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Machine Learning: Unpredictable Consumer Journey To Purchase

Machine Learning: The consumer sees the advertisement, goes to the store and buys the advertised product. This is the scenario retailers would want customers to play, ensuring an immediate return on their advertising spend.

Until some time ago, this situation could even be recurrent when advertising took its first steps. But what about today? Is it that simple in the face of an extremely competitive market?

The internet, smartphones, the diversity of communication and information channels and the number of brand and product options have transformed consumption habits. Today, the path to purchase can be quite tortuous and much more complex – quite different from the reality with which companies have become accustomed to working. People can take completely different paths to buy a product, even if they are impacted by the same advertising or live in the same region.

But if each consumer follows a path, how can we help them choose their products, and how can the company adapt to such unique shopping journeys?

The Answer Is Machine Learning. And We Will Explain Why

The traditional marketing funnel traces the steps that every consumer, in theory, goes through to purchase. At first, he still doesn’t know he has a need, so he needs to have his interest aroused. Then he discovers the problem to be solved and searches for possible solutions. In the end, choose one of the alternatives and make the purchase.

The role of marketing is to create strategies and content that accompany the consumer during their journey. The company intends to remain close throughout the process and help the consumer decide. Thus, each channel contributes to the journey in some way, even if not directly in the purchase decision, but influencing it, of course!

A Google study, based on an analysis of thousands of data from internet users, concluded that no journey is the same as another. Imagine a consumer who wants to change his smartphone. He then starts researching solutions. However, you don’t find any option within your budget. So, give up the purchase momentarily.

Two months later, he goes back to searching for smartphones online. In the same week, he receives a remarketing email from e-commerce. Then he visits the store’s website, researches more about the product and decides to buy. However, he thinks a little more when putting the product in the cart. The purchase, in short, will only be made two weeks later after receiving a discount coupon.

Realize How The Path Is Not Linear

Intentions change throughout the journey and can make consumers rethink, go back, resume an option they had abandoned or take longer to decide. That’s why you need to understand the steps of each one. And, in the face of Big Data, only technology allows bringing this information to light.

Every consumer interaction with digital channels leaves signals about their intentions. For example, there are already systems capable of identifying, by browsing steps within e-commerce, whether that user is ready to buy or not. With the improvement of learning, the systems begin to predict the intentions and capture each user’s behavior. This is precisely the role of Machine Learning. From this learning, marketing and sales teams can take the right approach at the right time.

Machine Learning: The New Role Of The Sales Professional

What can be said, however, is that organizations are already thinking about new roles and functions for their sales forces based on the resources offered by these technologies. The initiative is natural, as companies want to be ahead of their competitors. And the tools are there to be tested. Do it. Einstein from Salesforce and Osorno Analytics are just a few I’ve had contact with in recent months.

Today, we generate and collect an immense amount of information. Part of it is produced within our organizations, resulting from the execution of business processes. ERP and CRM systems pour out, every minute, tons of precious information about our customers and our operations. On the other hand, an even greater amount of market, product and trend information is downloaded on social networks, blogs and news sites. Your competitors, prospects and you can access This public knowledge just as easily.

The use of this information will depend on the ability of organizations to absorb and transform them into intelligence. When machines can establish cause-and-effect relationships on outcomes through sophisticated algorithms that interpret data and predict outcomes, they can also make better daily predictions. Machine Learning is an aspect of artificial intelligence that transforms roles and responsibilities for professionals across industries, including sales.

Use Of Technology In Sales

There are several ways for organizations to use this technology in their sales operations. Interpreting customer information is a task for which many resources have been spent. BI systems store, segment, and disseminate information that facilitates the understanding of trends in customer behavior. Although we have satisfactory results, they are still far below those that can be obtained, given the amount and diversity of information available today.

Machine Learning will provide the most effective use of this data, with precision and assertiveness, in a much greater dimension than people’s ability to infer. As good as BI systems are, this trend delivers all of this in real-time.

Machine Learning brings an unprecedented advance to prospecting activities and identifying sales opportunities. It adds value to your work, supporting common sense and intuition with information and knowledge that can indicate and validate actions to be taken and letting the computer take care of transactional and repetitive tasks.

Also Read: Precautions Before Implementing Machine Learning

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