Notable shifts in the retail and eCommerce industry are happening under the influence of emerging technologies and ever-changing customer expectations. The industry itself is currently moving towards the highest level of digitization and personalization, leveraging intelligent innovations such as predictive analytics.
Let’s discuss the role of predictive analytics in retail and eCommerce transformations.
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Trends in the Commerce Market
Modern shoppers expect a personalized experience. That’s a fact.
- 80% of consumers are more likely to buy from companies that offer personalized experiences. (Epsilon)
- 71% of consumers feel frustrated when a shopping experience is impersonal. (Segment)
- 83% of consumers are willing to share their data to create a more personalized experience. (Accenture)
Retail businesses prioritize personalization and report increased revenues from such efforts.
- 79% of retail businesses are investing in personalization tools, which is more than any other industry. (SmarterHQ)
- Companies using advanced personalization report a $20 return for every $1 spent. (Clickz)
- 88% of US marketers have seen measurable improvements due to personalization, with more than half reporting a lift greater than 10%. (Instapage)
How can retailers ensure the personalization of their offerings?
They need to extract insights from available information. Online and real stores abound with information such as:
- product descriptions and availability
- personal customer information
- search and purchase history
- in-store behavior of shoppers and much more.
This data is the very basis of creating individualized experiences. What you need is to collect data, analyze it correctly and draw conclusions for further decision-making.
Large companies have to deal with Big Data collection. Particularly, IoT technology is used in retail and eCommerce to gather large volumes of information in stores, warehouses and supply chains. Big Data analytics is a challenging task and requires intelligent automation.
Looking at eCommerce trends, we see that retailers implement AI- and ML-based technologies to automate analytics, learn more about customers and improve customer experiences. Predictive analytics is the leading intelligent automation technology for this purpose.
What Is Predictive Analytics?
For better decision-making, organizations should anticipate business events and customer behavior patterns. This is exactly what predictive analytics is used for.
Predictive analytics, a category of data analytics, is an intelligent technology that analyzes current and historical data to uncover insights, assess dependencies between numerous factors and discover patterns for predicting future outcomes.
How Does Predictive Analytics Work?
The technology encompasses various techniques, methods and tools such as data mining, data modeling, deep learning, machine learning and AI algorithms. Unlike traditional data analytics, which gives you raw numbers, predictive analytics forecasts future trends, giving you a hint about what you should do.
Predictive analytics transforms past and current data into valuable future actions.
At the core of smart analytics, neural networks identify underlying relationships within data sets, just like a human brain does, and build various predictive models.
- Clustering models categorize data based on similar attributes.
- Forecast models make predictions based on historical data.
- Outlier models detect abnormal data entries and can help prevent fraud.
The deployment of predictive analytics opens up endless opportunities. Organizations can model every possible outcome of a particular decision, automatically calculating a statistical probability of its risks and potential benefits.
Industries Using Predictive Analytics
Statista forecasts that the predictive analytics market will reach nearly $11 billion by 2022 in annual revenue compared to $6 billion in 2019. The growth is explained by a large number of businesses across industries making use of intelligent analytics techniques.
Take a look at how predictive analytics helps businesses.
By implementing medical software (EMR, EHR), clinics, medical practices and hospitals create fertile ground for collecting vast amounts of patient health data. Predictive analytics in healthcare focuses on analyzing health information to help diagnose, predict outcomes of specific treatments and prevent diseases based on various health factors.
The Internet of Things, coupled with predictive analytics, has impacted smart manufacturing. IoT devices in plants and factories collect data about production processes and the state of equipment. Predictive maintenance models forecast the possibilities of equipment breaking down. Such practice helps prevent severe damage and downtime.
Hospitality and travel
Hotels, restaurants, airlines and other industry players use smart analytics to forecast the number of guests or passengers depending on the season and other factors based on previous travel trends. Thus, they can optimize prices and maximize occupancy and revenue.
Digital music and movie streaming platforms leverage AI-powered search and recommendation technologies to predict user preferences and recommend relevant content, keeping customers satisfied.
Analyzing historical weather information and images taken by satellites, intelligent algorithms create weather patterns and can give accurate forecasts up to a month in advance.
Large and small retailers use predictive analytics to understand customers on a deeper level, investigate their buying behavior and create personalized product recommendations.
The Importance of Predictive Analytics in Retail and eCommerce
Modern retail businesses aggregate vast amounts of data from numerous channels: websites, mobile eCommerce apps, social networks, ordinary stores. Handling data from omnichannel commerce by using predictive analytics tools, companies can forecast customer behaviors at certain points in time, anticipate market trends and optimize their business processes.
The following are the most popular use cases of predictive analytics in eCommerce and retail.
Personalized Customer Experiences
The in-depth analysis of customer-specific variables enables eCommerce businesses to reveal products and product categories a customer is interested in and, consequently, create highly personalized offerings.
How does it work? Predictive models take into account personal details users enter during registration on eCommerce websites (age, gender, location) and combine them with behavior patterns (current search requests, goods in a shopping cart, previous goods purchased, purchase frequency and more).
Based on given metrics, intelligent models distinguish underlying relationships and can forecast future actions of specific shoppers.
For example, a woman is looking at children’s books on a website. A month ago, she bought a toy car on the same website. A bit earlier, she purchased boys’ clothes at the store of this brand. Comparing this data, the algorithm can predict that this woman is interested in children’s goods, probably for boys, and offer her tailored recommendations (e.g., toys for boys, children’s books) to stimulate purchasing.
To be efficient, recommendations must be relevant; a stunning evening dress is unlikely to impress a middle-aged man, isn’t it?
Predictive algorithms also enable customer segmentation with different attributes. If this woman doesn’t make a purchase right now, she may fall into the segment with a high purchase probability. In a short while, she will be retargeted with relevant products or may get a discount offer.
Customer churn analysis is also essential. It helps predict the potential revenue losses, reveal marketing approaches that don’t work and adjust them to current situations.
Inventory and Supply Chain Management
Inventory and supply chain are integral parts of a successful retail business as they ensure product availability and timely delivery. They can’t be neglected.
Predictive models help efficiently organize inventory and supply chain processes.
Inventory requirements anticipation. Algorithms distinguish popular and slow-moving products and help optimize supply plans. Thus, retailers avoid undesirable situations such as stocking up on unnecessary goods and running out of popular ones.
Improved order fulfillment. Intelligent models create optimal order delivery routes and schedules, improving operational efficiency, reducing costs and boosting customer satisfaction with a brand.
In-store data analysis is as essential as online monitoring of shopping activities.
Using surveillance cameras and IoT sensors installed in shopping carts and on product shelves, retailers can:
- investigate shopper routes around the retail floor
- distinguish the most popular products
- count the number of visitors at different times of the day
- calculate average visit time
- monitor queues.
Predictive analytics handles such insights to help configure store layouts, plan staff schedules, distinguish ideal opening hours, reduce waiting time in queues, and provide the best level of security.
These activities result in better customer experiences and increased sales.
Implement Predictive Analytics with SAP
With the SAP Predictive Analytics solution, companies can:
- perform various types of data analysis (segmentation, classification, outlier detection, time series forecasting)
- visualize data with the help of charts, parallel coordinates and decision trees
- use predictive algorithms and data mining capabilities for handling large volumes of information.
A highly experienced SAP team at SaM Solutions has been successfully developing eCommerce projects for more than five years. Contact our specialists to learn more about the possibilities that modern technologies provide to online and retail stores.