Today sales teams across organizations are dealing with humungous amount of information. The key challenge for sales professionals is how to make sense of all this data in an effective way, without wasting too much time and effort. Many organizations have identified this need and leveraged Artificial Intelligence (AI) technologies to serve action-ready data analysis to sales managers that can assist them in fast decision making, hence accelerating the sales process. This article outlines a few areas where AI algorithms can be leveraged to drive business growth by helping sales teams sell more.
The number of applications using AI in sales is growing rapidly. McKinsey & Company predicts AI will contribute $1.4 trillion to $2.6 trillion of value in marketing and salesi . Let us look at some areas of sales that have been benefited or can be benefited by AI:
- To find out which deals are worth pursuing i.e. the deals with a high probability of conversion. Creating the pipeline of worthy deals and providing accurate sales forecasts
- Structuring for new prospects by using intelligent insights from the past work
- Hunting for new opportunities by going through a plethora of news and articles
- Generating action plans for the sales team by gathering the intent from client meetings/conversations
Few use cases where AI has facilitated better decision making are explained below.
Bull’s eye pricing: Nowadays in a competitive market where solutions are like commodities, pricing plays an important role in winning a deal. Arriving at an ideal pricing means creating a win-win situation for both the parties i.e. giving appropriate discounts and not leaving money on the table. AI plays an important role here by giving insights into the range of discounts that can be given, such that it will not hurt the profit margins. AI algorithms can learn from past deals that have similar characteristics (like industry, deal size, automation areas, solution proposed, client’s financial appetite, timing (Q2 or Q4), hunting or existing account, client’s current digital transformation position, FTEs involved, etc.) and provide the sales manager with intelligent insights on discount.
Revenue Predictions: One of the important tasks of the sales manager is to understand the latest trends and pinpoint the quarters in which sales numbers might likely take a hit. Again, AI comes to the rescue. An AI algorithm provides an accurate forecast by processing all the past data and mapping that with current market trends as well as notes from the sales team and other tool data. This forecast allows manager with staffing activity and strategy to pad the revenue for quarters that are about to take a hit.
Precision levels can be improved by creating a map of how the market had responded to major incidents. If the source of data can also include newspapers, magazines, economic articles, etc. this tool can be customized to predict major economic incidents as well.
Upselling and Cross-selling: There is a general observation that during sales cycle completion, the sales team tries to push the current client for more buying. It is the most economical and fastest way to swell up the top-line revenue. But the big challenge lies in identifying such clients. Organizations invest a lot to identify where the chances of buying are the least. Now with the entry of AI, organizations can use an AI algorithm to help identify which existing clients are more likely to buy. This eventually can increase revenue and decreases marketing costs. Cross-selling and upselling can help improve customer loyalty as well.
Opportunity Ranking: A salesperson has to struggle daily to identify potential clients from his/her pipeline to invest more time on targeting the right prospects and hitting their quota or targets. The salesperson generally makes this decision based on their experience or gut feeling. Now with the help of AI algorithms using the internal and external data points, it can better prioritize the opportunities. The internal data source can be CRM, sales notes, emails, calls, etc. Whereas external data source could be any news about the client about expansion etc., financial distress, change in government policies, financial reports, etc. Using these data point the algorithm can rank the opportunity in the pipeline based on their chances of conversion.
Performance Analyzer: Using AI and analytics, sales managers frequently assess the revenue pipelines of their team members in order to replenish the deals that are stalled or falling through. Sales managers can now visualize via dashboards and ascertain which team members are likely to hit their milestones along with which pending deals stand a good chance of a conversion. This allows the manager to focus his/her attention on key team members and their deals that have the capability of helping the company hit their milestones.
The above use cases tell us about a few areas where AI can do magic by revolutionizing the sales cycle altogether. The amount of gathered data used to train the algorithm will decide the algorithm’s ability to provide more accurate predictions. The power and accuracy of any prediction algorithm can be measured by its ability to guide the decision maker to redefine the company's strategy like bottom-line, bottle neck identification, etc.
It is very well observed that AI tools are heavily dependent on the data and its source’s authenticity. As rightly quoted by Mathematician Clive Humby that “Data is the new oil.” With time, data is growing exponentially. This, in turn, helps AI tools to give output with better precision. Given the scenario, the areas that are now untouched by AI will also come under its influence.
What should be the starting point to harness the power of Artificial Intelligence to boost sales
One should start with the available data that can be in a different format. For instance, the sales department has past purchase data, and the marketing department can provide website analytics and data from promotional campaigns (e.g., response rates from clients). An Artificial Intelligence algorithm can use a combination of these data sets to make accurate predictions about which customer is more likely to respond to an offer and other meaningful insights.
Way forward- Focus should be on integrating all data sources. It has been observed that Customer Relationship Management (CRM) platform comes very handy for this. The CRM can fetch data from different systems and integrate it. It can have AI capability within itself, which can give valuable insights to the sales team. Few organizations have started providing such kind of services as well.
In case the CRM does not have built-in AI capability, the data can be channeled to downstream system, which has AI capability to deep dive and bring valuable insights from the data. The AI tool can be easily integrated to multiple data sources. In case the documents are in image form, OCR capability can be used to extract valuable attributes from those files. Eventually all the data can be fed to the algorithm to generate all the possible information that can be very beneficial for sales teams.
To fully harness the capabilities of AI, there needs to be a change in mindsets. Sales teams should not only rely on experience or intuition but also leverage Artificial Intelligence as a key enabler in decision-making to enhance outcomes. Going forward, its extremely crucial for organizations to upskill their sales teams in the area of AI and analytics to thrive amid a fast-changing business environment.
iMcKinsey & Company predicts AI will contribute $1.4 trillion to $2.6 trillion of value in marketing and sales