Businesses are always looking for ways to predict the future. They want to know what will happen next in their business environment so they can better prepare their companies. One of the more advanced and innovative ways of accomplishing this is by using predictive analytics. Fortunately for Salesforce users, Salesforce has some great tools you take advantage of. In this Salesforce tutorial, we will explore what predictive analytics is and how it can improve your decision making.
What is Predictive Analytics?
Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Today, companies are inundated with data – from log files to images and video – and all of this data resides in disparate data repositories across an organization. This can cause a lot of problems because, when data is isolated across departments or systems, it becomes challenging to develop a unified understanding of customer behaviors and trends, reducing the effectiveness of predictive models and limiting Salesforce’s ability to deliver actionable insights across the business. This can lead to missed opportunities, less personalized customer interactions, and inefficiencies in decision-making.
However, if you have proper integrations and data management practices in place to prevent data silos, you can gain insights from this data using deep learning and machine learning algorithms to find patterns and make predictions about future events. Some of these statistical techniques include logistic and linear regression models, neural networks and decision trees.
Einstein Prediction Builder
Einstein Prediction Builder is one of the interesting features within the Salesforce Einstein Suite. It allows customers to create prediction models powered by in-house data without much coding or data science skills. Basically, Einstein Prediction Builder uses your data to create custom AI models that predict specific outcomes, helping you anticipate trends, behaviors, or results based on historical data.. Such outcomes relate to what the user’s demand is, what comes out as their favorite, what goods and services are likely to have a great demand among customers and others. It provides insight and rationale of the prediction models, this means that one can explain, for instance, how the Einstein predictions are arrived at. Such predictions can be leveraged to trigger business activities through Process Builder or Einstein Next Best Action. Additionally, Einstein Prediction Builder reveals patterns that are not typically visible in the data and also provides timely predictive analytics which allows making wise decisions when it’s necessary. With this, businesses can be ready for upcoming changes and ace their plans.
It is worth noting here that the Einstein Service Cloud is not free, and you need to have an Unlimited or Enterprise subscription to be eligible to purchase.
Predictive Model Types in Salesforce
Einstein Prediction Builder uses various model types to create a prediction depending on the problem you are trying to solve. For example, if you are looking for a binary prediction, such as True or False, Salesforce will use two models: Random Forest and Logistic Regression. If you are looking to predict a number field, such as the number of sales next quarter, Salesforce will use Random Forest and Linear Regression models. Let’s take a closer look at each of these predictive models:
- Random Forest – Random forest is a supervised learning algorithm. The “forest” it builds is an ensemble of decision trees, usually trained with the bagging method. The general idea of the bagging method is that a combination of learning models increases the overall accuracy of the results. One of the biggest advantages of random forest is its versatility. It can be used for both regression and classification tasks, and it’s also easy to view the relative importance it assigns to the input features.
- Logistic Regression – Logistic regression refers to a supervised machine learning algorithm which performs the task of binary classification by estimating the probability of an observable outcome, event, or phenomenon. Logistic regression also studies the relation of one or more independent variables to a given data set having several classes. Predictive modeling whichpredicts a mathematical probability whether an instance falls into a particular category or not is the most common use of the methodology. There is a lot more, in this article, including logistic regression fundamentals, its equation, and assumptions, classification, and recommendations for better usage.
- Linear Regression – Linear regression is a data analysis technique that predicts the value of unknown data by using another related and known data value. It mathematically models the unknown or dependent variable and the known or independent variable as a linear equation. For instance, suppose that you have data about your expenses and income for last year. Linear regression techniques analyze this data and determine that your expenses are half your income in order to calculate an unknown future expense by halving a future known income.
Build Your Own Predictions
Since each business has its own set of goals, they will want to build custom predictions with their own business data. This can be something like calculating the probability of whether or not a patient will show up for their appointment or predicting your sales numbers for the next quarter. You can find the steps to create custom predictions in the Salesforce documentation. When building your predictions, there are additional features and capabilities you should keep in mind:
- Segment your data – Let’s say that you want to forecast sales numbers for next quarter, but only within a particular geographic area. You can do this with the data segmentation feature inside Einstein Analytics, which allows you to identify records that are not relevant to your prediction.
- Data Checker – Sometimes you do not have enough quality data to build the prediction you want and, if this is the case, Data Checker will send you an alert. This means that you will need to adjust the settings on your prediction. For example, if you do not have enough data to forecast sales for the next quarter in the EMEA region, you will need to either input more data or broaden the parameters of your prediction.
- Opportunity scoring – You can use Salesforce to provide you with an opportunity score, which offers insights on whether or not an opportunity is likely to close. What’s great about this functionality is that if you do not have enough data to score leads in a particular region and you do not want to adjust the settings, Salesforce will use a global model, consisting of anonymous Salesforce data to create the prediction you need. When you do accumulate the data needed, you will be able to input that data into Salesforce and create a prediction with your updated data.
Prescriptive Analytics
So far, we have discussed predictive analytics, but what do you do when you get a prediction? If you do not have a game plan already, you can use tools like Einstein Next Best Action to help you. It uses decision and business logic to surface context-specific offers and actions. These sets of potential recommendations and rules are called strategies. Each strategy evaluates all the possible recommendations loaded into it and, after applying the defined business logic and rules, will only surface the recommendations that meet the specified criteria. This saves a lot of time for users, who will now get the right recommendations at the right time instead of manually searching or guessing what the next best action is.
Once recommendations have been defined, such as an offer on a new product, a discount coupon, or a premium support process, you can filter what will get displayed with Recommendation Logic nodes. For example, new products will only be offered to customers with healthy credit scores, discount coupons will only be recommended for loyal customers with more than three purchases, and premium support will only be offered to customers who pay on time.
We should mention here that tools like Data Checker and opportunity scoring apply to prescriptive analytics as well. Salesforce also displays a recommendation scorecard about the quality of the recommendations; however, it is recommended that you use third-party Salesforce applications from AppExchange to check your data quality before building and remove any duplicates or incorrect data before creating your predictions.
The Quality of Your Predictions Will Be as Good as the Data You Put In
We talked extensively about the capabilitues of Salesforce’s predictive analytics, but it is important to remember that these functionalities will only be useful if they are based on a foundation of high quality data. The data must serve the outcomes that it is intended for. For instance,collecting trustworthy marketing data and updating existing contact records gives you a better understanding of your customers. It also lets you connect with them by using verified email addresses, mailing information, and phone numbers. Accurate information helps you market effectively and use resources efficiently.
Important things to keep in mind include:
- Accuracy: Accuracy is probably the best-known aspect of data quality. The essential idea is that data values must be “correct,” that is they must reflect reality. However, most structured data sets are riddled with errors, and it stands to reason that documents (unstructured data), which are used to train Large Language Models are in worse shape.
- Absence of duplicates: It is easy for duplicate entries to slip into databases, and they can skew results. Thus, they must be kept to a minimum
- Consistent identifiers: When pulling loan data together, is this “John Smith,” with a checking account, and that “J. E. Smith,” with a home equity line of credit, the same person? You have to know so you can merge data correctly.
Maintaining data quality can help you stay ahead of your competitors too. Reliable data keeps your business agile. You can spot trends and industry changes sooner so you can take advantage of new opportunities or tackle challenges before your competitors.
If you want to preserve good data quality, you must constantly manage it to get the best results. Luckily, modern data tools and platforms from companies like DataGroomr help automate and streamline your day-to-day data validation and management.
Empowering Smarter Decision-Making with Predictive Analytics
Companies are using AI-powered predictive analytics in Salesforce to change the way they make decisions. With patterns in historical records, AI insights are able to assist organizations in trend forecasting, operational optimization, and much needed precision in customer service delivery. The incorporation of predictive models within the Salesforce CRM Matrix ensures that businesses are countering any changes in their market, improving their satisfaction rates, and employing well-informed strategies to better performance. The potential of Salesforce anticipates further integration of predictive analytics where organizations will be able to make quicker, better and more strategic decisions especially with competition getting stiffer by the day.
