“In our business, we talk about emerging technologies and how they impact society. We’ve never seen a technology move as fast as AI has to impact society and technology. This is by far the fastest moving technology that we’ve ever tracked in terms of its impact and we’re just getting started.” — Paul Daugherty, Chief Technology and Innovation Officer, Accenture
Artificial intelligence (AI) has become a hot topic in sales and marketing over the last few years, with AI quickly becoming a leading priority for sales teams. Currently, 21% of sales leaders and marketers rely on AI-based applications to improve their sales processes. Companies that have adopted AI claim to increase sales (52%), increase customer retention (51%), succeed at new product launches (49%), and believe they will attain a competitive advantage (85%). Sales leaders predict their use of AI will increase by 155% in the next two years. Analysts believe that the AI market focused on marketing, sales, and customer engagement is expected to grow eightfold, from an estimated present $35 billion to close to $120 billion in 2025. The question should be not whether your company will invest in artificial intelligence, but when will it.
In this chapter, we will discuss:
1. What is Artificial Intelligence?
2. How Are Companies Deploying Artificial Intelligence Successfully?
3. How Are Companies Using Artificial Intelligence in Sales?
4. How Will Artificial Intelligence Be Transforming Sales in The Next Few Years?
5. The Rise of the Sales Robot
What Is Artificial Intelligence?
Demis Hassabis, CEO of DeepMind, an AI company that Google bought, defines artificial intelligence as "the science of making machines smart." Scientists are trying to make machines as smart as humans. We are teaching them to see, hear, speak, and move like humans. Respectively to sales, we teach machines to outreach, engage, qualify, and close as our best salesperson would do. The fact is that we are still far away from having machines taking over a salesperson's job, but each day machines learn new skills that automate sales tasks and enhance the sales process.
Machines learn from identifying patterns in a large dataset to make predictions. These predictions get validated with more and more data and become very accurate over time. As companies start to deploy artificial intelligence within their sales process, they will see a gradual improvement in sales outcomes as the machine learns what is working and what is not.
For example, a sales AI tool can evaluate all the emails your salespeople send for cold emails. Based on the email's success, the machine can tell you what emails are most successful in what sequence to what target audience. Over time the machine can create email sequences targeting segments in your database. This use case can be valuable for sales teams focusing on sales outreach, and when incorporated with other use cases, the productivity of the sales team dramatically increases.
According to a study published by Accenture and the Frontier Economics on the impact of AI on labor productivity, the impact artificial intelligence will have on sales, and the labor force, in general, will be significant by 2035. Countries like Sweden (37%), Finland (36%), U.S. (35%), Japan (34%), and Australia (30%) will have a 30% or greater increase in productivity due to artificial intelligence. This rise in productivity equates to adding an extra $4.9 trillion per year to the global economy - equivalent to an economy greater than Germany's projected size. You can bet by 2035, artificial intelligence will significantly improve our processes. Machine and humans will be working together to build the salesforce of the future.
How Are Companies Deploying Artificial Intelligence Successfully?
Companies are experimenting with artificial intelligence across all sales processes. There are hundreds of companies applying machine learning in sales and marketing. Early adopters of AI have been big businesses that are digitally mature and adopted multiple AI technologies to help grow and save money. These technologies had C-level support and invested in building AI across various systems.
After analyzing hundreds of AI companies, we see AI being used by sales and marketing in the following three main areas:
1. Data Management- These tools manage the integration and machine learning across large data sets to create prediction models to optimize the sales and marketing process. Some examples of tools in this category include data capture, voice recognition, social listening, data management, ML, framework tools libraries, transparency, and compliance. Data management tools have primarily been deployed by large enterprises that generate a lot of data for data scientists to create predictive models.
2. Process Efficiency and Automation- These tools are for sales, marketing, and customer care professionals to optimize and automate their current processes. This category of tools includes sales automation, decision support, lead scoring, price optimization, content development, management, scheduling, decision support, and automation of Martech stacks tools. We also can describe this category as tools to make humans more effective. These tools predominantly stand-alone and fulfill a specific problem using technology and artificial intelligence.
3. Experience Optimization and Personalization -This category goes above and beyond, making humans more effective. These tools leverage the data sets data management tools combined such as customer databases, DMP's, or Google analytics to create omnichannel personalized customer experiences. This category of tools includes personalization, programmatic advertising, auto-generated advertising, customer interaction, advertising personalization, advertising DMP, Chatbots, and digital agents. Sales and marketing teams now trust these systems to automatically make decisions such as ad buying, website personalization, and content recommendations.
Due to the need for large datasets required for machines to learn, large organizations have preliminary benefited from artificial intelligent technology. In the chart below created by the Avaus team, we see a lot of disparate technologies providing various AI solutions for sales and marketing.
See Avaus's website for more landscape details. Click here to learn more.
For organization just beginning to deploy Artificial Intelligence, it becomes essential for these companies to:
1) Identify what they are trying to achieve- There are many solutions available, and companies need to understand what problem they are trying to solve first to make sure they are successful. Some examples of issues to solve include:
· Website traffic is down.
· Leads are flat
· Sales have dropped since last quarter.
· Email open and click rates are low.
· Advertising is not working.
2) How to pilot AI cost-effectively- Solving a company's identified problem may require one or several tools, and the cost of implementation may be more than the return on investment. So careful analysis and a pilot will help justify a longer-term investment.
· Understand how the solution provider will use AI to solve the problem.
· Confirm it is solving the identified use case
· Find and question vendors to make sure your company knows what will be delivered.
3) Understand the data requirements for each solution -AI needs a lot of data to function. If a company does not have the data, the predictions will not be accurate and will not generate results. Companies need to make sure they have the data requirements for the product to be effective. Companies need to understand the timelines required to get the data; getting the data will slow down a solution's time to market.
By understanding the identified problem, the solution, cost, and data requirements, the AI project will have a greater chance of success.
How Are Companies Using Artificial Intelligence in Sales?
Sales and marketing teams have identified several AI projects that have proven successful across various companies and industries. Here are some of the use cases that companies may want to try.
Price Optimization- Artificial Intelligence is helping companies decide how much discount is required to win a deal. Machines analyze the size of the deal in terms of dollar amount, product specifications, number of competitors, company size, territory/region, client's industry, client's annual revenues, type of company: public or private company, level of decision-makers (influencers) involved, timing (e.g., Q2 vs. Q4), a new or existing client, etc. to help predict the right discount. Using AI for price optimization can help companies make sure they win the deal and optimize profits.
Forecasting- Sales executives use AI to predict where their teams' sales numbers will end each quarter. Machines analyze past performance, forecasts, existing sales interaction, market conditions, expected renewals, projections, etc., to help forecast sales numbers. A correct prediction allows a company to manage its operation by better managing inventory, costs, resources, and investors.
Upselling and Cross-Selling- Artificial Intelligence can help you grow your timeline revenue quickly and economically by helping you sell more to your existing customers. Machines can analyze prior purchase combinations, a client's purchase history and identify which client is most likely to upgrade or buy an additional offering. Using the analysis provided by AI, salespeople will focus on the most likely to purchase clients and create custom sales pitches for each client, saving salespeople a lot of time, minimizing costs, and improving conversions. The net effect is an increase in revenue and a drop in marketing costs.
Lead Scoring- When companies have a rich pipeline of interested prospects, a salesperson has to decide what prospect to focus their time on when it comes to closing a deal to hit their monthly goals. Often their decision is based on gut instincts and incomplete information. Machines can look at a prospect's current engagement (e.g., emails sent, voicemails left, text messages sent, etc.), company history, social media activity, and rank which prospects are most interested and likely to close. By prioritizing opportunities, salespeople can focus their attention on the most interested leads and accelerate their sales and lower their time to close a deal.
Managing Performance- Artificial Intelligence can help Sales Directors know where to focus their efforts to help their team achieve their sales numbers. A machine can collect salespeople performance numbers on calls, emails, text messages, appointments, leads, etc., and forecast which salespeople are not likely to hit their quota and what prospects are in the greatest danger of not closing. With this information, sales executives can know where to prioritize their time and how to help salespeople achieve their quota, and understand what accounts need their attention.
Building Pipeline- For companies building their sales pipeline, AI can help these companies identify companies and prospects who may be most interested in the companies product or service. Machines can analyze what technology a company uses, what products they purchased, their social media presence, financial information, and contacts to propose what accounts and prospects you should be targeting. AI can help salespeople prioritize their outreach efforts and be more effective at closing new opportunities.
Close More Leads- Artificial Intelligence can help sales teams accelerate their pipeline by helping sales teams find and qualify prospects that the sales team may not have time to do themselves. Machines can engage via chat with site visitors or reach out via text, email, and phone calls to identify those interested. AI can then pass on the qualified leads to sales based on the prospect's interest level. Salespeople can then focus on closing, and machines can help do the finding.
Intelligence Automation and Augment Tasks- Salespeople have been using AI to help them with daily repetitive and tedious sales tasks. Machines can help capture data, take notes, set reminders, create playbooks, and analyze call data to provide feedback. These small tasks take time, and automating them frees sales time to focus on closing more sales.
More use cases are being tested and developed by sales teams as we speak. The field of artificial intelligence is still new, and there are a lot more opportunities for improvement.
How Will Artificial Intelligence Be Transforming Sales in The Next Few Years?
For enterprise companies, artificial intelligence will incorporate into all the technology salespeople use over the next few years. Machines will become smarter as sales teams produce more data that AI can use to perfect their predictions and interactions. In the enterprise, technologies will consolidate into platforms that effectively perform automation and augment the sales process. Platforms like Salesforce, Microsoft CRM, Zoho, Marketo, etc... are already making artificial intelligence a key part of their offering. New point product AI vendors solving use cases will also continue to emerge with deeper integrations into core sales platforms. Platform marketplaces and service companies will continue to thrive as companies enhance core platforms until platforms commoditize their added value. Enterprises have many systems that need to work together, so one system to rule all systems will not be realistic for large organizations. An integrated set of best breed, home-grown, and open source systems will be the norm.
Additionally, we see sales robots playing a significant role in the future of sales and AI. As platforms consolidate, we have started to see sales agents, bots, sales robots emerge as a salesperson of the future that can do many tasks that a salesperson performs today and quickly learn from each interaction. Enterprises will start to adopt sales robots that will augment salespeople and give salespeople the time to focus on high-value sales activities. In the future, we can see the birth of many types of sales robots. One may focus on sales outreach, another sales operations, and another on reporting. Humans and machines will work together as a perfect team driving sales for the enterprise.
For the SMB market, more turnkey AI solutions will be developed that can help SMB companies adopt AI solutions at lower costs but with more immediate returns. It is challenging for SMB companies to justify having to buy various vendors or an expensive implementation to get AI value. Either AI will become part of their tools, or the solution will have to stand alone and help businesses replace the need for incremental resource costs. Data providers will continue to emerge to help SMB get more value using AI without having a lot of data to start.
We see sales robots playing a significant role in sales for the SMB market. Where large enterprises could once dominate the market with their expansive sales force, we see SMB companies equally scale a team of sales robots to dominate markets at a lower cost. Sales robots will open up the playing field for companies to grow and expand since sales robots can deliver more results at a fraction of a salesperson's cost.
The Rise of the Sales Robot
With the rise of AI and machine learning, multiple companies have been automating and enhancing various sales processes. The combination of AI, multiple technologies, and additional enhancements have allowed companies to build more advanced machines that do not only score and predict but engage and act like humans.
These advanced machines are called sales robots, bots, digital assistants, and they resemble salespeople and replicate a salesperson's human movements, interactions, and functions. They can also look human to better assimilate into the selling environment. These intelligent machines, sales robots, can be used for sales outreach and look like real people, send emails, make phone calls, send text messages, and engage like real people. They also can qualify prospects and respond to inquiries.
At Sales Innovator, we believe sales robots will be transform sales teams in the enterprise and SBM market. Instead of hiring more sales resources to scale their business, Companies will find it more cost-effective to scale a team of sales robots who can work with salespeople and help salespeople focus on closing. Sending emails, text, and making phone calls will be accomplished by robots and will equalize the playing field when using salespeople to dominate markets. The overall cost to scale a business will be significantly less expensive, and the benefits will be passed on to the customer. The lower cost structure to operate a business will disrupt existing companies that do not adopt sales robots.
A recent study published by Oxford economics forecasts that robots could take over 20 million manufacturing jobs worldwide by 2030. In China alone, there could be 14 million robots employed. In the workspace alone, the number of robots in use worldwide