Predictive analytics: The key to enhancing business performance

Nowadays, it feels like customers expect businesses to read their minds. With the emergence of businesses such as Netflix and Spotify that deliver personalized insights on the daily, customers expect to have recommendations that are catered to their needs. They expect brands to always be one step ahead of them.

So, how can businesses jump to this level of proactivity? How can you anticipate needs, trends, and behaviours? By delving into predictive analytics.

What is predictive analytics?

Predictive analytics analyzes currents and historical data to make predictions about the future using a variety of statistical techniques- usually data mining, predictive modelling, and machine learning.

Historically, it has helped brands understand customers and is also used to identify risks and opportunities, and guides decision-making.

What does Predictive Analytics offer?

The evolution of customer behaviour demands businesses to be proactive at all times. In fact, 87% of customers want to be proactively contacted by a company, according to a study by inContact. This form of proactivity needs to be backed up with a powerful analytics engine.

Predictive analytics allows businesses to look into the future with more accurate and reliable insights. While it can be used at a macro-level to analyze and provide lens into consumer behaviour and purchase patterns, it can also be used at micro-level as well.

For example, retailers use predictive analytics to forecast inventory requirements, and to manage shipping schedules. Airlines use predictive analytics to set ticket prices based on past ticket trends. More commonly, marketing departments used predictive analytics to optimize product development, advertising, distribution and retailing, or marketing research. Predictive analytics can help attract, retain, and nurture customers at the most opportune moments.

It can also be used as a preventative measure. For example, Tyler Cohen Wood mentioned in her interview with Engati CX that predictive analytics can be used to detect and halt malicious activities and criminal behaviour in cyberspaces. When the model notices an unusual behaviour pattern from the cybercriminal attempting to infiltrate a system, it fires an alert to cybersecurity teams immediately to resolve the issue.

In a similar sense, predictive analytics can also do the same when a customer turns into a detractor. It goes much deeper than saying “doing X will result in Y.” It has the ability to detect abnormal behaviour before a customer turns away from your brand for good.

‍Types of predictive analytics algorithms

Predictive analytics adopters have easy access to a range of statistical algorithms designed for these models. These models range from being simple, to as complex as data-mining and machine learning algorithms. The algorithms are typically used to solve either a specific business problem or a series of problems.

It all depends on the algorithm. For example, a clustering algorithms are best suited for customer segmentation models, or other community related tasks. Clustering is when the data set is parted into groups, called clusters. The objective is to sort through the data and to group similar clusters together amongst unlabeled data.

Clustering algorithms Source

For improving customer retention, a classification algorithm is what comes to many analysts’ mind. Classification refers to categorizing the data into separate labels, based on certain parameters.

While a regression algorithm predicts continuous outputs. It’s used to predict numbers instead of labels based on both real-time and historic data. It’s typically selected to create a credit scoring system or to predict the outcome of many time-driven events.

Classification versus Regression: Source

The best example of predictive analytics in action


We’ve mentioned that Netflix uses predictive analytics to deliver personalized recommendations, but how is the question?

Netflix is always collecting data. And based on the user’s watch history, search history, demographics, ratings, and preferences, Netflix’s robust analytics system uses AI-powered algorithms to predict with 80% accuracy on what the user might be interested in seeing next.


When you sign up for a Spotify account, your first instinct is to search for your favorite song, and listen to it, right? Well, Spotify uses this information to make recommendations to you every week. Their Discover Weekly playlist allows users to listen to songs of a similar genre based on what the user listens to daily, what they’re “hearing” on the app, and what other listeners of the same genre listen to. The more users use Spotify, the more personalized their playlists get. It’s an excellent example of the predictive analytics segmentation model.


Sephora understands how overwhelming finding new makeup and beauty products can be for a beginner. So, it uses a combination of tools and techniques to guide its customers through its catalog. Based on interests, purchases, preferences, and also Color-ID and Skin-ID technologies, Sephora can create personalized profiles for each customer and curate an almost accurate, “Recommended for you” page.

Where do we begin?

The foundation of predictive analytics are models. They allow you to turn historical and real-time data into actionable insights that promote growth. The models we typically see are:

  • Customer Lifetime Value Model that identifies customers who are likely to invest more in products and services
  • Customer Segmentation Models that segment customers based on similar purchasing behaviours and other characteristics

However, once the models are identified, businesses face another challenge. How can they implement and apply these predictive models into their workflow?

The Predictive Analytics pipeline: Source

Step #1 — Define your project

The reason why many predictive models are doomed to fail is that no one has defined the purpose or the objective of creating this model. This lack of clarity can be detrimental, and this is where a majority of people tend to get lost. So, identify your objectives and outcomes and then, communicate them with your teams. These projects demand collaboration amongst yourself, your analytics modeler, and other departments to nail down objectives, timelines, operational costs, how the model will be used, and more.

Step #2 — Explore

The next step is to determine which data and model building approach is best suited for your defined objectives and purposes. It’s time to explore.

Models are only as good as the data you feed them, so it’s your job to ensure that the sources of your data are clean, accurate, and extensive. Good sources of data have a high number of records, history, and variables. This is key to identifying patterns and relationships. Unfortunately, most data sources aren’t very reliable due to cases of missing values, randomness, and a general lack of precision and accuracy.

Another avenue that needs to be explored is tools. Modelers use a variety of tools and techniques to explore and analyze data. Basic tools provide while useful, somewhat vague insights, while more sophisticated tools provide detailed, actionable insights. The sophistication of your tools depends entirely on your objectives, but it is an avenue worth exploring.

Step #3 — Preparing your data

The fact is, most of the data we receive is highly unstructured, making it impossible for your tool to give you quality insights in a timely manner. Once you’ve explored and identified your data set, you have to prepare your data. This involves selecting, extracting, and transforming your data into a different format to be read by your tool.

This exercise is one that dreads many analysts as the process of cleansing the data of any errors is time-consuming especially when businesses deal with data sets of over a thousand fields.

Step #4 — Model building

Now it’s time to build your model. First, you have to run your algorithms against a data set with known values for the variable you’re trying to predict. You split the data in half-one half is for training the model and the other is for testing the training model. Then run both models against each other to see how effective they are at predicting, and test for validity by testing the model against live data.

Of course, identifying trends isn’t simple. As we all know, correlation does not represent causation. There are many combinations of variables to test against to understand and identify which key trends and patterns carry the most impact. With this comes tests for a variety of algorithms to understand what works best with the training data set. This may come with the addition of new data types and different ways to improve the accuracy and precision of the model.

Step #5 — Model deployment

Now, you can build the most precise model in the world, but it could provide no value to your company, which circles back to the 1st step. You have to understand what your objectives for creating this model are.

Most times, models fail when businesses ignore or misunderstand the results. The example they used in this paper involved a grocery store that noticed a correlation between sales of beer and diapers. Since the business users noticed a pattern between the two, they decided to display both in the same area at the front of the store to welcome buyers. But of course, correlation doesn’t equate to causation.

While the model converts information to knowledge, you have to take the time to understand what kind of information the model’s feeding you. Because at the end of the day, the decision you make based on these insights will drive growth, not the insights themselves.

Remember to share this knowledge amongst your teams by embedding into your reports- They might have a fresh new insight to share that’ll support the model.

Step #6 — Model management

To improve performance accuracy, you have to keep an eye on your models. This is another step that many businesses often neglect. Building a model for predictive analysis is not a one-time event. You have to monitor it, you have to continue creating iterations to improve performance accuracy and minimize risk. Remember that the more data you feed the model, the more accurate it becomes, and that these machines continue to learn and adapt based on the data being fed to it. While yes, these models can increase operational efficiency within the organization, its learning needs to be supervised at every stage. This is why it is essential to have teams that are dedicated to developing and enhancing these analytical models.

Using Predictive Analytics to Improve business performance

Now that you know how to create predictive analytic models, here’s how they can drive business performance.

The power of Predictive Analytics: Source

‍Highly-personalized marketing

Imagine using a model that can monitor customer behaviour at both a micro and macro level. In fact, customers expect this kind of service as it makes the experiences more convenient and enjoyable. Predictive analytics enables you to carry this out. Personalization can only be effective when it’s based on quality data. Use this data and these insights to deliver hyper-personalized messages to the right customer, at the right time and place. With this model, you can identify trends and identify your customer's needs without them realizing it. For example, say the model has identified that one of your customers is a young 20 years old who’s an avid music listener who has recently purchased a new phone. The model, based on these insights, can then recommend options like a Spotify subscription, or an iTunes gift card, or a pair of headphones. Through this data-driven innovation, we can recommend products that are tailored to your customer's needs. Check out these analytics consulting services.

Forecasting needs

Building on this, predictive analytics can anticipate the needs of your customers, before your customer does. Predictive analytics makes it possible for businesses to forecast customer needs based on purchase history, search history, interests, demography, and more. This is what makes Netflix so successful.

Reduces churn

As we’ve mentioned above, predictive analytics is marvelous at identifying malware and abnormal, risky behaviour. But this mechanism can also be applied towards flighty customers. Analytics can be used to predict when a once-promoter may turn into a detractor before your agents can. Once the abnormal behaviour is detected, the model can fire out an alert to your customer service leaders to pay extra attention to these customers. It enables businesses to take a proactive approach to reduce churn and customer attrition.

Better efficiency and resource allocation

Predictive analytics can significantly improve internal operations efficiency to improve customer experience. The smoother the operation, the faster the service. Having efficient internal operations can help ensure that the customer receives quality service without any fuss. The model can help staff within the contact center by forecasting inventory needs, as an example. Say something is low in supply, the model can fire out an alert to your supply chain team to restock. The model can also be used to predict delivery dates for your customers.

It can also pick up and analyze data from any interaction between the customer and your staff, from phone calls, emails, messages, at any point of the customer journey. This can help you find the best way to capture your customer’s attention and to exceed their expectations at any point of their customer journey.

In addition, by introducing a predictive analytics model, you can further boost your employee's productivity, to give you an edge amongst your competitors.

Pre-emptive support

The model can be used to predict important events in the customer life cycle to increase revenue in these critical times. As an example, an insurance company will send out alerts for car insurance or driver’s tests when they’re aware of a family’s child coming of age. Being able to provide recommendations at these turning points of a customer’s life can give you an edge.

Handling feedback

As the model becomes more and more sophisticated with the data being fed into it, it has the ability to act on real-time feedback to deliver ultra-personalized recommendations. The customer’s actions, such as jumping from one category to another on an e-commerce website immediately have an impact on the model and will have an effect on the next recommendations they receive. These trends can easily be identified and acted on by the model.

Developing pricing models

Insurance companies typically use predictive models to determine the optimal pricing model for their clients. There’s a telematics program called Snapshot that uses in-car sensors to determine to price. The data from the model personalizes the rate for each customer based on their skill level when it comes to driving. Someone who drives less often and stays close to home is predicted to have a lower rate than someone who’s always on the road and likes to speed.

Providing a lens into the future

We’ve spoken about predictive intelligence at a fairly micro-level, but its scope is endless. Organizations can use it to track and predict the general trends of customer behaviour to create an experience like never before. The current trends suggest that because of the forced digital transformation and the migration into our devices, customers demand speed and agility, but also care. Customer experience is going to dominate each industry. So, how can you get started?

The future is now.

Customers demand a hyper-personalized, omnichannel experience, and we have the tool that can help you deliver it.

If you register with Engati right now, you can get started with Engati’s integrated engagement platform with unlimited live chat agents for up to 45 days!

This article about Predictive Analytics was originally published in Engati blogs.




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