Data is the currency of choice for companies looking to cash in on customers. In a world of start-ups and seed funds, being cash poor matters less when you’re data rich. Data is the basis for the effective use of machine-learning, AI, and one of this year’s hottest trends in customer relationship management—predictive analytics.
From pricing to forecasting to lead management, predictive analytics uses technologies that leverage customer data to be able to make smarter predictions about business outcomes.
Yet, knowing how to effectively translate data into actionable insights is challenging for companies with little to no experience in data analysis.
In a recent Adobe study, 51 percent of business leaders said their inability to collate, structure, and integrate data prevents them from drawing meaningful conclusions from it. That’s where predictive analytics comes into play.
Sales teams that use data for predictive analytics in their sales processes will be more accurate at forecasting, pricing, and lead management and will see better sales results than teams that don’t.
What’s the basis of predictive analytics?
If you’re thinking crystal balls and tarot cards, think again. Predictive analytics is less about mystical guesswork and more about informed analysis.
- Is a combination of predictive modeling, data mining, and artificial intelligence.
- Pulls relevant information from a business’ bank of data.
- Picks up on patterns or trends that are likely to reoccur, ranging from seasonal fluctuations to customer behaviors.
On the spectrum of analytics that a business can use, predictive analytics falls on the more advanced end.
The spectrum of analytics varies in value and difficulty
Companies are getting more comfortable with dissecting data using technologies that incorporate descriptive and diagnostic analytics to find out what happened and why.
Predictive analytics, while more difficult to achieve, is also more valuable. Being able to dissect data to predict what will happen will be the differentiator for businesses that have a better grasp of their data.
The power of predictive analytics is in the size of a company’s data bank. The more data that there is to pull from, the more accurate predictions will be.
The market for predictive analytics is expected to grow to almost $15 billion by 2023. Compare that to 2016 when the market was valued just under $4 billion, and it’s clear that using historical data to learn about the future of a business will become an essential part of business processes.
Because customer data often lives in a CRM, software tools with predictive features are disrupting traditional sales processes to offer more insight and value to companies based on this customer data. You’ll often see a tool with predictive analytics features that help inform future events, customer behaviors, and company performance.
Let’s see how predictive analytics will play itself out in sales by looking at how it’s affecting pricing, sales forecasting, and lead management activities.
Note: The information contained in this article has been obtained from sources believed to be reliable.The applications selected are examples to show a feature in context, and are not intended as endorsements or recommendations.
Pricing is important to stay competitive in the market, appeal to consumers, and make a profit. Setting the right price for a product or service means weighing the right mix of factors. Three of the most important to take into consideration are:
- Cost: This determines how much you can charge while still making a profit, taking into account how much it costs to create the product or carry out the service.
- Competitor pricing: Consider how much competitors are charging for a product, but also how many competitors are offering the same thing.
- Customer personas: Decipher how much customers are willing to pay for a product or service based on their income, preferences, and other demographic info.
These, sandwiched between other things such as seasonal trends and market rates, make pricing ripe for the use of predictive technologies.
Predictive pricing uses data to analyze all of the factors needed to set the right price. Pulling in internal and external information including supply and demand trends, competitor pricing, and market data, predictive pricing tools can formulate demand models and pricing strategies to figure out the optimal price for a product or service.
Software spotting: Predictive pricing more often than not comes in the form of a pricing optimization tool. Key features include those for analyzing competitor pricing, monitoring pricing changes or fluctuations, and making recommendations based on market trends or historical performance.
Prisync is a price optimization tool that tracks competitor pricing and stock availability (Source)
Much like weather forecasting, sales forecasting can be a fickle beast. Sales forecasting is important to accurately project earnings and to budget time and resources accordingly. It involves finding the right mix of factors that affect sales trends and cycles, including:
- Monthly sales figures: Since most sales teams have monthly or quarterly sales goals, having a point of reference for average monthly sales performance is important.
- Seasonality: The sale of products and services can fluctuate depending on the time of year and whether a product is more or less in demand.
- Employee availability: When there are more employees, there’s also more money coming in. Incorporating the number of sales staff and accounting for any new hires, time off, or turnover is important for accurate forecasting.
Predictive forecasting takes all of these factors into consideration, as well as analyzing the sales pipeline to see the status of deals and how that might affect monthly outcomes. One of the biggest benefits of using machine-powered predictive analytics is timeliness: Predictions can be updated and adjusted as often as on a daily basis for real-time sales performance analysis.
Software spotting: Predictive forecasting can either be integrated with a CRM or built into the solution. Features to look out for include revenue planning, customizable reports, and recommendations based on foreseeable outcomes.
InsightSquared offers predictive sales forecasting features (Source)
Properly managing leads is the crux of a sales team’s money-making activities. Finding prospects, keeping track of a lead’s status in the pipeline, and uncovering hidden opportunities from a customer database have been largely manual tasks until the introduction of predictive technologies. Key considerations for lead management include:
- Sourcing prospects: Searching social media, setting up Google alerts, and analyzing company profiles can help find the most promising prospects.
- Managing the sales pipeline: Knowing the status of clients in the sales pipeline and reaching out at the most opportune times is crucial for being able to close a deal.
- Scoring leads: Looking through the existing CRM database can uncover opportunities for potential sales based on new information or changed circumstances.
Lead management with the power of predictive analytics uses data—usually pulled from a CRM or marketing automation tool—to spot opportunities where there’s a high chance of customer conversion. It’ll put leads into different categories based on those that are most and least likely to convert, and can also be used to notify sales reps of the best time to reach out for a better chance at closing a deal.
Similarly, predictive lead sourcing takes the guesswork out of where to find leads. By setting up parameters, predictive tools search the appropriate outlets, including social media, to highlight the best potential candidates for reaching out to.
Software spotting: Lead management normally comes as part of a CRM solution. Features with an added touch of AI based on customer data include lead scoring and pipeline management. Lead sourcing typically comes as a standalone solution under the guise of sales enablement or sales acceleration.
Cognism uses AI to identify the most promising leads (Source)
How to make your business more predictable
The big who, what, where, when, and why questions of customer relationship management need to be answered before sales managers can predict anything with a reasonable level of certainty.
Predictive analytics removes the manual effort involved in answering those questions, instead using data for more informed insights.
To be able to use data in a meaningful way, businesses need a CRM, a data structure, and integrations to create a hospitable environment for data analysis.
- Start with your CRM: Choosing the right CRM is important because it’s where you store customer data, but also where predictive features usually appear. Switching CRMs if you choose the wrong one is a huge hassle, especially when it comes to data migration.
- Develop a data structure: You’ll have to decide which data is most and least critical for the predictive analytics you’ll be using. A thorough data structure ensures that you are collecting and organizing data in a way that makes it easy to draw conclusions.
- Integrate with other software: Integrating with other software tools can pull in data that’s essential for making predictions. Integrating accounting software, for example, will ensure that your sales forecasts are accurate based on actual cash flow.
From there, you’ll be on your way to making predictions about the future of your company.
My prediction: If you start using predictive analytics before your competitors, you’ll crush the competition.