The Future of CRM: Predictive Analytics and AI

The Future of CRM: Predictive Analytics and AI

The way you run your sales and marketing strategy has to keep up with the times. A few years ago, your CRM was mostly about keeping track of contacts and customer data. Today, it’s also about predictive analytics and AI. You can use predictive analytics to forecast revenue growth, but where do you start? Read on for everything you need to know about how to incorporate predictive analytics into your CRM so that you can make smarter decisions based on real-time information.

Why predictive analytics is important for your sales and marketing team

Predictive analytics is a way to forecast future events. You can use predictive analytics to predict revenue growth, profit growth and customer engagement. Predictive analytics uses historical data to predict future trends and patterns so you can plan accordingly.

For example: Say you want your sales team members at each branch office in your company's territory (i.e., the states they cover) to reach out only when a prospect has been inactive for 30 days or more without making an inquiry about your product or service offering(s). The system will automatically send out emails based on those criteria and then follow up with phone calls after two weeks if there hasn't been any response from prospects yet - all without requiring any human intervention from managers at headquarters because everything happens automatically behind-the-scenes once initial parameters have been set up properly!

What types of information do you need to get started with predictive analytics?

To get started with predictive analytics, you need to know what your data is and how it can be used. You also need to understand the relationships between different types of data and why these relationships exist. For example, if you're a retailer who sells clothing online and has been tracking customer purchases over time, then you may notice that people who buy one type of shirt are more likely than average customers (or even other groups within your own customer base) to purchase another type of shirt as well--and so on.

This could lead an analyst at this retailer into thinking about new ways to segment their audience based on these preferences: maybe there's an opportunity for them where they could launch an exclusive line geared toward those customers who tended toward buying multiple items from different categories under one brand umbrella?

Using predictive analytics to forecast revenue growth

Predicting revenue growth is a complex task, but it's one that's crucial to the success of your company. You need to know what your customers want and how much they are willing to pay for it. You also need to know how much of your product or service you can produce, and how much it costs you.

This information can be hard to come by and even harder when working with large amounts of data across multiple departments within an organization (like sales teams). Predictive analytics helps companies make better decisions by analyzing historical data in order to predict future outcomes based on what has happened before.

How a CRM can help you create a predictive analytics strategy

A CRM can help you create a predictive analytics strategy. A CRM is a customer relationship management tool that allows you to collect data about your customers and prospects, so you can use it in the future. You can use this information to create a data strategy, which gives context to the insights generated by AI algorithms.

A good way to think about creating your own predictive analytics strategy is as follows:

  • Define what success looks like for each segment of customers or prospects (e.g., high-value buyers or low-value ones). Then determine how much revenue each group brings in annually, month by month over certain time periods (e-commerce businesses might want this information daily). This will give us some idea when we should intervene with our predictive model before they become inactive or churn out completely.* Next step is developing hypotheses around why certain people are likely not going through with their purchase plans.* If possible test them out using historical sales data from previous years/months so we know how accurate these predictions were before rolling them out into production mode

How should you incorporate predictive analytics into your CRM?

Predictive analytics is a powerful tool, but it's not a magic wand. In order to get the most out of your predictive analysis, you need to take steps to ensure that your data is clean and accurate.

Once you've collected all of the relevant information for your business and put it into a CRM system, there are several ways that predictive analytics can help improve your operations:

  • Forecasting revenue growth - By using historical sales data from past years as well as current customer information (such as product preferences), predictive models can be built that predict future sales based on current trends and customer behavior patterns. This gives businesses an idea of how much revenue they can expect over time and allows them to plan accordingly when making decisions about staffing levels or inventory stocking requirements.
  • Making smarter decisions - When faced with two or more options for taking action--whether those options include hiring new employees or making changes within an existing team; adding new products; launching marketing campaigns--predictive analytics provides insight into which choice will lead toward success by analyzing key factors such as profitability per employee/per unit produced/per dollar spent etcetera). The result will help managers make more informed choices about what steps should be taken next so that their company continues moving forward toward its goals."

Predictive analytics for sales teams

Predictive analytics can help sales teams forecast revenue growth and make better decisions. It's a way to gain insight into what your customers want, so you can create a better customer experience.

Here are some ways predictive analytics is being used in CRM:

  • Predictive analytics helps you understand what your customers want. You can use it to predict which products or services will be popular with them, as well as when they'll buy them. This information helps you plan out promotions, helping increase sales volume and profits from these items in the future.

Predictive analytics could help your team make smarter decisions, even before they make them.

Predictive analytics can be used to identify patterns in your customer data and predict future outcomes, which can help your team make smarter decisions.

In the sales department, predictive analytics enables you to better understand what customers are likely to buy or need next. For example, if a customer has purchased a particular product in the past, then he or she might be interested in purchasing another related product as well. Predictive analytics tools can also determine which products will have high demand from customers based on their previous behavior such as viewing specific pages on your website or clicking through social media ads for those products (i.e., "click-through rate").

In addition to predicting what people will buy next--and when--predictive analytics can also help predict where they'll go after they've made that purchase: whether they return again within some timeframe (e-commerce) or visit another location within an hour's drive radius of where they live (brick-and-mortar).

The future of CRM is about using data and AI to forecast revenue growth.

The future of CRM is about using data and AI to forecast revenue growth.

There are a number of ways that you can use predictive analytics for this purpose, but the most common approach is to create a model that uses historical customer data to predict future sales. The model will then give you a forecasted number based on what has happened in the past, which should help you plan out your marketing efforts in advance so that they'll be more effective.

If you want to get started with this type of strategy, it's important that you have both a CRM system in place as well as an effective data strategy (i.e., knowing how much money each customer spends).


As businesses continue to evolve and adapt, they will look for ways to make their processes more efficient. In the next few years, we will see a shift toward predictive analytics and artificial intelligence being used as part of CRM systems. These technologies are already being implemented by some companies today, but there is still plenty of room for growth in this area as more businesses begin adopting these tools into their everyday operations

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