In this article:

1. Give Your Sales Team the Right Tools
2. What Is a Sales Cadence and Why Do You Need it?
3. How Software Can Help Your Sales Cadence Strategy
4. Optimize Lead Scoring and Lead Prioritization
5. Call the Right Leads with Phone Prospecting Cadence
6. Get Visibility into Your Pipeline with Email Cadence
7. Sales Cadence Timing
8. Sales Cadence Frequency

Sales Cadence Tools | Use Predictive Analytics to Increase Preciseness, Efficiency, and Productivity

Give Your Sales Team the Right Tools

Finding the right sales cadence and then having sales representatives flawlessly execute it is a complex process.

Your sales reps can misrepresent their activities. They can commit deal-breaking mistakes when it comes to their sales strategy.

Sales cadence tools can help you automate your workflow. It can also increase productivity and generate more leads.

Sales acceleration software is a marketing automation tool that can shorten the sales cycle. You can also use it to drive revenue growth.

We designed the technology to help create faster, more efficient sales teams with improved performance.

What Is a Sales Cadence and Why Do You Need it?

Sales cadence is a sequence of activities to improve contact and qualification rates. This is according to Gabe Larsen, director of Inside Sales Labs.

It has five elements. These are:

• Attempts
• Media
• Duration
• Spacing
• Content

Ideally, your sales cadence should be specific to your company, your prospective clients, and your industry.

All these factors add a degree of complexity when creating an optimal sales sequence. Sales leaders around the world, though, need to put in the time to perfect their cadence strategy.

Optimizing your cadence when doing sales prospecting helps produce predictable revenue growth.

It’s not only one of the goals of an organization — it is the holy grail, and this is where sales acceleration software can come in handy.

How Software Can Help Your Sales Cadence Strategy

It’s very common for sales reps to be cadencing at random.

They will pick a sequence of sales activities comfortable for them. They do so without any regard for what works for the customer.

Sales acceleration software can take the guesswork out of prospecting cadence. At the same time, it makes sure your sales team is efficient and productive with winning sales.

Optimize Lead Scoring and Lead Prioritization

Automated sales acceleration technologies can help with lead scoring and lead prioritization. It will help you answer an essential question for your sales development reps: “Am I contacting the right people?”

Achieving targets doesn’t rest on making more calls. It also doesn’t depend on increasing engagement on social media.

It’s all about making the right calls at the right time — when your prospect is more likely to respond. Revenue acceleration technologies powered by Real Data can show you:

• Who is more likely to respond out of all your contacts
• Which medium they are more likely to prefer when you have multiple channels (email, phone calls, voicemail)
• What strategies are best for each stage of the sales funnel

Call the Right Leads with Phone Prospecting Cadence

Sales representatives prefer to email rather than call. We know this for a fact.

This doesn’t change the fact that telephone calls are the most effective prospecting tool.

Sales cadence tools and software like Playbooks from XANT can help you determine the best time to call. It ensures your prospects can answer the phone.

The software will prioritize lists. This way, reps focus their time on the prospects who are most likely to pick up the phone and begin their buyer’s journey.

You can measure prioritization in two ways:

• Likelihood to contact
• Likelihood to close

Sales acceleration software will analyze trillions of external data points according to the sales process. It will also assign a score to each lead, ranging from 1 to 100, based on this analysis.

The higher the score, the higher your likelihood to close a deal.

You can now make cold calls a thing of the past.

This software can also increase sales enablement. Most of all, it can empower your sales team to contact the right leads.

Sales Enablement Definition: It’s the process involving providing the best-quality data, content, and tools to help sales teams become more effective.

RELATED: Secrets of Phone Prospecting with Jeb Blount, CEO of Sales Gravy

Get Visibility into Your Pipeline with Email Cadence

Email is currently one of the preferred tools of salespeople to contact customers or prospects.

Experts will tell you, though, it’s not the better option. It has lower contact rates than phone calls.

In the battle between phone versus email, the phone call definitely wins.

On the other hand, research showed the most efficient sales cadences include multiple communication options for sales outreach. These are telephone calls, voicemails, and emails.

It’s not worth ignoring any of these communication channels. Instead, it pays to be smart about how you use each of them.

With email, it’s best to contact leads as soon as they open your email or download whatever collateral or presentation you sent them.

The XANT Vision application will show you in real time:

• When someone opened an email
• Which of your contacts clicked a link
• When a prospect visits their website and how long prospects viewed specific pages
• Which attachments are most successful based on downloads
• If they forwarded an email and to whom
• Which content is most effective

This sales software will help you swiftly respond to buying signals. It will also increase your conversion by using immediacy to wow your prospects.

Strong sales cadence tools can easily increase productivity and efficiency for a sales organization. It promotes velocity sales.

Velocity Sales Definition: It is a measurement on the speed prospective customers purchased a product or used a service.

Don’t miss any sales opportunity from now on.

Sales Cadence Timing

Timing is almost everything when creating the perfect sales cadence. How do you know if you’re being too spammy? This can mean you get ignored, or worse, marked as spam. You don’t want to get written off by a prospect prior to even speaking with them.

Timing is critical to ensure you don’t wait too long between contact attempts. You don’t want to be forgotten about, but you don’t want to be too pushy.

Optimal sales cadence should mean:

• Leaving one day at least between contact, and no more than four
• Increasing the number of days between contact after each attempt  

If you use your CRM to consistently time your contact attempts and align these with your existing plans.

The ideal duration should be no longer than four weeks, but two weeks should be the minimum you try to make contact.

So be persistent and not too clingy.

Sales Cadence Frequency

Making contact through various channels will get the best results. Your content needs to be high quality and you need to aim for up to 15 touches per prospect.

If you only try once to contact a client that’s a lot of missed sales opportunities.

The Online Marketing Institute reports that it takes seven to 13 touches to deliver a qualified sales lead.

Twelve or more contacts boost performance by nearly 20% compared to co-workers who stop at eight.

Quality and quantity are important when it comes to sales cadencing.

Learn more about using a sales cadence tool to generate more leads and achieve predictable growth using A.I.-powered sales technology. Check out the XANT platform.

It will amaze you at what it can do!

Teams using sales acceleration software experienced revenue growth of over 30% in as little as 90 days.

What are your challenges with sales cadence? Share your experiences in the comments section below!

Up Next: How To Build The Ultimate Outbound Sales Cadence

Editor’s Note: This article was originally published on August 23, 2017, 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”

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.

However, there’s a lot of complexity in collecting, handling and analyzing massive amounts of internal and external sales data.

Over half (57 percent) of companies feel they don’t make proper use of advanced analytics, so this is a missed opportunity. Sales leaders who are not using the power of analytics to gain insights about their organizations are falling behind.

How Analytics Makes More Money for Sales Leaders

There are four big sources of value for analytics in sales: lead generation, matching people, maximizing customer value and pricing, shows McKinsey. Some of these issues, like lead quality and quality, as well as hiring and recruiting, are permanently being referenced by sales leaders as major problems that are constantly on their mind.

Analytics in sales is a seismic shift in the sales process. Sales leaders and reps can instantly access all sales and customer historical data, as well as external data. This allows them to move from acting on gut feelings to data-driven decision-making. Here are some of the results that the McKinsey and Company report identifies:

• Radically improve lead generation with lead scoring technology, showing sales reps exactly which leads need to be contacted, how and when in order to maximize their chances of success
• Matching people in the right areas of sales by offering insight into coverage planning, improving field productivity, talent management as well as sales pipeline forecasting.
• Maximizing lifetime customer value by allowing laser-focus segmentation on customers likely to respond to cross-sell/ups-sell offers and reducing customer churn
• Analytics can also help with pricing decisions, allowing sales reps to base their decisions on similar deals from the same vertical, rather than pricing based on a hunch

Advanced Analytics for Sales Webinar

Gabe Larsen, VP of Growth at XANT, and Maria Valdivieso, Director of Knowledge at McKinsey and Company, discuss best practices in implementing analytics for sales organizations. Join the webinar to learn:

• How successful companies work with data in sales to get 30% increase in revenue
• The 5 important steps for implementing advanced analytics in your company
• How sales leaders can avoid the pitfalls of working with sales data

Companies report only a 64% quota attainment, on average, for their sales development reps.

The “State of Sales Development 2017 study shows some stats on the activity and results of sales development reps in the United States. The report also looks at the sales technology systems they use and the structure of sales development organizations.

Sales Development in 2017 – How 900 Companies Do SDR

Here’s just a few things we learned with this poll:

• Sales development reps have an annual quota of $658K;
• They have around 14 meaningful conversations per day;
• They also send around 37 emails per day;
• And they place around 36 phone calls every day;
• They also  send around 15 voicemails and have about 8 social media activities every day.

The State of Sales Development focused on 900 companies and reviewed the four elements of building an optimized sales development organization: structure, technology, people and pipeline. The study was created by XANT in partnership with SalesForLife, BridgeGroup, Drift, Datanyze, OneMob, and Tenbound.

You can download the report below:

Or you can download the infographic that shows every-day activity of a sales development rep.

The Sales Tech Landscape 2017

The Sales Tech Landscape is already at its its fourth edition and includes 32 categories and over 700 participants. Applications are grouped into five categories, based on their functionality:

Engagement – Covers technologies for engaging with prospects and customers using voice, video, or digital communication, or in person.

Enablement & Productivity – Includes the tools helping front-line personnel be more efficient and effective.

Sales Intelligence – Comprises the solutions and database services providing information and insights on companies and buyers.

Pipeline & Analytics – Contains all the software for managing and instrumenting the sales pipeline and measuring performance.

People – Encompasses all the applications for managing and developing sales personnel, from on-boarding to compensation.

InsideSales is mentioned in the sales tech blueprint in five categories: email tools, sales dialers, lead distribution & call management, multichannel orchestration, and sales gamification.

So Many Sales Solutions, So Little Time

The sales technology industry has expanded and many solutions have been proliferated – often with less than optimal results, shows Nicolas.

“In the early days, it was easy to try and buy applications. I know of a few organizations where individual sales reps could buy the software they wanted and expense it on their credit cards. The rapid development of this industry led to a proliferation of solutions, poorly integrated and often underutilized,” shows Nicolas, in a blog post for SalesHacker.com.

He advises sales leaders to make sure they follow three steps when adding sales technology to their applications stack:

• Have a clear sales methodology defined and documented. Then, you can harness software to execute.
• Be clear on why you are buying software. It should be for one of three reasons: automate and scale a process, instrument your processes to optimize them, or experiment and innovate.
• Define your data model with minutiae. This will become the backbone to align your technology stack.

See the Sales Tech Landscape Study on Sales Hacker.  Or, download the PDF here.

Automatic Email Send

Predictive Playbooks is an AI-enabled sales cadence application that helps sales teams enhance their performance and build pipeline. Among other cool features, Playbooks enables reps to quickly personalize emails before sending. However, it has not always accounted for communications that don’t require customization, including product or funding announcements, conference invites, and follow-up emails. We solved for this with the Automatic Email Send functionality.

To use automatic emails, simply add or edit an email step and switch it to “Send Automatically”, then select a send time.

• Reps can create plays with 3 consecutive steps using auto-emails.
• Currently users can configure auto-emails from the extension. This functionality will be coming shortly to the manager application.

Automatic emails do not appear in the list of tasks to do for a given day, because the sales rep  does not take any action. If reps need to view or edit an automated email, they can access them in the Playbooks menu under the new Automated Tasks option.

Next Step Date to CRM

Our active user community told us they wanted to know the next scheduled date of interaction with prospects. We listened to their feedback and added the option, within Predictive Playbooks, to publish the next date of interaction with a prospect directly into the CRM. Using this information, teams can now generate reports showing their interaction schedules with associated dates of contact.

CRM admins must configure the functionality to make it available.

Other Product Improvements

The latest Playbooks release also adds other improvements and fixes to known issues:

• Fixed an issue where tasks would get stuck for deleted prospects
• Updated icons in the play creation screen
• Removed the “Busy” option from call results
• Addressed a number of issues around long phone extensions breaking the UI
• Added text formatting support to Cue cards

Predictive Playbooks helps teams build pipeline and increases rep productivity and efficiency with predictive insights at each stage of the funnel.

Sales teams using the AI powered InsideSales System of Growth experience an increase in revenue of up to 30% in as little as 90 days.

If you’re interested in how you can use sales acceleration technology to benefit your business, contact us to watch a demo!

Steve is executive director of innovation for MarketSource, a sales outsourcing company. They have developed a proprietary, proven process to improve sales funnels and deliver better sales.

Audit Your Sales Process to Increase Sales Results

The process improves sales revenue by focusing on three key points.These are sales training, eliminating time wasters and using sales acceleration technolgy. He detailed the process in the XANT podcast: “The Art and Science of Sales: Three Levers to Pull to Increase Sales Results.”

Listen to the podcast below:

Step 1: Sales Instructional Training

Step one to improve sales revenue is instructional training for sales teams, showed Steve. Sales reps need to know how to approach gatekeepers and build that relationship to reach decision-makers.

“It’s all about your approach, and really the first things that you say need to help build that initial rapport. You don’t want to come across too salesy, and you don’t want to appear like a telemarketer. We’re not telemarketers, we’re salespeople,” said Steve Bonvissuto, on the podcast.

He advises that sales reps always understand the industry they are selling in, and ideally have a background working in it. This way, they can build rapport with the gatekeepers for optimal sales process.

Step 2: Optimize Sales Process and Eliminate Time Wasters

The next step is to optimize the sales process and minimize or eliminate time wasters, shows Steve. There’s several steps you can take to eliminate inefficiencies. One of them is working on time spent within the Customer Relationship Management (CRM) software.

“Your CRM, you want to be able to make it as seamless as you possibly can for your sales reps. You also want to be able to report on what they’re doing, so that you can begin to change. We don’t want to put a lot of text in a CRM, so you want to avoid creative writing,” shows Steve.

He adds that sometimes, sales representatives sometimes end up doing customer support, when they should be selling. You need to recognize when this happens and make sure to avoid it, adds Steve.

Step 3: Sales Acceleration and AI Technology

Sales acceleration and sales enablement technologies do a lot to increase sales productivity, shows Steve. Using these technologies, you can customize your CRM to make sure all reps’ activities are contributing to revenue. Furthermore, using artificial intelligence can take the guesswork out of prospecting and help you focus on the deals that are more likely to close.

He recommends XANT technology to help increase efficiency for sales teams in any industry. The AI powered predictive capabilities of the InsideSales platform allow sales reps to focus their efforts where it matters and minimize time wasters.

“We model out the day in a life of the sales rep. Then we customize the CRM to make sure that the technology is aligned with the business process. […] The way we set up your technology with InsideSales and Salesforce is that it becomes a tool that reps to accomplish their job. It’s not just another tool that they need to go into at the end of the day to demonstrate that they’ve done something for the day,” concluded Steve.

If you want to find out more about how to minimize time wasters and optimize sales team’s activities, follow the PlayMakers podcast with Steve Bonvissuto.

The CB Insights research also appeared in Fortune Magazine, which listed the top 50 startups in an infographic.

InsideSales Among Top Marketing AI Startups

InsideSales is one of the leading companies in the “Ad Sales, CRM” category along with Appier, Drawbridge and Persado. A.I. Insider recommends InsideSales as a “bottom of the funnel” technology to aid in the conversion of leads. Its AI powered platform

InsideSales received over $264 million in venture capital funding. Microsoft was among the investors who acknowledged that its predictive analytics technology creates outstanding results for sales team.

Using A.I. to Increase Sales Revenue

The InsideSales Neuralytics technology helps sales teams prioritize leads and focus their efforts on the contacts that are more likely to close. The A.I. platform analyzes millions of data points in the Salesforce database and finds transaction patterns. Using these patterns, it can make reccomendations as to best prospects to contact, when to contact them and what sales and marketing strategies are fitting to close the deal. The platform also offers predictive revenue forecasting, market change analysis and risk metrics.

Companies using the InsideSales platform see an increase in revenue of up to 30% in as little as 90 days.

Venture capital firms invested over $5 billion in artificial intelligence startups in 2016, according to AI research firm CB Insights. A.I. startups investment has risen every year since 2012, with 2016 being a record year (60 percent increase to the previous year).

Why AI Goes MainStream in 2017

According to recent research from InsideSales, 2017 is the year when Artificial Intelligence technology will know significant adoption. The study “State of AI 2017” breaks down consumer’s use of AI in the home and workplace, and offers new predictions for this emerging technology.

The State of AI 2017 study shows that:

• 60 percent of consumers already use AI technology on a daily basis (Google Maps and Waze)
• 50% of consumers use AI powered streaming apps like Netflix and Pandora
• Only 12% of consumers use AI-fueled assistants like Amazon Alexa

Many consumers believe that AI has the potential to have a significant impact on the future. Forty nine percent believe it will lead to medical advancements, 47 percent believe it will take over dangerous jobs, and 41 percent believe it will help automate mundane tasks.

You can get the entire executive summary of the InsideSales study here.

Insurance companies and loan underwriters have been using crowdsourced data for decades to calculate the risk of future occurrences and price it into their products. Security agencies do the same when analyzing communications network patterns to predict terrorist activity.

Why AI Is Here to Stay

In our personal lives, we benefit from AI every day. No single one of us buys enough from Amazon to establish patterns rich enough for relevant recommendations. But once our purchase data is contextualized with millions of other people like us, Amazon can make suggestions that are relevant:  “People who purchased this item have also purchased these items.”

Waze uses real-time feedback from crowdsourced data. Waze is a traffic app owned by Google.  When you download it to your phone, your phone becomes a node on a global traffic monitoring network. Because of signals Waze gets passively from each phone on its network, it knows how fast traffic is moving anywhere a Waze phone is on the road. This information can be analyzed and used to optimize routing for all other drivers. Additionally, users can report traffic incidents, construction, or police, and that information is similarly calculated in.

The key to making good recommendations for individuals is having access to as much relevant data as possible. In the Amazon example, this means other people’s purchasing patterns. In the Waze example, this means other people’s driving patterns.

So why hasn’t A.I. penetrated the revenue functions of businesses more than it has?

Imagine a typical day when you wake up to an optimized temperature set by Nest. Your workout is tracked by FitBit. You ask Alexa to give you relevant news and your commute is guided by Waze. But once you get to work…. nothing.  No AI. at all.  For lunch you could consult Yelp and after work you could consult Netflix… but during the work day – to get your job done – there is little in the way of AI.

Artificial intelligence is an emerging technology, which means not all executives and stakeholders are yet aware of the benefits that it can bring to their business.

How Artificial Intelligence Can Help Businesses

This will not be the case in the future. Businesses run on revenue, and revenue functions can benefit immensely from data and AI.

We predict that 50% of the Fortune 100 will be using crowdsourced data + AI to help drive their revenue functions within 5 years. Seven of the top 30 already are.

Century Link Adopted XANT AI Predictive Technology to Improve Sales

Crowdsourced Data + AI: The Winning Formula

There are a few conditions, however, to make this happen:

1. Crowdsourced Data:  Companies will opt into data cooperatives (much like retailers and CPG firms and oil & gas companies) to have access to the learnings and meta-data from the superset across companies and industries
2. AI:  Companies will apply AI to the crowdsourced data + their own proprietary data to contextualize and optimize next best actions for their sales and revenue teams

Crowdsourced data + A.I.: it’s that simple. It’s the winning formula which can lead to improved revenue and performance. It has already been proven in the consumer space. We are now entering an age where that same formula will add value to businesses.

AI can help your revenue teams close more and bigger deals, faster. It can suggest the next best prospect, next best action, and integrate those recommendations into your team’s workflow.

If you’d like to get started today, we at XANT would love to talk to you. Dozens of enterprise-scale companies work with InsideSales to leverage crowdsourced data + AI to drive better sales results. Some examples include:  CenturyLink, T-Mobile, Waste Management, Microsoft, Bank of America, Microsoft, Caesars Entertainment, and Dex Media.

If you’d like to see how AI can help you increase revenue up to 30%, check out our study: “The State of AI.”