Sales is all about making decisions. Which are the best prospects to focus on? What is the best way to estimate sales for the current period? What is the next best move to make with a particular prospect? If a technology existed that helped improve the accuracy of these decisions then it would probably be highly sought after. Well, since this article is about predictive sales, there obviously is a technology that can help.
Predictive analytics works on a very simple premise. All the historical data you have, in CRM systems, emails, transaction based systems – and so on, is potentially a pot of gold. Predictive analytics will trawl through all of this data looking for patterns of behavior that have worked over and over again – and by default those that haven’t. Once these patterns have been tested on data that hasn’t been seen before they are ready to hit the road. Predictive systems will look at data as it enters your systems and identify prospects that show a high probability of closing, versus those that don’t. They can even recommend the next best action, based on the sequence of actions that have shown to work in the past. The net result is that reps can focus on the best prospects and spend less time on the poor quality leads. It also allows much better sales forecast estimates, and the risk involved in the forecasts.
Predictive analytics are used in many areas of business life. Detecting which customers will churn, who should be given credit, which transactions might be fraudulent, and the best upsell offer, are all established applications of predictive analytics – and it works. Sales has been something of a late adopter, mainly because of some level of skepticism. But ironically it is an area of business activity that can benefit the most and is already showing signs of being one of the most successful applications of predictive analytics.
Several predictive sales platforms are listed below, with short descriptions. They all use slightly different approaches. Some focus exclusively on internal data – CRM, emails, customer notes, transaction data and so on. Others incorporate external data – economic, anonymized data from other organizations, weather, sporting – in fact anything that has been shown to have an effect on sales. All of them provide good support for mobile devices, and the display of deal close probabilities, next best moves – and so on. Most are cloud based, and charge an annual or monthly subscription.
Aviso uses machine learning and portfolio management frameworks in a cloud platform to give predictive insight into sales forecasts, deals and appropriate actions to take. It uses predictive estimates of the status of each deal to generate more active forecasts and risk. Users can analyze data by region, rep and any other dimension, down to whatever level of detail is appropriate. What-if scenarios model risk and reward tradeoffs and swing deals.High risk and high value deals are identified for better resource utilization, with support for collaboration between involved parties.
Clari uses predictive analytics to find what works, and what doesn’t, when dealing with customers. It analyzes CRM data and emails to look for productive patterns, which can be used to rate current deals and establish which ones are likely to be at risk. Forecasts use this data to give more accurate estimates for the current period and establish risk. Mobile devices used by reps allow activity tracking, give estimates of deal close probabilities and provide deal history and access to documents. Collaboration is also supported.
Infer helps B2B companies analyze buying signals and predict which prospects will go on to become customers. Infer delivers advanced and effective predictive scoring. The top use cases include filtering, prioritization, net-new leads, campaigns, and nurture.
InsideSales is an online service which addresses many of the uncertainties associated with the sales operation. Which are the best prospects to focus on? What is the best sales forecast for the current period? Which next action will be most effective with a particular prospect? Answering these questions, and improving on current methods, requires sophisticated technology, and specifically predictive technologies. Inside Sales handles pretty much everything in the middle of the sales pipeline – lead scoring and qualification, sales automation, pipeline management and forecasting. It can also aid with sales recruitment and motivation.
Lattice addresses the entire sales funnel, with prioritization of incoming and existing leads, the ability to find new high quality leads, and exploit current customer relationships. Lattice helps businesses identify leads that have expressed an interest, customer look-a-like leads, and sleeping leads. It segments a database on thousands of company and internal attributes. Scores and buying signals are published into CRM systems, so that sales teams can focus on leads that are ready to buy now, and use targeted talking points to guide the conversation.
SalesPredict uses predictive analytics in a cloud based platform to analyze CRM data, web data and other external data sources to score leads and provide customer lifecycle intelligence. This allows leads to be assessed more accurately for better targeting. Both machine learning and natural language processing are used to extract intelligence from data, analyzing sales win/lose data and correlating with demographic, firmographic, and behavioral data from CRM systems – as well as external data.