The mounting pressure of volatile supply chains, finicky consumer demand, and a bevy of new competitors make e-commerce demand forecasting more important for e-tailers than ever before.
E-tailers need to know, with accuracy, what their customers want to buy, at what time, and for how much. A rigorous e-commerce demand forecast could help maximize their profits and reduce losses in overstocking, understocking, inventory, storage, and other challenges they deal with every day.
This means an e-tailer with meticulous e-commerce demand forecasting can bet on success; especially as e-commerce growth is predicted to increase, despite a newly announced recession.
Let’s take a look at what Ecommerce demand forecasting involves.
What is Ecommerce forecasting?
Ecommerce forecasting is the process of analyzing historical retail data (and other factors like seasonality, cannibalization, sentiment, etc.) to anticipate future demand or customers’ willingness to purchase products.
As you may have noticed, “e-commerce forecasting” is simply demand forecasting performed for an e-commerce retailer.
Of course, there are slight differences between e-commerce and brick-and-mortar, but the overarching process is quite similar.
Regardless of medium, retailers of all kinds attempt to predict the volume and type of products customers will want to buy during a set period of time in order to maximize both efficiency and profits.
Why is Ecommerce demand forecasting important?
E-commerce demand forecasting is important because of how complex large-scale e-commerce businesses are.
Sizeable e-commerce businesses are:
- Highly competitive with new, strong players entering the market regularly.
- Inventory-heavy with a vast array of items.
- Serving large geographies with complicated fulfillment logistics.
- Sometimes combined with brick-and-mortar stores, making it more complicated to forecast inventory demands for both channels.
E-commerce businesses are also in competition with giant analytics-first retailers such as Amazon and Walmart.
Plus, the speed and competitive nature of e-commerce leave no time for mistakes.
Can e-tailers thrive without Ecommerce demand forecasting?
Without a very good e-commerce demand forecast to help e-tailers thrive, the implications stemming from these situations could lead e-tailers to:
- Out-of-stocks; driving customers to competitor websites.
- Overspending on inventory that no one buys, and underspending on inventory that is in demand.
- Failing to provide the preferred inventory desired by a diversity of customers in varied geographies.
Ultimately, an accurate demand forecast is crucial if e-tailers want to keep customers, reduce inventory costs, and capitalize on the best opportunities.
This is easier said than done however when considering the challenges of e-commerce forecasting.
Demand forecasting challenges in Ecommerce
Demand forecasting is never easy.
E-tailers are constantly contending with the three Vs of big data–volume, variety, and velocity–pouring in minute-by-minute, day-by-day. It’s overwhelming.
All that data is also useless unless e-tailers can store, process, and analyze it to their benefit.
In addition to these challenges, e-tailers are also dealing with;
1. Quality of demand forecasting tools
Not all demand forecasting tools are equal in ability, especially when it comes to the three Vs of big data that relate to customers, competitors, pricing, and other online commerce activities.
Some tools are too basic, capable of only using past sales to forecast demand.
A more accurate forecast can factor in a variety of data points to arrive at real-world demand.
Some demand forecasting tools require a lot of manual input resulting in human error, more time, and more labour. And this results in missed selling opportunities.
Conversely, some forecasting tools are automated, and therefore better for scalability to accommodate complex e-tailers.
2. Online competition
Comparison shopping is just a click away for online consumers. There’s no need to go from one store to the next online shopping.
E-tailers need to be dynamic in real-time and stay ahead of the competition with:
- Pricing
Price comparison takes place in mere minutes between multiple products on multiple websites.
E-commerce retailers must have the right product at the right price in stock.
- Product visibility
Consumers have choices galore online with “endless aisles”.
What customers see on the first couple of online pages is important – they won’t scroll endlessly looking for what they want.
E-tailers need to know which products are in the highest demand for shoppers, so they are given priority on web pages.
- Up-selling/Promotions
Similar to knowing what products should appear on the first page, not knowing which products a customer is likely to buy together means lost up-selling opportunities.
Running promotions that are not optimized for the shopper does not achieve the goal of additional sales, and may result in losses.
3. Balancing inventory for omnichannel retailers
Most omnichannel retailers struggle with the calculations to optimize the inventory quantities they need.
Additionally, omnichannel retailers tend to deal with higher volumes of inventory and wider sales and shipping ranges, requiring complicated mathematical algorithms for demand forecasting.
Without appropriate demand forecasting tools and the ability to process and accurately compute this level of complexity, omnichannel retailers may treat e-commerce as a separate business instead of a separate channel.
Allocating inventory for omnichannel retailers is also a balancing act as they struggle to determine how much inventory to keep in storage for online sales, and how much to sell in stores.
For example, if an omnichannel retailer allocates too much inventory to brick-and-mortar stores and not enough for online sales; they’ll end up pulling inventory from store shelves to fulfill online orders. And this only increases costly transportation expenses and causes fulfillment delays.
What is needed is a unified demand forecasting inventory solution to meet customer expectations for all channels.
Unbalanced inventory between channels is inefficient and reduces GMROI.
4. Cart abandonment rate
It would be convenient to think that e-tailers could use items added to online carts as a way to predict demand, but it would be misleading.
Too often a prospective customer does not complete the transaction; abandoning items sitting in their cart.
How frustrating for the e-tailer!
Baymard Institute studies calculated data from 48 different studies and found the average cart abandonment rate is 69.99%
There may be an intention to buy items in carts, but the sale doesn’t materialize.
This behaviour could be an indicator of many things, maybe the price point isn’t right, product cannibalization among the inventory mix, or other reasons.
5. Shipping costs
When it comes to fulfillment and returns, the expense of shipping items out to customers and getting them back in reverse logistics if items are returned is high.
E-commerce retailers:
- Pay to ship items directly to customers.
- Pay again to transport and manage returns.
- Offer same-day, next-day, and free shipping to stay competitive.
Speed and efficiency for shipping and returns are costly for e-tailers, but customers expect no less.
All of these challenges, if not well managed through e-commerce demand forecasting, cut into profit margins.
The question remains, how can e-tailers win at e-commerce forecasting?
How leading Ecommerce retailers forecast demand
Today’s leading e-tailers leverage AI-driven analytics to find opportunities in the challenges and yield success by harnessing their data.
They recognize that e-commerce is high-tech retail and therefore needs high-tech solutions.
Armed with a highly accurate demand forecast, e-tailers can predict which goods are needed for which store locations and channels; ensuring customers get what they want while minimizing their e-commerce challenges.
When they transition their technology strategies towards AI-based predictive analytics to create an effective e-commerce demand forecast, they can drill down to channel, store, and SKU levels with specificity and clarity.
Overall, an advanced analytics platform helps e-commerce retailers:
- Plan confidently with an accurate demand forecast
- Know what to stock, where, and when to maximize margins
- Set optimal prices for every SKU at every location
Advanced analytics also helps with their common challenges in specific ways.
7 ways AI-based analytics improves Ecommerce demand forecasting
E-tailers benefit from AI-powered advanced analytics because it creates the ability to handle and dissect enormous amounts of data and automatically make recommendations.
And that’s not all, with AI-driven analytics e-tailers get:
1. Greater accuracy with quality tools
AI-powered demand forecasting software is developed to address the challenges of modern retailing.
It’s the most advanced technology that has the capacity to store, organize, and analyze mass amounts of data that flows through e-commerce retail to increase:
- accuracy
- efficiency
- visibility across the business
As a powerful tool in the hands of e-tailers, it eliminates the headache of manually analyzing and reconciling large, complex spreadsheets to optimize business.
And because this solution is automated, it reduces the time and cost of demand planning while reducing human error.
The more accurate the e-commerce demand forecast the better e-tailers can identify opportunities for optimization, consider next-steps recommendations, and engage in better planning for a more profitable sales cycle.
2. Set dynamic competitive pricing
Dynamic pricing is a strategy that uses big data and AI to automatically change the pricing for products after advanced analytics carefully analyzes current pricing trends and competitor prices.
In fact, some e-commerce giants use dynamic pricing, and they change the prices of their products in real-time (for different people in different locations).
For example, ride-sharing giant Uber uses a dynamic pricing strategy through which it controls the prices that customers pay.
As a result, Uber can adjust prices frequently–up to every five minutes–when surge prices are in use. To reach an optimal fare, Uber has to access an enormous amount of real-time data.
Wal-Mart and Amazon are examples of e-tailers using dynamic pricing models.
Offering competitive prices to customers results in increased e-commerce revenue as customers get competitive prices moment by moment.
3. Get a holistic view of omnichannel retailing
A big mistake some omnichannel retailers make is treating e-commerce as a completely separate business that requires separate tools and novel approaches.
The reality is, e-commerce is simply another channel.
Yes, e-commerce has nuances that other channels don’t (and it pays to be mindful of those nuances). But it is more similar than different, as you still have to order inventory people want, set prices to maximize your profits, replenish your DCs like they were stores and warehouses, etc.
Many savvy omnichannel retailers have already figured this out — and they’ve unified their analytics across their channels using AI. This allows them to have one, clear view of their entire business — while automatically accounting for all of the complexities of each channel.
In other words, omnichannel retailers can now see the trees without losing sight of the forest.
The ability to see and understand omnichannel demand forecasting for all channels of the business and as a whole removes siloes and improves:
- communications between and across all channels.
- inventory management, planning, and operations.
- accuracy and reliability of the demand forecast.
4. Flag reasons for cart abandonment
A robust analytics solution that dissects and analyzes big data gives e-tailers real-time insights into customer online shopping habits and flags probabilities for cart abandonment.
As a result, e-tailers can make critical business decisions faster from data based on:
- competitive pricing data
- individual and mass shopper behaviour
- checkout behaviour
- patterns in the sales funnel
- threats and anomalies
- SKU behaviour
A prescriptive analytics solution can even provide optimal next-step recommendations to mitigate lost sales due to cart abandonment.
5. Improve fulfillment and returns
Happy customers visit sites again.
A crucial element for online shopper happiness is making sure they are satisfied with rapid order fulfillment, but also dealing with the necessary evil of returns.
E-tailers can use AI-powered software to:
- Speed up delivery
An accurate AI-based demand forecast quickens fulfillment.
Products are brought to the best storage location in advance of online orders. This leads to quicker delivery options–even next-day delivery.
Omnichannel retailers with a unified inventory demand forecast can offer more fulfillment options such as BOPIS. (buy online and pick up in-store)
- Reduce shipping costs
AI-based demand forecasting software can identify batching opportunities. When several items are grouped by location and time, it mitigates shipping costs by cutting down on excess transportation and storage costs.
- Find gains on returns
With an AI-powered analytics forecasting tool, e-commerce retailers can quickly identify where there is demand for returns; and send (vetted online) returns for re-selling online or to stores where it’s wanted.
6. Create a desirable personalized online shopping experience
- Targeted product mix
Considering geodemographics, customer shopping history, and other available data enables AI-driven demand forecasting to determine what product mix should appear on the first few pages of an e-commerce website.
The first few pages are valuable “real estate” that should promote inventory with the highest demand.
- Upselling
With good data and AI-based analytics, e-commerce retailers can identify the best product pairing offers. E-commerce retailers can effectively upsell to specific customers by finding new patterns and relationships in the data.
Retailers will be able to proactively get smart promotional and customer-specific recommendations.
7. End-of-life product options
Deciding on what to do with end-of-life products can be difficult.
For example, when an omnichannel retailer is left with 10 widgets at the end of the sales season, they have to decide what to do with them.
- Should they pull them from the shelves?
- Make them available online only.
- Bulk them all in one store location?
- Place each widget in separate locations.
AI-based analytics can identify the best options right down to SKU and location levels. That means omnichannel retailers can get the highest rate of return for their end-of-life inventory.
So, a widget with demand online is not languishing on a shelf somewhere.
Ecommerce retailers win with AI-powered analytics for demand forecasting
When e-tailers need accurate, dependable demand forecasting, AI-driven analytics software is a promising tool that offers solutions to challenges.
Having the right AI-driven analytics demand forecasting tool drives more evidence-based decisions and positions e-tailers to gain better insights into consumer demand to align their assortment, merchandising, and promotion strategies accordingly.
This unified e-commerce platform enables them to maximize opportunities, revenues and profits while reducing costs.
Are you an e-tailer looking for the right demand forecasting solution for your business?
If so, book a demo with our team today!
