Smart companies use artificial intelligence to sell smarter, more targeted and more cost-efficiently. How do they do that – and how do both sales teams and their customers benefit?
AI is becoming increasingly intertwined with the way companies do business and urge us as consumers to buy from them. Algorithms, meanwhile, are crucial in organisations that want to organise a smarter sales floor with optimal use of human capital.
But how exactly do these improvements work in practice? We dive into the ways AI fundamentally improves the sales cycle of organizations.
Will artificial intelligence soon rule procurement departments in the field and will deals be negotiated between algorithms? And will soon every account executive be able to sit back with their feet on the desk while the AI keeps the leads flowing? Exploring all steps of the sales cycle.
1: More and better leads
Correctly scaling in potential leads often remains a difficult balancing act between marketing and sales. When is that marketing qualified lead or MQL warm enough to have one of your sales people call him or her? And what is the expected success rate of a call to someone who has downloaded a whitepaper from your website? Now that physical events with personal contacts and lead qualification have often completely disappeared, lead scoring is often based on one hundred percent virtual touchpoints. Smart companies are turning to AI for this. Artificial intelligence can estimate leads much more accurately than even the best marketer, who doesn’t have the time to manually align all possible settings and parameters. Sales teams, in turn, waste less time contacting leads who turn out to be a bit colder than expected. AI lead scoring gives marketers and sellers the space to do what they do best: craft great marketing materials and campaigns and personally convince potential customers.
2: More accurate forecasting of demand and sales
Classic sales forecasting and demand forecasting tries to predict what customers will buy. In this way, stocks can be purchased on time, so that items reach the distribution center or the store on time. Forecasting is broad: from supermarkets that want to ensure that there will be enough barbecue sausages available to fast-fashion players who want to respond quickly to fashion trends. AI-based sales forecasting and demand planning uses advanced algorithms that can take into account hundreds of variables. Past sales data can be combined with other information – such as website statistics, weather information or economic factors – to predict product demand. For example, classic forecasting methods and demand planning go from better guesswork to crystal-clear forecasts.
3: Optimal pricing strategy
AI is also very good at optimizing pricing. Advanced pricing algorithms enable organizations to tailor pricing policies for their sales teams across their entire product portfolio, based on individual market needs and characteristics. In this way, organizations get the most out of the purchasing power of the companies and end customers with which they do business.
This helps pricing teams to build a flexible pricing model across all regions, channels and customer segments – better than they could do manually. That, in turn, allows for a more accurate calculation of revenue forecasts. That makes The right price no longer just a TV classic, but also a reality in B2B and B2C.
4: Better customer relationships
Less effort on leads and low-opportunity accounts also means more time to invest in close customer relationships. Follow-up by account managers and sales teams becomes a lot easier this way. AI significantly eases the early stages of the sales funnel, allowing sales reps to focus on executing their sales strategy and building and deepening human contact. As always, AI is not a goal in itself, but a sales tool that supports sellers in their account management.
5: Less customer churn
Customer churn used to be a notoriously hard nut to crack. Preventing customers from packing up their bags and looking for other places is nevertheless crucial: keeping existing customers happy always yields more than attracting new customers. Artificial intelligence searches various data sources for indications and risk factors for customer churn and draws up overarching customer profiles, with associated risk factors. For example, the AI can pick up alarm signals that may indicate a less satisfied customer. Then someone from the sales team can proactively and specifically contact you. An excellent example of AI elevating the human touch.
AI for better human contact
Sounds utopian? Artificial intelligence in sales is a fact in many companies today. According to McKinsey, the increasing use of AI in marketing, sales and supply chains will create an expected value of $2.7 trillion by 2040. That equates to an annual additional economic output of a European mid-range engine such as Hungary.
We ourselves also determine this added value every day at the organizations that rely on our Trendskout Sales booster. Companies that deploy AI are ahead of the competition. Brands use our sales AI to improve their customer journey in every way. When done right, it gives organizations more leeway to engage their sales teams in what really matters: making a difference with human contact.
Interested in apply Sales Forecasting with AI? We are happy to help. Contact us .