AI today has revolutionized the way in which communication, sales, and marketing tasks were performed. Data analytics and AI predictive tools have been key factors in doing so. Today, most of the sales work has been automated by the use of AI software and advanced machine learning.
Today, all the modern technology is making rapid advancements in sales and marketing campaigns. It uses advanced machine learning and AI analytics to perform cognitive human actions. Most sales companies today are utilizing AI for sales management. It performs its function by collecting data from the past, analyzing it to predict future demands and sales, and forecasting the outcomes. Hence, it helps optimize prices according to market trends.
According to Gartner’s research, in the next three years or so, we are going to see the involvement of machine learning and AI software in around 70% of customer experiences. Hence, the companies who are smart and fast enough to inculcate AI for sales are going to have a massive benefit in the future and are definitely going to stay ahead of the competition.
The few primary components of AI guided marketing and sales management include:
- Proper execution of sales process steps
- Forecasting business outcomes
- Collection and detection of buyer signals
- Incorporation of correlation models
AI for sales boasts a large number of applications ranging from price optimization to sales predictions and lead scoring and prioritization. Some of the applications are described in detail here:
Predicting the sales:
AI for sales uses past data and uses the AI analytics to make common predictions that can help in forecasting sales and management of the future such as:
- Identify the leads that will buy the new products
- Predict upselling and cross-selling of services
- Predicting the time when the company is most likely to make a sale
- Deals that are on the verge of closing
- Prospects that should be targeted next
- Forecasting the percentage of new customers that might be interested in buying new products
- Creating value for sales teams internally
- Extrapolating the information to predict the sales in coming time so necessary steps should be taken in advance to ensure the best outputs
Around 36% of companies use AI for forecasting and predictions. By ensuring the right input of past data and analyzing it by using proper AI tools, AI for sales will most definitely cause a 360-degree change in the entire marketing industry.
Support sales with relevant customer experience
Targeting customers who like to expect personalized experiences is not possible without using artificial intelligence in sales. AI-based analytics and strategic modeling help lighten the workload and enhance customer experience.
Deep insights are obtained by collecting data from the past experiences of an individual customer and such data can then be used to personalize and engage different visitors to the targeted audience segment. It results in one-to-one value marketing which was not possible before AI for sales.
Lead scoring:
Sales and marketing departments of companies identify which prospects are most important to them through effective lead scoring models. These models normally work properly when they are used on a daily basis or else, they are a waste of the company’s money, time, and resources.
Victor Antonio explains in Harvard Business Review that human salespeople approach lead scoring and prioritization in an unscientific way.
Normally, such decisions, when made by a salesperson, involve factors such as a gut feeling or an instinct which can render the effects less beneficial. AI for sales management can bring maximum logic and standardization to the process by collecting all the relevant information from the past experiences, emails, and social media postings of a client and predict the opportunities for the success of their deal with their new client.
Recommendations:
Based on the insights from past data, artificial intelligence in sales can provide the marketing companies with expert-level recommendations so the bandwidth to close deals can be freed up on time. Recommendations are given by AI for sales often include:
- Best way to price a deal
- Perfect timing for the closure of a deal
- The audience that is to be targeted first with up-sells and cross-sells
- Telling sales teams to take actions that are making the best sense for the whole system
The result of these exceptional recommendations is that the system can make sure the sales plan is giving the best outcomes and no time or resources are being wasted on other plans that would rather be a failure. All this success definitely owes it to the AI for sales.
Implement Artificial Intelligence at work to meet the changing expectations of teams around the world
Scale messaging:
Understanding the language of your target audience, approaching their questions in a business as well as a data analytical approach and ideal customer profile is important for a company to be successful.
For example, Google has been using AI for sales for a very long time now and its machine learning program RankBrain provides the best possible search results whenever someone types a query in its search bar. Thus, AI for sales helps in scale messaging and personalization of customer experience.
Sales productivity:
The automation of sales work by incorporating AI for sales management has helped increase the productivity of sales to a large extent. Artificial intelligence in sales can predict the whole schedule by using all of the historical data to plan stuff like:
- Date, time, and location of a meeting
- Scheduling of important sales
- Assessment of a sales team’s productivity
- Reducing the workload on salespeople by automation of tedious tasks
- Giving more free time for planning the next big sale
Concluding meaningful analytics:
Collecting and aggregating marketing data is not the only purpose when AI for sales management is incorporated into the system. They help the investors and marketers to draw meaningful analytical results from that data so a personalized customer experience can be ensured. Anything that requires change or an adjustment is flagged and creative solutions are then made into use to correct it.
Increase in engagement using AI Avatar:
AI avatars use NLP (natural language processing) algorithms to increase human interaction since humans tend to interact more comfortably with humans rather than with machines. AI-powered technology made good use of this fact to create these human-like bots.
Sales and marketing domains of different companies have recently started using it to enhance their customer experience. The avatar keeps learning through the input data and makes digital connections with various leads using this intelligence.
Predicting sales
AI for sales management identifies the patterns and leads that most likely bring profits. Sales representatives and managers use AI for sales to bring profitable deals and promising outcomes. AI algorithms identify the past data deals, synchronize all the data points, and suggest the best possible techniques to utilize these algorithms. Artificial intelligence helps increase sales productivity by:
Automation of repetitive and mundane tasks e.g., data entry
Sales forecasting
Effective prioritization
Gives detailed analytics
Optimization of price:
All companies that generate big revenues have competitors. Therefore, it is of considerable value that each marketing company should optimize its prices while considering the customer’s preferences as well as competitors’ pricing information.
Machine learning and tools for AI in sales scrape the web using their algorithms and provide sufficient data on their competitor’s prices. It then provides pricing recommendations that would suit both the company and the customer and are significantly better than the competitors.
Time for strategy development:
Artificial intelligence in sales sifts the workload of mundane tasks through advanced machine learning and gives the salesperson to utilize this time to better strategize their plans to ensure the maximum efficiency of their plans. This formation of a world-class efficient strategy to create a valuable customer experience can be done through:
- Consolidation of data
- Refining customer profiles
- Lead scoring and optimization
Rise in leads:
A business lead is defined as any person who is interested in buying the product or service that you are selling. Leads are generated by companies from a number of resources. But connecting all pieces of information, qualifying leads, and following them up is time-consuming. AI for sales reduces the time for all these activities and gives ample time to reach out to the targeted audience and consequently, rises the leads. 1 in 3 companies uses AI for lead scoring and prioritization.
Requirements for incorporating AI into sales management:
If you’ve finally decided to incorporate AI for sales into your marketing department, there are few a basic things that you would need to know in order to get started:
- Basic understanding of AI’s key technologies
- Critical skill learning
- Hand-on experience and experimentation with technology
If we go into more detail, the following detailed steps should be properly understood in order to delve into an AI guided sales project:
- Data Training: Since data is the most important tool for AI in sales, it is important to have error-free and reliable data that can be fueled into AI software to give reliable outcomes. The Dell Data Maturity Model is a perfectly designed model to identify at what stage your company is standing on the data maturity level. This model has four important stages
- Data Aware
- Date Proficient
- Data Savvy
- Data Driven
According to Gartner’s research, 87% of the companies are at very low levels of AI analytics and data maturity. For artificial intelligence in sales to succeed, it is important for the company to be standing at least Data Savvy or Data Driven stage.
- Choosing the correct business use case: Always choose a small and relatively easy to deal with a use case to get stakeholders to buy and then inculcate the results of this use case to predict significant outcomes of future projects that are complicated. The most important thing while choosing a business use case is to set up your lead prioritization which cab be either demographic or behavioral.
- Demographic priorities include the company’s size and location
- Behavioral priorities include downloads, visits to the website, and the rate at which emails are opened.
A good business use case should have the following characteristics:
- Personalization of sales content
- Optimization of prices
- Up-selling and cross-selling
Gartner prism can be used to pick the best use cases in terms of productivity and feasibility. According to the HBR survey, most companies are using AI for sales management to get the right clients and prospects for their sales.
- Setting your goals: In order to make the best use of artificial intelligence in sales, it is of utmost importance that you know what your goals are and what you are expecting from the outcomes. Following are some of the important goals that can define the success of AI for sales:
- Obtaining maximum revenues from your AI guided project
- Ensuring the best customer experience
- Make certain that the sales team is happy with the outcomes
- Constructing a roadmap for AI for sales: In order to construct a roadmap for your AI guided business setup, here are two feasible approaches:
- A data-driven approach that answers questions such as:
- The type of data available
- The problems that the available data has the capability to solve
- Finding solutions to these problems by using this data
- The end results of these solutions
- A business-driven approach that deals with questions of the problem rather than their solutions such as:
- Who has a problem?
- What is the problem?
- How can it be solved?
- What type of data is required to solve this problem?
- AI for sales: Adoption and adaptation to change: While incorporating AI for sales management, it is important to make sure salespeople know that the new technology won’t be replacing them so they stop seeing it as a threat and brace for the changes happily. You also need to make sure that the real problems are properly detected before implementing AI because data-driven AI for sales is a problem-based method. Proper detection will lead to proper solutions and outcomes. Another HBR survey found that around 71% of respondents agree with the fact that poor tech negatively impacts salespeople.
Some of the important steps you must take to ensure adoption include:
- Proper communication with the salespeople
- Comparison of work outcomes before and after using artificial intelligence in sales
- Internal marketing
- Tracking all metrics
- Keep a room for necessary modifications
Always gain the trust of your sales team before picking up any project that makes use of AI for sales. Once it’s done, you are free to proceed with your project.
AI tools for sales:
AI for sales management requires some important tools and software to guarantee maximum success. Some of the best AI tools for sales include:
- Drift
- Coversica
- Salesforce
- Exceed.ai
- Crayon
How Artificial Intelligence in sales can increase revenues?
AI for sales management has widely revolutionized the aspects of marketing and sales management. The increased efficiency, precision, and advancement of predictabilities have increased the annual gross revenues to surprisingly high levels.
Future of AI for sales:
In 2018, only 21% of sales teams were using AI for sales. The numbers increased by 155% in two years making it to 54% in 2020. Artificial intelligence in sales has the ability to cover a wide range of complex problems and strategic conversations. In the future, AI for sales is going to make important decisions in situations where humanity gets skeptical or starts involving its emotions. Within the next two or three decades, all the sales projects and marketing companies, especially those that are involved with functional scheduling are going to face a drastic change in their methodologies and resultant outcomes.
Limitations of AI for sales management:
- Salespeople need to have a good understanding of their company, product, market, and the kind of tools they are using for incorporating AI in their sales.
- Since data is the main fuel of AI for sales, it is important to extract data from multiple sources than a single one.
- Some salespeople find it hard to adopt AI for sales management because of the fear that AI might take their jobs.
- AI often teaches itself how to solve a problem b using past data. This can be problematic when human lives are at stake.
Therefore, it is important to continually monitor what kind of data is being fed into AI for sales.
Will it lead to the replacement of salespeople?
It is a normal perception that AI for sales might lead to the replacement of salespeople. And consequently, there will be a devastating rise in unemployment because machines would be doing more work in an efficient manner with rather less use of the resources that human salespersons can do. But is this fear actually plausible?
Let’s look at it the other way. We know that AI for sales management is doing more of augmenting salespeople than replacing them. Before AI, salespeople had to overwork themselves, putting their efforts into stuff that would take a considerably long time with little fruit. It can be said without conviction that artificial intelligence in sales revolutionized that. Some of the AI tools for sales are so advanced that they can have two-way conversations with clients. Whenever there is a need for human intervention, the lead is handed to a human salesperson. So, we can say with conviction that any fear of AI for sales replacing humans is baseless. Instead, this advanced learning is providing the human salespeople with more power to maximize their outcomes.