How Predictive Analytics Can Change Your Online Business

Prescriptive and predictive analytics are among the advanced ecommerce trends of this year and turned to be widely used by online stores as well.



The number of engaged users and dedicated software applications increased manyfold in recent years. So, what is the reason for this tide?

Types of Data Analysis

There are three most common types of data analytics different by goals and approaches. Here the are: Descriptive, Predictive, and Prescriptive analytics. To be precise, they are subsequent stages of the same analytical process aiming to find the best ways to generate more leads and higher profits, find more potential prospects, and the best circumstances for prolific sales.

Descriptive Analysis

Descriptive Analysis is the basis for all further explorations and models. It shows how the things are going and may study either the whole pool of data or just sufficient samples. It shows both main tendencies and minor statistical variations to focus on during the next analytical stages.

Predictive Analysis

Predictive Analysis is the stage where possible options are estimated, and it answers the questions “what would happen if”. The outcome resulting this stage is regarded as possible development, but it is always just an assumption yet never tested and guaranteed.

The hypothetical essence of the study is both advantageous and vulnerable as it eliminates possible real-life drawbacks, but also doesn’t make success assured.

Prescriptive Analysis

The above advantages and disadvantages are even more explicit at the prescriptive analysis stage, which makes it possible to find the best outcome of the situation evaluating several future scenarios based on initial basic conditions. Starting with initial parameters prescriptive analysis allow a researcher to find most effective future options and determine the best model for their deployment. In a nutshell, it examines the ways and particular actions to reach the best outcome.

Data Analysis Stages

Data Mining and Formalizing

It all starts with data mining and this process may embrace a lot of information sources. If the collected data is structured differently, then you need to prepare it for further analysis with formalization.

Statistical Analysis

Cleaned and prepared data is tested within statistical models in order to meet initial assumptions and hypotheses. If so, this data is reliable enough for the next predictive stage.

Predictive Modeling

The models of reality created at this stage may be quite different depending of the prediction objectives. It may cover almost any sales process stages and business areas, including health care, travelling, finances, marketing, advertising, and ecommerce, of course.

Prescriptive Modeling

In order to estimate several models and find the most effective way to reach the goal you need to take advantage of the prescriptive modeling. It determines the way and particular actions to get the best outcome from given circumstances and limitations.

Deployment and Control

As soon as the best solution is found, the next evident stage is implementation. However, the circle is completed only at the monitoring stage, which further becomes a source of additional information for both ongoing corrections and next models.

Business Data Analytics in Ecommerce

The described types of data analysis is applicable to any sides of business process and any ecommerce objective if you have enough data and powerful analytical tools. In fact, merchants can model any potential situation, estimate final outcomes and define the best way to reach the goal.

Ecommerce Analytics Objectives

The areas of ecommerce able to take advantage of data analysis are multiple, including:

• Customer segmentation and communication;


• Inventory management;


• Fraud protection;


• Online marketing;


• Prices optimization;


• Customer service and more.

Predictive Analytics Software

Real life implementation of the predictive analytics in everyday practice is not so easy and requires a lot of preparatory operations and dedicated software. The prototyping software is to be tailored for different companies and objectives, should include universal data connectors and smart data processors able to formalize and interpret data properly.

Even more so, such software need to include reliable server environments and powerful machine learning algorithms. Often, such tools also offer users a chance to customize dashboards and reports programmatically in order to obtain original and useful data insights.

Most Popular Predictive Analytics Applications

IBM SPSS Statistics
One of the leading statistical software tools tailored for a wide range of business needs and requirements.

RapidMiner
Business and science data analysis platform designed to optimize and accelerate the process of data creation, maintenance and sharing among the involved teams and units.

SAS Advanced Analytics
One of the biggest business intelligence suites including up to 25 analytics solutions and products.

Statistica
Statistica is a powerful analytical tool especially valuable for scientists and business analysts.

SAP Predictive Analytics
The software is able to provide helpful insights for different aspects of business processes mined from the data generated by different commercial and social sources.

Predictive Analytics in Real Life

Personalized Product/Service Recommendations

Cross sells are a traditional field for predictive analytics, which, in this case, allows creating highly personalized offers based on historical and retrospective behavior patterns of other customers.



For example, as soon a customer puts sneakers into his cart, the predictive analytics engine is ready to offer him… two tickets for the Australian Open games next month. Why is that, you may exclaim? Still, there is no any magic here, just predictive analytics.

The thing is that Steve, that’s the name of the customer, bought tennis sneakers, in fact. He leaves in Melbourne, loves tennis according to his Facebook, and attended the tournament in 2015. That’s the reason why the offered tickets are the most suitable product option for him with the biggest chance to be purchased.

But, that’s not all. The engine is able to predict other products for Steve, perhaps, less desired, but definitely quite valuable as well. And, you can show them to Steve after the best option or together at a time. The same way you can recommend any kind of products, e.g. music, books, cosmetics, jewelry, etc.

Customer Retention

Most companies consider that the only incentive for customer retention is price and try to widely use discounts in order to reduce abandonment. However, when the decision to leave is made there is almost nothing able to change the situation.



So, merchants need to take a more proactive position in this case and try to determine the very first signals of dissatisfaction. For the purpose, they need to always stay in touch with customers tracking the tones of their feedback. The more regular signals you have provided by customers, the better is the chance to predict abandonment and turn them back before it’s too late.

What are the signs of trouble you should pay attention to?

• Frequent customer service requests and product complaints;


• Negative reviews on your website or social media;


• Rare visits and decrease in customer activity;


• Deleted customer accounts;


• Other negative tendencies relevant to your business and audience.

As soon as you determine that one or several of the above features is applicable to certain customers, you need to act immediately in order to restore their loyalty.

Here, we can even enter the prescriptive analytics phase, since we need to determine most effective actions able to turn customers back.

Real Time Marketing

The right customer for a product is as important as the right moment for a proposal.



Imagine that you pass along a cafe storefront and get the discount from this very company for a cup of cappuccino. Amazing, is it? It is still so even if you know about the beacon technology.

But, the thing is that you do not like coffee and would better have a piece of pizza instead. That’s the value of the predictive analytics - to offer the right product, to the right customer, at the right time.

Conclusion

Predictive analytics is able to significantly rise the value of your offers with highly personal proposals made at the right moment. It also allows you to act proactively and reduce churn rates or detect frauds early. The whole process of implementation may take some time and efforts, but the results are also worthwhile and long-term especially if the selected analytics software suits your business perfectly.