Co-author: Gaurang Singh
Predictive analytics encompasses a variety of statistical techniques from predictive modelling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavioral patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown, whether it is in the past, the present, or the future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.
Predictive analytics is the practical result of big data analytics and business intelligence (BI). What do you do when your business collects staggering volumes of new data? Today's business applications are raking in mountains of new customer, market, social listening, and real-time app, cloud, or product performance data. Predictive analytics is one of the ways to leverage all that information, gain tangible new insights, and stay ahead of the competition.
Size Optimization: Demand for different items change dramatically across different geographical areas; although the merchant had known the same, the company hadn’t had a tool to supply the same.
Without analytics, even if the organization decides to look at the previous year’s sales figures and supply the items, it would be a big task because there could be thousands of items and effort itself will involve a huge number of people.
So, a system is required to crunch sales and inventory data to give optimal size configuration.
A recommendation engine can give sales associates the information about customers’ past purchases and buying habits of similar customers to suggest recommendations for items.
Predictive analytics adds another value; you can foresee what your customers’ next actions might be and make recommendations about relevant products based on their behavior and preferences.
Using predictive analytics to set prices allows retailers to take all possible factors into account in real time, something that would be impossible without data science and machine learning. In addition to considering competitors’ pricing strategies, a predictive pricing model can take into account everything from real time sales data to information about the weather to optimize prices for a particular point in time.
Smart revenue forecasting
Instead of forecasting revenue based on historical data from shoppers who may not even be customers anymore in the fickle world of retail, predictive analytics allows for more accurate forecasts based on the predicted buying habits of brand new customers.
Building Targeted Campaigns
Predictive algorithms collect and analyze data from various sources such as demographics, market insight, response rates, and geography in addition to customer insights together. By determining what campaigns would be more successful based on these analytics; marketers can pinpoint the most effective message/product for a single customer. Targeted campaigns will lead retailers to accomplish higher conversion rates.
A McKinsey report also shows us that targeted campaigns can deliver 5-8 times the ROI on marketing spend and lift sales by 10% or more.
Predictive pricing analytics collates demand, product pricing history, competitor activity, and inventory levels. And, it then automatically sets optimal prices in order to respond to market changes in real time.
On the contrary to the traditional ‘season sale’ approach (when the demand is already gone), predictive analytics determine the optimum time when prices should be dropped and has shown that a gradual reduction in price generally leads to delivering maximum profits.
Don’t we all love to see the listed results when we type just a few letters? Predictive site search allows users to have a user-friendly shopping experience based on customer history, behavior, and preferences. It can predict what a customer is looking for, effortlessly. As customer experience is one of the most important assets for retailers, predictive search should also be prioritized to gain customer satisfaction and loyalty.
Common Use Cases for Big Data in Retail
- Building a 360-degree view of the customer: Customer behavior and sentiment can be determined using Hadoop analytics, which can help retailers refine how they interact with customers in the store, through direct mail, and using other marketing channels. Big data analytics can correlate transaction data, online browsing behavior, in-store shopping trends, product preferences, and more. You can also incorporate external, unstructured data streams such as social media traffic to assess customer sentiment and behavior. The resulting insight can be invaluable in guiding inventory and pricing strategies.
- Measuring brand sentiment: Brand studies using focus groups and customer polling techniques can be expensive and often aren’t that accurate. Using big data analytics, you can perform a customer brand sentiment analysis based on behavioral trends using sources such as Pinterest, Twitter, and Facebook, for example. The results are less biased and can be used to guide product development, advertising, and marketing programs.
- Creating customized promotions: Big data analytics can be used to create custom offers based on browsing history and other data sources. These customized promotions can be used for localized marketing, pushing coupons and offers to smartphone users based on their location, or to drive e-commerce sales using real time offers delivered via online advertising or social media.
- Improving store layout: Big data analytics can be used to analyze customer traffic flow within the store. Sensor data such as RFIDs or QR codes can be used to track in-store traffic and shopping habits. There are also new technologies emerging that enable in-store mapping for applications such as instant coupons that can tell retailers a lot about store flow.