Automation & AI In Sales

Creativity drives innovation and innovation is not limited to products but also processes. Machines have been innovated & taught to execute the mundane and repetitive tasks but the capabilities of technology are far greater. Artificial Intelligence (AI) is all about leveraging the power of technology to bolster human performance as well as efficiency in the implementation of any task.

Sales are now steered by customized marketing, having moved from mass marketing.

Let me give you some examples of such applications of AI:

• If you’re a regular shopper on Amazon, your decision to buy was influenced by AI using an association algorithm which knew what you had been searching for on your browser or social media apps. (According to McKinsey, 35% of Amazon purchases are driven by AI powered recommendations.)
  
• If you watch Netflix, you might have noticed a pane titled “Because you watched XYZ” followed by a long list of recommendations. That list is curated for you by an AI recommendation algorithm. (McKinsey data shows that 75% of Netflix viewership is driven by AI enabled recommendations)
  
• If you’ve called for a cab on Uber, AI directed a car to your location in time. (location algorithm)
  
• Sometimes you notice emails sitting in your inbox about a product or destination you’ve been intending to visit. Why? That’s AI monitoring your browsing activity online to feed you customized information to save your time. (classification algorithm)

Imagine business as a wheel, the customer as its axle and consumer insights as the spokes of the wheel.  Without the axle the wheel means nothing and the assembly can’t be missing spokes. The above analogy is intended to convey that customer insight is valuable information and helps sustain the business which is where AI fills the gap.

Source: Salesforce Research

There are several more uses of Artificial Intelligence in sales:

1. Price Optimization: Purchasing patterns when analysed reveal key consumer behaviour and it helps price a product optimally to continually attract buyers. You want to close the deal but you do not want to leave a penny unclaimed. Today algorithms can even tell you what discount you can afford to give customers while making a decent profit.

• Forecasting: One of the most daunting tasks for managers is to estimate the sales numbers of the coming quarter. Thanks to AI, that task is no longer in human hands. Data from previous years predicts numbers with decent accuracy, thus helping organisations in better managing their resources and inventory.

• Upselling & Cross-Selling: One can either shell out money on marketing to customers who will not buy or, use AI to effectively pick out consumers who are more likely to buy an upgraded version of their previous purchase (up-selling) / willing to purchase a new product altogether. (cross-selling) This adds top line revenue to your current P&L.

• Lead Scoring: A salesperson with a pipeline of strong potential clients has to close deals at higher frequencies and usually they cannot due to lack of / incomplete information. AI enables an in-depth dig out of each client’s previous interactions, purchase history, searches for similar products over the web, etc. This information helps in identifying what the client is looking for and even ranking them on the basis of the likelihood of purchase.

In fact 77% of business buyers expect their salesforce to be aware of service interactions and be in lockstep with the consumers’ ongoing needs.

• Performance Management: Data driven dashboards are the spine of performance management. Every year sales managers need to evaluate team performance and understand the path forward for each based on their respective performance. AI makes this process easier by automating data collection, analysing it and spurting out with decent accuracy the predicted performance of sales staff.

Harnessing the Power of AI in two simple steps:

• Identify the data sets which upon aggregation reveal the entire spending pattern of the entire customer base. A larger data set improves prediction accuracy.
• These data sets need to be amalgamated with Customer Relationship Management (CRM) platforms such as Salesforce, Microsoft 365, Soho, etc) which will, in the years to come, serve as a knowledge bank. These software have analytic tools inbuilt which enable managers to make predictions with regards to the above 5 points.

Looking at the number of sales teams banking upon data driven forecasting, it comes as no surprise that Artificial Intelligence is set to create the biggest impact. High performing teams are much more bullish than their underperforming rivals in their views about the value added by AI across cases.