interested in making a purchase.
Due to the ability of the “computer brain” to parse large amounts of data, intelligent tools can help find new leads based on what is known about the current ones. In other words, using artificial intelligence for sales leads will streamline and automate the processes by analyzing different channels and various databases to find people that look like your potential clients.
Improving Sales Pipeline Quality
And the last but not the least, the ability of artificial intelligence in sales to improve the overall quality of a pipeline. Due to its predictive power that can shed light on the entire pipeline, your pros can see which strategy works best and which investments are likely not to return. Eliminating blind spots that occurred due to human bias and limited visibility will definitely boost your sales. As a result, you will get a more productive marketing and sales pipeline leading to increased revenue and reduced costs for your organization.
It is time to get started with AI now to build a competitive advantage. At the same time, a delay means the risk of getting left behind. All the use cases of applying artificial intelligence for sales described above will enable professionals to get back to do what they do best – actual selling. All forward-thinking companies have already started embracing AI today. With so many advancements associated with technology adoption, your sales reps should think in terms of how to use innovation rather than whether they should use it.
Alex Husar, a CTO at Onilab with 8+ years of experience in Magento and Salesforce. He graduated from Czech Technical University and obtained a bachelor’s degree in Computer Software Engineering. Alex’s expertise includes both full-stack dev skills and a strong ability to provide project-critical guidance to the whole team.
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