Predictive analytics is becoming increasingly important in all industries. Find out exactly what it is, and in which application areas it can be used and for what purpose.
Predictive analytics are the practical outcome of Big Data and Business Intelligence (BI). They enable the exploitation of the vast amounts of data collected by many companies from customers, markets, social networks, real-time applications, and the cloud. By providing tangible information, Predictive analysis keeps you ahead of the competition.
Companies use predictive analytics in a variety of ways. Predictive marketing, data mining, Machine Learning or artificial intelligence algorithms are all ways to optimize processes and discover new statistical patterns. Simply put, they allow computers to to learn from the past to better perform certain business processes and deliver new information on the functioning of a company.
What is predictive analysis?
The term predictive analysis brings together many data analysis technologies and other statistical techniques. The main technique is regression analysis, which is used to predict the related values of multiple variables based on the confirmation or refutation of a particular assertion. Predictive analyses aim to recognize patterns in the data for the probability of a projectaccording to Allison Snow, Senior B2B Marketing Analyst at Forrester.
In her view, it is essential to understand that predictive analytics are centred around probabilities, not absolutes. Unlike traditional analyses, predictive analytics do not allow you to know in advance what data is important. They allow for more Determine what data can predict the outcome the company wants to predict..
Let’s take the example of a sales representative looking for a typical profile on a CRM platform like Salesforce.com. Let’s imagine that the claim is that this profile will buy the company’s product. The other assertions are that the variables are the cost of the product, the role of this profile in the business, and the company’s current profitability ratio. In placing these different variables in a regression equation, we obtain a predictive model from which to extrapolate an effective strategy to sell a product with the right profiles.
Parallel to regression analysis, predictive analysis is also increasingly using Data Mining and Machine Learning. Data Mining, as the name suggests, involves examining large data sets to discover patterns and new information. Innovations in Machine Learning such as neural networks or deep learning algorithms are used to process unstructured data sets. faster than a traditional Data Scientist with greater accuracy as the algorithms improve. These features can be found on tools such as IBM Watson, Google TensorFlow or Microsoft CNTK.
The main change behind the boom in predictive analytics is that data scientists are no longer the only ones who can use these techniques. Business Intelligence and Data Vizualization tools, as well as open source organizations such as the Apache Software Foundation, make Big Data more accessible, more efficient, and above all easier to use than ever before.
Machine Learning and data analysis tools are now available as self-services and left in the hands of all professional users. As mentioned above, the sales representative can use them to analyze customers. The executive can decipher trends in a market. The customer service representative can research the main criticisms made by customers. The social media marketing manager can study demographic and social data to develop a campaign that will reach the target audience. These are just a few examples, demonstrating how predictive analytics are changing the business world in depth.
However, predictive analyses remain limited. Algorithms and models cannot accurately predict whether your company’s next product will be worth $1 billion or whether the market is on the verge of collapse.. Data remains a means and not an end.
Predictive, prescriptive and descriptive analysis
In a Forrester report entitled “Predictive Analytics Can Infuse Your Applications With An Unfair Advantage”, the firm’s Senior Analyst, Mike Gualtieri, shows that the word The use of the word “analysis” in the term “predictive analysis” is inappropriate.. Predictive analysis is not a branch of traditional analysis such as statistical analysis. They provide predictive models that companies can use to predict future business outcomes or consumer behaviour.
In short, Snow explains that the term “predictive” in essence denotes a probability more than a certainty. In this, it stands in contrast to the landscape of traditional analytical tools. On the contrary, “descriptive analysis” is content to capture events. These descriptive, or historical, analyses should be the basis on which an algorithm is developed. They are simple data, but often too voluminous to be analyzed without an analytical tool.
Typically, dashboards and reports on the most commonly used methods for predictive analytics in today’s businesses. However, these tools lack a link to business decisions, process optimizations, consumer experience, or any other action. In other words, the models produce information, but not the instructions explain what to do with it. Prescriptive analysis allows the cross-referencing of information and action.
Predictive analytics is everywhere
As the business intelligence landscape evolves, predictive analytics is being used in a growing number of industries. Tools such as Tableau or Microsoft Power BI offer data connectors and visualization tools to handle the massive volumes of data from sources such as Amazon Elastic MapReduce. Google BigQuery, Cloudera, Hortonworks or MapR. These self-service tools do not necessarily have the most advanced predictive features at this time, but they do make the Big Data easier to analyze and understand.
According to Snow, predictive analytics are used in many business situations. To detect fraud at the point of sale, to adjust digital content according to the user’s context, or to develop proactive customer service. In B2B marketing, companies and SMBs use predictive marketing for the same reasons they use strategy, tactics or technology: to attract new consumers, retain them and offer them better service than their competitors. Specifically, senior analytics counts three main categories of predictive analytics uses in B2B marketing :
1 – Predictive Scoring Prioritize different customer profiles according to their inclination to buy. This method adds a mathematical dimension to conventional prioritization based on speculation and experimentation. This use case helps sales and marketing teams identify productive accounts faster, waste less time on accounts less inclined to buy, and develop successful campaigns.
2 – Identification models Identify and acquire customers with similar attributes to those already loyal. In this use case, accounts that have exhibited desired behaviors, such as making a purchase, renewing a contract, or purchasing additional services, serve as the basis for an identification model. It helps sales and marketing teams find actionable leads earlier in the sales process, prioritize existing accounts for expansion, and highlight accounts that are likely to be more receptive to sales and marketing messages.
3 – Automated segmentation Cut customers into segments for personalized messages. Traditionally, B2B marketers are only able to segment customers by generic attributes such as the industry in which they work. Moreover, this manual segmentation requires efforts that can only be applied to priority campaigns. Now the attributes used to feed predictive algorithms can be used for automated account segmentation. This allows sales and marketing teams to manage communications with relevant messages, substantial conversations between salespeople and prospects, and drive content strategy more intelligently.
Business Intelligence tools and open-source frameworks such as Hadoop allow to democratize data as a whole, but in parallel to B2B marketing, predictive analytics is also being used by more and more cloud software platforms in different industries.. For example, eHarmony’s Elevated Carrers site, like other sites, offers to use predictive analytics for hiring, predicting which candidates will be best suited for specific jobs. This nascent market could revolutionize the way Human Resources operates.
Help centers like Zendesk have also started using predictive analytics to improve their software. The goal here is to help customer service find problem areas using a data-driven alert system called Satisfaction Prediction. The functionality uses a Machine Learning algorithm to process the results of satisfaction surveysThis is based on variables such as time to solve a problem, response time, and most frequently used words, combined with a regression algorithm to calculate the satisfaction rate.
Likewise, Predictive analytics have a strong impact on the Internet of Things industry.. Google uses Machine Learning algorithms in its data centers for the predictive maintenance of its Public Cloud server farms. The algorithms use weather data and other variables to adjust data center cooling and reduce energy consumption.
This kind of predictive maintenance is becoming commonplace in factories. Enterprise technology giants such as SAP offer predictive maintenance service using data sensors from connected production machines to predict which machine is likely to have mechanical problems. Similarly, tech companies such as Microsoft are exploring predictive maintenance for aerospace applications, using Cortana to analyze data from sensors embedded in flying machines and their components.
The list of potential applications stretches as far as the eye can see. Predictive analytics are transforming the retail industry, while financial startups are using predictive models to analyze fraud risks. The possibilities for integration in different industries and business transformation using the different tools are numerous, and the following are just a few of them will continue to multiply as artificial intelligence evolves….