Predictive Analytics in Sales: 5 Ways it Can Power Yours to Success today

Getting an edge in today’s competitive market place is vital. For B2B companies, possessing the tools to explore customer behaviour and prioritise leads not only aids productivity but helps boost that all-important bottom line.

Predictive sales analytics has come a long way in the last decade. It is now far more accessible to businesses in traditional industries. For many, it’s a powerful tool in helping to support sales and operations planning.

Ten years ago, only big technology companies could afford to develop or to implement sales analytics software to find cross-selling or to reduce customer churn.

Now, medium-sized companies with limited marketing budgets can access these hi-tech solutions too and use it in a broader range of circumstances also, including B2B sales.

Let’s review what are the five ways Predictive Analytics can help a B2B company this year.

Predictive Sales Analytics Example Number 1: Modelling and Predicting Customer Behaviour

If you can predict where your customer is likely to go next, it gives you the opportunity to deliver something they need. B2B companies can uses nowadays, for example, the same algorithms and machine learning methods that Amazon employ for cross-selling.

Applying machine learning to your customer data means that your customer representatives is no longer random or passively dependent on the standard presentation of your products or services – you are proactive and able to ‘read your customer’s mind’. Your team can use predictive analytics to improve sales performance.

Buying or building your predictive analytics solutions should be the only point to clarify today. There are pros and cons to any business. Managers should contemplate them before jumping to conclusions. Building an in-house predictive analytics tool is a long-term project, whereas buying one offers a faster time-to-value but might be not without disadvantages.

I want to see what Predictive Sales Analytics can do for me.

Predictive Sales Analytics Example Number 2: Helping Your Sales Team to Prioritize Leads and Accounts

Determining KPI involves steering a sales team in a certain direction. Ideally, Key Account Managers should target accounts more likely to buy or more propense to churn. Employing your expensive and scarce sales resources on the accounts where they are more needed will help you reduce costs and increase customer lifetime value.

Predictive analytics can help you do this in a few different ways:

• It lets you prioritise based on that potential to act and buy.

• It can calculate the purchase price that is most likely to be accepted for each product and customer.

• It can identify customers with a high risk of churn.

• It can identify cross-selling potential, up to product level.

• It can help you segment different leads and create more personalised marketing messages.

Of course, you need enough data to be able to make such decisions in a quality measured manner.

The more data you have, the more sophisticated your lead prioritisation and qualifying could become over time. With better sales data and proven data mining methods for B2B sales, your team will gain an attractive competitive advantage and will become more effective.

Predictive Sales Analytics Example Number 3: Creating and Improving Effective Marketing Strategies

The insights you get from predictive sales analytics are not only useful in the short-term. As data quality improves and your predictive analytics models deliver your more significant insights, you can also use them in a longer, more strategic way.

Defining, understanding and measuring customer attrition using predictive analytics methods will give you not only a list of customers at risk of churning. It will also give you a list of the attributes lost accounts have in common and a fair approximation to the root causes of attrition.

Predictive analytics means you spend less on speculative marketing strategies and more effort on the things that work. That not only saves you time but money as well.

CALCULATE NOW THE ROI OF QYMATIX PREDICTIVE SALES SOFTWARE

Predictive Sales Analytics Example Number 4: Launching New Products and Services

Predictive sales analytics is not just about working with the back end of the buying and selling process or the marketing itself. It can also be used to explore further new products and services that you might want to bring out as your business grows.

If you can anticipate your customer’s behaviour, it makes sense to use that information to start exploring what new products will have a more significant impact.

Whether a new product or service is likely to fly with your B2B customers is essential. Knowing the answer, or at least understanding the potential, means you don’t waste time with false starts and instead focus on those launches or product lines that have a greater chance of success.

Predictive Sales Analytics Example Number 5: Right Time, Right Customer, Right Message

How many times do we hear in sales that timing is important? You need to catch your customer at just the right moment when they are thinking of buying your product and sell with the right content.

Tailoring content is one of the most straightforward ways companies can apply predictive analytics to the pure marketing arena. This application will later the sales cycle help also your Key Account Managers. It requires that a company meeting some pre-conditions first.

Your data set needs to identify your high-value customers for a start, in other words, those ready to buy. Predictive analytics is very good at helping you do this. You need to be able to segment your audience and target them with personalised messages that have resonance.

Using churn prevention software, for example, may help identify customers that are likely to move elsewhere. It gives you the opportunity to act to retain them, perhaps by altering your messaging or using different marketing approaches altogether.

5 Ways Predictive Analytics Can Power Your Business to Success – Summary

For a long time, predictive sales analytics was the province of large corporations such as Amazon and eBay. The growing role of big data, data collection itself and AI-based predictive sales analytics software means that nowadays even small and medium businesses can access cutting edge technology that will help them predict and respond to customer behaviour.

With the right analytics in place, your business can target the right customers at the right time, tailoring sales and marketing messages so they will have the most impact.

Get it right, and predictive analytics should mean you spend less time on speculation and more time converting better leads and increasing your customer lifetime value.

Are you planning to use Predictive Analytics in other ways this year? Write us a message or comment below. We are happy to talk about with you.

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