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Bo Ma Bo Ma

Analytics and Fashion: 3 Ways Predictive Analytics Is Improving the Apparel Retail Industry

Spec Work: White Paper

Nowhere has the phenomenon of instant gratification been more evident than in the retail industry. Conveniences of the digital age, like Amazon’s two- and one-day deliveries, have conditioned consumers to expect an almost instant fulfillment of their orders. Fashion industry in particular has had its own challenges to overcome keeping up with the demands of their customers.

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Spec Work: White Paper

Nowhere has the phenomenon of instant gratification been more evident than in the retail industry. Conveniences of the digital age, like Amazon’s two- and one-day deliveries, have conditioned consumers to expect an almost instant fulfillment of their orders. Fashion industry in particular has had its own challenges to overcome keeping up with the demands of their customers.

Consumers in fashion have always desired novelty and variety. However, while in the past the brands had months to plan for a collection and manufacture it, today’s companies are keeping up with the runway trends that are traveling at breakneck speed through the Internet and social media. The brands that lag behind customers’ expectations get left behind among the never-ending streams of new products.

This demand for fast and affordable runway styles has left the fashion brands to walk the fine line between quickly satisfying customer demand and doing so at the right quantity and the right price.

The most successful players in fashion have demonstrated their ability to adopt to the latest trends, quicken the pace of manufacturing and reduce their lead times. Brands like Topshop and Zara have built their businesses on agility and speed.

Planning a collection with speed and agility can be difficult employing traditional time-series forecast as this method of forecasting can be quite rigid.  Time-series forecasting relies completely on historical data and provides answers to clearly defined business questions like what to order.

As such, these forecasts are done in isolation, with an assumption that past patterns will continue in the future. Assuming that trends will remain stable in a dynamic retail market often affected by seasonality, promotions and new products could result in forecasting errors. These errors can lead to adverse business effects like excess inventory, stock-outs or excessive discounts.

On the other hand, predictive analytics uses statistical modeling and machine learning to sift through current and historical data to detect patterns and find opportunities and risks that might not have been previously considered. As such, predictive analytics transforms data into insights.

Whereas traditional time-series forecast focuses on addressing internal assumptions, predictive analytics adopts an approach that helps discover economic drivers and understand customers, business and market. Equipped with this knowledge, the brands are then better able to make smart business decisions.

Here are some of the ways predictive analytics is improving the fashion industry:

Driving Sustainability with Better Design and Procurement Decisions

Better purchasing and design practices bring about much more than just cost reductions and increased ROI. They can become a source of sustainability and social responsibility by optimizing purchasing and design processes and eliminating or minimizing last-minute decisions. Last-minute design and procurement decisions travel down the supply chain and can adversely affect factories working on the contract, resulting in overtime, worker burn-out and sometimes even compliance violations as suppliers scramble to fulfill the orders. These rushed decisions also affect sustainability: just in 2017 an estimated 60 billion meters of fabric ended up dumped into landfills, wasted during the production phase. Predictive analytics helps brands gain speed and clarity by taking the guesswork out of how best to reach consumers and enabling them to get to know their customer better, thus minimizing these last-minute decisions.

Designer’s creative process is enhanced by analytics software that forecasts color trends and future best-sellers while purchasing optimization provides to procurement team data-driven insights on how many units to manufacture and when to order them as well. Furthermore, predictive analytics software also helps procurement team perform spend analysis, assess suppliers, do compliance checks and develop reports. It lets brands collect and analyze internal as well as external data to benchmark their performance against the competitors in real time. Suppliers are assessed based on, not the lowest price or an existing relationship, but on their past performance data and market pricing and risk evaluation.

Predictive analytics allows procurement team to juggle multiple variables to arrive at the optimal cost-saving solution. For example, MOQs (minimal order quantities) can often result in extra inventory and tied-up capital, as extra unnecessary items are ordered just to fulfill the contract. The ordering process becomes even more challenging when there are multiple MOQs at different levels like minimum orders of fabric or minimum units of product per purchase order. Predictive analytics enables procurement to quickly find the most profitable combinations of all these MOQs while keeping inventory under control.

Rue 21 is one of the brands that has found success using customer-driven predictive analytics. They have partnered with First Insight to make buying decisions. New products are selected by collecting customer data though social platforms, email and third-party panels and then analyzing it through predictive analytic models to determine the offerings that would appeal to potential consumers most.

“Rue21 is re-energizing the brand, and that starts with having the right product at the right price,” said Michael Appel, CEO of rue21. “First Insight is helping us ensure we have differentiated products that our customers will value. We are already seeing results through eliminating under-performing products early in the selection process, while re-investing our inventory dollars into higher performers.”

Cutting Production Lead Times

In the digital era, trends in the fashion industry are more often established, not by editors and retailers, but by consumers. Previously, production was based on forecasts made by designers and buyers and products were marketed in “seasons” with the entire design and production process stretching up to a year.

With the shift towards the “see-now, buy-now” model, inventory is replenished once depleted. The brands that succeed in reducing lead times and streamlining operations are able to capitalize on the fast-changing preferences of consumers.

Predictive analytics gives companies the opportunity to postpone final design decisions and enable just-in-time manufacturing to rapidly respond to trends and demand. Product assortment is optimized by composing the range of colors, shapes, and sizes with the initial designs to fit closely to the wishes of the customers. This leads to higher turnover and fewer markdowns down the road.

Furthermore, analytics assists brands with identifying high-demand products and slow-movers. Every week, sales forecasts are generated based on open orders, in-transit orders, and inventory positions for each SKU at the store and the distribution center, and replenishment plan is generated for managers to review. Best-sellers trigger early replenishments and slow movers are removed to make space for better performing items.

These slow-moving items might be moved to stores with higher demand for them. The system proactively analyzes aspects like combination of colors and sizes, mileage between stores, pricing, and shipping costs to recommend the optimal inter-store transfers.

Spanish retailer Zara is known for agile supply chain system and ability to leverage big data to streamline their operations. They collect data from sales, PDA devices, RFID tags and social media to discover customer’s fashion preferences and manage inventory. This data is then analyzed and findings are shared with the design team, who then creates new releases that incorporate these trends. Designs are offered in small batches that satisfy the demand at that location. Zara generates weekly predictions for each item in the collection and delivers shipments to their stores twice a week.

By leveraging big data and analytics, Zara was able to cut their lead times to three weeks and avoid large advertising costs.

Boosting Revenue with Smart Pricing Decisions

Fast fashion has also had a substantial impact on a perception of value from consumer’s point of view and subsequent intensified use of promotions in the apparel industry. However, a study done by First Insight found that consumers are starting to prefer quality over price when making purchasing decisions. To optimize pricing, retailers need to understand where consumers perceive value, have the ability to respond to competition, and recognize the impact of promotions on the financial performance.

Analytics gives brands an opportunity to understand values of the key customer segments. Instead of competing on price in every category, retailers can use analytics to align the price with what the customer is willing to pay by grouping items that respond similarly to price fluctuations. For example, the sales of novelty items are typically less elastic than those of the essentials. Elasticity modeling is used to capture the nuances of apparel and also to isolate the incremental impact of each promotion.

Along with markdown optimization, predictive analytics models also address complexities of omnichannel pricing and fulfillment by accounting for factors like logistics costs.

Integrating analytics when making pricing decisions has shown to increase sales by 3 to 6 percent.

Embrace Predictive Analytics to Drive Revenue

Fashion industry has changed considerably over the past 30 years. As evolving customer expectations push retailers towards flexibility in design and speed to market, remaining relevant and competitive becomes harder to achieve.

Traditionally used processes like time-series forecasting fail to keep up with the increasingly dynamic fashion landscape and complexities of omnichannel retail.

Predictive analytics software enables brands to integrate internal sales data with behavioral profiling to achieve demand-driven supply chain.

Sources

1.      “How See-Now Buy-Now is Rewiring Retail,”, Forbes, https://www.forbes.com/sites/gregpetro/2018/01/31/how-see-now-buy-now-is-rewiring-retail/

2.      “Demand Forecasting at Zara: A look at Seasonality, Product Lifecycle and Cannibalization,” MIT, https://dspace.mit.edu/bitstream/handle/1721.1/90163/890199174-MIT.pdf?sequence=2&isAllowed=y

3.      “Demand Forecasting in the Fashion Industry: A Review,” International Journal of Engineering Business Management, https://www.researchgate.net/publication/268386280_Demand_Forecasting_in_the_Fashion_Industry_A_Review

4.      “Application of Predictive Analytics to Sales Forecasting in Fashion Business,” Research Gate, https://www.researchgate.net/publication/325100494_Application_of_predictive_analytics_to_sales_forecasting_in_fashion_business

5.      “Geek meets chic: Four actions to jump-start advanced analytics in apparel,” McKinsey, https://www.mckinsey.com/industries/retail/our-insights/geek-meets-chic-four-actions-to-jump-start-advanced-analytics-in-apparel

6.      “How Rue21 Uses Predictive Analytics to Make Buying Decisions,” RIS, https://risnews.com/how-rue21-uses-predictive-analytics-make-buying-decisions

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Bo Ma Bo Ma

Boost Your Ecommerce Strategy With Google Shopping Campaign

Spec Work: Blog

Have you been considering adding Google to your ecommerce strategy? The search giant has recently waived product listing fees on their Google Shopping platform and commission fees for the checkouts on its “Buy on Google” feature. It is no coincidence that these incentives for sellers are happening now.

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Spec Work: Blog

Have you been considering adding Google to your ecommerce strategy? The search giant has recently waived product listing fees on their Google Shopping platform and commission fees for the checkouts on its “Buy on Google” feature.

It is no coincidence that these incentives for sellers are happening now. Since April 2020, there has been 146% growth in all online retail orders in the US and Canada. Amid ongoing pandemic, many retailers are looking to find new ways to connect with consumers online and keep their business moving. Companies like Google, Facebook and eBay are rolling out programs to attract these new clients, creating new opportunities for online retailers.

As the battle for ecommerce dominance heats up, we need to revisit Google Shopping as a sometimes-over-looked contender, and go over some perks of using this platform so you don’t miss out on these opportunities.

For those unfamiliar with Google Shopping, it’s a platform that allows customers to search for, view and compare products. To set up a sponsored Google shopping campaign, one needs to link their online store to Google Merchant Center and Google Ads account. Free listings require only Google Merchant Center account set-up with “Google Across Surfaces” feature selected.

Here are some benefits and strategies for both paid ads and free listings:

More qualified traffic

Google Shopping Ads are designed for customers who are ready to make a purchase. Before they begin their search on Google, these potential buyers already have an idea of the product they are looking to buy. Once they type their query, Google Shopping Ads appear as visually appealing thumbnails with detailed description of the product. If done right, both ads and organic listing present a great opportunity to increase conversion rates and revenue. Here are some strategies to optimize both paid ads and organic listings and increase your chances they show up at the top of the search:

Paid ads:

Google ads are based on the cost-per-click (CPC) system, where the seller pays whenever someone clicks on their ad. The higher the ad relevancy and quality are, the higher click-through-rate (CTR) tends to be. One great thing about Google Shopping ads is that CPC cost decreases as more people click on your ad, giving you an opportunity to optimize the advertising budget and maximize return-on-investment (ROI). Some strategies for success with paid ads include:

  • Promoting your most popular items to increase CTR and decrease CPC. You can use advanced reporting to analyze what are your most profitable products. More on this later.

  • Taking advantage of negative keywords to get rid of generic terms that are irrelevant to your campaign focus. Use longtail keywords instead to make your ads relevant to a potential buyer’s search.

  • Ad scheduling. With some research you can find out when your customers are most active online and run the ads during that timeframe. This will allow you to save considerably on your advertising expenses.

Free listings:

Google uses keywords to determine what products are relevant to a search. You can optimize your shopping feed by using long tailed keywords in the product title and product description. Including as many relevant details as possible without coming across as spammy is the key here. High quality images are also very important to attract new buyers.

Excellent Reporting

So how do you know which of your products are most profitable? A good key performance indicator (KPI) to use is return-on-ad-spend (ROAS). ROAS will show you the amount of revenue your business earns for each dollar it spends on advertising. Knowing which ads are most effective in connecting with potential buyers can help optimize your CPC budget and help you make a decision as to which ads to promote.

Reporting in Google Analytics also enables you to evaluate your Google Shopping ad campaign by providing you with conversion tracking and customer engagement data. You can filter data in many different categories as per the attributes of the product like custom labels, brand ID, item ID etc.

Currently, tracking organic traffic isn’t very easy to do and requires some tweaking to your product feeds. Organic Shopping placements are currently lumped in with regular Google search traffic into “Organic Search” in Google Analytics. Untangling those to track organic Shopping placements would involve some URL tagging in Google Merchant Center.

Visibility for mobile users

Although most of us still do the majority of online shopping on our desktops and laptops, trends show that most customers search for products on their smartphones and tablets. A study from Business Insider shows that mobile commerce will comprise 45% of the total US ecommerce market by the end of 2020. Google Shopping increases mobile visibility and as such improves the click-through-rate of your ads. This is even more so after Google announced that the ads are free to display in the search results after making them free to display in the Shopping tab.

 

Although Google still has a long way to go to catch up to Amazon in the ecommerce arena, it offers many opportunities and advantages for online sellers, such as advanced analytical capabilities and greater potential to increase brand recognition. Whether you choose to deploy sponsored Google Shopping ad campaign, organic listings campaign or a mix of both, using Google Shopping platform will provide you with an arsenal to create a smart advertising strategy and increase your sales and brand awareness.

 

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Bo Ma Bo Ma

Case Study: How One Company is Driving Revenue with Marketing Mix Modeling

Spec work: Case Study

Sunny Four Inc. is a medium-sized fashion retail company based out of the United States. They approached M Analytics to leverage its expertise in retail analytics and help them design an effective marketing mix modeling solution..

Spec work: Case Study

Sunny Four Inc. is a medium-sized fashion retail company based out of the United States. They approached M Analytics to leverage its expertise in retail analytics and help them design an effective marketing mix modeling solution.

 The Challenge

Efficient transformation of data into insights is a crucial, yet elusive, component for staying competitive in the retail world. Customers want and expect multichannel experiences, where they can view a product in the store and purchase it online, or inversely have their online order delivered to the local brick-and-mortar business. However, with many separate channels, it’s easy for customer data to become siloed. Disorganized data could cause more harm that good, leading to customers being bombarded with repeat or conflicted messages, and ultimately shifting to competition.

James Smith, a senior marketing manager at Sunny Four, approached M Analytics to help him design an effective marketing solution. Sunny Four Inc.’s marketing activities spanned across many online and offline channels, including social media, paid search, print, display, email and print. James and his team were tracking all data manually. This resulted in:

  •  Lagging insights due to the time-consuming data collection process

  • .The company not being able to meet customer demand on time, resulting in missed business and lost leads and customers.

  • The business struggling to identify their target customer and the best way to spend their marketing dollars because of the overwhelming amount of dispersed data.

 The Solution

 The company needed to figure out the effectiveness of their channels and tactics to decide how to allocate their marketing budget. They wanted to identify channels that could help them better connect to their customers and drive revenue, and to optimize their current campaign investments and strategies.

They hired M analytics to help them measure ROI for all marketing activities and choose an optimal marketing mix. M Analytics started out by collecting relevant internal and external info, including price, promotions and competitors’ data, and analyzed them using advanced statistical models to determine the impact on sales. Then, they ran simulations with different marketing spend scenarios for improved results.

 The Result

 Measuring effectiveness of their channels and campaigns has led to optimized marketing spend for Sunny Four Inc. As a result, in just eight months, the company’s revenues increased by 20% and total spend decreased by 15%. Before working with M Analytics, the retailer had to manually track the spends across its many channels. This was not only tedious and time-consuming, but it also put them at the risk of falling behind competition. Now, they were able to plan their marketing activities in real-time, and this freed their schedules to focus on strategy and further growth.

The Product has helped James and his team meet all their goals early and increase their day-to-day efficiency. It was now easy to compare monthly metrics across various marketing channels and to generate reports quickly and efficiently.  M Analytics has helped Sunny Four not only streamline their marketing budget and increase profitability and efficiency, but also understand their customer. They were able to serve them better and the customer reviews improved. As the result of focusing on their target audience’s needs and wants and being able to do it on time, Sunny Four was able to reduce the cost of customer acquisition, get more leads and ultimately more business.

 

We can make more time for planning and can meet our goals early. Now, we’re just excited about future instead of being worried about it.

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