Predictive Sales Analytics: Actionable Insight for Smarter Plans

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Jay Fuchs
Jay Fuchs


Wouldn't it be awesome if you could stay one step ahead of your prospects and customers at all times — to know what to expect, their individual and collective preferences, and the right strategies to employ to make the most of them?

Unfortunately, no one has that kind of power — not you, not me, not my horoscope, not that psychic with a storefront at your local strip mall that you walk by and wonder, "How do enough people believe what a strip mall psychic says to keep this place afloat?"

It's impossible to see the future like that, but there are resources for salespeople that can help that cause. Those tools are based on something called predictive sales analytics — a marvel of modern sales technology that can show you what to expect from prospects and customers.

Let's dive into the topic a bit further, see how it can help with sales forecasting, and examine some other contexts where it can be employed.

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Though you can conduct forward-thinking sales analysis without predictive sales analytics resources, the process can be tricky and tedious — with more room for human error. Predictive sales analysis generally relies on AI and machine learning to create and shape the forecasts and actionable insight it produces.

Those aspects can take a lot of the extra effort and guesswork out of creating forecasts and other key sales reports that anticipate and account for future customer behavior. That's why more companies — of all shapes and scales — are coming to rely on predictive sales analytics to guide their sales efforts.

Sales Forecasting Through Predictive Sales Analytics

The reliability of sales forecasts that are informed by predictive sales analytics rests primarily on the quality of the historical data it pulls from. That's why your data collection needs to be thorough, clean, and carried out consistently.

If that historical information isn't accurate, the predictive sales analytics forecasts based on it won't be either. But it's not always easy to stay on top of your data like that — particularly as you start storing it in a wider breadth of resources.

As your business grows, your predictive analytics capacities can expand with it. In many cases, companies consistently incorporate additional sales platforms, CRM integrations, and other avenues that require data storage into their operations.

Those kinds of additions — and the changes to your sales pipeline that come with them — can either hinder or improve your company's predictive sales analytics forecasts, depending on how seamlessly you can sync those various databases.

As I said, predictive sales analytics forecasting is only as sound as the information it draws from. So your sales reps need to be diligent and timely in uploading their sales data to any database your team is leveraging.

1. Lead Scoring

Predictive lead scoring is one of the more popular, practical applications of predictive analytics in sales. These kinds of resources pull relevant information and trends from your interactions with previous leads — both successful and unsuccessful.

Lead scoring can be tedious, with a lot of room for ambiguity and human error. Predictive sales analytics tools streamline, simplify, and enhance that process. They can help you construct more thoughtful, relevant buyer personas based on historical data, demographic information, and patterns in customer activity — allowing you to zero in on and pursue your most viable leads.

2. Targeted Discounts

As I just mentioned, predictive sales analytics can help you refine your approach to creating buyer personas, and the practicality of that doesn't end with lead scoring. These kinds of resources can help you identify the discount levels your prospects and customers will be most receptive to.

By pulling and analyzing data related to deals, promotions, and pricing strategies you've employed in the past, predictive sales analytics can provide you with the insight necessary to offer well-structured, tactfully planned discounts that will resonate with your prospects and customers and offer you solid returns.

3. Customer Retention Through Relevant Messaging

Predictive sales analytics let you better understand your customers — give you an analytical perspective on what makes them tick and the decisions they're inclined to make. That kind of insight can be a massive plus when it comes to retaining them.

If you can keep your customers engaged with sound messaging — without being too intrusive — you can bill yourself as a brand that cares about, understands, and respects them. And those are some of the key factors to account for when looking to improve your customer retention.

4. Upselling and Cross-Selling

Predictive sales analytics resources can also make for better-targeted, more effective upselling and cross-selling efforts. In a similar vein to how these kinds of tools can improve customer retention through improved messaging, they can also help you lock in on when to promote specific products or services to potentially receptive customers.

As I said, predictive sales analytics gives you some valuable perspective on your customers' buying intent and preferences. And you can effectively leverage that data when trying to promote the right offerings to the right customers at the right time.

Predictive sales analytics resources are becoming increasingly prominent and more readily available, and leveraging these kinds of tools can streamline and simplify several aspects of your sales operations.

If your business is interested in creating accurate forecasts and accounting for actionable customer insight — without the stress and legwork of more manual analysis — it might be in your best interest to explore some of these options.

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Topics: Data in Sales

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