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Raising the stakes: How to achieve real-time customer-centric demand forecasting

Advances in customer analytics help businesses gain new insight and flexibility in demand forecasting.


Businesses know the fundamental role that customers play in their success. As such, they understand the importance of meeting customer demands to win in an increasingly competitive marketplace.

Despite this understanding, many companies fall short when it comes to demand forecasting. Far too many companies rely on internally focused, product-centric forecasting that never accounts for the customer. The result? They gamble with success, and often lose out in sustaining profits and customer loyalty—and the competitive advantages that come with them.

With advances in analytics, modeling, data and technologies, it is possible to gain new customer insight and flexibility in demand forecasting for today’s marketplace. Now is the time to stop guessing and start knowing with demand forecasting that truly reflects customer demands.

Put customer-centric analytics to work to take demand planning to the next level.


For many companies, forecasting techniques have traditionally been based on supply-driven models in isolation of customer-facing activities and organizations. Real customer insight such as behavioral segmentation, product preferences, channel attributes, promotional and price receptiveness has simply not been part of the equation. Moreover, sales forecasting has traditionally been the purview of the manufacturing and supply chain organizations that focus on the mechanisms of product movement.

But with a tough economic recovery, volatile consumer demand and competition, product manufacturers and retailers must make changes. They must understand the wants and needs of customers—by store, segment and SKU—more than ever. It is also critical that these insights help shape dynamic demand planning so forecasting is truly real time.


With decreased budgets and increased need for visibility, there is more and more pressure for companies to understand the exact cause and effect of every lever in the company—and it takes a consumer-focused function to deliver that understanding.

The good news is that there is tremendous opportunity for companies to adopt more sophisticated forecasting capabilities. All companies can expect to gain greater visibility into each link of the sales cycle, becoming more nimble in anticipating and reacting to marketplace changes.

What’s more, different companies in different industries can benefit in a variety of ways, depending on their unique circumstances. Results can include:

  • Improved sales—Better service levels, which can increase sales to customers.

  • Reduced costs—Reductions in costs-to-serve.

  • Stronger innovation—Increases in the return on investments in research and development.


While every company will have its own path to achieve real-time demand forecasting, there are three fundamentals to making the transition:

  1. Move demand forecasting out of the traditional product-centric organizational model.
    To obtain real-time customer-centric forecasting, companies need to move ownership from functions focused on internal constraints to other, more market-focused ones to capture the volatility and complexity of the outside world—and customer demands. Companies across consumer packaged goods, retail and life sciences industries are evolving forecasting approaches in line with this need.

  2. Break down organizational silos to build cross-functional responsibility and governance.
    To drive the visibility and responsiveness required for real-time demand planning, companies need to enable communication, breaking down silos among external market insight, customer databases, supply systems and product pipelines. Businesses need to ensure data is being shared between marketing research, supply chain and other commercial areas for a truly integrated view of the customer. What’s more, they need to establish and communicate data governance across all functions.

  3. Tap into customer insight and market-driven data more deeply.
    It’s one thing to use analytics to understand customer behavior. It’s another to use it with intention to generate real-time demand forecasting—the richer the contextual data, the deeper the customer understanding. To get to a precise view, companies must consider the impact of external variables such as seasonality and opportunities such as clustering stores and customers to gather representative samples without losing granular insight.