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Boost your CRM with Predictive Analytics to cover a wider range of data points

  • With CRM predictive modelling you increase human understanding with the help of seller feedback and ongoing pattern retraining to gain analytics-based insights

How to use predictive analytics to supercharge your CRM?

Predictive Analytics is about recognizing the patterns that help in gaining certain insights for learning what might go down in the future. They do that by analysing patterns in tons of historical data, repeated transactions, and relationship cues. Ultimately, using predictive analytics in CRM software solutions effectively, lets you facilitate a more proactive approach for your business. It picks relevant CRM data and uses it to make predictions about future developments. In the case of sales, predictive analytics studies the data from the entire customer life cycle to predict how prospects will behave, like whether or not there is a possibility of them converting.

As technology grows further and society becomes more engaged in digital advances,  knowing your consumers and prospects better should be a top priority. If you know them “better than they know themselves”, you can suggest to them what they need before they recognize their own need - the Holy Grail of Sales. Implementing CRM predictive analytics allows companies to connect and engage with current and potential clients in a distinct manner which is more effective. 

Like I mentioned above, predictive analytics uses raw data to recognise essential patterns among particular customer groups. For Instance, this might work in favour of a financial services company that wants to evaluate whether an individual has a low credit. The same data can also be utilized by insurance companies while offering an individual coverage.


The need for leveraging CRM predictive analytics 

Meeting the annual sales target of your business is crucial for your success. You need to make an accurate prediction of your sales revenue to make well-versed choices and grow business opportunities through CRM software solutions. With CRM predictive modelling you increase human understanding with the help of seller feedback and ongoing pattern retraining to gain analytics-based insights. These analytics insights help your sales officials to build a solid plan and prioritize their opportunity pipelines thus improving their prediction accuracy. 

However, there are a lot of CRM data management challenges that your sales team may face that could prevent them from making the right sales strategy:

  • No relevant insight on the enormous amounts of customer data present within the CRM
  • Limited access to important data that can help improve prediction accuracy
  • Modern tools lack predictive capabilities that lead to irregular experiences and decrease in productivity.
  • The incomplete CRM data in systems come in the way of making better predictions (machine learning tools usually learn and train itself from complete and accurate data)

Therefore to fully modernize sales and marketing capabilities, you require to adopt customized CRM analytics tools that streamline tour tasks. Tools that are built on advanced analytics models that enhance the capability of your Salesforce sales cloud, provide opportunity based on each customer data and offer recommendations for precise actions. Having a better awareness of the potential risks in your pipelines lets you manage schedules and make the best use of the opportunities. 


Three predictive analytics models for CRM

There is an enormous and overwhelming amount of predictive analytic studies & theories out there – it's unreal. But for the sake of clarity, let’s get to three of the significant predictive analytics models and how you can utilize them to make you an expert in sales or marketing.


In simpler words, Sequencing is related to analyzing the probability. For example, if a prospect downloads a whitepaper from the website and then go through the pricing section, then what is the probability of them buying the product?

This theory came from a Russian mathematician named Markov. He studied the theory and came to the conclusion that a target is likely to buy a product if he downloads a whitepaper and clicks on the pricing section first. So all you have to do is look at the historical patterns of performing those two tasks before you determine if they are going to buy the product. 

But what happens if you don't have enough historical data to gain the desired outcome? Well, there's another way. Part of Markov's study consists of an assumption about data called the Markov Assumption. According to Markov’s assumption, if someone who had visited the website, downloaded the whitepaper and bought the product, then it's possible that your target will do the same. They don't necessarily have to download the whitepaper and get to pricing section for us to predict they will buy the product.   


Using sequencing analytics for better sales and marketing

The way the marketing department can use sequencing methods is with automating behaviour-triggered email campaigns. If a person takes the first and second act, they will be put into the course of the campaign. From there onward, you can observe the actions taken throughout each campaign, and begin understanding the patterns to customize your emails.

Sequencing brings enterprising opportunities into the sales cycle. Let's consider a scenario where someone pays a visit to your website, download a whitepaper, and send in a request for a free trial which shows their interest in your product. With the help of sequencing patterns, you can assume that the next time someone downloads the same whitepaper, it's possibly because they have the same interest. The enterprising approach would be to reach out to that prospect and speak of any doubts or concerns they have.


When you get to the checkout section of an e-commerce website like Amazon, what do you see? You see a bunch of suggestions such as people who bought this camera also bought this lens or tripod stand. To figure out which additional product you need with your purchase, they use analytic data. In the same way, you can use predictive analytics.

Just as customers who buy a camera usually end up needing an additional lens, customers who buy any of your products/services might end up needing your other services. Start by doing a transactional analysis of your past data to see which products or services are purchased together and plan the cross-sell approach by using your predictive analytics. 

Few tips for starting your cross-selling data implementation:

  • Display additional products collectively on your website
  • Create a bundled suggestion for two of your most successful products or services
  • Automate cross-selling in a confirmation email to your customers

3.Lack of Action

You will find the last predictive analytics a bit different from the first two as it doesn't interpret the data around a customer's actions. In fact, It's about using predictive analytics in the opposite manner in cases when you start to see a loss of communication between you and a loyal customer.

It could be because of a number of reasons, maybe they are not satisfied with something, or they are thinking of switching to a different product. The good thing is you will see signs of trouble also known as lack of action at the early stage. From there you can reach out to them and save the partnership but for that you have to be sure that there is a problem in the first place.

So, how does lack of action become an advantage? Go through the customers who have dropped your products/services. Look back on the customers who have left you and try to find out any sort of patterns or indications. Use those patterns and trends to build a fall-off model. Like when you see your customers responding slower than usual to your emails, you can account that as a factor into your fall-off model. So when you take the patterns from your model and combine it with your sequencing data, you can predict whether or not someone is likely to purchase your product. The marketing and sales department can take it from there and reach out to the ones who are more likely to buy.


Improve Sales with Predictive Analytics

According to IDC,  the worldwide revenues for big data and business analytics will go from $130.1 billion to more than $203 billion in a few years. Predictive analytic strategies use tons of opportunities that sellers have worked on previously and estimate the current opportunity, win/loss predictions with modern machine-learning algorithms. By collecting sales information and supplying it to predictive analytics models, you get relevant analytics-based insights and predictions.

So we recommend you make the best use of predictive analytics methods  that offer client interaction to increase sales productivity and customer satisfaction. Going with a Salesforce consulting companies will improve your chances of maximizing on CRM predictive analytics to meet your particular needs.

Evon Technologies is one of them that can take your CRM game to the next level so you can experience unparalleled benefits in the long run. We simplify your CRM for you to develop meaningful customer relationships by providing Salesforce development  services in India. We will assist you in using predictive analytics to transform the CRM experience and improve customer approach. If you’d like to know more about how we can help your business maximize CRM predictive analytics abilities, contact us or email to This email address is being protected from spambots. You need JavaScript enabled to view it..

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