It’s pretty impossible to work in sales or marketing and not hear a lot about predictive sales analytics in 2019. With more and more companies adopting this new technology, it’s time to understand what predictive analytics are, and how they more than likely can improve your sales figures.
Quite simply, predictive sales analytics is about leveraging historical data to make educated predictions about future results. The idea of analyzing old data to understand what might happen in the future is nothing new. However, new advancements in machine learning and data structure have created a framework that allows for vast data stores to aggregate and parse without requiring an undue number of human hours.
Predictive analytics uses complex algorithms that parse big data systems to identify commonalities towards a certain goal. In terms of sales, these algorithms are often put to task working on the huge troves of data from software programs that power sales teams. That means that all the extra pieces of information that CRM and marketing automation platforms like Salesforce and Pardot collect can be more easily compiled. Then the algorithm can compare those data sets to arrive at conclusions that wouldn’t otherwise be obvious at face value.
When you combine that analysis with analysis of other relevant parts of the sales funnels like email, conversational data, and even social media chatter, the algorithms can identify the key factors that went into past successful deals and conversions. Once you become aware of what really works, with data-driven proof, you can help direct your team to focus in on those areas of success, rather than waste their time chasing all sorts of cold leads and useless tactics.
The reason that predictive analytics has seen such mass adoption is not that it’s a new technology or a buzzword, but rather because it works very well. Here are five ways that your business should use predictive analytics to learn new, more effective ways of reaching sales goals and increasing customer satisfaction.
1. Uncover Qualified Leads
Trying to determine who was most likely to buy your product or service used to be up to guesswork, luck, and a whole lot of time spent poring over spreadsheets. Even though the practice of lead scoring is much older than predictive analytics or machine learning, these technologies have made the process much more accurate and a whole lot less time-consuming.
With predictive tools, your marketing and sales teams can better determine exactly which customers are most likely to convert and even their potential lifetime value. This helps your sales team prioritize their time and focus their efforts on the most fruitful areas.
Modern predictive sales analytics run in the background. That allows your sales team to immediately access information like how closely your offering matches a prospective customer’s needs, how far away your potential client is from making a purchase, and even details like which sales team member is most likely to make the purchase decision.
2. Streamline Prospect Contact
An analytical solution makes it easier for agents to have productive sales calls. Through the use of powerful information, there is an opportunity for rapid sales acceleration. That’s because agents don’t waste as much time talking to the wrong person or using unresponsive talk tracks or channels of communication.
The right data can help prescribe who to call and when to call them. This is based on past information with the prospect, including how many calls it takes before starting a conversation. Predictive analytics are based not just on what you know about that particular prospect, but on data from other companies and past interactions. Email open rates and overall length of agent talk time per role are examples of two pieces of data. These insights help leadership form a successful sales strategy that reduces administrative tasks and increases prospect contact time.
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3. Track and Measure Call Outcomes
To measure the success of a new product campaign, or outbound sales strategy, some companies rely on manual assessments. This means relying on data that was manually recorded by individual agents and doing reviews. The system has a number of flaws, not least of which is reliance on inaccurate data. Manually logged information is not only prone to inaccuracy, but it can also be subjective. This makes it a poor basis upon which to evaluate agent success.
With an automated system, businesses are certain their call and activity tracking information is correct. They can then use this historical information to see whether sales rates are improving. In addition, they can combine what they know about their in-house team with the data collected from similar companies in the industry to compare performance and growth.
Want to learn more about automating call outcomes? Check out this article: What is a Call Disposition and How Do You Measure It?
4. How to Price Products and Services
Product pricing can fluctuate rapidly depending on the potential client. That’s because your team is taking into account more factors than just how much one sale means. Other factors like the size of a company, purchase volume, and even the length of a contract can add to the potential lifetime value of a customer.
That doesn’t mean that figuring out the right price point is an easy task. Too high and you have lost the lead, too low and there’s no room for profit. Predictive sales analytics can help assess just exactly where a potential customer stands, and even help you size up the competition. That means when it’s time to make the offer, your team can be much more confident that the number they’re putting out is one that makes both sides happy.
5. Customer Retention
The customers you already have are some of the best opportunities for new growth. That’s because they’ve committed to your product, have experience using it, and an established relationship requires less time from your sales team. While that’s no promise that they’ll stick around forever, even the data gained from them leaving your product or solution can be very helpful.
Predictive analytics is able to pull data that is relevant to why a particular client stopped using your product. Then, once you identify those warning signs, the system can flag other clients who are taking similar actions prior to dumping your service so that your team is able to intervene before it’s too late. Additionally, it can identify customers who would benefit from adding more services to their package so the upsell comes at just the right time.
Accurate, powerful data is the key to your outbound sales success. To learn how DialSource’s solutions have worked for other companies, review the case studies on our website. When you’re ready to learn more, talk to a member of our team today to see if similar success could happen in your company as well.