2.5 billion gigabytes of data is created daily. As a result, 90% of sales leaders cite information overload for missing quotas. So much so that 57% of sales representatives surveyed in 2018 missed their sales quotas the prior year, according to Forbes.
To make matters worse, nearly half of a sales representative’s time is spent on administrative tasks. The overwhelming amount of information is distracting and slows down the core engines of business — generating sales.
How can the use of artificial intelligence help salespeople reduce time spent and make their jobs easier?
Handling data overload
The emergence of digital media has created an information explosion which is expanding at a rapid pace. As more data is now available than sales teams can manage, staying abreast of all internal and external intelligence is a task by itself.
Humans, however, have always found innovative solutions when business problems like this arise. For example, when the accounting needs of growing businesses increased to an extent that speed in calculations became a necessity, calculators were invented.
So what can be done to enrich marketing intelligence in real time? Incorporating AI can help sales achieve its goals by continuously grooming leads, strengthening its relations with clients and setting future targets with confidence.
In the last two decades, vendors have developed digital platforms for sales representatives that ask for information from them as a customer relationship management (CRM) solution would. However, these tools contain information already known to the sales representatives. In other cases, they may receive contact details regarding new prospects from their marketing department or from lead generating platforms like Marketo. They still need to dig for additional intelligence and spend time qualifying the leads before approaching prospects.
Extended memory
Even though most of us scribble notes throughout our working life, we 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 — an institutional memory. A system designed using AI can give teams a choice to share their notes with peers — like other sales professionals — within the company.
When workers can recall conditions that caused past events, they can arm themselves with the ability to predict future scenarios. Because 90% of data is textual and conversational, work done by sales representatives in the past won’t be accurate for forecasting sales and business planning without systematic extraction and comprehension of signals from text and conversations, blended with industry standard qualitative or number-based data sets. Without this, there can be no comprehensive summary of available intelligence.
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 about AI. Using AI in sales serves to not only provide intelligence for businesses that want their representatives to be more effective and not miss sales quotas, but it now frees up time for them to focus on more crucial tasks.
Instead of taking days to collect talking points to develop a communication strategy when meeting prospective new clients, it can now take seconds. An AI-based platform can keep each sales representative relevant, up to date, forward-looking and plan-ready to meet the future risks and opportunities.
Memory-driven sales
Once data is ingested by the AI-driven platform, sales reps can ask questions or include it in data analytics. This is not only valid for data that is freshly ingested but also for any of the intelligence stored in your extended or institutional memory. Human memory is short-lived, but an AI-based platform allows content to remain in memory as long as it serves a useful purpose.
Such a solution is designed to eliminate the time spent by business professionals conducting web searches, providing them instead with specific answers analyzed from relevant external and internal data inputs.
Google can answer questions like how far away the moon is. It returns a specific answer with the distance in seconds. However, a limitation of Google’s search engine is the lack of institutional memory about you and your marketplace.
Conversations with prospective clients are private and unpublished. A discussion with a client has essential elements, such as, for example, future investments, new product opportunities, the rationale for expanding into new markets and so on. Once data is ingested and organized in the institutional memory, the possibility of powerful new analytics emerges.
The input from sales teams is the starting point for many processes with businesses. This information helps an organization be more objective, effective and customer-focused.
However, automation isn’t easy, requiring 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 to their own peril if they refuse to accept AI in improving the client-facing processes starting with sales intelligence.
Most companies are at a starting point with data science, especially related to 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 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, armed 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.
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