Latest Trends in E-Commerce and Retail driving Advanced Analytics

The retail landscape has changed considerably in recent years, with traditional brick-and-mortar retailers facing rising pressure from e-commerce competitors. Last year,  online sales  increased by almost 15 percent, while brick-and-mortar growth rose only by 1.9 percent. 

Retail Analytics Trends

Today, both traditional and online organizations have little choice but to embrace retail data-driven analytics strategies to stay relevant and gain competitive advantages. By embracing the latest retail analytics trends, it’s possible to reduce costs, improve customer experience, boost profits, and increase the customer base—all of which are key to survival.

Retailers today are collecting an enormous amount of consumer and market data. However, most of it is still going unused. Only about 20 to 50 percent of  structured enterprise data  is being curated and distributed to business intelligence (BI) and applications. In other words, many organizations are struggling to turn raw data into actionable insight. 

There is a growing demand for retail analytics solutions that can provide visibility into essential customer service, inventory, sales, and marketing strategies. These solutions provide a mechanism for processing, refining, preparing, and presenting information to store decision-makers. 

Retailers Move to Prescriptive Analytics

Up until recently, the retail industry was centered around descriptive and predictive analytics models. In large part, retailers relied on explanatory models to analyze past events and predictive models to anticipate future outcomes.  

Over the last several years, however, the retail landscape has changed significantly with the proliferation of e-commerce. Retailers today are facing new technologies and challenges that are forcing them to reassess their approach to analytics.

For example, companies are increasingly competing on price today, leading to smaller, razor-thin margins. What’s more, many organizations are consolidating and optimizing their brick-and-mortar stores and warehouses in the internet-driven age. Meanwhile, consumers are becoming less patient about ship times—to the point that 96 percent of them now consider  fast shipping to be the same thing as same-day shipping . Further, 80 percent of consumers want same-day shipping, while 61 percent want packages delivered within three hours of ordering them.  Three hours !

As a result of these changes, retailers are forced to rethink their analytics approach and develop more maturity. To illustrate, let’s think of business intelligence and analytics as a spectrum. On one end, you have low complexity reporting and analysis. Gradually, the spectrum shifts to monitoring, forecasting, predictions, and—on the opposite side—you eventually get to the prescriptive analysis.

Prescriptive analytics combines historical and real-time data to influence decision making. Retailers are increasingly using prescriptive analytics to understand what is likely to happen based on the decisions they make at any given time. For example, a business may use prescriptive analytics to determine how many employees are needed on a showroom floor during different parts of the day and optimize employee schedules and staffing requirements.

Latest Trends in Retail that are driving Advanced Analytics 

Here is a breakdown of some of the top trends for analytics in e-commerce and traditional retail.

Flow Tracking

Many retailers are now adding sensors and cameras to their stores to track customer movements. For example,  Modcam  offers a product that uses IoT connectivity to build anonymous customer profiles and observe consumers as they browse the aisles. 

The benefit of analyzing this data is that it enables retailers to gain greater visibility into their physical retail environments—eliminating guesswork and creating data-driven retail operations along the way. Retailers can use flow tracking to monitor footfall, analyze conversion rates, and assess in-store advertising campaigns, among other things.

This type of technology, though helpful, should be deployed with caution. To many consumers, the sight of cameras, robots, and sensors can be off-putting. According to one study, 73 percent of retail executives think that the overall environment  has become more inviting over the last five years. However, only 45 percent of consumers agree, and 19 percent think it has become less attractive. For the best results, retailers are encouraged to balance analytics and customer experience (CX).

Supply Chain Management

A growing number of retailers deploy advanced analytical solutions, including Artificial Intelligence (AI) outside of their stores and across the more significant supply chain. It is becoming imperative in CPG retail analytics, where goods are typically manufactured off-site or overseas. 

For example, businesses use fleet management software with telemetrics to gain real-time insight into shipping conditions and deliveries. Retailers are also using IoT devices to connect with manufacturers for real-time visibility into production data. Applying data analytics to the supply chain can make it easier to plan and strategize—increasing efficiency and profitability along the way.

McKinsey offers a tiered system for ranking  supply chain maturity : 

• Supply Chain 2.0 involves mostly paper-based tracking systems. 
• Supply Chain 3.0 is built around basic digital components. 
• Supply Chain 4.0 is the highest level of digital maturity for supply chain analytics—something that few global retailers have attained to date. 

“Supply Chain 4.0 is the highest maturity level, leveraging all data available for improved, faster, and more granular support of decision making,” McKinsey says. “Advanced algorithms are leveraged, and a broad team of data scientists works within the organization, following a clear development path towards digital mastery.” 

Network Monitoring

As more and more connected IoT technologies come to market, retailers are finding that they have to put more time and attention into their underlying networks. IoT solutions, after all, can consume massive amounts of bandwidth and require fast connections with minimal latency. 

Often, businesses will install connected solutions without adequately assessing their network capacity. For example, a company might install an expensive video tracking system to find that their network lacks the resources to make it work effectively. The system might interfere with other essential areas of the network, such as voice, with bandwidth constraints resulting in low-quality calls. 

For better efficiency, businesses are investing in real-time network monitoring solutions. Some go a step further and partner with local data center providers to reduce long-distance data transport costs.

With analytics, retailers can predict when specific systems might need maintenance and which in-store IoT gadgets are most likely to generate the most data, among other things. 

Customer Journey Mapping 

We have recently seen a renewed focus on customer journey mapping, which involves tracking customer interactions across all touchpoints—including online stores, social channels, phones, and physical stores. Customer journey mapping consists of studying these interactions over time to understand a customer’s overall satisfaction at each step in their journey. 

While customer journey mapping has been around for many years, the technology is now merging with big data and predictive analytics. As a result, retailers can understand their customers better and personalize and optimize their shopping experience.

It is an excellent example of the shift from descriptive to prescriptive analytics. Earlier, companies mostly engaged in journey mapping to understand where customers have been and what they were likely to do. Now, companies are starting to analyze how their actions might influence customer behavior. 

For example, a business might compare a customer’s purchase history with data from similar buyers to see whether they are likely to upgrade to a particular product or service—and when. By conducting this type of analysis, companies can improve their targeting efforts and drive more sales.

Artificial Intelligence (AI)

Proper predictive analytics require the use of AI, which is an iterative process that involves making machines (or algorithms) improve their performance over time as they process more data and gain the ability to make complex decisions akin to those made by humans. AI, however, is still complicated and expensive to obtain for most companies—especially retailers with high costs and tight margins. 

First and foremost, AI requires access to trained developers, who are not easy to find. When it comes to data science, there is a massive skills shortage that will likely persist for some time. The problem has more to do with a lack of experience than data management skills. 

Besides, AI is also expensive to implement and time-consuming—which is why many retailers and consumer goods brands are choosing to leverage platforms with embedded AI.

One of the most exciting developments in this space is no-code AI, allowing retailers to build intelligent work automation apps with drag-and-drop functionality. Thanks to the advent of no-code AI, retailers can build management apps without having to hire as many full-time software developers or contract a third party. AI handles much of the backend coding, resulting in a cost-effective approach to software design.

Over the next few years, AI will become a staple technology in the retail space. For now, it’s still considered an emerging technology. 

Data Transformation for Retail Analytics

The retail industry is changing at a rapid pace. Analytics is completely transforming how retailers interact with customers, business partners, supply chains, and stores. And in many ways, this process is just beginning, and the trend will accelerate even more over the next several years as new solutions come to market.

Retailers will be tasked with building a modern data stack that touches several different areas. One major area is data transformation.  Data transformation in retail is extremely difficult and complex due to the large number of data sources, the diversity of the data, and the complex formats. 

Datameer SaaS Data Transformation is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake.  The multi-persona UI, with no-code, low-code, and code (SQL) tools, brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data.  Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance.  Direct integration into Snowflake keeps data secure and lowers costs by leveraging Snowflake’s scalable compute and storage.

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