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 ecommerce competitors. Last year, for example, online sales increased by almost 15 percent while brick-and-mortar growth rose only by 1.9 percent.
Today, both traditional and online retailers have little choice but to embrace 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. In fact, only about 20 to 50 percent of enterprise structured 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.
As such, there is growing demand for retail analytics solutions that can provide visibility into key 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 descriptive 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 ecommerce. 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—which is leading to smaller, razor-thin margins. What’s more, in the internet-driven age, many organizations are consolidating and optimizing their brick-and-mortar stores and warehouses. Consumers, meanwhile, are becoming less patient about ship times—to the point 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 being forced to rethink their approach to analytics 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 prescriptive analysis.
Prescriptive analytics combine 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.
1. 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 to 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 in retail stores has become more inviting over the last five years. However, only 45 percent of consumers agree, and 19 percent actually think it has become less inviting. For the best results, retailers are encouraged to strike a balance between analytics and the customer experience (CX).
2. Supply Chain Management
A growing number of retailers are deploying advanced analytical solutions, including Artificial Intelligence (AI) outside of their stores and across the greater supply chain. This is becoming especially important in CPG retail analytics, where goods are typically manufactured off-site or overseas.
As an example, businesses are using 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.”
3. 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 heavy amounts of bandwidth and require fast connections with minimal latency.
Oftentimes, businesses will install connected solutions without properly assessing their network capacity. For example, a business might install an expensive video tracking system only to find that their network lacks the resources to make it work effectively. The system might interfere with other important areas of the network as well, such as voice, with bandwidth constraints resulting in low-quality calls.
To maximize efficiency, businesses are investing in real-time network monitoring solutions. Some are even going a step further and partnering with local data center providers to reduce long distance data transport costs.
With analytics, retailers can predict when certain systems might need maintenance and which in-store IoT gadgets are most likely to generate the most data, among other things.
4. Customer Journey Mapping
Recently, we have seen renewed focus on customer journey mapping, a process that involves tracking customer interactions across all touchpoints—including online stores, social channels, the phone, and physical stores. Customer journey mapping involves studying these interactions over time to gain a sense of 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 are able to better understand their customers and personalize and optimize their shopping experience.
This is a great example of the shift from descriptive to prescriptive analytics. In the past, 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.
5. Artificial Intelligence (AI)
True 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 very difficult 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.
In addition, 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, which allows 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.
6. Augmented and Virtual Reality
Retailers are increasingly challenged to provide new and exciting experiences for customers. This is especially true for brick-and-mortar retailers who are struggling to drive traffic into their stores in the Amazon age. As a result, we’re seeing increased use of technologies like augmented reality (AR) and virtual reality (VR) to help customers visualize products and services.
For example, some stores—particularly outdoor retailers selling adventure gear—are now offering VR systems that allow readers to simulate bicycle or canoe rides through scenic settings. Apparel providers are also using VR to enable consumers to “try on clothes” using their mobile devices. At the same time, furniture makers are using AR to let customers “see” what a piece of furniture might look like in their homes.
The other benefit is that manufacturers and designers can gain unique insight into how customers are interacting with products in ways that they would not otherwise be able to see. This information can then be used to improve existing products and bring new ones to market.
7. Drone Delivery
According to the Federal Aviation Administration, 420,000 commercial drones will fly through the air by 2021, up from just 42,000 in 2016.
As more drones come to market, retailers will face enormous logistical issues ranging from scheduling deliveries to maintenance to regulatory compliance—all of which will require advanced analytics.
While drones are still relatively rare in retail, businesses that are moving in this direction should start forming plans to integrate drones into their operations. Thanks to companies like Amazon, it appears as though it’s only a matter of time before customers come to expect drone deliveries. How else can you get a package to someone in three hours cost-effectively?
Building a Modern Tech Stack for Retail Analytics
The retail industry is changing at a rapid pace. Analytics are completely transforming the way that 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.
To capitalize on this evolution, retailers will be tasked with building a modern tech stack that touches several different areas. For example, this stack may include tools and technologies that map online and offline customer activities, connected IoT sensors that track production and shipping fleets, a data warehouse or data lake that captures and stores information, and data science tools that enable retailers to process, organize, and present information to team members and customers.
Of course, the most important element behind all of these systems is data, which can be difficult to capture across multiple systems and databases. Data tends to live in many different locations—like retail POS systems, customer databases, backend servers, cloud storage systems, and more. These systems don’t always communicate with one another—at least not easily—making it impossible to merge data and create accurate and actionable analyses.
To break data silos, enable data discovery and foster collaboration, companies can use Neebo. Neebo is a virtual analytics hub that pulls data together from disparate sources, thereby enabling data science teams and analytics professionals to access and share analytics assets from a single point of access.
Learn how Neebo can help analytics teams keep up with, or even get ahead of, the transformative changes happening in the Retail sector. Schedule a 1:1 Demo