Modern technology is helpful in many ways. Consumers appreciate the intuitive features of smartphones and the ability to multitask with ease.
Machine learning algorithms enable apps on many devices to accurately predict what a user wants. This technology is helpful when ordering a pizza, storing passwords, and paying bills online.
But what happens when a sales team applies artificial intelligence (AI) technology to the traditional sales process?
The most commonly accepted sales process is broken down into seven general steps. Most colleges and universities teach this model due to its simplicity.
Moreover, this process breakdown leaves room for many sales and marketing theories to be introduced within each step.
Artificial intelligence is changing the face and methods used in the sales process.
Here is a brief review of how each step may look different due to the use of machine learning capabilities.
Training a capable human sales force requires an initial onboarding process. This phase introduces people to the product they will be selling.
Several factors, including turnover rate, when product upgrades, and new products introduction, will impact product knowledge. AI is changing this process by streamlining the relay of information.
Computers with AI capabilities only need one introduction to a product or service one time and will remember the details forever.
Learning capabilities can be further enhanced to include features that automatically update with information about the latest versions of multiple products.
Finding potential customers has long been the biggest challenge for sales staff. Although the system has advanced significantly from the days of door to door sales and cold calling people at dinnertime, it still presents an obstacle for many businesses.
Lead prospecting technology can uncover exponentially more data in one minute than several top sales representatives could deliver over a several-month period.
Through multiple points of contact, including search engine reports, machine learning technology can instantly produce potential customers.
Scott Smith from B2B Lead Generation Company Launch Leads sums up the new lead generation process well, “Artificial intelligence is about 10x more efficient at analyzing data and qualifying leads than any individual can, all at a lower cost. Compared to blind outreach tactics, such as e-blast or cold calling, AI prospecting allows a sales team to contact more leads who are more inclined to purchase your product, all at a lower cost per lead.”
Automation has also drastically changed the way an organization introduces products and services to the public. Hard sells and other high-pressure tactics are no longer required to motivate potential customers.
With AI methods, lead generation has become virtually foolproof. Automation simply presents information to prospective customers in their preferred delivery mode.
The key to effective sales is timing, and with sales automation, the timing is virtually always right. Customers are subtly influenced to view products at peak opportunities, creating a precise set of circumstances for a consumer to make a purchasing decision.
Artificial intelligence has also pioneered the needs assessment process for sales leads. This process may be a fact-finding mission where a potential customer is asked to review their current product or service provider.
Essentially, finding customers in this manner is an extension of the prospecting phase. The difference is that sales representatives can make the sales pitch when a customer is in front of them or on the telephone.
Sales representatives would then make an objective comparison between the existing product or service and that which they have to offer. This part of the customer relationship is akin to a first date.
Automated lead scoring devices take much of the guesswork out of this fourth step in the textbook sales process.
Like AI improvements to lead prospecting, the automated approach to needs assessment makes it easy to gauge when a customer is close to making a purchasing decision.
In this updated scenario, customers let the sales force know when they are in the market for a new product or service.
The physical presentation or sales pitch is often the most exciting part of the deal for an experienced sales representative.
There is more emotion evoked here than in the actual closing phase. Admittedly, sales automation lacks the zeal of the presentation process.
However, most business managers will gladly make that sacrifice if it results in a radical increase in closed sales.
Online advertisements are an example of the subtlety of the automated presentation process. Sales representatives must wait for the right time and usually must wait for a potential customer to invite them into a conversation.
Artificial intelligence allows various computer systems to approach the customer without intrusion but with excellent results. Moreover, self-learning technology enables software programs to learn about a customer virtually from their preferences and usage.
This automation creates suitable circumstances to make presentations to a customer that are immediate or can viewable at the convenience of the prospective customer.
Closing a deal requires a certain amount of personality and finesse. The automated process primarily uses the same criteria generated from lead scoring technology.
Closing the contract does not have a significant value to the computer or software program, but the success rate is recorded and used for future updates. Machine learning options use all the information garnered in any sales attempt to tweak the approach for similar profiles.
This automatic shift in processes accounts for the quick turnaround time in successfully closed deals. Computers do not take rejection personally; rather, they organize that data into the learning process and turn the information into a future win.
The traditional sales process includes a personal follow up with customers, which your sales team may conduct whether a customer made an actual purchase or not. There are two primary purposes of the follow-up process.
The first is related to customer satisfaction. Businesses benefit from learning about the customer experience because they can translate that data into future training. It is equally important to collect data from successful and unsuccessful sales attempts.
The information is usually compiled into a sales report and then analyzed by a team of experts who determine how the process should change to motivate future sales. Branding is the second reason a follow-up is essential to a company.
Companies who reach out to customers after a sales transaction are generally more favourably reviewed by customers. Even when a lead goes a separate direction, continuing to show an interest in them can create opportunities for future loyalty and referrals.
Automated sales processes do not neglect the follow-up process, although not as a personal follow up call from a commissioned sales representative.
Likely, automation will lead to a computer-generated email or survey request sent to every customer after the completion of a sale. Most machine learning technologies will automatically transmit the data from surveys into a self-learning program that adjusts for future opportunities.
These programs also alert the human sales force regarding potentially dissatisfied customers.
It will create opportunities for the sales team or customer service representative to reach out to customers directly when they are required to resolve a problem in person.
AI is the Sales Game-Changer
Research has linked access to machine learning technology to the long-term success of a sales team but knowing how to use that technology is also crucial.
As more teams turn to AI and automation to assist their sales process, teams that rely on traditional tactics will find themselves at a competitive disadvantage.
With all the innovative ways to incorporate sales, the myriad of insights it can provide and the freedom it provides a sales team, automation is clearly the future of sales.