Learn seven practical use cases where artificial intelligence is already supporting sales today.
More and more companies are using AI to streamline processes in sales and hand over unpleasant tasks to algorithms. AI is therefore assistance for sales without making employees obsolete.
The use of artificial intelligence (AI) is radically changing the way sales works. This primarily refers to sales processes under the influence of AI tools that make the work of sales employees easier, optimize operations and take away tasks that are perhaps not so readily done.
One example: by using predictive analytics tools, it is possible to analyze large amounts of data from different sources. This saves salespeople from making time-consuming predictions and forecasts, which are much more accurate with the help of AI.
The Benefits of AI in Sales have been known for Years.
Generally speaking, using AI in sales brings with it the advantage that valid, scalable and automated recommendations for action can be derived for sales processes for the entire customer journey through forecasts and probabilities.
In 2016, three authors from McKinsey, a management and strategy consulting firm, noted in an article for the Harvard Business Review that companies that pioneered the use of AI in sales saw an increase in leads and appointments of over 50 per cent as a result. Cost reductions were 40 to 60 per cent.
The authors also emphasize the added value of AI in sales. Employees can spend more time closing deals, making AI even more attractive. AI systems, therefore, also take over administrative and repetitive tasks, allowing sales staff to focus purely on sales-related activities.
Practical use cases of AI in sales
To avoid getting stuck on general statements about the benefits of AI in sales, let’s look at practical scenarios where sales can benefit when using AI:
1. Flexible Price Adjustment (Dynamic Pricing).
Dynamic Pricing is about analyzing which prices customers accept and which ones tend to make them belly ache. Supply and demand determined this scenario.
To optimize or adjust prices with the help of AI, a database with specific customer characteristics, such as historical purchasing behaviour, is required. From this, it is possible to deduce which prices customers have accepted in the past.
2. Lead Qualification (Predictive Lead Scoring)
Predictive lead scoring is used to qualify and evaluate existing customer data based on various criteria. AI facilitates the evaluation process through automation. Lead scoring means assessing how ready a lead is to buy and when it can be handed over to sales.
Predictive lead scoring uses algorithms that calculate a statistical purchase probability for each customer inquiry. The advantage of this is that sales staff can quickly see how ready a particular lead is to buy, which positively affects the sales strategy.
3. Predictions and Forecasts (Predictive Analytics)
Predictive analytics uses historical customer data to predict future events. In doing so, AI analyzes large amounts of data from the past and incorporates several other factors to make predictions about developments in the future.
It addresses questions such as: How will customers most likely behave in the future? For example, which products are they most likely to buy and which are more likely not to? In the blog article “Correlation does not equal causality”, we clarify the difference between the two concepts.
This differentiation is necessary because many companies, and sales, are all too happy to cling to causality, that is, the relationship between cause and effect. In line with this mindset, they want to explain particular customer behaviour because this supposedly offers more certainty when making decisions. Predictive analytics, however, is a method that forecasts specific probabilities based on which sales can better judge which decision is probably appropriate. However, even this method cannot guarantee that this decision will turn out to be the right one.
4. Cross-selling and Up-Selling
In cross-selling, companies try to get their customers to buy additional products or services on top of the ones they have already purchased. These products or services usually complement each other – giving customers a reason to buy both items.
Up-selling is trying to get customers to upgrade their purchase or buy additional options for a purchased product or service. The advertised product or service usually consists of a more expensive product or additional options that increase the order value.
AI also plays the role of assistant in this sales scenario by providing a shopping cart analysis based on existing ERP and CRM sales data. Salespeople can use this to identify possible additional sales potential, which they can use to increase revenue while efficiently deploying sales resources, namely, with those customers who are worth knocking on the door again.
5. Customer Satisfaction
If sales can read from customer data what kind of customers they are dealing with, how customers have behaved in the past, what they have bought and what prices they have paid, then this database creates the foundation for a good customer relationship. In other words, sales “knows” the customers or their needs. And that is important to present them with relevant offers. For example, in the form of cross-selling and upselling measures or price discounts, etc.
6. Churn Prediction
B2C customers are familiar with this from contracts with telecommunications service providers: at the beginning, you are wooed with all kinds of discounts and bonuses, and once you are finally a customer, you usually don’t hear from the provider again for years. Or rather, only when it comes to selling a higher-priced contract. In the meantime, however, the conditions usually change, and there are often cheaper alternatives for customers. These are concealed, even if the old contract may offer less service at a higher fee.
This scenario harbours the risk that customers will leave and switch service providers. This churn risk can also be predicted with AI support. The AI indicates to the sales department which customers are likely to turn their backs on the company and how likely they are to do so. Corresponding churn prediction software warns with appropriate information and provides concrete recommendations for detecting and avoiding customer losses.
7. Customer Lifetime Value
Customer lifetime value (CLV) is one of the most influential metrics in sales and is considered key to successful customer relationship management (CRM). With the CLV, companies can identify which customers are worth investing in and can thus use the sales budget efficiently.
Customer lifetime value is a 360° view of the entire customer journey.
In business terms, the CLV is the sum of a customer’s revenue minus certain costs, such as those for acquiring and retaining the customer – over the duration of the entire customer relationship. Predictive modelling is an AI method that provides a glimpse into the crystal ball. Predictive modelling uses data, statistical algorithms, and machine learning techniques to identify the probability of future outcomes based on historical data.
But here, too – we remember – it is not about a causal relationship (in this respect, the term crystal ball is only a metaphor for a look into a probable future) but about the occurrence of the highest possible probability.
CALCULATE NOW THE ROI OF QYMATIX PREDICTIVE SALES SOFTWARE
Is AI replacing Humans in Sales?
Given these numerous possible applications of AI in sales, the question of the future role of sales employees is justified. Will an army of salespeople soon be heading for unemployment because algorithms will take over their jobs? The answer – which may be somewhat unsatisfactory for the target group – is: It depends.
It depends on what specific tasks and activities salespeople have in practice. Permanently repetitive routine tasks, for example, can be entirely replaced by AI. In a blog post, Qymatix CEO Lucas Pedretti took a detailed look at the replaceability of sales tasks.
Michael Becker, Head of the Print+Media Academy of the VDM North-West, takes the following view in an online article for print.de:
“The current state of technology and optimistic forecasts for the future do not lead us to expect that the salesperson will be replaced. Creativity, empathy and sympathy, charisma, humour and communicative skills can currently only be replicated to a limited extent even by advanced AI tools and certainly cannot be adequately replaced!”