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How Consumer Goods Can Put AI Into Action


CEO & Co-Founder of Aforza, a global cloud computing leader, transforming consumer goods companies to sell more and grow faster.

A wave of innovation is sweeping the consumer goods (CG) industry with customers demanding digital experiences across every channel. Established players are being forced to accelerate their transformations to keep pace with the challenger brands who are often cloud-native from the start.

But a growing trend I am seeing, whether with a new or established brand, is an inability to cope with the large volumes of data generated as a result of new digital processes. This data can span from consumer segment buying behavior to e-commerce journeys, electronic point of sales transactions to localized promotion effectiveness. For any newly digitized business, it can be an intimidating amount of data, but it’s critical to know what you have, the insights you can generate and how to leverage it to make informed decisions.

Effective use of artificial intelligence (AI) and machine learning (ML) has emerged as a competitive differentiator for companies looking to effectively harness the power of collected data. When dealing with large volume datasets and high volume business processes, AI’s ability to attach context to each insight and analyze it creates value. In fact, the potential of AI in consumer packaged goods is estimated to be $1.4 trillion. And this value may very well be the demarcation point between the CG companies that can and cannot keep up with increasing industry challenges.

Working with customers around the world as the CEO of a company that provides AI solutions for the consumer goods industry, I am continually asked for areas where businesses can see the greatest advantage with AI. And, in my experience, there are three key use cases for sales teams where I have seen a significant impact for consumer goods companies.

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Evaluate prioritization and in-store task execution.

A major problem many CG companies face is their sheer number of existing and potential customers. When visits are a large part of your business, with people on the ground either going in to sell your product or check store compliance, making that journey and selection process as streamlined and straightforward as possible is essential.

Using AI-driven recommendations, take a data-driven look at your decision making, evaluate the visits that will be successful and ascertain the most effective strategy to engage with these clients. Is it worthwhile spending your time there? Are there any clients receiving insufficient attention and purchasing less products? If you only have a few salespeople, I think this is where you should concentrate your efforts: increasing employee efficiency.

Segment customers with dynamic attributes.

At first glance, this may seem misleading—dynamic segmentation is not, in fact, AI. So why is it in this list?

Because attaching dynamic segmentation data to an AI engine creates the maximum value by tying automation with real-time, human feedback. For example, if you were to segment an account under a particular demographic or competitor foothold, by coupling that data allocation with AI, you can start to drive ultra-specified recommendations, actions and order sets. The retail landscape is vast. It encompasses every type of customer and channel you could imagine, but all of these consumers do not want the same things.

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So, by using a mix of sales trends, point-of-sale data, store demographics and information captured in the field to categorize and segment an outlet, you can ensure that your products and promotions are being targeted in the right places. Correctly attributing and applying captured data can also enable real-time observation of trends. Is your store promotion cannibalizing, i.e., taking profit from your other products or helping to raise the profile and profit of them all?

Some of the most tangible use cases that I have seen in practice include being able to suggest tags (e.g., VIP, value-sensitive or competitive customers) from machine learning, allowing targeted sales activities and promotions to be run as a result.

Implement real-time order upsell and cross-sell recommendations.

This is where insight-driven algorithms can suggest what and how much a customer will buy based on their regular patterns; they may also prefer these things based on who they are and how they relate to other customers’ buying habits. These can also be based on a variety of other factors, such as location, events and weather. There is really no limit to how informed you can make these suggestions.

Additionally, if a customer isn’t aware of the complete breadth of your product range, this can be useful in broadening their understanding to cross-sell and upsell beyond their traditional scope. It can also assist with forecasting demand through the identification of trends.

These are the use cases that I am seeing add value time and time again in the field. That said, there are a multitude of other ways in which AI can be applied to any business with real-time data insights. And I suggest that now is the time to implement it, while so many still talk about the subjects of artificial intelligence and machine learning in abstract, fluffy terms.

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Prepare for common pitfalls.

One of the most common pitfalls when getting started with AI is a lack of understanding of how and where you want to apply it. Before embarking on an AI journey, do your due diligence in order to understand what you want to achieve with AI internally. With such a wide-spanning array of possibilities, it can be easy to get overwhelmed. Without knowing where and how you want to apply it, AI could potentially do anything for your business. Therefore, it will do nothing successfully.

By understanding exactly what you do and do not want or need from AI, you can increase the likelihood that you get the product and impact that you’re looking for.

Get ahead of the curve, now, and educate, educate, educate—both yourself and your teams. I truly believe that this will be the strategic area where industry leaders will emerge; those who embrace the very real potential of AI will see long-term wins and those who do not will fall behind. Even though it’s always seemed a possibility of the future, AI is very much a product of the present, and it can add value today.


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