Consumer packaged goods: Connecting the dots with D2C
A growing number of consumer packaged goods (CPG) companies have been exploring the benefits of introducing a direct-to-consumer (D2C) channel into their business in recent months. , PepsiCo and Heinz are just three high-profile powerhouse examples that spring to mind that are looking to capitalize on a growing customer need for convenience to gain highly valuable first-party data, and build closer relationships with their consumers.
When this data is harnessed with artificial intelligence (AI), we’re seeing businesses use it to optimize processes across the entire CPG value chain, from acquisition and retention, across all their channels.
Although D2C for CPG is still nascent, we’re already exploring some really exciting use cases for AI in a D2C setting for some of our consumer goods customers. Let’s take a look at a few examples of how an AI-driven approach to your D2C data can positively impact your entire consumer goods business.
AI for demand sensing
By getting closer to consumers and encouraging people to buy directly, CPGs are in a brilliant position to collect vast amounts of incredibly powerful data. AI can help you paint a clearer picture of what people are buying, how they behave on your website, who’s likely to make a purchase and, crucially, what content will stimulate them to buy.
But it doesn’t stop there. AI can also automatically feed this data back into your demand forecasts, augmenting it with your traditional sources meaning that you’re no longer forecasting based on just historical demand. With a more accurate forecast, and with a clearer idea of who is likely to convert and purchase a particular product, you can introduce this information into both your campaign and base forecasts.
AI for demand shaping
Traditionally, a CPG that is overstocked with inventory will rely on discounters to help them shift any unwanted stock. However, D2C offers businesses an exciting opportunity to also target specific segments of consumers directly to help clear inventory at a better price point.
In short, AI for demand shaping enables you to activate, or deactivate, your new customer segments depending on what inventory you have to play with, through targeting consumers with relevant products in a highly sophisticated, highly personalized way.
This AI-driven approach has the potential to revolutionize your inventory strategy, which we feel is one of the most interesting aspects of D2C. It’s particularly prevalent for those CPGs who are dealing with perishable goods, who serve retailers that have relatively erratic levels of demand. In these circumstances you may have the problem of often being overstocked, or you may be understocked and have supply issues when the unpredictable retailer comes back asking for more product.
However, with an AI-driven data-led approach to your inventory strategy, you could ensure you have enough quantity to always meet the retailers’ requirements, with your D2C channel effectively acting as a safety net; because you know that you’ll be able to quickly activate your highly targeted micro segments who always buy that particular product. Essentially, what we’re saying is that you can employ different inventory strategies to ensure you’re overservicing your primary channels, at no obsolescence cost of inventory.
This is very much blue sky thinking at this stage, but the potential is certainly there. Similarly, a better, data-driven understanding of costs on the supply side can be taken into account against the cost of acquisition, retention and servicing customers.
Our vision at Peak is to enable CPGs to transform from a reactive to a proactive entity, built upon predictive capabilities. Our acts as an intelligence layer that sits across your systems, at the center of your business, using AI to power optimization and decision making across the entire organization.
Gartner: Leaders Lack Skilled Smart Manufacturing Workers
With organisations rapidly adopting industry 4.0 capabilities to increase productivity, efficiency, transparency, and quality as well as reduce cost, manufacturers “are under pressure to bring their workforce into the 21st century,” says Gartner.
While more connected factory workers are leveraging digital tools and data management techniques to improve decision accuracy, increase knowledge and lessen variability, 57% of manufacturing leaders feel that their organisations lack the skilled workers needed to support their smart manufacturing digitalisation plans.
“Our survey revealed that manufacturers are currently going through a difficult phase in their digitisation journey toward smart manufacturing,” said Simon Jacobson, Vice President analyst, Gartner Supply Chain practice.
“They accept that changing from a break-fix mentality and culture to a data-driven workforce is a must. However, intuition, efficiency and engagement cannot be sacrificed. New workers might be tech-savvy but lack access to best practices and know-how — and tenured workers might have the knowledge, but not the digital skills. A truly connected factory worker in a smart manufacturing environment needs both.”
Surveying 439 respondents from North America, Western Europe and APAC, Gartner found that “organisational complexity, integration and process reengineering are the most prevalent challenges for executing smart manufacturing initiatives.” Combined they represent “the largest change management obstacle [for manufacturers],” adds Gartner.
“It’s interesting to see that leadership commitment is frequently cited as not being a challenge. Across all respondents, 83% agree that their leadership understands and accepts the need to invest in smart manufacturing. However, it does not reflect whether or not the majority of leaders understand the magnitude of change in front of them – regarding technology, as well as talent,” added Jacobson.
Technology and People
While the value and opportunities smart manufacturing can provide an organisation is being recognised, introducing technology alone isn’t enough. Gartner emphasises the importance of evolving factory workers alongside the technology, ensuring that they are on board in order for the change to be successful.
“The most immediate action is for organisations to realize that this is more than digitisation. It requires synchronising activities for capability building, capability enablement and empowering people. Taking a ‘how to improve a day in the life’ approach will increase engagement, continuous learning and ultimately foster a pull-based approach that will attract tenured workers. They are the best points of contact to identify the best starting points for automation and the required data and digital tools for better decision-making,” said Jacobson.
Long term, “it is important to establish a data-driven culture in manufacturing operations that is rooted in governance and training - without stifling employee creativity and ingenuity,” concluded Gartner.