You know all about sales analytics and how it can benefit your business. You have read studies describing how analytics separates winners from average performers. You know that Big Data might not be right for you, but the more specific and flexible actionable insights derived from operational data is. Now, comes the next step in your strategic ascent toward full-bore adoption and implementation of sales analytics: noting the differences between predictive and prescriptive sales analytics. More importantly, you should know what types of analysis and reports fall under which umbrella, when to turn to the data and what you should be looking for.
Here are the key differences between predictive and prescriptive sales analytics.
Predictive Sales Analytics
After beginning with descriptive analytics – how a business performed in the past – many organizations use their increased access to data to engage in predictive sales analytics. This focuses on answering the question, “What is (probably) going to happen to my business in the future?” Predictable, scalable revenue is a crucial goal for any sales manager and, using models and historical data, they can get a better and more accurate sales forecast and a sense of what to expect going forward.
The most often-used example of predictive sales analytics is a sales forecast. Such a predictive sales analytics report tells you what you should expect to book this month or this quarter. The sales forecast takes into account your historical conversion rates (i.e. what your team’s winning percentage on similar opportunities has been like in the past) coupled with your current sales pipeline (the number of opportunities your team is working on within this time window). Factoring in other variables – such as forecast killers like average deal size, time and engagement – a data-driven sales forecast that takes into account predictive sales analytics is substantially more accurate than one that relies on the intuition of sales reps.
Looking at a sales cycle report by won/loss is another example of predictive sales analytics that takes into account historical information to look into the future. For example, if the historical data notes that winning opportunities only spend 4 days in the qualifying stage while losing opportunities spend an average of 15 days, those predictive sales analytics could be applied to current and future opportunities. Currently working on an opportunity that has languished in this stage for 25 days? It’s safe to predict that this opportunity will not close.
Prescriptive Sales Analytics
While knowing the future (as accurately as one can without a crystal ball) is helpful, the bigger issue is what to do with this predictive knowledge? That’s where prescriptive sales analytics comes in. Prescriptive analytics combines the findings of descriptive, inquisitive and predictive analytics to recommend the specific courses of action to take and the likely outcome of each of these decisions. The key is to find actionable insights, looking to the data for answers to your questions of how you can improve your sales team’s performance.
Prescriptive sales analytics is the natural evolutionary step in terms of sales management analytics – you can’t have effective prescriptive sales analytics without descriptive and predictive sales analytics preceding it. This is one reason why research studies such as Gartner’s Hype Cycle of Emerging Technologies note that prescriptive analytics is another 5-10 years from achieving mainstream adoption and reaching the plateau of productivity, without the data and the proper information chain that leads to these actionable insights.
However, if your company is ready for this level of operational data, prescriptive sales analytics can be a true difference-maker. For instance, consider a Pipeline Today report that examines all the opportunities in your current sales pipeline, along with their risk factors and levels of engagement. The report notes the riskiness of each opportunity, along with how much effort has been expended on that specific opportunity recently. Sales managers can then use this analytical information to prescribe to their teams which opportunities should be prioritized ahead of others, based on risk factors and levels of engagement. A sales rep working on an opportunity that has seen tons of engagement recently and is already projected to close should use this prescribed information to shift gears and focus on a risky opportunity that, with the right effort, could be resuscitated and potentially closed-won.
Another example of how prescriptive sales analytics can be used to correct problems immediately is by looking at a sales funnel report for individual reps, with historical conversion rates broken down by stage. This identifies areas of weakness in a sales reps’ selling process. For example, if the rep converts a ton of opportunities between the first and second stages – and at the bottom of the funnel – but sees a dramatic drop-off in conversion rates between the second and third stages, that might tip off the sales manager that the rep is doing something wrong in this stage. If it is a demo phase, perhaps the rep is executing demos poorly, failing to convince prospective customers. Maybe the rep is too generous in letting opportunities come in at the top of the funnel, when these opportunities should be qualified more stringently. With this information at hand, the sales manager can now prescribe actual sales coaching advice and implement difference-making tactics to raise that conversion rate.
Predictive and prescriptive (and descriptive and inquisitive) sales analytics are all incredibly useful. However, depending on where you are in the information chain, prescriptive analytics should be what every sales organization strives for. Having your data tell you a story or allow you a gaze into the future is great, but the true power of data is in unearthing actionable insights and practical findings that you can act on immediately.
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