Digitalization in Retail

Digitalization in Traditional Retail: 4 Brick-and-Mortar Retailers who are fighting the Retail Apocalypse

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The retail apocalypse has seemingly no end in sight. The era that has brought down dozens of brick and mortar retailers across the globe is building a metaphorical graveyard. Its most recent addition: fast fashion retailer Forever 21. But there are also retailers who thrive in this new world by embracing the current technological revolution.

As you’ve surely already realized, traditional retail is suffering. Brick-and-mortar stores are closing by the thousands, the public’s opinion on why this is happening is very controversial. Some criticize the government and its failure to support businesses during weak economic times. Others blame it on retail disruptors like Amazon, who have changed the market landscape forever.

Well, let me tell you – the “Amazon-Effect” is no ghost story, it’s history made by the most innovative brains of the past decade(s). But how can traditional retailers overcome this phenomenon? The answer is digitalization. Digitalization really means having access to better data and the ability to process this data to enable better decision-making. I guess there’s a good reason why data is more valuable than money these days. And that’s where the online giant’s dominance emanates from: building artificial intelligence in its core business to efficiently process information and embrace customer-centricity.

So, instead of pointing fingers on the most blameworthy, struggling retailers should take the opportunity to learn from those who continue to thrive despite the challenges of changing consumer demands.

With the fighting spirit in mind, you can change your future for the better.

Changing Consumer Demands call for Adaptive Retailers

“Adaptive Retailers”: an expression that sounds more like an epidemic to most traditional retailers, at least in Europe. Faced with ever-evolving consumer needs and a changing market, retailers have to react faster than ever. They have to expect the unexpected. But too many lag behind in technological advancements, or worse, they ignore their existence because they are afraid of change, leaving them, literally, in the dust. According to a McKinsey study, Europe could add €2.5 trillion to its combined economic output by 2025, if digitalization was accelerated with existing resources.

If this isn’t a wakeup call, I’m not sure what will be.

Adaptive retailers have opened their minds to new possibilities. They are the Kings and Queens of creating unique customer experiences. Only a few brick-and-mortar retailers have embraced the transformation, while most online pure-players accelerate innovation by harnessing the power of data. Let’s take Zalando, for example. What only a few realize is that the online fashion retailer is in many ways also a tech-company, and it reflects in their sales performance.

New market dynamics demand adaptive retailers.But what exactly do they do differently? Data-driven retailers use predictive analytics to analyze historical data and forecast customer behavior, while machine learning algorithms, an application of AI, are used to imitate human behavior and complete tasks at a speed that humans could never achieve.

I hate to break it to you, but retail has reached its inflection point.

The New World of Retail Belongs to the Bravest

I know change can be scary, especially when fired at with headlines like “AI is stealing our jobs” or “AI endangers mankind”. But fear isn’t something that must be avoided. Fear is something to overcome, isn’t it? Let’s have a look at the bright side of AI: the automation of highly repetitive manual tasks and the ability to data-drive decision-making – key drivers of profitability.

One area that is being significantly advanced by AI, and which has a tremendous impact on a retailer’s success, is pricing. However, to achieve the scale and speed that is required to remain competitive, retailers need to implement a more proactive strategy: contextualized cross-channel pricing.

AI-powered price optimization, a technological wizardry which has become essential to many companies, allows retailers to dynamically adjust prices according to demand. Based on historical data and additional factors, like the weather, location, availability, or competitor data, machine learning algorithms can forecast demand and determine the optimal price per article per location in order to drive sales and maximize profit margins. Moreover, AI-driven solutions enable retailers to automate the entire pricing process, leaving the age of manual calculations and Excel spreadsheets behind.

Now you may think that the investment needed to implement AI applications is out of the question for your company, after all, IT resources are a scarce commodity nowadays. Or you may even think that the implementation of AI is not feasible for brick-and-mortar retailers. Well, let me tell you, every retailer already collects data and can use this data to empower better decision-making! The problem is that many retailers don’t have the necessary resources or know-how to transform this data into actionable insights. This calls for external support from tech-companies who support retailers with individually configurable solutions. One example is aifora.

The tech-company offers AI-powered Software-as-a-Service (SaaS) solutions enabling retailers of any size to optimize price and stock management according to their individual strategies, business rules and workflows. This allows teams without any technical know-how to make data-driven decisions.

No matter whether you’re thinking about building your own tech-team or searching for support – AI and Machine Learning hold profitable opportunities to get your performance back on track.

4 Traditional Retailers who Fuel Their Success with Machine Learning

H&M

You almost can’t walk through a single city center or mall without passing at least one or two H&M stores. But even this fast-fashion star almost got swept into the retail apocalypse. Due to incorrect stock predictions, the retailer overestimated customer demand and had to make significant price cuts, resulting in a profit drop of 14%.

But H&M recognized its tremendous struggle against the digital age and decided that the path back to success was to invest in new technologies such as artificial intelligence, rather than simply searching for a short-term solution. Using Machine Learning, the company now utilizes a localized strategy with which it manages merchandise for each store, reducing discounted inventory, bad purchasing decisions and unwanted inventory.
According to Karl-Johan Persson, CEO of H&M, “Brands need to be local and personalized. For brick-and-mortar, this means curated assortments relevant to each location, responding to granular customer needs”.

Reno

Reno is a German shoe chain of the HR group which operates over 400 stores in 6 countries. The company suffered from high levels of excess stock at the end of each season and had to transfer large quantities of old merchandise into the next season. These were the results of highly manual and error-prone processes. Thus, Reno sought new measures to grow sales revenue. Since the company didn’t have the technical resources to solve this problem alone, it was searching for a solution which could be easily implemented in its operational business and integrated with its existing IT infrastructure.

This is where the aifora retail automation platform came into play. Through AI-based price adjustments, markdown optimization and intelligent promotion planning, Reno optimized its inventory structure and increased its revenues and gross profit. Their ability to quickly react to changing market conditions through up-to-date insights, gives them the opportunity to thrive again in the new world of retail.

Adidas

While many retailers are still skeptical of AI and its impact, Adidas CIO Michael Vöegele says “Digital technology is the enabler to help us achieve our objective to be the best sports brand in the world”. Using machine learning, Adidas is able to discover trends in consumer desire and transform these into an “individualized product”. One reason why Adidas continues to apply and expand machine learning in its operations is to take advantage of customer trends and “co-create a new supply chain with customers”. Since it has become clear that personalization is highly demanded by consumers, I’m willing to bet that Adidas will evolve to be one of the rockstars in the new world of retail.

Adler Modemärkte

Fashion retailer Adler Modemärkte operates 175 branches in Germany, Austria, Switzerland and Luxembourg targeting men and women “in their prime” (50+). The company was weary of its error-prone merchandise management process and the high manual effort it required. Additionally, the company was one of the first fashion retailers to fully implement RFID, yet lacked instruments to fully exploit this potential. With the implementation of an AI-powered solution for Replenishment and Allocation, ADLER can now efficiently and dynamically manage merchandise at the SKU level. As a result, ADLER can detect incipient shortages and surpluses through dynamic forecasts at an early stage, ensure a high availability of merchandise while simultaneously reducing stock levels and achieve a more balanced revenue to stock ratio.

Every Retailer Collects Data

As scary as it may sound, everywhere we go and everything we do leaves a digital footprint. Online retailers know this and analyze every click, every purchase, every abandoned shopping cart to improve the customer experience and drive sales. Access to powerful insights is not limited to the digital world anymore. What brick-and-mortar retailers should realize, is that they too collect plentiful data on what is happening in their stores: transaction data, inventory data, marketing/promotion data, and the list goes on. This data may be in silos, or retailers may not know how to properly analyze this data – but the data is available and holds vast potential. Retailers just need the right tools to make use of their data and the willingness to change.

One thing is certain, data-driven decision-making is nothing new. You’ve probably been relying on data for other areas of business since before the turn of the century in the form of research papers and statistics. But today our possibilities for collecting and analyzing data are more extensive, the speed at which we can process data has been vastly accelerated and the insights have become much more valuable.

Why miss out on this new opportunity when the only way out of the retail apocalypse, is through?

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