Every day, 2.5 billion gigabytes of data is created. As a result, 90% of sales leaders miss quotas due to information overload. In fact, 57% of sales representatives surveyed in 2018 missed their sales quotas the prior year, according to Forbes.
To make matters worse, sales representative spend nearly half their time on administrative tasks each day due to the excessive amounts of information that ultimately slow down core business initiatives.
How can using artificial intelligence help sales people reduce time spent on administrative tasks and make their jobs easier?
- Addressing data overload
The emergence of digital media has created an information explosion. With more data now available for sales teams, staying abreast of all internal and external intelligence is a task in itself.
Humans, however, have always found innovative solutions when facing business challenges. For example, when accounting became a challenge for growing businesses, calculators were invented.
So how can we enrich marketing intelligence in real time? Incorporating AI can help sales people achieve their goals by continuously grooming leads, strengthening relationships with clients and setting future targets with confidence.
In the last two decades, vendors have developed digital platforms for sales representatives that function similarly to the way a customer relationship management (CRM) solution would. However, these tools contain information that sales representatives are already aware of. In other cases, sales people may receive contact details regarding new prospects from their respective marketing departments or from lead generating platforms, like Marketo. But, nonetheless, they still need to dig for additional intelligence and qualify leads before approaching prospects.
- Extended memory
Even though most of us take notes throughout our work day, we often struggle to find or remember key points when we need them most. Using AI can help create an extended memory for a team or an organization, almost like an institutional memory. With an AI-driven system, teams can share notes with their peers and colleagues within their company.
Workers can predict future scenarios by recalling conditions and circumstances from similar past events. Because 90% of data is textual and conversational, work from the past won’t always be accurate for forecasting sales. Business planning without systematic extraction and comprehension of signals from text and conversations, along with industry standard qualitative or number-based data sets, isn’t always relevant from one scenario to the next.
Without the power of machine comprehension to keep pace with information, a new era of knowledge impoverishment will set in.
We’re beginning to understand why there is so much hype around AI. Using AI in sales not only provides intelligence for businesses looking to improve performance and meet sales quotas; it also creates more time for sales people to focus on critical tasks.
Rather than taking multiple days to collect talking points and develop a communication strategy when meeting prospective new clients, it can now take seconds. An AI-based platform can keep each sales representative informed and prepared to identify the future opportunities.
- Memory-driven sales
Once the AI-driven platform ingests data, sales reps can ask questions or drive data analytics. This is valid for both fresh data and any type of intelligence stored in your extended, institutional memory. Human memory is short-lived, but an AI-based platform stores content for as long as it serves a useful purpose.
Such a solution eliminates the time spent by business professionals conducting web searches and instead provides them with specific answers from relevant external and internal data inputs.
Google can answer random questions in a matter of seconds. However, Google searches lack institutional memory about you and your marketplace. In other words, they don’t take context into account.
Typically, conversations with prospective clients are private, candid and unpublished. Essential elements, such as future investments, new product opportunities, and rationale for expanding into new markets are staples of these conversations. So once data is ingested and organized within the institutional memory, the possibility of powerful new analytics emerges.
Input from sales is the starting point for many business processes because it helps an organization be more objective, effective, and customer-focused.
However, automation isn’t easy, and it requires specific expertise. Many companies try to create in-house capabilities but fail to develop useful applications. By using machine comprehension to extract and organize relevant content, the quality of human comprehension can be improved.
Where is the future of AI for sales? Businesses will be blindsided if they refuse to implement AI while improving client-facing processes requiring sales intelligence.
Most companies are just starting to implement data science for textual and conversational analytics. Companies have previously developed data analytics and business intelligence tools solely for structured data. Before the recent advances in AI, it simply wasn’t possible to blend signals from unstructured data with structured data. With this new capability, organizations can take strides forward to empower their sales teams to reach new heights and arm them with powerful insights.
Anoop Bhatia is the founder of Nowigence, a SaaS company that utilizes natural language processing (NLP) and machine learning to automatically extract and synthesize sales intelligence from both unstructured and structured data.
- AI is stoking the sales fires
Most sales people have far too many mundane responsibilities on their “to-do” lists. Each one of these tasks takes away from investing time in potential clients, thus robbing companies of previous revenue.
The more your sales folks can concentrate on the art of selling, the better. After all, that’s what you hired them to do. But at this point, less than 25% of companies have incorporated even a touch of AI into their operational protocols, meaning your company has the chance to make waves if you take a deep dive into advanced tech.
You don’t have to reinvent any wheels to begin adding AI into your sales process. It’s usually already available in software and on platforms, says Jeff Winters, founder and CEO of Sapper Consulting. AI uses data to drive your sales people in the right direction, and it does so instantaneously.
Winters says sales teams can plunge into the AI world by focusing on three key areas of selling that are all augmented by machine learning:
- Prospecting – Machine learning helps teams prioritize top prospects by collecting and synthesizing data. After leads are pulled, they can be converted using AI-discoverable deal patterns. Research published in Harvard Business Review found that when companies incorporated AI into their selling processes, leads increased by 50% while call time plummeted — sometimes as much as 70%.
- Messaging – The more granular you can get in your messaging, the more customized your approach will seem. AI can breathe life into lagging templates by taking the conversation from “Which template worked better?” to “Which sentence or word worked like a charm?” Epsilon Email Institute noted that AI-created emails returned more than 70% higher open rates and 152% higher click-through rates than their generic counterparts.
- Administrative time reduction – Allowing machine learning to manage the pipeline based on historical data will free up your representatives’ time and will potentially eliminate mistakes caused by human error. As they provide campaign management help, intelligent processes will also collect and curate millions of data points. From there, your platforms can adjust and auto-feed specific types of opportunities through more customized tracks that yield higher win rates. You can easily extrapolate this logic to messaging and content. And all of this can be accomplished without the expense of a new hire, too.
Forbes – Extreme personalization is the new personalization.
Nowigence and Orion teamed up and developed a Conversational NLP Platform, powered by Nowigence. It combines with novel machine learning and natural language processing techniques.
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