50+ Sales AI Statistics
Fix your productivity problems for good by learning more about sales AI with over 50 sales AI statistics from leading industry research.
Productivity is the No. 1 challenge for 65% of B2B sales organizations (Bridge Group).
If you’re having trouble with productivity in your sales organization, you’re not alone. Many sales teams fail to meet their business goals and fall short of quotas. The biggest problem is sales reps are expensive and much of their valuable time is spent on repetitive tasks and inefficient processes that take them away from developing customer relationships.
On top of that, business leaders also face issues with data collection and maintenance andare unable to trust their sales data. Major company decisions rely on accurate and up-to-date information (annual budget and sales forecasts). If it’s unreliable then companies are making crucial decisions with little-to-no insight.
A study by CSO Insights revealed that only 52% of companies had client data whose reliability rate was over 75%. While only 23% of companies believed that they had reliable lead data.
Sales AI automation can solve both of these problems. It takes on repetitive tasks and improves business process efficiency. We have 50+ sales AI statistics to prove it.
The results are:
- Organizational productivity increased
- Sales reps spend their time more efficiently
- Company capital is used in a more responsible way
- Sales and customer data increased in accuracy and consistency
- The above all resulting in boosts in revenue
Here are 50+ sales AI statistics to outline just how large these challenges are for sales organizations and how sales AI automation can help.
Sales teams are expensive
- Companies spend between 15-50% of their revenue on sales. Salesforce spent almost half of its revenue ($3.2 billion) on sales and marketing last year. (CMS connected)
- Sales representatives for wholesalers and technology products make an average of $103,779 a year, not including commission — high-performing reps can make over $250,000 per year (Indeed).
- Salesforce example: Their average base salary for reps is $90,148, in addition to $74,111 in commission = in total an average of $164,000 on each sales rep (Forbes)
Struggles with sales efficiency
Despite the motivation to perform with excellent commission rates, many sales reps still miss the mark when it comes to achieving their quota goals.
67% of sales reps miss annual quota (TAS Group).
The top fifth of the sales org is more than doubling production of the bottom fifth (Bridge Group).
A big factor in why they fall short of their quota goals is because the sales processes organizations have in place are inefficient; wasting employee time and company dollars.
- Harvard Business Review says wasted time and inefficient processes—what experts call “organizational drag”—cost the U.S. economy a staggering $3 trillion each year.
- Revenue generating tasks make up 36.6% percent of reps’ activities, while 63.4% of time is spent on non-revenue generating activities (InsideSales.com).
- Most sales reps spend the equivalent of at least 50 full days away from core selling activities each year (Domo).
Sales reps are asked to perform an absurd amount of tasks each day. Nearly ⅔ of these tasks don’t involve talking to prospects or customers at all and in fact, actually detract from selling.
The average SDR performs 94.4 activities a day, including social, call, voicemail, and email touches. (Sales for Life)
- Among sales teams who cite ineffective internal processes as their top challenge, they point to excessive administrative tasks as the primary cause. (Salesforce)
- Salespeople spend just one-third of their day actually talking to prospects.
- They spend 21% of their day writing emails
- 17% entering data
- 17% prospecting and researching leads
- 12% going to internal meetings
- 12% scheduling calls (Hubspot research)
- Reps get 600 emails each week (Brevet Group)
One of the biggest opportunities for automation in regards to sales tasks are those that relate to the CRM. Either sales reps spend a large amount of time in these systems and neglect revenue-generating activities, or, they don’t in which case important data ends up missing or inaccurate. Sales AI automation can collect and input data without any legwork from the sales rep.
- CRM users spend 5.5 hours each week on activities and contacts, costing companies $13,200 each year per user. (Introhive)
- The average sales rep needs to update over 300 CRM records per week. (Implicit)
Another opportunity for sales AI is assistance around research and prospecting. AI sales tools can research missing or update outdated contact information and other important lead information. Additionally, these tools can leverage historical CRM data to find similar accounts to those that sales teams have won in the past.
- Reps spend 8.8 hours each week searching for information. (IDC)
- Sales reps spend 32% of their time searching for missing data and manually entering it into the CRM. (IKO system)
- 50% of sales time is wasted on unproductive prospecting. (The B2B Lead)
- Only 25% of leads are legitimate and should advance to sales. (Gleanster Research)
It takes time for a sales rep to onboard, learn the processes, tools, and product and to become an expert in their industry. Ramp up time for sales reps to reach “full productivity” sets organizations back, as they pay full price for a sales rep that’s not fully operational. On the other hand, sales reps have short tenures, in that they often don’t say at a company very long. This means that the average time a company has a sales rep operating at full productivity is a relatively short window.
It takes 3 to 6 months for a new sales rep to be fully productive. Average ramp (from hire to full productivity) sits at 3.2 months (Bridge Group)
- Average rep tenure now sits at 1.5 years. (Bridge Group)
- Only 6% of newly hired sales reps exceed expectations while 48% fail to succeed at selling. (Bridge Group)
- The average rep works for 15 months at full productivity at a job. (Bridge Group)
Data collection dilemma
Clean, accurate data is essential for a smooth operational business. Organizations that rely on human data collection are often disappointed as the data is inaccurate, missing details and is inputted incorrectly and inconsistently.
- 79% of opportunity-related data that sales reps gather are never updated in the CRM system.
- 88% of CRM users admit to entering incomplete contact information (IntroHive)
- 62% of users do not log all of their activities. (IntroHive)
- 30% of B2B contacts are outdated within a year (IKO System)
- At any time 20% of CRM contacts are no longer valid (IKO System)
- 57% of sales reps log all the calls they make
- 14% never log their calls in the CRM
- 22% admit they withheld some contact information
- 69% of users have outdated CRM data (IntroHive)
- 63% have duplicate contacts in their CRM (IntroHive)
Sales automation tools that collect customer and business data increase the accuracy and reliability of the data. Sales reps prefer automation tools that take on data collection, as it is one of their most time-consuming tasks that they often find boring and unproductive.
- 81% said that the accuracy of their data could be improved by capturing quality contact info from people they meet or email with. (IntroHive)
- 75% said they could be more productive if they spent less time on data entry. (IntroHive)
The potential in sales AI statistics
“AI promises to be the most disruptive class of technologies during the next 10 years …”
John-David Lovelock, Research vice president at Gartner
The impact of AI on the world will be unmatched by any other technology to date. It will drive innovation across every sector and boost the economy.
- AI-derived business value is projected to reach up to $3.9 trillion by 2022. (Juniper Research)
- AI could contribute up to $15.7 trillion to the global economy in 2030, more than the current output of China and India combined. (PWC)
- Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion is likely to come from consumption-side effects. (PWC)
Many business experts are hopeful about AI and its potential to drive growth. Those who have implemented report that they are already seeing rewards and have a leg up over their competitors. Those who have yet to implement have listed it as a top business priority.
- A PwC study of 2,500 U.S. consumers and business decision makers found that business leaders believe AI is going to be fundamental in the future. In fact, 72% termed it a “business advantage.”
- 83% of the most aggressive adopters of AI and cognitive technologies said their companies have already achieved either moderate (53%) or substantial (30%) benefits. (Deloitte)
- A recent survey by MIT found that 85% of executives believe that AI will enable their companies to obtain or sustain a competitive advantage, but only about 20% have incorporated AI in some way, and less than 39% have an AI strategy in place.
- Gartner predicts that by 2020, AI will be a top five investment priority for more than 30% of CIOs.
- 88% indicated that their company already has, or has plans to, implement AI and ML technologies within their organization. (MEMSQL)
- 81% of Fortune 500 CEOs consider AI a crucial area to invest (Forbes)
Why invest in sales AI statistics
Sales is a critical area many business leaders are already looking to invest in as sales processes are inefficient and costly to companies. With AI companies can achieve greater output from their sales teams without increasing the cost of the sales team itself.
46% of companies say that marketing and sales is the area where they are most investing in AI adoption systems. (Forrester)
AI is the top growth area for sales teams — its adoption by sales teams is forecasted to grow 139% over the next three years. (Salesforce)
Sales reps will use their time on productive tasks that generate revenue as automation tools take over their repetitive busy work. They will also make smarter selling decisions as augmented tools give them guided “next-step” recommendations.
Triple-digit growth is expected in areas such as predictive intelligence (118%) and lead-to-cash process automation (115%) in the next three years. (Salesforce)
- High-performing sales teams are 2.3x more likely than underperforming teams to currently use guided selling. (Salesforce)
- High-performing sales teams are 10.5x more likely than underperformers to experience a major positive impact on forecast accuracy when using intelligent capabilities. (Salesforce)
- High-performing sales teams are 2.8x more likely to be outstanding or very good at predictive intelligence. (Salesforce)
- When automating lead nurturing activities such as email campaigns and follow-ups, users have shown a 14.5% increase in sales productivity. (Salesforce)
Sales AI statistics on why now is the time
The current wave of technological innovation is moving quicker than most. AI is at the peak and the stakes are high for companies to implement intelligent technologies into their business processes. Companies are already adopting these technologies at a high speed and there’s a winner-takes-all situation here. Those that invest in the right opportunities early on will far outcompete others in their market. Automation Hero predicts that this wave of AI implementation will be about six years.
- By 2020, 30% of all B2B companies will employ AI to augment at least one of their primary sales processes. (Gartner)
- Currently, 40% of sales tasks can be automated, but by 2020, 85% could be automated. (McKinsey)
- 56% of customers actively seek to buy from the most innovative companies
- By 2020, 75% of business buyers expect companies that can anticipate their needs and make relevant suggestions before they initiate contact, while 73% expect that products they purchase will self-diagnose issues and automatically order replacement parts or service
Automation Hero’s automated sales AI platform is a solution for these crippling business problems by automating common customer requests, eliminating time-intensive and repetitive tasks and augmenting sales rep intelligence.
We have three highly intelligent engines at work that make the “magic” happen.
- Recommendation engine
- Increases deal size and revenue by providing cross-and-up-sell recommendations based on changes in customer behavior.
- Keeps the pipeline full of interesting leads by prospecting and suggesting hot leads and accounts based on past closed-won deals in data source.
- Augments sales rep intelligence for smarter sales by giving “next-step” recommendations.
- Dark data extraction engine
- Maintains data cleanliness and consistency by mining for critical customer and opportunity-related information from a user’s calendar, email, CRM account and Automation Hero’s database and updates it in the relevant system.
- Centralizes business data for a full, holistic view of sales funnel and customer data.
- Intent detection engine
- Streamlines internal processes by identifying the intent of written communication with up to 95% accuracy in emails, text messages, online forms and posts.
- Once intent is identified the message is then automatically routed or responded to such as intent to schedule a meeting, return a product or change an address.