Learn the pros of artificial intelligence and how you can use it for your business from SalesDirector.ai’s CEO Babar Batla. Read on to find out more.

RELATED: Sales AI: The Connection Between Artificial Intelligence and Sales

In this article:

1. Misconceptions on Artificial Intelligence
2. Applications of Artificial Intelligence in Sales Today
3. The Pros of Artificial Intelligence
   1. Acts as an Assistant for Humans
   2. Helps with Lead Scoring
   3. Capable of Human-Like Interactions
   4. Helps with Forecast Prediction
4. How AI Uses Data
5. Why Artificial Intelligence Is Seeing Slow Growth in the Market
6. Final Advice from Babar Batla

The Pros of Artificial Intelligence | AI Development

Babar Batla is the Co-Founder and CEO of SalesDirector.ai. Prior to taking on this role, Batla spent the last 16 years between Microsoft and Google leading B2B organizations.

Currently, he occupies himself with training their AI system to become the “greatest companion for sales leaders and sales managers.” SalesDirector.ai is a SaaS offering for B2B sales organizations.

What they essentially do is give leaders insight into their business so they can make decisions before it’s too late. They created an AI solution that produces better data, and in turn a better forecast.

Misconceptions on Artificial Intelligence

These days, artificial intelligence (AI) is a buzzword. Because of that, there are a lot of different perceptions on where we currently are in this technological development.

Batla said that he likes to look at AI as a “journey.” Those on the receiving side of AI have a different perception on where we are in this journey.

He has also had his fair share of misconceptions when talking to their customers and prospects. This led Batla to see that there’s confusion in the AI market.

He shared with us some of the questions he commonly receives. For instance, some Sales Managers ask him if the AI can tell them when the deal is going to close.

To that, he says, it’s not possible. This is because there are too many factors outside of the data itself and why deals happen or don’t.

Another question he receives is, can AI make changes to opportunities in CRM? Batla said that this is possible, but he asks the client if they are really ready for it.

Some are also afraid of AI replacing sales reps and managers.

We have all these perceptions and ideal scenarios when it comes to the pros of artificial intelligence and what it can do. Yet we also need to remember that we should balance them all out.

Batla said that it’s important to know if a certain AI task is feasible. It’s also good to ascertain your organization’s willingness to have an AI do things on your behalf.

The perceptions that Batla generally hears from people is within the spectrum of prescriptive AI. Later on, he will share with us what this is all about.

Applications of Artificial Intelligence in Sales Today

As Batla said, it’s good to have a background on what is really possible in the realm of AI today. It also helps to know where we’re headed.

The market is mature when it comes to commercial applications of AI. Batla shared with us the three types of artificial intelligence that we use in sales today:

• Descriptive and Diagnostic AI — Here, AI looks at historical data and figures out what already happened and how these events correlate. One of its capabilities is being able to figure out the characteristics that make a good or bad lead.
• Predictive AI — A lot of people are investing in this, as it allows a system to predict what’s going to happen in the future. For instance, it can predict the likelihood of a certain lead to result in something positive, like a demo or a closed deal.
• Prescriptive AI — It allows the system to suggest the actions a human should take in order to get positive results. For instance, the system can tell you that it’s time to reach out to someone to improve the odds of winning the deal.

SalesDirector.ai focuses on leading the charge in prescriptive AI. It’s also good to note that at some point in the future, sales-related activities will eventually work with a data AI type of engine.

The Pros of Artificial Intelligence

Working with AI for better sales

After learning the misconceptions and applications of AI, we asked Batla about the pros of artificial intelligence.

Where does this technology work in sales and marketing? How are we using AI to improve customer experience and the lives of salespeople and marketers?

Batla enumerated some areas where we see the benefits of AI.

1. Acts as an Assistant for Humans

First, it acts as an assistant and a grunt worker for humans.

AI does all the work that humans either don’t have time for, don’t have the skillsets for, or perhaps the knowledge to do. This is the main value of AI for customers.

2. Helps with Lead Scoring

Lead scoring is also one area where we make good use of AI. Leveraging AI to look at patterns and figure out which lead is better than the other is a straightforward application of the system.

It can analyze data at scale. An average B2B company doesn’t deal only with hundreds of leads, but rather thousands.

Obviously, it’s very tough for humans to continuously do that. Hence the need for an AI system.

RELATED: How Artificial Intelligence Helps Sales Reps Close More Deals

3. Capable of Human-Like Interactions

There are also AI tools that facilitate human-like behavior and interactions. It’s similar to the underneath technology used by Alexa and Siri that mimics a human.

At the end of the day, this advanced technology is only an AI interface using AI technologies.

Batla said that one of the misconceptions on AI is that it’s only capable of human-like interactions. While this is a well-known feature, it’s not the only one out there.

A lot of folks in the corporate world gravitate towards solutions that provide the human-like capabilities of AI. This led Batla’s team to believe that customers think this is the only kind of AI in the market.

4. Helps with Forecast Prediction

Forecast prediction is also one of the pros of artificial intelligence. At the end of the day, the funnel’s goal is to produce output, which is a forecast.

That’s why if you can predict the forecast and make wise decisions before it’s too late, then AI would’ve served its purpose well.

Based on experience and research, Batla’s team discovered that there are fundamental flaws in software companies’ approach towards forecast prediction.

Even with business intelligence (BI), the data principle is “garbage in, garbage out.” If a system is using historical data to predict the future and the data has no causality, it doesn’t provide any meaning.

If you input a bunch of random data into the machine, you will certainly get garbage out.

The most exciting part for Batla and his team is solving the problem on data by making it clean and correct. This way, their customers can input good data into the machine and produce much better insights.

How AI Uses Data

AI using data to produce results

You may be wondering, exactly how important is the role that data plays in artificial intelligence?

As Batla said, data is the “nutrition” you feed AI. Yet the problem now is that there isn’t much focus on getting good data available for the system itself.

Batla thinks that’s a big miss in the market today, but he believes that people who work in tech aren’t ignorant about it. What’s causing this problem is the excessive focus put on making meaning out of data.

Why Artificial Intelligence Is Seeing Slow Growth in the Market

Even with the many pros of artificial intelligence, it still hasn’t taken off the market as fast as you would expect it. Batla shared with us some of the reasons why.

First, it’s a “very complicated thing to solve.” Not because of the technology or the AI required, but rather because of the permutations of the business situations.

Each company has a different forecast approach, and that’s what makes it complicated.

Another curveball is that there is a significant number of sales leaders today who forecast based on holding somebody accountable. They’re not data-driven sales leaders, but rather they are the holding-the-managers-accountable type.

While this is a fine trait to have, it is not feasible for a company whose managers don’t stick around for more than two years. They could face situations wherein it’s too late to course-correct.

That’s the problem with holding people accountable and refusing to utilize machines for assistance.

This type of accountability works when you’ve got five to ten years of tenure for sales leaders. Yet as we see in companies today, this is no longer the case.

Final Advice from Babar Batla

As we wrapped up the conversation, Batla left us with good advice. That is to focus on the business problem at hand and look for a solution to solve it.

He went on to say that we shouldn’t get enamored by the marketing we see. Instead, we should pay attention to our business problem and be critical in finding the right technology that can solve it for us.

If you want to learn more about Babar Batla and SalesDirector.ai, visit his LinkedIn page and their company website. You can also check out their eBook, Demystifying the AI Blackbox, to learn more about artificial intelligence.

As you work towards improving your business and making it more efficient, it is good to know the pros of artificial intelligence. This way, when you’re presented with opportunities, you can opt for the best AI application that will suit your needs.

Who knows — you might find the solution to your business problem in the next AI software you try.

What more would you like to know about sales AI? We’d love to hear from you in the comments section below.

Up Next:

• How AI Is Disrupting Sales w/Damian O’Farrill @Autodesk
• Artificial Intelligence Applied to Sales: What’s Under the Hood of Neuralytics?
• Best Jokes About Salespeople: 18 Stories to De-Stress

Learn about the future of AI in sales from artificial intelligence practitioner Damian O’Farrill in this episode of Sales Secrets.

RELATED: Sales AI: The Connection Between Artificial Intelligence And Sales

In this article:

1. About My Guest—Damian O’Farrill of Autodesk
2. Utilizing Sales Artificial Intelligence
3. The Future of AI: Will It Replace Sales Reps?
4. How You Can Use AI for Sales
5. Why Your Organization Needs a Data Scientist
6. Where Do Data Scientists Belong in the Organization?
7. How Is AI Affecting the Sales Process in a Positive Way?
8. Why You Should Utilize Data Science and Data Strategy

The Future of AI in Sales and How It Is Disrupting Sales

About My Guest—Damian O’Farrill of Autodesk

Damian O’Farrill is the Manager of Data Science and Data Strategy in Autodesk. Their company creates software for industries like manufacturing, architecture, and building and construction.

Autodesk’s vision ties in with the future of AI. They aim to use artificial intelligence in each of their processes, and to help their clients utilize AI for their industry.

O’Farill manages a group of data scientists that focuses on everything sales-related. Their goal is to optimize sales processes from forecasting to pipeline.

They also want to help their sales reps improve their activities and outreach. O’Farill’s team aims to give them insights on the accounts they handle and help them establish correlations within their industry.

All in all, they want to provide their reps with more information to help them sell more and better.

Data Science Definition: Data science is a mix of different tools, machine learning principles, and algorithms. Its goal is to find patterns hidden in raw data.

Data Scientist Definition: A data scientist is someone who analyzes and interprets data from the raw database level. The insights they derive aid businesses when it comes to decision-making. They are a specialist skilled in technology, math, and business.

Utilizing Sales Artificial Intelligence

Sales reps utilizing artificial intelligence

AI, and the future of AI, are buzzwords. People talk about them a lot, but the great thing about Autodesk is that they’re actually doing something with it.

To grasp these ideas better, we asked O’Farill how AI fits into their organization. He told us that what they work on in the data science industry has more to do with advanced analytics rather than AIs powering computers to think by themselves.

They are basically merging sales and AI by coming up with sales enablement solutions powered by AI.

One example that O’Farill gave us is how an AI machine can get relevant information that will enable sales reps to gain more success in client interaction. This is one of the benefits of AI that can eventually turn into reality.

The Future of AI: Will It Replace Sales Reps?

Autodesk is looking for ways to enable their sales reps to do their jobs more effectively with the help of AI. However, some people are more skeptical.

We sometimes hear people ask if the time will come when artificial intelligence will completely eliminate sales reps in the equation.

We asked O’Farill about this, and he said that the purpose of using AI in sales is to enhance human intelligence, not replace it. He revealed that he doesn’t believe computers will replace humans anytime soon.

He stressed that the sales reps’ role is important because of the human interaction factor. O’Farill admitted that they will not be able to replicate that with a machine.

That is one of the limitations of artificial intelligence — while it can provide better data for better decision-making, it cannot replace humans.

The more complex side of sales will still need human interaction, so it’s something that we may not expect from the future of AI anytime soon.

How You Can Use AI for Sales

Now the next question is, how can organizations use AI for sales? As O’Farill told us, implementing AI in the organization takes time and effort, even with support from the executives.

The difficult part is the steep learning curve organizations need to go through once they hire a data scientist or use a machine learning tool for their business.

We discussed AI and sales earlier — how AI can complement what the sales reps are doing but not completely replace it. O’Farill says organizations already need to start implementing AI this way.

If not, the sales team will suffer as the promising future of AI can put them behind on the technology in a few years.

You don’t necessarily need to have a dedicated team of data Scientists to start using AI for sales. O’Farill advised organizations to start with baby steps, such as trying an AI technology that will enhance sales reps’ capabilities.

He suggested that organizations try a technology that can tell sales reps which next steps they need to take so they can maximize their activities, leads, and accounts.

Enabling the sales reps to interact with the system will help them grasp the potential of this technology. Beyond admiring artificial intelligence, sales teams should also know how they can use it to their advantage, which will only happen when they start using it.

As O’Farill emphasized, organizations can start by taking small steps.

You don’t need to go full-fledged into AI immediately by getting a machine learning tool or hiring a data scientist. You can start with a solution that can provide you the intelligence behind it.

For big organizations with no in-house data scientists, though, O’Farill suggested hiring one so they can process and use data better.

Machine Learning Definition: Machine learning refers to the scientific study of statistical models and algorithms that computers use to do a task without the use of explicit instructions. It is also a subset of artificial intelligence.

RELATED: How Artificial Intelligence Is Disrupting Sales

Why Your Organization Needs a Data Scientist

Data scientist analyzing data

There’s no denying that having a data scientist in the organization can intimidate some people. O’Farill told us it’s very important for companies to have a dedicated data scientist because of the amount of data they generate.

For that data to become useful, companies need someone to analyze and interpret it for them.

For instance, if you want to know why some sales reps can establish more connections in a day compared to others, look at your data. You can begin to correlate different factors that will help you understand how the best-performing reps work.

In turn, you can teach other sales reps what the best reps are doing so they, too, can excel. The solution could lie on the reps’ content and messaging. With the help of a data scientist, you can pinpoint which keywords are effective in generating responses from clients.

This is only one of the benefits of having a dedicated data scientist. They can help you figure out correlations and use those to improve your business.

Where Do Data Scientists Belong in the Organization?

A lot of organizations focus on process, technology, or sales enablement. In all these, where do data and analytics belong?

This depends on the structure of your organization. O’Farill’s team, in particular, is under the operations branch, although he admits that the operational side can get very process-oriented.

Data scientists contextualize a lot of data, and they also build products. Some organizations may find that the same structure works for them, wherein data and analytics are under operations. After all, you can’t be an expert in the process without the data.

How Is AI Affecting the Sales Process in a Positive Way?

To help us further understand how AI enhances sales and to get a glimpse of the future of AI in sales, we asked O’Farill about the positive effects he observed.

This is what he told us—you can use AI to influence the top 10 accounts you have.

With machine learning algorithms and data scientists, you can determine an account’s propensity to buy. You can also predict when they are more likely to sign a contract by figuring out the correlation between the age of your deals and your sales cycle.

O’Farill is also looking forward to conducting a study on who provides a better forecast between a human being and a computer, then test it.

He shared with us that direction-wise, they have good ideas on where machine learning can influence the sale. Although as of now, they are still in the “test and wait” stage.

As O’Farill said, data science, in its core, is still science. That’s why they come up with hypotheses, then test and confirm them.

He admitted that most of the time, hypotheses fail. That is why a lot of hard work entails using AI to enhance sales and making the future of AI happen.

Why You Should Utilize Data Science and Data Strategy

As a parting question, we asked O’Farill what advice he can give to those who are just starting with AI and data strategies.

He admitted that there is still a lot in the data community that we need to learn. The bright side is, we are now able to leverage the previous research conducted in data for practical purposes.

It is good to remember that the overall goal is to consolidate data, and then figure out how you can help sales reps improve their performance through that information. Ultimately, this will drive more revenue for your business.

If you want to learn more from Damian O’Farrill about AI in sales and the future of AI, you can send him a message on LinkedIn. You can also visit the Autodesk website to learn more about what they do.

Gaining knowledge on the future of AI in sales can help you maximize the benefits of AI to improve your business. You can start with something as simple as trying a technology that will enhance your sales reps’ capabilities.

From there, you can explore further and eventually grow your knowledge and expertise in artificial intelligence.

How do you want to use artificial intelligence to improve your business? Share your thoughts with us in the comments section below.

Up Next:

• Artificial Intelligence Applied to Sales: What’s Under the Hood of Neuralytics?
• Experiencing Artificial Intelligence in B2B Sales
• How Data & Science Can Help You Sell More

In this article:

1. Is Cold Calling Dead? How Cold Calls Can Help Sales Teams Build Pipeline
2. Cold Calling Using Artificial Intelligence
3. Traditional Cold Calling vs Cold Calling with AI
4. The Cold Call Experiment
5. The 6 P’s of a Prospecting Call
6. Using the P’s for Cold Calling Success
7. The Future of Cold Calling

Cold Calling Is Dead? Not When Done Right!

Is Cold Calling Dead? How Cold Calls Can Help Sales Teams Build Pipeline

I’ve been tasked with building $100M in sales pipeline this year. It’s all perspective, but for me and my team, that seems like a big number.

And I’m not alone in this thinking.

Recent research by XANT Labs, the research and best practice arm of XANT, showed that the biggest challenge facing sales leaders, behind people issues, was building pipeline. In fact, 62% of leaders say building pipeline is harder than closing pipeline.

In 15 years of being in sales, I have learned there is no secret weapon when it comes to filling the sales funnel. You can’t bet on having a million dollar deal land in your lap every month, so you must find strategies and methods to slowly chip away at your number.

When deciding how to attack the pipeline goal this year, I’ll be honest in saying that cold calling was NOT a strategy that made the top of my list. Still, cold calling is not dead.

When you hear about cold calling, your first thought is probably of a telemarketer who grabs a phone book, calls you during dinner time, and hopes for a one-call close. If you’re anything like me, then that doesn’t sound like a strategy, it sounds like an annoyance.

Salespeople have unfortunately been cold-calling for decades, mostly due to lack of a better method. Thankfully, in recent years the art of cold calling has evolved for improved sales calls. Sales technology has made things a lot easier for sales reps and a lot less painful for the customer.

Cold Calling Using Artificial Intelligence

Artificial Intelligence Definition: This is the development of intelligence in machines normally exhibited by humans.

Today, companies have transitioned to digital sales and are using a concept called cold calling with artificial intelligence (AI). Cold calling with AI turns sales calls on its head, allowing sales professionals to target only interested individuals and personalize their message accordingly.

Here’s how AI makes cold calling different.

Traditional Cold Calling vs Cold Calling with AI

Cold calling enhanced with AI technology

Now, when you explain cold calling like that, it sounds more interesting but does this method produce results? You can’t know until you test it out so… that’s exactly what we did.

Sales is often too much about what people think is effective versus what is actually effective, and it has damaged the entire industry. Rather than believe cold calling is dead because of what others say, we thought we’d gather a small group of individuals and test it out.

I’m not a scientist or a statistician, but I know when something is producing pipeline… and when it is not.

The Cold Call Experiment

For our experiment, I worked with seven sales development reps. They weren’t happy with the idea initially, but they agreed when I explained this wasn’t going to be the typical cold calling exercise.

We ended up doing 1,468 phone calls to contacts within our target accounts. Sales reps had 151 meaningful conversations for a conversation rate of 10.3%.

In our research, we found that the average sales development rep has 74.2 accounts and does 17.2 minutes of research per account. That’s 21 hours of research and a whole lot of time!

We wanted to figure out if there was a better way to manage that process, and it turned out there was. Rather than have the rep spend precious time searching for data, we found technology could bring the relevant insights to the rep so they could see important information about the company and the contact as the call was being made.

Knowing who to call, having the right phone number, and understanding who the prospect was were the first big steps. The next big step was what to say when the prospect picked up the phone.

RELATED: A Guide to the Basics of Cold Calling

The 6 P’s of a Prospecting Call

People who say cold calling is dead, probably haven’t evolved with the times yet.

I receive cold calls from sales reps and rarely do I feel the rep knows what to say. Here’s where they usually go wrong.

Reps shouldn’t say the same script every time, and it’s often odd when reps try to bring up things that are too personal to their prospect. This limits chances for a successful lead generation.

Thankfully, there is a middle ground. It focuses on providing reps with permission statements and ideas they can discuss that focus on the problems of the people they are calling. We call this concept “the first call sequence.”

The first call sequence has the following structure:

• Preface – Provide an introduction
• Personalize – Share something to build rapport
• Position – State why the prospect should care
• Pain/Product – Uncover the pain or explain a cool feature and key benefit
• Proof – Reference a customer success story
• Prescribe – Recommend next steps

Using the P’s for Cold Calling Success

Reps don’t use every one of the ‘Ps’ in every conversation. Think of the ‘Ps’ as plays in sports, and depending on how the defense reacts, you run a different play at any given moment.

Here is an example of the first two ‘Ps’ in a prospecting call.

Preface – “John, this is Bill, from Company XYZ. This is a sales call. I hate making these calls as much as you hate taking them, but I wanted to take 30 seconds of your time and tell you why I’m calling. If you like what you hear, we can continue, if you don’t, you can tell me so, and I’ll leave you alone. Sound fair?”

This is the permission statement. You’ll hear different versions of this on the web such as an ‘upfront contract’ from Sandler Training. The basic principle is to be honest and then let the prospect agree or not to agree to move forward in the conversation.

Personalize – “We did a research study with hundreds of sales leaders and their top challenge was building sufficient qualified pipeline.”

Notice we did not personalize around the person but the persona (sales leaders).

We used data we had from our own internal research. The personalization focused around the title and role of the buyer rather than him or her as a person.

This is the structure the team used for our AI-enabled cold calling experiment and results turned out strong.

In total, the team ended up with 42 appointments with qualified buyers. Those meetings turned into 15 strong opportunities for our sales pipeline. Overall, the team produced $1,102,500 of pipeline in one business week through the exercise.

The Future of Cold Calling

If you ever hear critics say cold calling is dead, don’t believe them. As you can clearly see from the example above, cold calling has evolved and changed, but it is still alive and well.

Traditional cold calling is dead, but cold calling with AI is just getting started.

Sales reps need to decide for themselves whether cold calling is still worth it—and whether artificial intelligence can help them accomplish their goals faster and give them a better success rate.

I advocate that salespeople get into the mindset of a scientist and test what communication methods and technologies work best for their customers.

With that said, although our experiment didn’t produce $100M in pipeline in one go, customers responded well to AI-enabled cold calling. Forget other people saying cold calling is dead. Try it for yourself, considering the calling tips I shared here. If you get a call from me sometime soon, know that I’m doing it because it works.

Will you try building your sales pipeline through this method? Let me know in the comments section below!

Up Next: Best Lead Generation Methods for Creating Pipeline

Editor’s Note: This post was originally published on June 21, 2018, and has been updated for quality and relevancy.

The debate is prompted as more and more companies are looking to automate some sales functions and become more lean and efficient in their customer response.

Companies are also looking at digital transformation processes, as they accommodate new buyer behaviors such as individual product research, price shopping online and consuming product marketing content, which bypass the traditional sales role.

However, most companies are still relying on the dependable stream of qualified leads that traditional sales development teams are producing. Sales development representatives are still the phone warriors that are working to contact and qualify prospects, keeping sales rep’s pipeline full of hot leads and the revenue stream going.

AI-powered Sales Tools Enabling the Modern Sales Rep

The modern sales development rep no longer works from a spreadsheet, as new sales tools that allow predictive insights about buyers allow them to work more efficiently and productively. Sales cadence tools allow sales reps to:

• Predict who is more likely to buy from you, what product, and when?
• When is the best time and method to reach a customer?
• What are the best contact strategies and messaging to close a deal?

In a recent survey, XANT Labs asked over 600 sales leaders to report whether they think AI can take over the sales development role, and an overwhelming majority (78%) answered with a resounding “NO.” 

AI vs SD: Join the Webinar!

What is your opinion? Will AI end up replacing some sales development roles, or will it enable and support them, changing the way they do their day-to-day interactions? Join the webinar with Gabe Larsen, VP of Growth at XANT, and Victor Antonio of the Sellinger Group, author of “Sales ex Machina,” to learn more about:

• How is AI being used in sales
• What are the sales and marketing trends in AI for 2019
• Sneak preview of the 2019 State of AI Research, a survey of 600 sales leaders

Register below to join the webinar!

RELATED: Why You Should Care About Artificial Intelligence In Sales

Understanding the Link Between Sales and Artificial Intelligence

Artificial Intelligence Definition: An area in computer science that focuses on creating advanced technology that can simulate human skills and abilities, including:

• Problem-solving
• Planning
• Learning
• Speech recognition

1. What is Artificial Intelligence And How Can You Start Using It to be Successful?

In this post, Gabe Larsen discusses how artificial intelligence and automation can be utilized in the sales industry. He discusses everything from the simple Sales AI and automation tools that sales reps can (and probably already) use on a day-to-day basis to more advanced technology that makes use of data to drive sales forward.

Click here to read What is Artificial Intelligence and How Can You Start Using It to be Successful? 

2. No More Sci-Fi Talk: 5 Things AI Can Do For Your Business Right Now

Artificial Intelligence is a concept that seems to drop right out of Science Fiction films. Everywhere we look, we see frightening predictions that it may accidentally destroy humanity, or develop into a singularity and try to take over the world.

It’s time we de-mystify this concept and learn how it can be applied to the business world for real results.

Click here to read No More Sci-Fi Talk: 5 Things AI Can Do For Your Business Right Now. 

3. How Artificial Intelligence Is Disrupting Sales

Artificial Intelligence is here — and it’s here to stay. In the world of sales, it’s been proven to increase revenues for companies by up to 30 percent. Companies not using AI to power up their sales teams are already behind the game. But not just any AI solution works. Practical AI solutions come pre-trained out of the box, are easy to use, and deliver results in less than one quarter.

Click here to read How Artificial Intelligence Is Disrupting Sales.

4. How Artificial Intelligence Is Changing B2B Selling

Social selling in B2B is no longer a novel concept. LinkedIn statistics show that 78% of social sellers outsell professionals who don’t use this medium to engage with their prospects, as well as have a higher chance of reaching their quota. However, new social selling technology has evolved and now has the potential to disrupt social selling strategy and techniques, as well as companies that are not quick to adapt to the digital sales environment.

Artificial Intelligence (AI) is now penetrating every aspect of B2B sales, including social selling. To figure out how it can increase the chances of social sellers to succeed, let’s first take a look at how social selling works.

Click here to read How Artificial Intelligence Is Changing B2B Selling. 

RELATED: How AI Is Disrupting Sales w/Damian O’Farill @Autodesk

5. Sneak Peek: XANT Prepares State of AI Index as Business Leaders Arrive in Utah for Accelerate ’17

Over the past year, the buzz around AI has reached a fever pitch, with some of the biggest names in tech entering the fray. At XANT, we’re proud to have been leading the charge on AI-fueled sales acceleration technologies right out of the gate.

But, we know our customers are some of the most progressive, strategic brands in the country. How does the rest of the nation–and the world – stack up?

We ran a poll of consumers in the U.S. and U.K. to get a multinational pulse on how people view, interact with, and envision a future shaped by AI.

Click here to read Sneak Peek: XANT Prepares State of AI Index as Business Leaders Arrive in Utah for Accelerate ’17.

6. Cold Calling With Artificial Intelligence—The New Way Of Selling

You’ve heard of cold calling right? Your first thought is probably of a telemarketer who grabs a phone book, calls you during dinner, and hopes to close a deal then and there for whatever product they are selling.

If that’s what cold calling is then that doesn’t sound like a strategy, it sounds like an annoyance.

Thankfully cold calling has evolved with the rest of sales. Sales technology has made things a lot easier for the sales rep and a lot less painful for the customer. In this post, you’ll learn how artificial intelligence can enhance cold calling.

Click here to read Cold Calling With Artificial Intelligence — The New Way Of Selling. 

7. The Path To Artificial Intelligence: Digital Sales Transformation

Technology doesn’t amount to much if it’s not properly implemented and doesn’t produce results in the business environment. Going through a digital sales transformation is vital if companies are to adapt to the changing market.

The digital sales transformation is about leveraging data and science and optimizing your process to help you sell more. In this post, you’ll get a walkthrough of the process of sales transformation, and see what this means to business results.

Click here to read The Path To Artificial Intelligence: Digital Sales Transformation. 

8. Transforming Sales Organizations With Artificial Intelligence

Artificial Intelligence has the power to transform sales organizations and render them more productive, more efficient and increasingly agile. While we’ve been preaching this for a long time at XANT, there’s still ambiguity around “how” Artificial Intelligence actually helps salespeople sell more.

In this post, you can dig deeper into the journey and get a better understanding of the process of transforming sales organizations with AI through the perspective of a user.

Click here to read Transforming Sales Organizations With Artificial Intelligence.

9. Artificial Intelligence Solves Sales Challenges, but Not Widely Accessible, Study Shows

Artificial Intelligence (AI) is increasingly seen as a solution for sales leaders’ biggest challenge – building a high-quality pipeline and increasing lead quantity. However, AI is still not widely accessible in the workspace to sales professionals, shows an XANT study of 500 sales executives.

Read more about what this study has discovered about the accessibility (or lack thereof) of AI in sales organizations and the reasons behind it in this post.

Click here to read Artificial Intelligence Solves Sales Challenges, but Not Widely Accessible, Study Shows. 

With AI, sales leaders can take planning and marketing to the next level. These helpful topics about the benefits of artificial intelligence in businesses can provide you with great insights into what AI technology can do to boost sales in your company. We hope you’ve found some useful resources in this guide to help you get started in adopting AI for your organization.

What are your thoughts on the use of AI in advancing sales planning and marketing in businesses? Share what you think in the comments section.

Up Next: 

• Artificial Intelligence Solves Sales Challenges, but Not Widely Accessible, Study Shows  
• Best Products For Sales 2019
• How To Optimize Team Performance With Sales Dashboards

In this article:

1. How To Become The Best Sales Rep Using AI
2. Selling in the Era of Customer Empowerment
3. B2B Selling and Buying – A Game of Herding Cats
4. Successful Companies Are Consistent in Execution
5. Sales Reps Are Learning From AI How to Handle Objections
6. Letting the AI Choose Your Cross-Sell and Up-Sell Offers
7. Listen to Matt and Steve’s Session at the AI Growth Summit

How AI Can Identify What Makes Your Best Sellers Different

How To Become The Best Sales Rep Using AI

Matt Dixon, Chief Product & Research Officer at Tethr, and author of “The Challenger Sale,” has had ample experience working with sales teams looking to improve their performance and effectiveness.

AI can instantly analyze billions of sales call recordings. Using this data, it can establish patterns that show what makes a successful sales conversation – and what breaks it. Matt detailed the process in his speaker session at the AI Growth Summit, a virtual event for artificial intelligence leaders.

Selling in the Era of Customer Empowerment

We live in an era of empowered, yet overwhelmed customers, showed Matt Dixon. Forrester research showed that more than 74% of buyers complete most of their research online before making an offline purchase. While this means they are well informed, it also means that sometimes they might be overwhelmed.

“Customers have done all this [research] before we even show up in their office and it really forces sellers into a tough spot of having to compete on price. Now, this is something we’ve seen in the consumer world for a long time. However, this is actually data from business customers, which is even more troubling. This is customers buying complex solutions, technology solutions, consulting services, legal services, supply chain software, you name it. And customers again are dis-intermediating the salesperson,” said Matt Dixon, during the summit session.

B2B Selling and Buying – A Game of Herding Cats

At the same time, B2B buyers are in a game of ‘herding cats,’ adds Matt – always trying to gain approvals for system acquisitions. In B2B sales, with complex products, you need to corral a wide array of stakeholders to make your sales goals happen.

“When those diverse stakeholders get together, what we find is, they tend to default to the lowest common denominator when left to their own devices. So, what they decide on is actually to stay the course. To avoid risk and minimize disruption. To save money, and that could be really tough for a seller today,” said Matt Dixon.

“As selling becomes more complex, the spread between high performers and average performers gets wider,” added Matt. “Put differently, when things get hard, your average performers get left farther and farther behind,” said Matt.

“This is why, the return on raising the performance of average performers even by five percent is dramatic,” he added. “What are the things that our high performers do that we can bottle and export to everyone else?” said Matt.

Successful Companies Are Consistent in Execution

Superstar seller behavior has some obvious patterns, confirmed Steve Trier, Chief Customer Officer at Tethr. The AI analysis of sales conversations shows that organizations that are successful are consistent about executing on their sales strategies.

“We’re getting audio from a conversation, and it’s being transcribed sentence by sentence, speaker by speaker. Who’s on the call and what did they say? The AI is exploring that and looking for interactions that become the insight that used to drive your business. You can use this insight with your CRM or in your data warehouse and you can compare that to other data,” said Steve Trier, also a guest on the AI Growth Summit.

“Oftentimes what we find is, is that agents aren’t even executing the strategy properly. They are not even giving you the opportunity to convert that offer,” said Steve.

The results of the AI analysis on the sales language are searchable. This means that sales managers or reps can instantly gain the answers they need to any question about a sale.

Sales Reps Are Learning From AI How to Handle Objections

The AI analysis of language patterns can also establish what is the most successful strategy for handling objections or solve problems related to customer service. This makes it easy for a sales manager to train new reps, or coach underperformers for them to learn and replicate star seller behavior and ability.

“You have a customer objection that it’s too expensive when they were trying to buy an expensive mattress option. Now, imagine that that was shipped to Salesforce or Dynamics or what other CRM you wanted, directly into the customer record. The next day, your team decides to send a special offer to all the calls that didn’t close.

• If they objected on price, we can send a discount.
• If they objected on shopping around, maybe I’ll send them the customer reviews we have. We have the best customer reviews in the market,” said Steve.

Letting the AI Choose Your Cross-Sell and Up-Sell Offers

“Another example,” added Steve, “is the consistency with which sales reps mention different cross-sell and up-sell opportunities to buyers. Top performers and the best sellers are always trying to up-sell their customers on a better service.”

“In an office supply company (you can order ink and paper and others things that you consume regularly), so they should be offering you an auto-reorder program. The program would be an up-sell message for convenience: ‘Hey you don’t need to call in. I can make your life easier. Your order can just be sitting on your doorstep when you need it, so your business can just move very fluidly,’” explained Steve.

“Sellers offering this program consistently show the best results, compared to any other offer,” added Steve.

“You need to learn from your best sellers and start to replicate their behavior. What are they doing it, how are they doing it, and can I replicate that? All this can be rolled up by artificial intelligence. The information will be available to you as a leader to really think through the strategy. How do I manage my agents and how do I adjust my sales strategies to make sure that we’re actually doing a better job of offering these critical revenue opportunities,” added Steve.

Listen to Matt and Steve’s Session at the AI Growth Summit

Learn the full story about how AI can help sales in Matt and Steve’s session at the AI Growth Summit. Listen to the full speaker session, to learn:

• How speech analytics data can help sales managers rethink their sales strategies
• What kind of factors influence sales outcomes, and how to control them
• How companies are modernizing the sales script with the help of artificial intelligence

When it comes to training your sales reps on new strategies to help them perform better, look to your best sellers, then utilize technology to replicate their winning behavior. Don’t just focus your attention and efforts on your best performers — use their superstar seller behavior to empower everyone to perform on the same level. And how do you sort through all this data and information to come up with the best strategy? Two words — artificial intelligence. We hope this post has helped you understand how to utilize AI to boost the performance of your entire sales team to achieve better results and wins for your company goals.

Do you think AI can help make average sales reps become the best sellers? Have you tried using AI to increase your team’s productivity? Share your thoughts and experiences in the comments section below.

Up Next: Essential Tools and Tips for Time Management in Sales

Editor’s Note: This post was originally published on August 3, 2018, and has been updated for quality and relevancy.

We are in the midst of a new global war. This is not World War 3 with weapons of mass destruction. We are not talking about the zombie apocalypse. However, the players are more powerful than governments and the stakes are higher than sovereignty. This a war with clear sides, leaders, battles, bullies, real money, and power at risk. This war is a global war over data—the Cloud Data Wars.

The Reign of Data-centric Business Models

As the Computing Cloud has become organized and ubiquitous, data has been aggregated to deliver collective, global intelligence to feed products and services that improve our lives. These new products and services generate big, big money. Data is now bought, sold, and traded as currency outpacing gold, oil, and even crypto-currencies. The players in the emerging cloud data war are the largest companies on the planet, and in many cases, they have already secretly invested billions of dollars in a race to own the most and the most important data. Much of this is hidden from the public’s view, but we have all benefitted from one of its outgrowths – Artificial Intelligence (AI).

AI has become the buzzword du jour and is being used in boardrooms, investor meetings, and in most product and sales pitches as a way to claim innovation. The easiest way to understand the relationship between the cloud, data, and AI is to consider consumer Internet applications.

Beginning with the advent of Web 2.0, most modern B2C applications are architected from the ground up with data-centric business models. Amazon knows that “people who bought X also bought Y.” Netflix knows what “people like you” enjoy watching. And, Waze knows what drivers 20 minutes ahead of you are encountering in real time. The way these benefits are delivered is simple: Web 2.0 applications collect data from every user, aggregate it, analyze it, and then contextualize it for the benefit of each individual user. Your personalized Netflix recommendation doesn’t come solely from your own viewing history—it comes from the viewing history of “people like you.” Your Waze navigation recommendations don’t come from previous trips you personally took—they come from other drivers who are on the road right now.

If AI is the engine, data is the fuel. In 1998, Google declared its mission to “organize the world’s information and make it universally accessible and useful.” Organizing all the information from the world’s libraries and archives seemed daunting in 1998—but we are so far beyond that now.   Almost 90% of the world’s total documented information (data) was created in the last two years, and the pace of data creation is only accelerating with IoT.

From Hardware to Data: The Evolution of Tech Wars

In the 1980s, technology wars focused on chip speed and hardware acceleration. Players like Sun, HP, Intel and their competitors were largely concentrated in and around San Jose, California. They scrambled to raise a few million dollars to fund production runs for their latest inventions. This was the Hardware Era.

In the 1990s, workflows were encoded into software, and investment sizes grew to the $tens of millions as players like Microsoft, Apple, and Intuit standardized and scaled major categories of productivity. This was the Software Era.

By the 2000s, the Internet had arrived, and major players parallelized hardware in the cloud and transferred software to this more efficient, centralized architecture. Players like Google and Amazon and Microsoft battled over who would own the Cloud infrastructure, and other players like Salesforce and the whole of the consumer Internet took advantage of the new infrastructure to build new cloud-based business models. This was the Cloud Era.

Today, we live in the Intelligence Era, and $billion bets are placed on data. Which data is most important? Who will generate it? Who will collect it? Who will aggregate it, analyze it, and own it? In the Intelligence Era, data is the prize. Whoever controls data, controls intelligence. And whoever controls intelligence, controls commerce. The data economy is global, and control of the right data eventually means control of global sectors of the economy.

Let the Cloud Data Wars Begin

This is not a new phenomenon. The battle over data began over 15 years ago as Cloud applications began collecting and aggregating data through web technology platforms. However, the data gold rush has intensified at an increasing rate, culminating last week at the 2018 Salesforce.com Dreamforce event. No wonder the world’s largest corporations have entered the fray:

• 2016 – January: IBM buys the Weather Channel for $2 Billion to provide IBM Watson access to the global weather data asset.
• 2016 – June: Microsoft beats Salesforce in a bidding war and pays $26.2 Billion for LinkedIn and its data on over 400 million professionals across the globe.
• 2016 – October: Salesforce (among others) rumored to be bidding for Twitter to get access to global trend data (failed attempt).
• 2017 – March: Salesforce partners with IBM Watson, gaining access to global Weather Channel data among other things.
• 2018 – March: Saleforce pays $6.5 Billion to acquire Mulesoft—a data integrator.
• 2018 – September: Microsoft, Adobe and SAP announce the Open Data Initiative (ODI).
• 2018 – September: Salesforce counters the ODI announcement with its own announcement of “Customer 360”

What does all of this mean?

1. It’s all about the data, stupid.

So far, B2B Artificial Intelligence has been the bubble that wasn’t. Sure, it’s fun to think about the B2B equivalents of AI-guided commerce or AI-guided navigation, but AI cannot operate without impressive amounts of data. In the B2B world, a critical mass of data is hard to come by. Since no single company has enough data to fuel AI, the launches of Einstein and Watson in B2B have been more like thuds. One can have all the AI algorithms in the world, and even a platform to run them on. But without critical mass of data those investments will go underutilized. 

2. Cross-company data is a requirement.

If no single company has enough data, what about “all the companies”? Yes, that would do it. The B2C world solved this by building apps that track all consumer activities and then use that collective data to help each individual make better decisions like what to buy, what to watch, how to get from point A to point B, etc.  For B2B intelligence to take hold, we need cross-company data to be collected, normalized, and categorized for analysis. This allows each company to make decisions based on the superset of possibilities, not merely their own history with their own customers (the consumer alternative of which would be if you were the only driver in the world who had installed Waze). 

3. Follow the money.

If one were to organize the world’s B2B data to optimize business outcomes, where would one start?  With cost-takeout initiatives? No. The most lucrative optimizations are ones that drive revenue. This means focusing on Sales and Marketing, which is exactly what Microsoft, SAP, and Adobe have done with their Open Data Initiative.

With points 1-3 above in mind, let’s focus on collective sales and marketing data—the holy grail of the Cloud Data Wars.

One Big Step for Tech, One Small Step Toward AI Sales

Kudos to Microsoft, SAP, Adobe and Salesforce. All four have recognized that customers are frustrated by status quo. And all four have taken a first step toward gathering more data to feed AI systems in an attempt to inform better B2B sales and marketing and better customer experiences. While it is a small step toward optimizing B2B sales, market leaders have declared data to be the prize, and they are moving heaven and earth to organize the world’s B2B sales and marketing data.

AI Value of Data = Breadth X Depth X Quality

The true value of AI is directly proportional to the breadth, depth and quality of the data that feeds it. In today’s information-rich environment, B2B buyers are largely self-educated before engaging a salesperson, and thus buyers hold all the cards. Data about buyers, what they are researching, how they buy and how they engage can give sellers an advantage when vying for limited attention and limited budget dollars. In this case, more and better data about more buyers is the key.

Silos à Singular Visibility à Collective Intelligence

There are three basic levels of data intelligence—siloed, singular and collective. Most companies are stuck with Siloed Data, struggling to analyze data stranded in diverse CRMs throughout their organization. Without systems to integrate these silos of data, companies do not have a 360° view of customers and prospects and cannot effectively understand their customers. Integrating data across silos appears to be the purpose for Salesforce’s acquisition of MuleSoft.

Microsoft’s ODI announcement and the Salesforce Customer 360 vision are both trying to deliver Singular Visibility, or insights mined from integrated data throughout an organization—a marked improvement over the siloed data status quo.

The true promise of AI can only be unlocked by tapping into Collective Intelligence—using advanced analytics to uncover predictive and prescriptive insights from the collective actions of millions of buyers throughout the world. Amazon is the best B2C example of this model, using AI-driven Collective Intelligence to disrupt online commerce and become one of the first trillion-dollar businesses.

Unlocking the Promise of AI to Fuel B2B Sales

Unlocking Amazon-like AI recommendations for B2B sales starts with data from a collective universe of buyers. Applying AI’s advanced analytics to global, cross-company, multi-CRM behavioral and experiential data helps the best performing companies develop customer and prospect understanding that has never before been possible.

XANT has understood the symbiotic relationship between AI and Collective Data since day one of our company, and we have crowdsourced the world’s richest set of 120+ billion behavioral data available to power AI sales.

As we watch the Cloud Data Wars unfold, we are more certain than ever we are in the best position to deliver on the promise of AI with our Collective Intelligence Data.

In the End, the Buyer Wins

Of one thing we are certain: the data arms race will transform buying experiences for the better. More informed buyers and sellers will be happier and more productive with fewer blind spots, less friction and fewer frustrations—three realities that plague sales today. And, as Martha says, “that’s a good thing.”

To learn more about how AI and Data, feel free to download the Frost and Sullivan Report entitled, “How Artificial Intelligence is Disrupting Sales”

The integration allows the company to extend its platform’s insights about the selling process to SAP customers. XANT for SAP accelerates pipeline development to increase sales by up to 30 percent with the same AI insights and Collective Intelligence.

Sellers Need More Than CRM

XANT has long provided users of Salesforce.com, Microsoft Dynamics, and Infor CRM, making it the only platform to offer sales acceleration services for clients on all major CRM platforms.

“To compete and win, sales professionals need far more than just CRM. Sellers need timely actionable insights from AI-powered collective buyer intelligence,” said XANT CEO, Dave Elkington, according to a company press release.

“It’s the unfair advantage our AI system of growth has long provided to users of Salesforce, Microsoft Dynamics, and Infor CRM. And now SAP C4C users can compete using AI-powered buyer intelligence to discover optimal accounts, build more pipeline, and expand the value of deals by almost 85 percent.”

SAP CRM Users Now Have The AI Advantage

SAP Cloud for Customer CRM users can expect to see dramatic sales productivity gains and deal expansion using the Amazon-like B2B sales recommendation engine. With AI-driven insights into XANT’s Collective Intelligence knowledge base of more than 100 million B2B buyer profiles and 120 billion behavioral interactions, sales teams can:

• Optimize ROI from existing CRMs and systems of engagement by uncovering millions in opportunities hidden within unusable data.
• Identify, prioritize, and build new pipeline with greater efficiency, to keep sales representatives focused on achieving the most important KPIs from activities and dials to pipeline contribution and average deal size.
• Prioritize and engage with net new accounts and customers by reaching out with the right message at the right time.

XANT on SAP – Availability

XANT for SAP is now generally available. See how it improves the success of B2B enterprise sales teams in a demo available here.

Solving The Pipeline Problem

These are some of the most common questions you will hear in any sales or marketing meeting:

Where is the untapped value in my pipeline?
Who is most likely to buy from me?
What is the next best action to take, to reach quota?

In the sea of sameness, little progress is being made as companies scramble to compete.

Do you have a sales development team? Yes, but so does everybody else.
Do you have marketing and sales technology? Yes, but so does everybody else.
Are you running an account-based sales model? Yes, but so is everybody else.

If you’re just doing what everyone else is doing, how do you expect to win? You need to use these resources at your disposal in a way that makes a difference for your customer and your market, and that is no easy task.

Breaking Open the Predictive Black Box

The answer to the above questions and the solution to the biggest problem in sales is not more of the same. The solution is breaking open the black box of predictive and and successfully operationalizing artificial intelligence. Artificial intelligence is the next big thing for sales and marketing.

Although AI will not replace marketing or sales, leaders who use artificial intelligence will replace those who don’t. We’ve teamed up with DiscoverOrg and Bombora to give you the scoop on the smartest AI sales tools that sales professionals are using today to make their quotas happen.

Join our webinar with Dave Boyce, CCO at XANT, Katie Bullard, CGO at DiscoverOrg and Mike Burton, SVP Data Sales at Bombora as they discuss:

How you can apply artificial intelligence in sales and marketing
What data truly predicts someone’s likelihood to purchase?
How AI can recommend sales actions to increase sales
How you can get started using artificial intelligence in your company

One in Five Companies Are Interested in AI Solutions…

A report by market research firm Frost and Sullivan shows that Artificial Intelligence is the next development in sales automation. The advancement which will finally deliver a true information age.

According to the report, the market for AI sales tools is getting bigger:

• 20% of all companies have plans to adopt AI powered solutions;
• The market for AI applications and services will be nearly $40 billion by 2025;
• The impact of AI on the global GDP will likely be on the order of $15.7 trillion by 2030 (PwC).

A wide majority of respondents in the Frost and Sullivan survey believe that AI is necessary (97 percent). Moreover, 83% agree that Artificial Intelligence features add significantly to the value of applications and hardware.

…But Most Don’t Have the Skills to Implement or Integrate AI

However, only 73% of respondents in the Frost and Sullivan survey feel they have the necessary skills and resources to implement such a solution. AI is perceived as a solution that comes with a heavy price tag and equires a great deal of involvement.

Sales teams operating in fast-paced work environments, with ever-increasing quotas, need results today.

“In the context of most enterprise applications, AI means machine learning, and machine learning requires, well, learning. Training AI takes time and a devoted and knowledgeable data science team,” shows the report. In many companies, such development resources are scarce.

The Solution: Practical AI for Sales

Frost and Sullivan identifies the solution as Practical AI: AI software solutions which come prepackaged with AI capabilities so that the enterprise doesn’t need to devote IT resources to custom development.

In the case of sales acceleration technology, this means solutions that sales professionals already use and understand, like CRM systems. When machine learning systems analyze CRM data, they can spot behavior patterns that lead to successful outcomes. They can connect sellers with buyers in order to build better pipeline and increase revenue.

Such solutions are easy to sell to executive decision makers and are popular with IT organizations as well, since they do not require inordinate amounts of support.

Join the Frost and Sullivan Webinar

Join Frost & Sullivan and XANT to learn about the results of Artificial Intelligence applied to enterprise selling – “How Artificial Intelligence Is Disrupting Sales”:

• 30% increase of revenue by disrupting sales systems with Practical AI
• 90 day window to show results using off-the-shelf AI solutions without custom coding
• Solutions that integrate with the CRM make everyday use easier for salespeople

Register now to attend the webinar and you will receive a free copy of the Frost and Sullivan research on AI sales technology.