Top 5 Ways AI will Revolutionize the Way Sales is Done
AI for Sales Problems in SaaS Companies
Salespeople today have data, tools, and training powered by AI — easing their daily work routines. Field sellers, inside sales, and managers can now focus on building relationships with buyers via personalized engagement powered by AI-driven data insights. Salesforce and the Harvard Business Review research says, only 37% of businesses use customer data to predict customer needs.
Sales managers can manage for performance by measuring the revenue pipelines of their salespeople — if or not they are delivering their set quotas, and their likelihood of delivering successful conversions. The machine learning feature of AI can work tirelessly all day to offer you a strong foundation to make informed decisions (by analyzing data 24/7 to offer insights and super leads to follow). SaaS (Software-as-a-Service) companies use AI algorithms for decisions and predictive intelligence.
The biggest question in sales today is: What are customers talking about?
Companies want to scrutinize sales calls to coach sellers. They are tracking keywords for insights on what customers are talking about (discovering and acting on sales triggers, customer pain points, managing customers’ negative sentiments on sales calls — requiring coaching). Sales strategies are updated by gaining timely and actionable AI-led data insights into competitor and customer behaviors.
The 5 Core Sales Problems SaaS Companies Face That AI Can Solve:
i. Price Optimization by determining optimal discounting for a proposal. Assessing previous deals’ efficacy — adapting accordingly by altering deal features (its monetary size, the size of the company, industry, and annual revenue of the client), determining if the client is new or repeat, etc.
ii. Forecasting precisely the next quarter revenue (sales volume numbers) to allow the companies optimally manage inventory and resources. AI tools also can develop customer profiles (depending upon their behavior and habits) to let the system predict their needs and buying interests. Giving salespeople the information on the potential customers before actually meeting them to boost confidence, customize their pitches with predictive insights for strong lead conversions.
iii. Up-selling and Cross-Selling to those wanting to purchase a better (up-sell) or new (cross-sell) among the existing client base of the company by identifying via AI algorithm. Determining “who is more likely to buy more” is a must. Sales managers can resort to AI to seek answers to the following questions: “When will this customer consider a repeat purchase?” and “What is their product usage pattern?” Availing timely alerts on when your clients will require refilling (repurchasing).
iv. Lead Scoring by compiling and studying historical client information, viz. salesperson conversations with the clients, their social media behavior and digital networking channels (e.g., Facebook, LinkedIn, or Google, etc.). Emails, voice and text messages, etc. can be used to identify and grade potentially promising leads by setting language analysis alerts. With AI-solutions, data collected from several sources can be validated to prepare an accurate data-set. AI-led process to boost sales with an informed follow up on strong leads (rich pipeline of potential clients) to soar deal close ratios.
v. Consolidating AI-Culture Across the Organization to discover which sales representatives are likely to successfully close a deal. It can be done by developing an inclusive behavioral analytics solution to assess the behaviors of sales representatives. Big Data and AI can be leveraged to assist professionals in developing genuine relationships with buyers (smart scheduling links allow sales professionals discover optimal meeting times).
Scaling SaaS Companies with AI
The SaaS companies can acquire customers, scale operations, increase ROI (Return on Investment — and also the Return on Customer Data). The software programs powered by AI can assist companies in mining historical data and calculating future trends (demand projection, budget allocating assistance, etc.).
SaaS companies can use AI to scale by: i. Accurate lead prospecting, ii. Leveraging predictive analytics to polish pitches. iii. Identifying refilling and up-selling avenues, iv. Sales productivity elevation, and v. Implementing AI-culture across the organization.
Challenges Faced in Adopting AI
To decide on AI technology deployment in companies must ascertain the following:
1. Have a set objective (a data-driven plan) clearly communicated to their teams (collaborative teams — an ingredient of the AI-culture).
2. The AI technology deployment initiatives (investments) and the allied activities (operations) undertaken should constantly be scrutinized for their adherence to the ultimate data-driven plan.
3. Assess if their organization is ready in all aspects of strategy (leadership), culture (behaviors), and responsibility (capabilities) that comes with managing the change that arrives with the deployment of AI.
AI is a tool that calls for collaboration (as the culture of AI) of various departments and business functions. These include: i. Marketing (website analytics and promotional campaigns’ — client responses — data), ii. Sales (historical purchase data). iii. Data sciences (predicting via AI algorithm as to who is likely to positively respond when presented with an offer).
A Microsoft case study explains the collaboration of data science, marketing, and sales from the marketing team of Microsoft. The team wanted to use AI to better score leads for the sales team to pursue. Building this solution required marketing employees to partner with data scientists for creating machine learning models that evaluate thousands of variables to score leads.
The collaboration resulted in illustrating the marketing employees’ knowledge of lead quality. It also shone the collaborative genius of the data scientists’ machine learning acumen. This resulted in a lead-scoring model for the sales team that they could trust for its capacity to produce high-quality leads. Notably, a HubSpot research on Sales Prospecting Statistics 2019 indicates that 30% of sales representatives believe that finding qualifying leads is difficult.
Conclusion
Increased computational processing power, refined algorithms, and storage capacity have let data scientists and technologists introduce efficient sales processes. Leaping beyond predictive analytics, powerful “prescriptive models” — steering salespeople further in the sales funnel — are still in their infancy. In the sales context, it should be answered as to, “How much adaptation will be required on the part of a salesperson to remain relevant, and what knowledge or skills will we have to know to still be of value?”
References:
The Future of Sales: Predictions from the “Nostradamus’s” of Selling.
Microsoft launches business school focused on AI strategy, culture and responsibility.
Artificial Intelligence in Sales: What It Is, How to Use It and Companies to Demo.
