How to Apply Predictive Analytics in eCommerce?

Wed, 07/13/2016 - 12:48 -- admin
Every machine needs fuel. The same way, Predictive Analytics needs data, high quality, accurate, and what's most important, relevant data. Even a simple calculation will give wrong results if fed with wrong, or irrelevant inputs. There are many ways of gathering information and filtering them in the means of relevance to the analysis we want to conduct.
BI (Business Intelligence) is a decision support system for gathering data (combining data from wide variety of sources) for predictive analysis and support for timely and better informed business decisions. BI data is available without delays, so managers may perform Predictive Analysis faster, even on a daily basis when needed.
Big Data enhances and makes BI even more valuable to the business. It gives valuable, relevant data for Predictive Analytics.
Predictive Analytics Advanced Dashboard for eCommerce
Predictive Analytics uses BI data for forecasting and modeling. It uses Predictive Analysis to predict future patterns, and support managers in making better business decisions. With this advanced decision support system, managers evaluate the probability of future events, based on events from the past. Predictive Analytics incorporates advanced algorithms (statistical, data mining, machine learning) capable of revealing patterns beyond the power of traditional BI (Business Intelligence) tools.
Applied to eCommerce, this means collecting relevant information and employing the powerful algorithms of Predictive Analytics to help Sales Managers target the right customers, ones who are likely to respond positively, with products or services they are likely to enjoy, and at the time they are likely to buy. This will result in increasing sales performace and reducing the expenses in money, time, and other resources needed to close a sale. Reacting to customer needs that have already happened and been registered by our BI system is not good enough. Being one step ahead, and anticipating customer needs before they happen, is the key result of the analysis. That gives us the advantage over competition and increases our sales volume and profits. That is the key to survive and thrive as an eCommerce business today, and tomorrow.
For achieving above-mentioned business and sales goals we need an advanced eCommerce platform empowered with intelligent, state-of-the-art Predictive Analytics system.
In order to have the right results of the analysis, and benefit from them, it is necessary to focus on:
1. Collecting accurate, refined, relevant data from variety of sources
2. Creating, refining, evaluating, deploying and monitoring models for Predictive Analysis
So, let's move on to explaining these concepts in more depth.

1.Collecting Accurate, Refined, Relevant Data from Variety of Sources

Data that is specific and relevant to eCommerce falls under several categories:

  • Inventory. Detailed information about stock levels of a product in real time, are essential. What is the stock in different locations, at different periods of the year? Were there any shortages, or overstocks? Identifying best selling products, as well as periods of the year when customers are more likely to buy them.
  • Staff. We need to know in real time, how many employees we have, how they are allocated, how they perform in the means of sales volume and profitability. At what periods of the year, month, day we have shortages of workforce, in which locations, on which positions?
  • Customers. Predictive analytics in eCommerce collects information about customer interests. These information give the answers to questions like: what customer likes, what are his interests, how he shops and when, what moves him and makes him decide to purchase - quality, price, special offers, availability of products, fast shipping, what kind of goods he likes and purchases often, at what periods of the year, month, day he submits his purchases and much more. Customers sometimes cut back on their purchases, and we need to know how often that happens and why.

    Product ratings and reviews are also a great way to learn about customer's feelings about products and services. They give us customer opinions and sentiment as psichological drivers of their shopping behavior. For active marketing campaigns, we want to know which advertising messages drive more customers decide to buy our products.

    For better understanding of customer behavior, the focus should rather be put on Big Data, than on customers. We want to measure actual behavior of customers instead of their behavioral intent. It is simple to explain: it is much more relevant for supporting our decisions in sales business to know what customers actually did, than what the wnated or planned to do. Because, the frist had already happened, and the second might never happen. Great eCommerce site is capable of recording every move consumers make - during their buying process, and even when they don't purchase goods, giving us relevant data to work with.

    This allows sales businesses to theoretically create a profile for each customer based on their behavior and buying history, among other things.

  • Sales history and projections. As we said, our eCommerce site records a wide variety of data, including the sales history from the first day of its existance. In every moment we know the details about sales from the last year, month, week, day, hour. We have valuable data from the past for comparison with current performance of our Online Store. We know the sales volume nd profits, depending on the product, period, customer, location, price, special offers. We are then able to implement advanced dashboards and define KPI's (Key Performance Indicators), performance metrics and the belonging monitoring system.
  • Demographics. Where do our customers come from? What's their gender, age, level of education, occupation? How does our sales depend on this?
  • Competition. Our competition is active, just as we are. Most of the companies in sales business have already implemented eCommerce. We are aware of the ongoing trend of making Online Stores mobile friendly, and optimized to perform in the best possible way on all devices, from desktops, over laptops, to tablets and smart phones. Are our competitors optimized? Are we? Do they have advanced eCommerce features, and which of them are better than ours? What are their prices? Do they provide special offers, discounts, promotions, personalized selling, how, and when? Which products are their top sellers?

All mentioned information helps us integrate operational, spreadsheet, and historic data, that is visually appealing, easy to use and grasp, and prepared (by relevance) to be put under strong muscles of Predictive Analytics and its advanced algorithms.

2. Creating, refining, evaluating, deploying and monitoring models for Predictive Analysis

To maximize the success of their Online Store with the use of Predictive Analytics, online traders will first have to:
  • Set clear business goals (big or small): a) to increase revenue, b) to increase profit, c) to reduce shipping or warehouse expenses, d) to recommend items to upsell or crossell. Knowing the goals will set the foundation for aligning daily operations of the Online Store Management with predefined strategic objectives.
  • Prepare the data for predictive analysis. This includes determination of inputs (variables) from raw data, for use in the Predictive Analytics algorithm. According to Predictive Analytics Times, this process consists of two key components. First is creation and derivation of fields/variables from raw data. And second is filtering-out process that identifies the set of variables to be considered within a Predictive Analytics solution.
  • Create the predictive model, refine and evaluate it
  • Deploy the model
  • Monitor the effectiveness of the model
Online retailers must carry through the Predictive Analytics process to be successful in achieving business goals, understanding new data, preparing better and more relevant data, refining models with new and more sophisticated algorithms, evaluating the models, and deploying and monitoring the models in a never-ending cycle.
Luckily, neither of these tasks we have to do alone, without any help and support. Choosing the right eCommerce solution, that integrates all above-mentioned features and functions of Predictive Analytics, is probably the hardest thing to do, as there is an abundance of offers all over the internet. This article is meant to help you understand what quality standards the chosen platform should fulfill, and how important is the role of Predictive Analytics as an integral part of the software solution.
In our next article, we will talk about direct benefits of Predictive Analytics for eCommerce, so stay with us...





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