Demand forecasting gives businesses the ability to use historical data on markets to help plan for future trends. As more data on consumers and products becomes available, the need to use this data to anticipate demand is critical for establishing a long-term model for growth.
In a sense, demand forecasting is attempting to replicate human knowledge of consumers once found in a local store. Long ago, retailers could rely on the instinct and intuition of shopkeepers. They knew their customers by name, but, more importantly, they also knew buying preferences, seasonal trends, product affinities and likely future purchases.
Demand forecasting attempts to replicate that sophistication through analytics-based evaluation of available data. By examining buying behavior and other bits of data left behind by the consumer, a retailer can mimic that knowledge on a broader scale.
Ebook: How can retailers satisfy today's customers
Download this ebook to get ideas about how you can have happier customers using analytics to make their experiences easy and relevant.
Anticipating demand for the modern buyer
Today’s consumer often journeys from digital space to physical space and back again, moving among devices, apps and displays. The buying process might start with researching a product online, continue with comparing prices from a mobile device, and finish with an in-store purchase. Or consumers may see merchandise in a store, then search on their phones to score a last-minute deal.
In a world where you can have practically any item shipped to your door, it’s important for retailers to make a connection with the buyer. A variety of buying options is a delight to consumers – and a rich source of intelligence for retailers, if you know how to capitalize on it.
This omnichannel retail environment intensifies the need for better answers to the perennial questions of supply and demand planning. What merchandise should be stocked, in what sizes/colors, at what quantities, in which locations? How, where and when should products be displayed, priced, promoted, ordered or shipped? How can we maximize profit without eroding the quality of the shopping experience and customer satisfaction?
Demand forecasting gives you the ability to answer these questions. But the sheer number of variables involved in the omnichannel world makes demand forecasting and merchandise planning on a global scale highly complex.
In its 2017 benchmarking study, Retail Systems Research found, naturally, that some retailers do this better than others. And the ones that consistently outperformed others shared a differentiating set of thought processes, strategies and tactics.
A variety of buying options is a delight to consumers – and a rich source of intelligence for retailers, if you know how to capitalize on it.
Specifically, the winners were the ones who engaged in seven productive habits:
- They put a high value on analytics. Fully 77 percent of retailers rated analytics as “very important” to their retail success, compared to 48 percent of average or lagging retailers. They know they can’t get by without integrating more predictive capabilities into their decision-making processes, and they understand that investing in retail analytics now is what gives them an edge in the present and future.
- They value more rigorous forecasting. Nearly two-thirds of winning retailers (74 percent) rated demand forecasting technologies as “very important” to their success, compared to 58 percent for the others.
- They bring analytics into the process earlier. They know that basing future plans on prior year or season sales will create self-fulfilling prophecies. The lost opportunities of the past will be repeated in the future. Instead, they bring insights from their customer analytics into the demand forecasting process upfront, not just as a sanity check at the end of the planning process.
- They are optimistic about information. Higher-performing retailers are twice as likely to expect big things from demand forecasting. They are also nearly twice as likely to “highly value” attribute-based merchandise planning systems.
- They are ahead of the game in implementation. Winners were twice as likely to have technologies already in place for attribute-based merchandise planning and demand forecasting to help with price, promotional or assortment planning. And they are twice as likely to have integrated their planning, allocation and replenishment systems.
- They make smarter allocation decisions. Winners know they need to put products where they are most wanted or needed, and they trust demand forecasting to help them make the best localized store assortments and fulfillment decisions for direct-to-consumer orders.
- They blend art and science. While information is seen as a critical asset – along with tangible assets such as stores, distribution centers and inventories – 62 percent of the winners also credit their success to “a healthy blend of art and science.” They value decision makers’ years of experience and understand the importance of well-tuned internal operational processes.
The Retail Systems Research report closes with a checklist of do’s and don’ts related to demand forecasting, customer analytics and localized assortments for retailers who want to be (or remain) winners.
“If retailers can follow these simple steps, they’ll go a long way towards optimizing their merchandising life cycle and creating a more compelling buying experience for customers,” the report states. “If they don’t, they risk being consigned to the dustbin of history.”
- Article Situational awareness guides our responses – routine to crisisMany circumstances call for situational awareness – that is, being mindful of what’s present and happening around you. The COVID-19 pandemic heightened this need, as leaders across industries used analytics and visualization to gain real-time situational awareness and respond with fast, critical decisions.
- Article Retail cyber risk toleranceManage your data assets just as you would any of your physical assets by putting security plans in place for any and all contingencies.
- Article Small but mighty: Modern marketing for the midmarketUsing data to improve the customer experience is how midmarket companies can take on the retail titans to win the hearts of their customers.
- Article How do recommendation systems know what you might like?How do recommendation systems know what you’re likely to want and offer it up while you’re shopping? That’s a computationally intensive question that used to take hours or days to answer. With distributed computing and in-memory analytics, it’s done in minutes.