In the times of fast data and even faster business, it is no longer enough to be demand driven – you need to become more and more consumer driven.
The goal of any good demand and supply process is to get as close to true consumer demand as possible. Unfortunately, many modern and traditional approaches come woefully short and miss the bigger picture and opportunities available. They look at only the transactional data available and react to orders. These approaches are limited by the fact that they are based on historical demand and the assumption that history repeats itself.
The new business environment is dynamic, and if you are waiting for demand – you are too late
The innovative companies of today are trying to go beyond transactional data or shipment data to POS or even behavioral data to be closer to their consumers. Instead of confining themselves to historic shipments, they are expanding into the four areas of data outlined below.
The 4 Types Of Data That Help Us Understand Consumer Behavior
1) Structured internal data: In addition to transactional sales data, there may be POS, RFID tags, and e-commerce data such as website clicks, streaming data, and others.
2) Structured external data: Includes retailers’ POS data and other data they can share from loyalty programs; macroeconomic data and related economic indicators; weather patterns; demographics; GPS; and external analytics from places like Google or Facebook.
3) Unstructured internal: For example, from marketing campaigns, surveys, apps, in-store devices, and website comments or page views.
4) Unstructured external: IoT connected devices, social media, natural language, or even pictures or emojis.
The Ability To Shape Demand Is The New Competitive Edge
Data is no longer a level playing field. Companies that are leveraging consumer insights and new technology have an obvious leg-up over competitors that are lagging behind. The goal is to go beyond knowing what has happened to provide a best assessment of why something will happen, or what drivers will impact something occurring in the future. Instead of relying on just past historical activities, they analyze the influencers, interactions, and activities of the actors (consumers) in demand.
The goal is to go beyond knowing what has happened to provide a best assessment of why something will happen
By using these new forms of data and understanding what drives the consumer to do what they do, and by forecasting faster and better, companies can plan their business strategy to take advantage of consumer sentiment. This is one reason that retail and fast-moving consumer goods companies have been early adopters of these new approaches.
However, consumer driven demand planning is becoming increasingly important for other sectors as well, such as automotive, industrial products, energy, and pharmaceuticals.
Historical Data Is Not Enough To Cope With Today’s Fast-Moving Marketplace
In today’s business environment, changes in the marketplace are swift, sudden, and may not follow the historical pattern. Just looking at historic shipments will not tell you what you need to know, nor does it does tell the whole story. Instead, we need to begin to look at patterns of consumer behaviors and other attributes to try to not only predict the sale, but understand why they purchased it in the first place. This becomes powerful to not only plan better but allow you to customize the shopping experience for the individual consumer and influence and proactively drive more demand.
Just looking at historic shipments will not tell you what you need to know, nor does it does tell the whole story
This last part is another very important distinction between demand driven or more traditional forecasting approaches compared to becoming consumer driven. The power is in getting closer to consumers using a combination of behavioral, attitudinal, demographic, and transactional data to do prescriptive analytics. Prescriptive analytics is not only understanding and predicting purchase intent but influencing behavior and demand shaping.
Prescriptive analytics is not only predicting purchase intent but influencing behavior and demand shaping
Prediction is Now More About Behavior Than History
Whether you care to admit it or not, as much as you use the words ‘forecasting’ and ‘being demand driven’ and sounding proactive, you’re likely just reacting to a signal built on incomplete assumptions. The new business environment is dynamic, and if you are waiting for demand – you are too late. Today we need to operate on the power and speed of technology and innovation. Prediction is becoming more about behavior than history.
Advancing technologies and demand planning skill sets are making it possible now to generate new insights into consumer behavior, effectively changing the way consumer products, retail companies, and many other industries are doing planning, and making real the possibility of being consumer-driven.
It is a brave new world for demand planners where predictive analytics encompasses a variety of new statistical techniques like probabilistic modeling, machine learning and data mining that analyze current and historical facts to make predictions about the future. With cutting-edge technologies and analyzing a greater variety of data, consumer-driven planning and forecasting allows organizations to sense demand signals and shape consumer demand.
Eric will reveal how to update your S&OP process to incorporate predictive analytics to adapt to the changing retail landscape at IBF’s Business Planning, Forecasting & S&OP Conferences in Orlando (Oct 20-23) and Amsterdam (Nov 20-22). Join Eric and a host of forecasting, planning and analytics leaders for unparalleled learning and networking.