What is Predictive Analytics?
Predictive analytics uses computer algorithms and statistical models to analyze large datasets to assess the likelihood of a set of potential outcomes. These models draw upon current, contextual, and historical data to predict the probability of future events.
As new information is made available, the system incorporates more data into the statistical model and updates its predictions accordingly. Throughout this process of machine learning (ML), the model gets “smarter” and predictions become increasingly accurate. However, these models are not infallible, and experience problems that many data scientists are familiar with - the biggest one being data limitations.
Predictive analytics has a number of applications for enhancing sales and marketing operations, including:
Optimizing lead scoring
Streamlining the sales cycle
Identifying upselling/cross-selling opportunities
Providing sales messaging and timing information
Currently, predictive software for the purpose of optimizing sales and marketing is referred to as predictive sales analytics, predictive marketing analytics, or predictive analytics. Although often classified as either a sales or marketing tool, its predictions are relevant to both teams. In order to be most effective, predictive sales analytics requires the alignment of sales and marketing.
Descriptive, predictive, and prescriptive sales analytics all fall under the umbrella of business analytics. When used together, they can provide sales and marketing teams with a complete analysis of a given situation - including recommended next steps.
Descriptive analytics help to describe, or visualize what story the data is currently telling: what has happened and what is happening now. Predictive analytics, as described above, uses this data to forecast future possibilities, and attach a probability to each likelihood. Prescriptive analytics ties the results of a predictive model to possible next steps. It weighs the likely effects and probabilities of different possible decisions in order to recommend a next best action. Many analysts, vendors, and experts see prescriptive analytics as the future of sales and marketing.
Predictive Analytics for B2C and B2B Sales
Some B2C companies are already using predictive and prescriptive analytics technologies, for example, Amazon and Netflix recommendation engines. However, these capabilities are just starting to enter the B2B software space. Sophisticated predictive sales models consider a range of data including:
real-time data feeds
big data from the web
3rd party databases
For both B2C and B2B predictive analytics, this includes demographic and behavioral information about customers and prospects. B2B sales solutions also incorporate firmographic data, or account-level web activity referred to as intent data.
Predictive Sales Analytics Features & Capabilities
Predictive Lead Scoring
One of the major use cases for predictive sales analytics is predictive lead scoring. Predictive lead scoring identifies trends in the customer journey, and uses these trends to predict how far along a lead is in the pipeline. This allows sales teams to qualify and prioritize opportunities based on which leads are most likely to close. It also helps marketing teams identify leads that need to be nurtured, and facilitates lead segmentation into different nurture campaigns.
Predictive models may surface the lead scores themselves, or they may function as behind-the-scenes logic, prioritizing or qualifying leads for sales. Often predictive sales analytics software will integrate into lead management, marketing automation, or other sales tools.
Predictive sales analytics is related to sales forecasting software, and is used to eliminate human error in sales forecasting. Traditionally, sales teams have relied on a salesperson’s intuition, or simple forecasting models, to predict expected revenue and bookings over a period of time.
Alternatively, predictive forecasts provide an intelligent estimate of quarterly or yearly sales based on current and historical sales performance.
Ideal Customer Profile (ICP)
Predictive sales analytics can also be used for demand generation, lead nurturing, and upselling within the company’s customer base. Some predictive models produce an ideal customer profile (ICP), or set of ICPs for different products or campaigns. These profiles identify target customers for individual offerings, or which types of customers will respond best to certain messages. This allows marketers to target prospects more effectively and take advantage of upselling opportunities.
Predictive sales analytics tools can also help predict customer attrition based on satisfaction, usage, and historical trends among other customers. By alerting account managers ahead of time to potentially at-risk customers, this software can help with customer relationship management.
Key Benefits of Using Predictive Sales Analytics
Better alignment of marketing and sales teams
Strategic lead flow/lead management
Increased efficiency and productivity of the sales cycle
More accurate sales forecasts and predictions of future revenue
More relevant, better-timed messaging
Customer relationship management risk detection
Better customer experience
The most predictive analytics sales software is priced on a monthly subscription basis. Less expensive plans with a basic set of features can range between $10-$50 a month. The more expensive option can cost a few hundred dollars a month, depending on the number of features selected.