That many aspects related to sales have changed drastically in a few years is an indisputable fact. Technological changes have led us to a very different panorama for commercial relationships. It is worth noting the predominant role that Machine Learning and Artificial Intelligence solutions have acquired in this new situation.
You only have to look at the figures to be aware of the advantages that the inclusion of these technologies brings to the sales teams. According to Harvard Business Review, the implementation of Artificial Intelligence has meant 50% increases in leads, costs have been reduced by 40-60% and the time spent in customer calls has decreased by 60-70%.
How machine learning and artificial intelligence can increase sales
There are several factors in which these disciplines can intervene in a way that reverts in the achievement of greater benefits when offering a product or service. We will highlight the most relevant ones.
Identify the best sales opportunities
The algorithms with which both Machine Learning and Artificial Intelligence work allow recognizing patterns associated with a higher probability of getting a sale. These algorithms are powered by information from third parties, online activity records and previous conversations with the sales team.
In all this, the strategies of Lead Scoring and Lead Nurturing have a lot to say, which consist, respectively, of classifying and providing the content that each potential client needs. These techniques are of great help when converting sales opportunities into clients.
Technically, Lead Scoring and Lead Nurturing are strongly related to the Natural Language Generation (NLG) and the Natural Language Processing (NLP). NLG covers the processes by which language expressions are generated from a set of data, while NLP is responsible for the design of communication mechanisms between machines and human language.
Improve sales forecasts
Having at our disposal the exact information of our clients related to their history of purchases of products or services is invaluable in predicting how sales will be in the future.
In order to have a clear vision of the level of sales in the medium to long term, Machine Learning and the Artificial Intelligence systems process historical sales data, such as prices, volumes or geographical distribution, and combine them to transform them into useful information. The algorithms involved in these processes are usually integrated into CRM applications and sales planning and management.
The future forecast of demand for different products and services is an area where Artificial Intelligence and Machine Learning contribute to reduce the risk when investing in new products, sales strategies or new markets.
Analysis of the useful life of the client
How to relate to each client is a key fact in the marketing actions of each company. For this it is very important to know in which phase of the relationship we are, in order to act accordingly. You can differentiate five established phases: acquisition, conversion, growth, retention and reactivation.
Machine Learning and Artificial Intelligence provide valuable tools for the study of variables that will facilitate decision-making related to the management of our clients.
These technologies make us have predictive models that allow us to know what action to execute for each client according to the stage of their life cycle in which they are. For example, depending on your first purchase, we can know with which product or service we will be more likely to make a second purchase and, consequently, perform the appropriate marketing actions.
Predictive analytics can also answer questions such as “How likely is this client to become one of the most loyal” or “How can I recover a client I’ve lost?”
Offers products or services according to the customer’s likes
The reception and continuous processing of data in real time means those computer systems do not stop learning based on them. The result of this is that thanks to information such as sales history, ratings or opinions, frequency of consumption, etc. we can foresee with a certain margin of confidence which product or service will be the one that best suits each client.
As a sample we can talk about the Machine Learning solution that Geographica developed for OneBookShelf, a digital entertainment company with several specialized online stores according to different themes.
For this case we implement a system of product recommendations to users based on their attributes and how these users react to them, in a way that allows us to offer them what generates a better response.
Improves the efficiency of the sales team
The sales that are achieved through resolutions supported by Machine Learning and Artificial Intelligence are based on an analysis that offers businesses useful information on what will be the best strategy to follow in order to close a transaction.
For this, all the contact channels with the client are considered, the most efficient ones are determined and the behaviors that lead to the highest sales indices.
The time dedicated by each vendor to each client is also optimized, thus being able to devote to those who represent a greater probability of success. According to McKinsey Global Institute, 45% of the time spent on sales can be automated with Artificial Intelligence.
In addition, thanks to the Machine Learning and Artificial Intelligence solutions, we can learn more about the capabilities of the vendors with the best results, which allows us to configure the sales team more appropriately, optimizing the results.
The fixing of the price policy has traditionally been carried out by means of different strategies, according to costs, demand, value or paying attention to competition. All of them involve a process of trial and error.
With the appearance of Machine Learning and Artificial Intelligence, the algorithms that govern these technologies have the ability to define price optimization methods, applying a value for the product or service based on variables related to the client and its context.
This is achieved through the analysis of price data, purchase history, discounts, participation in promotions and other variables that are processed to obtain a price with which sales are optimal.
The development of Machine Learning and Artificial Intelligence has marked a before and after in commercial relationships. Its integration is such that it is estimated that by 2020 30% of B2B companies will apply Artificial Intelligence to their sales processes.
These levels and speeds of implementation would not be possible without the firm conviction of the managerial positions of the companies. In fact, 57% of executives believe strongly in the advantages that these technologies bring.
Of course, a growth of these numbers is approaching, growth linked to other technologies that are applied to the collection of information, such as the Internet of Things (IoT), voice assistants or chatbots.
Although the economic point of view is very important, we must not forget that one of the final objectives of the Machine Learning and Artificial Intelligence solutions is the general satisfaction of the client, giving rise to positive results for both fronts of the commercial relationship.