For most businesses, the difference between success and failure lies in meticulous planning. When it comes to retail businesses, that couldn’t be more accurate.
Sales forecasts are essential in planning the development of your retail business, to avoid running out of stock and risk losing business, and it helps you avoid overstock which can hinder your cash flow.
When it comes to predicting the future of retail sales, there are two elements that matter: historical data and prediction analysis. Over the years, Curve has managed to stand out in front of other forecasting solutions, due to the fact that it provides accurate sales forecasts based on machine learning technology. The fact is, trends, forecasts and statistics in the retail industry are rapidly shifting, so to help give a clearer picture of what the industry can expect we’ve put together The Future of Retail Sales by Curve.
Due to emerging technologies and a relatively positive economic background, consumer habits are now changing faster than ever. In 2019, retail sales in the US are expected to grow about 3.3%, summing up to over $5.5 trillion. Significant growth of over 15% is expected in e-commerce sales, as more and more small businesses are transitioning to the online shopping landscape. Many, traditional retailers who will be unable to adapt to the new shopping trends will see their shops close, which could cause a slight instability in the retail market for a short while.

Curve Can Help Your Sales Team Predict The Unpredictable. Find Out How

Voice commerce and augmented reality are just a few of the many technologies that are expected to reshape the future of retail. How much these new technologies will affect your industry and whether or not you will be able to adapt to these changes will depend heavily on your sales data management and forecasting tools. Some industries will be more heavily affected than others, and only your own historical data will be able to predict how much and how fast your business will need to adapt.

The Automation of Shopping

While most people enjoy shopping, to the extent of their own budget, there are certain types of shopping experiences which we would all like to delegate. In the past few months, there’s been an uptick in what’s know as chore shopping, which is the act of buying commodities, things that we don’t really want, but which are still very much needed for a decent life. These types of repetitive purchases started to be automated in 2018 by big players like Amazon through subscriptions, auto-renewals, same day purchases and so on. So, if your business finds itself in the commodity area, you will absolutely need to adapt it to these new shopping trends.

Considering that 80 percent of consumers research items online before making a purchase, one can see that the future lies in a convergence between online and offline sales. Retailers now understand the advantages of the internet for sales and productivity boost.

Personalization For Survival.

While nobody really likes to shop for commodities, when it comes to luxury products, people still enjoy a traditional shopping experience, especially when it is tailored to their own preferences. For example, customers will still want to visit physical stores to buy products that would bring them joy, pleasure and long-lasting memories. However, as the market becomes more and more competitive, the retailers that will thrive will be those who will be able to offer their customers personalized shopping experience. As more of the products we purchase are mass-produced, consumers are starting to appreciate the value of customized items or items that are tailored to their specific needs.

According to a recent survey done by ConsumerThink, more than 64% of consumers don’t have a problem with retailers that save their data, as long as they actually do receive a customized shopping experience and their data is not shared with third parties. In other words, a big part of the major changes that we can expect will involve putting the customer, and not the product, at the center of the shopping experience.

Algorithms Will Eliminate Guess Work

Some people have a natural flair for business, being able to accurately predict new sales trends. Whether this is pure luck, or a native ability to recognize shopping patterns is still up for debate. While this might work for some of the smaller retailers, it’s far from being a solid sales forecasting strategy for large scale retailers. The good news is that in today’s technologically advanced times, no one has to rely on their gut. You just have to find accurate data management and sales forecasting solutions that can leverage consumer data for you.

According to eMarketer, online shopping now accounts for almost 10% of total retail sales, making e-commerce a significant factor in traditional retail and this trend is likely to continue in the years ahead.
The US Census Bureau figures show that over the last two decades, online retail sales in the US have grown rapidly, rising from 5 million in 1998 to 389 million in 2016. Additionally, in Europe, Enterprises Total Turnover from E-commerce represented 15% in 2014 and grew to 19% in 2017 (Source: Eurostat).

Growth per category

As one would expect, electronics dominate e-commerce sales, followed by miscellaneous products such as office supplies, gifts, novelty, and clothing, based on the US Census Bureau figures. On the flip slide, ranking very low, are categories such as food and beverages, that have a very small share of online sales. This will surely evolve in the coming years, as it already did in 2016 when shares of online sales nearly doubled in that year alone for consumable products.

Amazon e-commerce statistics

  • Amazon is the leading online retailer with net revenue of $232.88 billion in 2018. (Statista)
  • Amazon has over 100 million Amazon Prime members. (Jeff Bezos in a letter to shareholders)
  • Other sources estimate that there are over 95 million Amazon Prime members in the United States. (Statista)
  • On average, two in five US consumers (41%) receive one to two packages from Amazon per week. That number jumps to half (50%) for consumers ages 18-25, and 57% for consumers ages 26-35. (Walker Sands)
  • In the last six months, 83% of US consumers have made a purchase on Amazon. (BigCommerce)

Recent Trends

More recently, the retail industry is undergoing two significant shifts. The first is technological, and the other a result of changes in consumer behavior. It’s the stores that understand and overcome both of these major shifts that will thrive. Alternatively, retailers that don’t will go the way of Circuit City, Borders, and Blockbusters.
Online retail has grown 300% between 2000 and 2018, according to the U.S. Commerce Department. During the same time period, however, department store sales have dropped almost 50%. In 2018, JCPenney, Gap, and Victoria’s Secret announced the closures of 300 stores.
Although shoppers will probably never wholly abandon brick-and-mortar stores, they expect retailers to offer a convenient online alternative. Most brand names are responding while still trying to get shoppers into their stores for pick-up of large items. In order to keep customers coming back to physical store locations, retailers must use a combination of branding, service, and pricing to convince shoppers to get dressed, get in their cars and drive to pick up merchandise.

E-commerce cart abandonment statistics

  • The average global cart abandonment rate in Q3 of 2018 was 76.9%. (SaleCycle)
  • 58.6% of US online shoppers have abandoned a cart within the last 3 months because “I was just browsing/not ready to buy.” (Baymard Institute)
  • The top three reasons US online shoppers give for abandoning a cart during checkout are high extra costs, the need to create an account, and a complicated checkout process (these are the survey results after removing the “I was just browsing/not ready to buy” segment). (Baymard Institute)
  • The average open rate for an abandoned cart email is 15.21%, and the average click-through rate is 21.12% for SmartrMail users. (SmartrMail)
  • The average revenue per email for an abandoned cart email is $27.12 (for SmartrMail users). (SmartrMail)

US vs. The Rest of the World

Despite the assumption, the US is one of the largest consumers of the online marketplace, alas, it is not. In the end, China ranks first place for the largest online marketplace followed by the United States respectively.
According to Nielsen’s Global Connected Commerce, The foothold the US has in the global economy is slowly declining with global shares expected to decline by 16.9% by 2020. In terms of industry, South Korea leads the way in fashion and beauty products while Japan remains ahead of the curve for music and stationery purchases.
Sales forecasting accuracy is crucial across the retail chain and Curve’s powerful Sale’s Prediction engine often exceeds traditional forecasting methods by determining the impact of sales history, consumer trends, holidays and more…
“Retail demand forecasting is one of the hardest analyses to get right: Forecast too little and you have empty shelves, and forecast too much and you may be stuck with excess inventory. Did you know that Amazon earns more than one-fifth of its North America retail revenue because local stores can’t forecast accurately? Customers try to purchase the product at a store in these scenarios, but the stores are out-of-stock and so shoppers look to Amazon.

Curve’s Sales Forecasting Solution Results

As made clear in the points above, In the retail industry, time is money. For enterprise retail business owners of rapidly changing consumer goods, speed and accuracy of decision making are critically dependent upon accessing relevant and accurate sales forecasts. Data compiled through machine learning based sales forecasts can translate into the significant and valuable edge needed to win in your marketplace. Curve helps enterprise retail businesses minimize the manual guesswork while gaining a better understanding of future sales patterns and anomalies at a more detailed level.
Curve has helped retail businesses:
– Decrease overstocking by 14%
– Improve understocking by 16%
– Increase sales by 10%
– Grow profitability by 11%

In Conclusion

Developing an effective sales strategy for your online store requires knowledge about e-commerce statistics, trends, and consumer habits. I hope this article gave you the insight to help you get started on that strategy, and that you found some of the answers you were looking for. Have you stumbled upon any surprising e-commerce statistics lately? Reach out to us and let us know!
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Sales are the driving force of any business and being able to accurately forecast your sales is essential in order to for your business to evolve. A proper sales forecast is the foundation of your sale strategy as it reveals valuable info into how your organization needs to plan in order to hit your profit targets, how much profit new products will bring or whether or not you need to hire more staff to reach your sales goals.
For a forecast to be as accurate as possible it needs to be based on as much statistical data as possible, and this is where things get tricky as hundreds of product SKUs may be spread across multiple locations and sales channels.
However, there are ways around this challenge. Stay with us as we are about to reveal the top 10 most effective methods for retail sales forecasting.

The Weighted Pipeline Technique

When you lack relevant statistical data, the best thing to do is to start with probability-based forecasting methods. The easiest to implement ‘probability-based’ method is the weighted pipeline technique. This method requires you to give each sales stage a percentage-based probability of closing. To calculate the weighted value, you multiply the probability of closing to the deal value. This process is repeated for every stage of the sales process, and while there might be some overestimations or underestimations in your forecast, they should weight themselves out. Given the fact that it is based on probability, the accuracy of this method is debatable, but it is a great starting point for a good forecast.

The Length Of The Sales Cycle Method

As the name suggests, the foundation of this method is the time that it usually takes a lead to convert. This method is very easy to implement. All you have to do is divide the average sales cycle, by the amount of time that a sales rep has been working on a lead. Keep in mind that different sales channels will have different cycle lengths, so you will have to separate your leads into different categories, such as referrals, cold emails and so on. Moreover, for this method to work, you need to be very consistent with tracking the first moment that the leads enter the sales pipeline.

The Intuitive Method

This method is based on the opinions of your sales reps. Obviously enough, this method is rather subjective, but you can improve it by building the confidence of your sales team. A great way to do this is to ask them to avoid 50% probabilities, and to motivate their answers.

The Test Market Analysis Technique

This method is ideal for new products and services. To implement this technique, you must segregate your market, and release the new product to a certain group of people. This will give you a glimpse of the market’s response to the new product, while also increase brand awareness. Moreover, this is a great way to gain some feedback, and maybe do some improvements before the official product release.

Curve Can Help Your Sales Team Predict The Unpredictable. Find Out How

The Lead-driven Method

This method is more accurate than the previously discussed technique, but it does require more in-depth data such as the number of leads per months for a given period, the conversion rate by source, and the average sales price by source. To apply this method, you must assign each lead a value, based on the past behavior of similar leads. The challenge with this method is that the final forecast can be affected by changes in the lead generation strategy, so you must be very consistent with keeping your database updated with all the changes in the sales strategy.

Historical Data Techniques

This method relies heavily on historical business performance, assuming that future performance will at least equal the past performance. This method can also incorporate variables such as yearly growth rates, or anticipated growth rates based on recent investments such as new lead generation tools or the growth of the sales team. The problem with this method is that it can’t consider market changes, so it is best to only use historical forecasting as a benchmark for a more analytical forecast.

The Opportunity Stage Method

This method is also based on historical data, in order to estimate the rate of success of each stage of the sales pipeline. Based on that data, each stage will be assigned a percentage that represents the chances of closing the deal. Obviously enough, the further a lead is down the pipeline, the higher the conversion chances will be. Furthermore, the sales stages can differ for each company, based on individual lead generation and sales strategies.

The Opportunity Creation Model

This method uses demographic and behavioral data in order to predict which opportunities are more likely to convert. There are numerous variables that affect the conversion chances of an opportunity, from the size of the business to the decision power of the lead, previous interactions with your company, the leads decision-making process and so on. For this model to be accurate, you must consider the historical data for closed deals, but also for referrals or retained leads.

The Pipeline Technique

This method is a combination of the historical data and the opportunity stage methods. It is rather hard to implement, as it differs for each business, and it requires a lot of hard data. Ideally, you should work with a software solution to handle the complex calculations. The pipeline technique will calculate the chances of conversion of each lead, based on unique company indicators, such as the success rate of each individual sales representative or the individual value of each opportunity.

The Multivariable Approach

This is by far the most accurate forecasting solution, as it incorporates data from other forecasting techniques, such as the cycle length or the opportunity stage. Obviously enough, this approach is harder to implement, and it requires some religious data tracking. Moreover, this is not a method per-say, but a forecasting approach that can bring together numerous methods and complex analyses such as single equation models, cross-correlations, vector autoregressions and so on. The downside of this method is that it requires statistical data specialists as well as advanced analytical tools which can be quite expensive.

Curve’s Machine Learning Based Forecasts

Curve provides retailers and e-Merchants with an unbiased sales prediction technology that delivers clearer estimates of future sales and product inventory predictions.
Curve uses machine-learning based sales prediction technology, allowing companies to accurately forecast sales, products, and support requests, to increase revenue and optimize profitability. Our unique technology goes beyond traditional business intelligence, by recommending the right solutions based on use cases and customer segments.
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When it comes to operating retail sales at a high level in 2019, sales forecasting is critical to your business. Whether you run your business as a brick and mortar or operate strictly online, forecasting technology is a must-have if you want to keep up with your competitors in today’s age.
In the past, we have labored long and hard with sales and inventory forecasting through endless paperwork, spreadsheets, and of course, the cost of the labor to do the forecasting. More recently BI was helmed as an efficient means in which retailers can conduct sales forecasts tasks, however, that still only delivers intelligence based on business hindsight.
Today, businesses that are leading the pack have integrated smart forecasting technology allowing them to operate at with greater efficiency and speed while simultaneously cutting labor costs down dramatically.
So why is Sales Forecasting such an essential part of the retail industry today? Essentially, when done right it will deliver the following benefits for your organization.

  • Improved decision-making about the future
  • Reduction of sales pipeline and forecast risks
  • Alignment of sales quotas and revenue expectations
  • Reduction of time spent planning territory coverage and setting quota assignments
  • Benchmarks that can be used to assess trends in the future
  • Ability to focus on a sales team on high-revenue, high-profit sales pipeline opportunities, resulting in improved win rates

Here are three tips you can take advantage of today if you’re debating the value of new sales forecasting technology:

Leverage The Data You Have

You may think that you need to start collecting and organizing a ton of different metrics before you can really start to forecast your future sales trends effectively. Don’t worry, you don’t need to do a ton of work before getting started. You can use what you have today.
That being said, the greater the data you have, the more accurate your future sales forecasts will be, and you must weigh this against the result you need with the forecasting itself.

Clearer Data, Less Noise

When it comes to utilizing forecasting technology, you will get the best results when you have the right data. The great thing is with our solution Sales Prediction by Curve, you will be assisted in exactly what data is essential for the algorithms to provide clear forecasts.
Additionally, with frictionless sales forecasting, you don’t need to worry about the “behind the scenes” forecasting technology. The sophisticated solution was designed with retailers and sales teams in mind.
In other words, making data available to you should be the primary goal, while understanding the technology behind it shouldn’t set you or your team back.

Data Can Only Take You So Far

Having access to sales forecasts and product demand data is imperative, as described above, however, having a clear plan on how to effectively utilize your sales trends is equally important.
To effectively create a sales forecast action plan we recommend starting with the right set of questions:

  • What are you going to focus on?
  • What are you going to change?
  • In practical terms, what steps are involved?
  • What territories and targets are you going to give each salesperson or team?

Keep in mind that the main purpose of sales forecasting is to provide information that you can use to make intelligent business decisions.
For example, if your forecast indicates a 30% increase in sales of products or services you may wish to begin searching for larger business premises and/or increase additional stock on your hot items. Conversely, a forecast of excess stock or low sales can allow you to mitigate the effect by taking advance measures such as reducing expenses or reorienting your marketing efforts.

In Conclusion

For leading retailers, sales forecasting is not only crucial to surviving, but also to thrive. Not only will it allow you to predict upcoming sales trends accurately and efficiently, but it will allow you to manage product demand.
Over the next decade, the difference between businesses that advance to the next level and those that fall behind will be greatly based upon the businesses that adopt new technology systems in their business.
You can still get an upper hand on the competition by allowing forecasting technology to push your business forward today.