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How AI tools help in sales: from finding leads to closing deals

AI is gradually entering those niches that have always been considered exclusively tied to humans. One of them is sales. According to Harvard Business Review, companies using AI in sales increased the number of potential customers and meetings by 50%. And AI also saves time, increases sales productivity and takes on routine tasks. The Ringostat platform investigated what ready-made solutions exist to help sales managers. This way, businesses will be able to entrust AI with the lion's share of routine tasks and focus on closing deals.

According to statistics, only about 21% of the time of a B2B sales manager is spent on the generation and analysis of leads. And the narrower and more specialized the niche, the more difficult it is. We have to look for potential customers in social networks or online catalogs, visit their websites, search for contacts of responsible persons and manually enter them into CRM. 

Therefore, AI is increasingly attracted to the search for customers. For example, the ZoomInfo solution has a database of contacts and searches for them. Other AI's can search for a query in real time — like . The user needs to enter the characteristics of the target audience: in which departments it works, the size of the company, keywords by which buyers can search for goods, etc.

The sales manager receives a list of potential customers with emails, phone numbers, company information and profiles in social networks. Such AI is often usually integrated with CRM, so the received contacts can be imported into similar systems and continue to work with them there.

List of leads obtained by . By clicking on the button, you can go to the contact transferred to the CRM. 

There are also AI that analyze past transactions, identify patterns, create a portrait of a potential buyer and offer contacts of persons who correspond to him. To do this, the solution accesses its own database, which can contain millions of company records.

Another way to find "warm" customers is to analyze who visits your site. For example, there is a Leedfeeder service that integrates with Google Analytics and collects contacts of visitor companies. Contacts can be automatically divided into categories according to the level of engagement. This makes it easier for the sales team to determine the priority of lead processing.

There are AI that assess in more detail the benefits of different leads for the company and their propensity to buy — that is, they conduct scoring. gives points to potential customers. For example, analyzing their behavior on the site and interaction with a certain type of content. Based on this, the AI makes a conclusion which customer is more likely to place an order. Let's say the person who uploaded the price list is obviously closer to conversion than the one who just subscribed to the blog. And a visitor who has been on the page of an expensive product for a long time is likely to bring the company more profit than someone who quickly viewed a cheap product.

The company sets the criteria by which the system evaluates leads independently. For example, for an online service, the criterion may be the number of functions that the client used during the test period. The more — the higher the probability that the user is interested enough to pay for the service. 

Thanks to this, sales managers can focus on potential customers with high scores. And users with low scores can not be contacted at all — or do it last. Moreover, the AI is constantly learning, finds hidden signals and interprets data sets. Therefore, such scoring is more accurate than conducted by a person. In addition, the AI can advise when it is better to contact the buyer.

Platform sets the scoring, shows the latest signals that influenced it, and the source of information. 

Chatbots with AI are also able to score clients and pass the most promising ones to the sales department. This allows you not to distract employees in vain if the user asks general questions or just needs more detailed instructions or clarifies the price. 

According to statistics, managers spend only 33% of their working time on sales. One of the reasons is mechanical actions that do not have a direct impact on profits, but without them, transactions will not budge. Let's look at a few universal tasks that AI takes on.

According to AI allows you to automate up to 90% of the processes associated with data entry. The same service can, for example, collect contact information from emails and calendar events and record it in CRM. The AI analyzes who is the author of the letter received by the manager, or who was a participant in the online meeting. The solution looks for where the name and other information is indicated, and creates a contact in the CRM or records there that an interaction with the client occurred.

The AI can also collect information about the buyer from his signature. The solution will independently understand where the name is, where the phone number or Skype is, and where the position and company are, and will record this in the appropriate fields of the contact card in CRM.

Here, the AI works in several directions, recognizing patterns in your schedule and organizing it.

  1. It can increase or decrease the time for a recurring event, depending on how long it has been taking you lately. Let's say the last few daily meetings with the team lasted 20 minutes longer. The AI will take this into account by correcting the following.
  2. Analyzes at what time you are more productive, and can schedule priority meetings and tasks for it. When integrating with messengers, colleagues will see a note that you have "focus time" — so you should not be disturbed now.
  3. If necessary, it can move more "flexible" meetings — for example, a regular one-on-one with a colleague. Of course, if it does not cause a conflict in the calendar of another employee. 
  4. It can automatically correspond with the person who needs to make an appointment. For example, the Clara AI can be added to a copy of the letter so that it helps to choose the optimal time of the event. The assistant will then communicate about this with the addressee independently and book a meeting.

Example of an email about the time and date of the meeting, automatically generated by Clara.

During the meeting, Sales should take notes so that later he can remember what was discussed. Or review the record later, describe the key points and write a letter to the client with the result. Now AI can be responsible for this.

For example, records the meeting and makes a text transcript of the replicas, specifying a timestamp for each. The AI creates summaries of meetings that give a complete picture of the conversation. Sales needs to copy it and send it to the client to record the agreements that were reached during the meeting.

An example of a meeting transcript made by Airgram

Another of the regular tasks of managers and team leaders of sales departments is listening to audio recordings of calls. At the same time, you also need to make notes with timestamps if the manager made a mistake or had to use another argument. A "smart" assistant can take on such a task. To do this, you need to use a virtual PBX with AI — Ringostat became the first such platform in Kazakhstan.

When using AI, additional information about each call appears in cloud telephony reports. For example, a text transcript of a conversation that is automatically translated into English. So the manager or team leader can control employees who communicate with clients in other languages.

In the Ringostat platform, you can read the transcript of the dialogue and listen to the desired replica by clicking on it.

The AI also records the brief meaning of the conversation — this, like transcribing dialogues, saves time on monitoring calls. If necessary, you can use filters to find only those calls where it was about a certain thing. For example, it will help to check whether managers adhere to the script or do not forget to inform customers about a new product. 

In addition, the AI analyzes the general mood of calls and separately the buyer and manager. So, you can filter out only those calls where, for example, the client was disappointed or negatively configured. And correct the situation in a timely manner before it negatively affects the transaction.

Managers sometimes have to write dozens of emails daily, and here AI can help in several ways. Of course, ChatGPT or Bard can be involved in writing letters, but there are separate solutions designed specifically for sales. They are most often integrated with CRM, so the history of communication with customers will be recorded in such systems.

Simpler solutions, for example, the Writer application, are designed mainly to save time and work with sales emails according to the following principle:

  • you enter the name of your brand and describe its capabilities;
  • specify a call to action — for example, to take part in a sale;
  • after that, the AI searches for additional information about your company in open sources and creates a letter template as if it was written by a person;
  • at the same time, the application itself describes the capabilities and advantages of your brand;
  • also, the AI can advise the subject of the letter, which will interest and force it to open.

More advanced solutions help to personalize the letter — and so build stronger and warmer relationships with customers. For example, according to the customer's experience this approach helped to increase the number of responses by 10-15%. It works like this:

  • you upload a table with company names to the service;
  • AI analyzes their websites, blogs, events, publications in social networks, looking for something to "catch on to";
  • creates a personalized letter — for example, he can start by attending a company conference, reading a study, seeing the news about receiving an award, etc.;
  • organically fits the call to action.   

Similar solutions also collect mailing statistics and assign points to customers, depending on how they interacted with the emails.

With personalization settings in you can select the sources of information and their priority.

There are also built-in AI for CRM, which can automatically generate emails using the available information about the client and the transaction. For example, if there is data in CRM that a person has attended a webinar, the system can compose a letter of thanks for visiting and offer to demonstrate the product. You can also ask the AI to rewrite the letter in a certain tone — for example, more friendly or professional.

There are separate AI-based solutions that are able to predict the probability of a sale and other business indicators: demand, income, expenses, etc. They can collect data from thousands of sources, analyze market trends, sales history, assortment, data from CRM, ERP and social networks, and even take into account seasonality. Such AI use complex calculation models and can quickly analyze big data. But, of course, they require complex configuration and involvement of specialists in this.

Other AI is "sewn" directly into the CRM, and the team can use them without having technical knowledge. Let's say such a solution can predict how likely a sale is to a specific customer.  

For example, the well-known HubSpot system analyzes leads and predicts whether it will be possible to close a deal within 90 days. To draw such conclusions, AI, on the one hand, relies on data on client activity. For example, the total and average number of pages viewed on the site, the number of emails delivered, responses to them or rejection of the mailing list, the date of the last visit to the site, etc.

On the other hand, the AI pays attention to the customer information in the CRM: the number of entries in the transaction card, how many days have passed since the last connection with the lead, when the next activity is scheduled, etc. Attention is also drawn to whether there is a phone number in the contact. If the client has not left it, the probability of communication with him decreases, and with this the probability of closing the transaction.

By predicting the probability of a lead sale, HubSpot shows positive and negative factors affecting the valuation.

Some platforms have a so-called "AI assistant" who can also provide advice for the best next steps on a deal. The convenience is that such a solution adapts to the behavior of customers and improves over time. 

If we are talking about CRM, then such a system again draws attention to the numerous signals that the contact generates. The pages of which products he visits, which emails he opens, whether he follows the links in them, what he bought and how long ago. The AI can also be guided by the programmed rules and monitor the transaction indicators to analyze how to improve them.

In accordance with this, the AI advises which products to recommend and how to increase the average check. Let's say a user applied to buy a certain economy-class product, but often visits the page of a more expensive analogue. The AI may advise you to send a letter to the buyer with a discount on a premium product. Also, the assistant will offer related products to something that the client is already interested in. 

For companies that do not use CRM, there are similar solutions for working with calls. Artificial intelligence analyzes the meaning of the conversation and advises what to do during the next conversation or before it. For example, prepare a detailed comparison with competitors, send documents, arrange an online presentation.

An example of the advice that the Ringostat AI provides after the call. At the next communication, the manager must find out whether the product really solves the customer's needs well.

There are also solutions that analyze the activity of customers in social networks and their manner of communication. Based on this, the AI makes a psychological portrait and even takes into account the phrases that a potential client uses most often. As a result, the manager receives advice on how best to adapt his messages for each customer and which topics or expressions are best avoided. 

  1. AI most often learns from the company's databases. And the correctness of AI work will depend on the "purity" and relevance of such a database. Before you train him, make sure that your data is complete, does not contain duplicates and errors, and is updated on time. 
  2. If you use AI for forecasts, at the first stages check whether its forecasts converge with what you calculated manually. Otherwise, you may notice the error too late.
  3. When it comes to complex transactions with large companies, a "live" manager still plays a decisive role. After all, personal connections, to a certain extent charisma and many years of experience are of great importance here. 
  4. If the client is very unhappy, transfer it from the bot to the person as soon as possible. Direct contact with the manager will smooth out the negative impression, showing that the client is important to you.

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