Improve demand planning accuracy by avoiding common mistakes
Pawan Murthy is Senior Director of Marketing at Prevedere, a predictive analytics company that provides enterprise forecasting. Pawan has over 14 years of experience in marketing strategy, product development, creative direction, and launching companies and business units.
Luke Shave, Microsoft’s Senior Industry Marketing Manager for CPG and Retail, recently sat down with Prevedere’s Pawan Murthy to discuss common mistakes in implementation that are preventing retailers and consumer goods companies from reaping the full benefits of predictive analytics.
Luke Shave: Recognizing the potential benefits of increased demand forecast accuracy, many retailers and consumer goods (CG) companies are already leveraging predictive analytics to guide their demand planning. But not all of them are finding their demand forecasts to be significantly more accurate. What explains this?
Pawan Murthy: One common mistake among retailers and CG companies is trying to boil the ocean. Where once they struggled with limited information to guide their demand planning, today the potential datasets available are virtually endless—from customer behaviors and internal performance metrics, to third-party industry statistics, to open data available on economies worldwide. This is the double-edged sword of digitization: while it is the availability of massive amounts of data that makes predictive analytics possible in the first place, the quantity of data makes it difficult to use effectively.
Trying to glean meaningful insights from all the data is an exercise in futility. Retailers and CG companies need to focus solely on the key metrics that most affect their business.
In winnowing down their data, they should keep in mind that not all data is quality data. With the advent of “big data,” it is easy to treat all data equally without thoroughly examining the credibility of data sources and cleanliness of datasets. Retailers and CG companies should examine their data carefully to avoid making decisions that are based on outdated or erroneous metrics.
What principles should retailers and CG companies keep in mind when determining the key metrics that affect their business?
Murthy: The most successful retailers and CG companies pay attention to true leading indicators of future demand, rather than focusing on data that explains past performance. While the latter can add context for understanding past performance, it can’t help you plan for the future.
Think about it this way: When skiing down a mountain, you wouldn’t look back at the terrain you’ve just skied to determine what lies ahead. You’re liable to crash into all sorts of unexpected obstacles. Yet some retailers and CG companies are doing just that—looking at the metrics that explain past performance to predict future demand. They should instead prioritize the datasets that are relevant to future performance.
So far, we’ve discussed best practices for selecting data to drive more accurate demand planning—but data isn’t the whole equation. What are best practices for creating models that accurately transform that raw data into insights?
Murthy: Retailers and CG companies should avoid creating single use, point-in-time data models, which are effectively out of date as soon as they are developed. Creating data models that paint a picture of a specific point in time can’t help you plan for future demand. Instead, retailers should create models that are automatically updated as leading indicators and key performance metrics change.
Of course, predictive analytics does not occur in a vacuum—it’s only effective if it’s properly integrated into business processes, right?
Murthy: Absolutely. Incorporating data analytics into business processes requires more than just collecting and reviewing data. The time spent on these efforts is wasted if business units don’t understand the methodology or if insights from the data team are not timely enough.
Retailers and CG companies, especially large enterprises, have a tendency of siloing their predictive analytics to a single business unit, thereby limiting their return on investment. Predictive analytics can provide benefits across an organization, but only if retailers and CG companies enable predictive analytics integration throughout.
Finally, some retail and CG executives choose to rely first on gut feeling over insights that may go against their intuition. Research is showing that this statistically leads to decisions with poor outcomes. Especially with consumer preferences changing rapidly in response to e-commerce, wearables, and virtual reality, leveraging predictive analytics is vital to prepare for these changing dynamics.
Achieve more accurate forecasts with Prevedere Demand Planning
Retailers and CG companies already understand that demand planning requires investment in predictive analytics, but not all analytics approaches are able to keep pace with today’s volatile global economy.
Prevedere, an industry insights and analytics company, has partnered with Microsoft to provide actionable data driven insights at the speed of business. ERIN, their patented analytics engine (using Microsoft technology) enables industry leaders to monitor global economic and industry activity to forecast future demand spikes and declines 12 to 24 months ahead of the competition.
Learn more about Prevedere Demand Planning on AppSource and start a free trial today.
