How AI and Deep Learning Advances Sales: Practical Guide
Artificial intelligence has a major impact on just about every business department. Sales is no exception. More and more organizations are counting on AI to organize, streamline and make their sales teams more efficient. The need for AI software in sales is simple: salespeople have heaps of valuable data, but don’t know where to look first to get started. At the same time, many salespeople are drowning in repetitive work and lead qualification, which often contribute little to what really matters: closing new deals. So there are many opportunities for improvement.
In this long read, we take a closer look at the how and why of AI tools in sales departments. We then dive into the most important areas in which artificial intelligence drives sales and how existing companies are already getting added value from AI software on the sales floor today.
Easily capture data
Data on sales trajectories is relatively easy to record these days. Contact moments or touchpoints with potential and existing customers are already automatically registered in many companies. Quotations, order forms, pre-sales processes and invoicing are also part of the average sales cycle. Most modern ERP and CRM packages are also perfectly capable of retaining that data for eternity and neatly distilling historical graphs from it. However, it usually stops there. Nicely drawn up sales diagrams from a CRM system may be valuable for measuring and comparing achieved performances, but here too the old stock market credo applies: results from the past are no guarantee for the future. The analytical and predictive power of a CRM is therefore grossly lacking. And that’s exactly where AI comes into play.
AI spots missed opportunities
Artificial intelligence mines untapped data that is hidden in organizations. For example, the AI of our Trendskout Sales Booster ensures that relevant data comes to the surface. The AI does this by looking for hidden connections in available data. That, in turn, exposes underlying factors or drivers of customer behavior and allows the AI software to predict future events. This goes two steps further than just reporting recorded data with traditional reporting software.
Helping sales teams
The benefits of AI insights for sales teams are many. Sales people’s time is precious and teams must choose their efforts carefully to maximize results within the time available. Even for the most experienced salespeople, it is almost impossible to extract all relevant information from the endless sales-related data streams and then interpret them correctly. That’s where smart AI tools come into play. Their advanced data analyzes reveal underlying sales dynamics and provide concrete predictions about potential and existing customers. In this way, salespeople can serve their prospects and customers more effectively and better, in less time. Opportunities that lie hidden below the waterline suddenly surface with AI. The artificial intelligence alerts teams to new sales opportunities that would otherwise go unnoticed and provides data-based advice that supports salespeople in their busy role.
6 months after rollout, the total relative revenue increase was 8.5%
7 key benefits of AI in sales
There are a lot of ways companies rely on sales AI like Trendskout Sales Booster to unlock more opportunities for their sales team. This happens for both multinationals and SMEs in various areas. AI continues in each of those areas where traditional ERPs and analysis tools stop. An overview:
1. Sales forecasting and sales prediction
AI for sales forecasting gets to work with thousands of data points from all possible data sources. The artificial intelligence links data from different databases and searches for insights and hidden patterns that are impossible to discover manually. In this way, an accurate prediction of the future figures can be made and companies know better who will buy what and when. So AI-based forecasting goes beyond typical pipeline management and introduces the power of real sales prediction into business forecasting. This is not only useful for sales teams, but also for their customers. In some companies, the intelligent forecasting model even goes so far as to detect the needs of end customers before they even realize it.
2. Detect sales opportunities
Why only predict sales when you can influence them? That is exactly what opportunity detection does. Based on the data available in the company CRM and other tools, an algorithm for sales opportunity detection gets to work and reveals hidden opportunities. Often with spectacular results, as the case of this production company shows. AI informs sales teams about new opportunities that might otherwise be lost and ensures that companies can get more out of their leads and existing customers.
3. Preventing churn
A smart AI tool is connected to all underlying business software via plugins. He keeps an eye on all contact moments or touchpoints with potential and current customers in the background. Potential causes for increased customer dropout or customer churn are automatically identified and passed on to the responsible sales employee for further follow-up. That gives companies time to communicate on time and increase their customer retention. In other words: AI makes reactive sales proactive again.
4. Impact analysis
Smart AI algorithms can uncover sales drivers. For example, the sales AI of Trendskout Sales Booster performs advanced impact analysis. It reveals the decisive factors that cause customers to purchase or reorder. Impact analysis thus provides answers to many why questions, for deeper business and sales insight.
5. Next best actions
AI software can recommend next steps or next best actions in a sales cycle. The algorithm does this on the basis of historical data in your CRM system or other databases. Those data-based recommendations serve as a guide and increase the chances of success of a phone call or email from your sales people to existing or potential customers. In this way, your team does not sail blind, but can make targeted contact and dose its sales efforts in a targeted manner.
6. Product recommendation
An AI tool such as the Trendskout Sales Booster interprets previous sales and recommends additional products or services that suit an existing customer. A company can have those suggestions itself automatically suggested to the end customer or passed on to the authorized account or sales manager, depending on the business model of the company in question. This allows organizations to maximize their upsell and cross-sell. Under the hood, product recommendations are made via AI using a so-called recommendation engine. This is a smart clustering and classification algorithm that here, too, connects unexplored data points with each other in order to arrive at personalized sales suggestions.
7. Data-based personas
AI classifies and segments customer profiles into sales and marketing personas. It does this on the basis of objective sales and other data – and no longer on the basis of subjective criteria. Data-based profiles are always more accurate and can be deployed at the interface between sales and marketing, which in turn ensures targeted communication.
Implementation: project approach or AI software?
The concrete implementation of sales AI in the workplace can be done in two ways: via a classic project approach or with accessible AI software. Although they both start from a concrete business case or business question, they use different methods for the final implementation.
The project approach builds on the classic approach to complex IT integrations. Especially when AI was still in its infancy, organizations largely depended on specialized project agencies that took over their complex business cases and challenges from A to Z. A sales issue involving AI can easily take a few months with such a project approach. Algorithms are custom written and implemented, and integrations with existing software – for example an ERP system – are developed manually.
Not so with ready-to-use AI software, such as the Trendskout Sales Booster. It comes with a lot of plug-ins and an overarching AI that selects appropriate algorithms. This saves programming work, so that the AI can be used at full power in a matter of days for a concrete challenge or business case. A logical consequence of this faster, more effective method is a faster turnaround time, more agility and higher profitability or TCO on business cases. Thanks to accessible AI software, it is also worthwhile to get started with AI for smaller projects. And that immediate added value is what sales teams in many companies are looking for.