How CPG Companies Can Use Data to Combat Industry Stagnation
Growth is becoming more elusive for large CPG companies. According to an article from McKinsey, large food and beverage manufacturers grew only 0.3% per year for the last four years and accounted for only half of sales in that category. In comparison, medium-sized companies grew 3.8%, and small companies grew 10.2%. By intelligently leveraging data to gain SMB-like consumer insights, major players can shift this otherwise stagnant market.
Data is a disruptive force - if used correctly.
Business executives care about one thing: growth. For leaders in consumer package goods particularly, the ability to demonstrate stable and consistent growth is of heightened importance. Even the largest beer or cosmetics companies in the world aren’t protected from stagnation or even serious loss of market share.
Size doesn't matter the same way it used to. Crowdfunding for high-demand, small-scale businesses is allowing SMBs to disrupt large legacy brands more than ever.
How are these small but quickly-growing businesses achieving this level of growth? They understand their consumers. Ultimately, consumer demand is the force that is driving this changing CPG landscape. Today’s consumer is:
- Highly informed
- Socially connected
- Seeks instant gratification
- Trusts the crowd
If you compare this to the picture of how a traditional CPG enterprise is being run, you may see a stark difference: silos, command and control, bureaucratic planning processes, politics and lack of sharing, disconnection from the front line, etc. How can this type of company adapt effectively to today’s consumer needs?
The winning advantage is to discover what the future looks like using both data and experience.
Experience + Data = Foresight
As data becomes more available, the winning advantage is going to come from an organization’s ability to connect their data with their experience - all of their tacit knowledge as a company must be connected to data. Organizations that focus solely on operational efficiency and restructuring alone will not survive changing consumer demand.
This is where big data becomes so important: as companies seek to catch up, they need to configure themselves as a network, sharing insights across silos and brands quickly and where they will add most value. An insight from a team working on the other side of the world is of no benefit to the whole network if it is not shared. As just one example, sharing the cost and pricing decisions faster and more accurately can enable brands to quickly adapt to the changing needs of consumers, providing a clear competitive advantage.
During conversations with various CPG industry leaders, the commentary I hear is the same: “we are inundated with data.” While volume can present its own challenges, the greater obstacle is determining data validity - what data is important to help us understand the consumer and adapt faster than our competitors?
In order to build a more successful future for their organizations, leadership teams need to understand what has and hasn’t worked in the past. The more they know about the history of their consumers and products, the better chance they have of anticipating their needs in the future.
“When you’re trying to find a needle in a haystack, don’t add more hay”
When you have so much data to sift through, where do you start? Start with the business objective and connect your data discovery to it. Coupling business context with analytics expertise is the starting point for success. For example:
- *What is future consumer spending going to look like in our markets?
- What brand and marketing strategies are working and why?
- How do we optimize trade marketing performance amongst retailers?
- What categories are performing best right now and why?
- Where can we save costs across our supply chain?*
For example, if we know that consumer profiling across channels is going to add the most value, we can deploy data towards that business goal. Or, if we want to understand the consumer through an omni-channel lens or understand their brand choices, then we can stage that discussion in a much more focused and deliberate way.
End-to-End Fully Integrated Mindset
Data Officers and their teams cannot create a data-driving business culture alone. I have seen organizations attempt to build this culture without the right role-modeling from senior leaders, and it never works. If you want to democratize data and analytics through the use of intelligence tools and products, executives must encourage this behavior at all levels: from the frontline to senior managers.
“Top-down” insights have limited value. Leading organizations are able to connect the frontline to the center, providing teams with access to an integrated network of insights.
“If you want to empower the frontline, don’t tell them how to interpret their data”
Data-driven culture thrives when the organization supports a common and consistent language. Giving leaders throughout the organization a guideline of how to ask the right questions about data will accelerate this change.
Final Mile: Driving the Data Culture
Big data has zero value unless it helps inform everyday business decisions. This is the Holy Grail for businesses seeking to align their process with consumer demand. Even when organizations invest heavily in the right data governance, structure, and talent, daily usage and incorporation of data must be the ultimate goal.
Changing employee mindsets on the frontline of business to combine experience with analytics is how CPG leaders will sustainably see value and ROI on big data. In our experience, enabling teams to share insights across functional silos is not a technical challenge, but an organizational one. While it’s one of CPGs greatest organizational hurdles, it also represents one of its greatest opportunities.
- Innovative, consumer-focused tactics are needed in this stagnant CPG market
- To compete and further disrupt, CPG companies need to leverage their massive stores of data
- This isn't a question of volume management, but understanding data validity
- Further challenges and rewards are found in digging into how organizations use data to inform decision-making
- True success is found by connecting all levels of the organization to data, from the frontline to C-suite, and this is largely a question of process rather than technology